Investigation of the Kinome in Pancreatic Ductal Adenocarcinoma

INVESTIGATION OF THE KINOME IN PANCREATIC DUCTAL ADENOCARCINOMA

John Nathaniel Diehl

A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum in Genetics and Molecular Biology.

Chapel Hill 2021

Approved by

Adrienne D. Cox

Channing J. Der

Lee M. Graves

Katherine A. Hoadley

Charles M. Perou

Jen Jen Yeh

ABSTRACT

John Nathaniel Diehl: Investigation of the kinome in pancreatic ductal adenocarcinoma (Under the direction of Dr. Channing Der)

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease that remains incurable for a majority of patients. Constitutive activation of the KRAS protein, and subsequent hyperactivation of its effector pathways, is the key genetic event responsible for nearly all PDAC cases. In an effort to overcome the toxicity and limited efficacy of conventional chemotherapies, we set out to comprehensively describe the signaling intricacies that drive tumor growth and metastasis. This work is divided into two major parts: (1) how PDAC cells respond to ablation of mutant KRAS, with an emphasis on measuring the activity of kinase signaling networks and (2) the identification of an ERK inhibitor as an unexpected, but highly synergistic, addition to a pan-RAF inhibitor to suppress PDAC proliferation and induce apoptosis.

Oncogenic KRAS drives proliferation through a diverse, but incompletely delineated, network of signaling proteins. KRAS modulates intracellular processes by direct and indirect regulation of both protein activity and protein abundance. Using a chemical proteomics strategy, we established a comprehensive profile of kinase signaling

(the kinome) in PDAC and compared this profile in KRAS-suppressed cells. We found that > 12% of kinases detected were significantly altered in our panel of six PDAC cell lines, and that many of these kinases are critical for PDAC growth in the presence, or

iii absence, of mutant KRAS. These findings led us to the conclusion that system-wide delineation of the KRAS-regulated kinome can help identify therapeutic targets in PDAC.

Mutant KRAS signaling is dependent on the RAF-MEK-ERK kinase cascade, which consequently serves as an attractive therapeutic target. Prior attempts to use

BRAF-selective inhibitors induced rebound activation of ERK via stabilization of other

RAF isoforms, which led us to trial a pan-RAF inhibitor as the basis of targeted therapy combinations. Chemical and genetic screening against a diverse set of proteins led us to identify ERK as the most promising clinical target in combination with pan-RAF inhibition.

Concurrent inhibition of RAF and ERK at low doses caused apoptosis in the majority of

PDAC cells, despite each inhibitor only exhibiting cytostatic effects alone. Subsequent comprehensive profiling of this combination showed that inhibition of RAF and ERK was insensitive to feedback reactivation and induced a system-wide collapse of pro-survival processes. These findings led to our conclusion that low-dose vertical inhibition of the

RAF-MEK-ERK pathway is a viable therapeutic strategy for the treatment of KRAS- mutant PDAC.

For Molly, Judy, Alan, Michael, and Addison i

ACKNOWLEDGMENTS

This work would not have been possible without the members of the Der and the

Cox labs and their scientific advice. To the members of my thesis committee, your guidance and conversations with me have guided my career more than you know. Special thanks to the leadership of the MD/PhD program, who gave me the opportunity of a lifetime to train in their curriculum among brilliant future physician scientists and some of my best friends. This work was directly funded by the Lineberger T32 Cancer Cell Biology

Training Program, the Slomo and Cindy Silvian Foundation, and the Ruth L. Kirchstein

National Research Service Award.

TABLE OF CONTENTS

LIST OF TABLES...... xii

LIST OF FIGURES...... xiii

LIST OF ABBREVIATIONS...... xv

CHAPTER 1 – INTRODUCTION...... 1

1.1. IMPORTANCE AND PATHOGENESIS OF PANCREATIC CANCER...... 1

1.1.1. TUMORS OF THE PANCREAS...... 1

1.1.2. CLINICAL FEATURES OF PDAC...... 2

1.1.3. GENOMIC FEATURES OF PDAC AND PATHOPHYSIOLOGY...... 2

1.1.4. RESEARCH MODELS FOR PDAC...... 4

1.1.5. CURRENT TREATMENT STRATEGIES FOR PDAC...... 5

1.2. THE KRAS PROTEIN...... 8

1.3. MAPK SIGNALING...... 12

1.3.1. ERK-REGULATED PHOSPHORYLATION...... 13

1.3.2. ERK-REGULATED TRANSCRIPTIONAL ACTIVITY...... 14

1.3.3. ERK IS THE KEY KRAS EFFECTOR...... 15

1.3.4. ERK ACTIVITY IN CANCER DEVELOPMENT AND PROGRESSION...... 16

1.3.5. RESISTANCE TO TARGETED THERAPY...... 18

1.4. THE PATH FORWARD IN PDAC THERAPY...... 21

vii CHAPTER 2 – DEFINING THE KRAS-REGULATED KINOME IN KRAS-MUTANT PANCREATIC CANCER...... 24

2.1. INTRODUCTION...... 24

2.2. RESULTS...... 28

2.2.1. KRAS SUPPRESSION ALTERS THE ACTIVITY OF DIVERSE KINASES IN PDAC...... 28

2.2.2 INHIBITION OF DDR1, BUT NOT JAK, IMPAIRS PDAC GROWTH...... 31

2.2.3. LOSS OF KRAS OR MAPK SIGNALING CAUSES LOSS OF G2/M AND DNA- DAMAGE RESPONSE KINASES...... 35

2.2.4. INHIBITION OF WEE1 KINASE INDUCES GROWTH ARREST AND APOPTOSIS IN PDAC CELLS...... 39

2.2.5. COMBINED INHIBITION OF WEE1 AND ERK CAUSES SYNERGISTIC GROWTH ARREST AND APOPTOSIS...... 39

2.2.6. INHIBITION OF WEE1+ERK1/2 ARRESTS GROWTH OF PDAC ORGANOIDS...... 43

2.3. DISCUSSION...... 43

2.4. MATERIALS AND METHODS...... 51

2.4.1. CELL CULTURE...... 51

2.4.2. ANTIBODIES AND REAGENTS...... 51

2.4.3. RNAI KNOCKDOWN STUDIES...... 52

2.4.4. MIB/MS...... 52

2.4.5. IMMUNOBLOTTING...... 55

2.4.6. QUANTITATIVE REAL-TIME PCR...... 55

2.4.7. FLOW CYTOMETRY...... 56

2.4.8. RPPA...... 57

viii 2.4.9. PROLIFERATION ASSAYS...... 58

2.4.10. BLISS SYNERGY ANALYSIS...... 59

2.4.11. PROTEASOME INHIBITOR STUDIES...... 59

CHAPTER 3 – LOW-DOSE VERTICAL INHIBITION OF THE RAF-MEK-ERK CASCADE CAUSES APOPTOTIC DEATH OF KRAS-MUTANT CANCERS...... 60

3.1. SIGNIFICANCE...... 60

3.2. INTRODUCTION...... 61

3.3. RESULTS...... 63

3.3.1. ARAF, BRAF AND CRAF CONTRIBUTE TO GROWTH OF KRAS MUTANT PANCREATIC CANCER...... 63

3.3.2. CHEMICAL LIBRARY SCREEN IDENTIFIES SYNERGISTIC ERK MAPK VERTICAL INHIBITION COMBINATIONS...... 67

3.3.3. RAFI/ERKI TREATMENT SUPPRESSES ERK SIGNALING DESPITE LOSS OF NEGATIVE FEEDBACK INHIBITION...... 71

3.3.4. RAFI/ERKI TREATMENT DECREASES CELL CYCLE PROGRESSION, SUPPRESSES PROTEIN TRANSLATION SIGNALING AND INCREASES APOPTOSIS...... 75

3.3.5. COMBINATION RAFI/ERKI TREATMENT STIMULATES MESENCHYMAL-TO-EPITHELIAL TRANSITION...... 78

3.3.6. CONCURRENT INHIBITION OF COMPENSATORY SIGNALING ENHANCES RAFI/ERKI CYTOTOXICITY...... 81

3.3.7. LOW-DOSE RAFI/ERKI VERTICAL INHIBITION IS EFFECTIVE IN KRAS-MUTANT ORGANOIDS AND TUMOR-BEARING RATS...... 84

3.4. DISCUSSION...... 85

3.5. MATERIALS AND METHODS...... 92

ix 3.5.1. DATA AND CODE AVAILABILITY...... 92

3.5.2. CELL CULTURE...... 92

3.5.3. PATIENT-DERIVED ORGANOIDS...... 93

3.5.4. RATS...... 93

3.5.5. SHRNA AND PLASMID TRANSFECTIONS...... 94

3.5.6. IMMUNOBLOTTING...... 95

3.5.7. PROLIFERATION ASSAYS...... 96

3.5.8. DESIGN AND CLONING OF THE CRISPR LIBRARY...... 97

3.5.9. CRISPR/CAS9 LIBRARY LENTIVIRUS GENERATION AND INFECTION...... 98

3.5.10. DRUG SENSITIVITY RESISTANCE TESTING (DSRT) CHEMICAL LIBRARY SCREEN...... 99

3.5.11. RPPA...... 100

3.5.12. RNA SEQUENCING...... 102

3.5.13. MIB/MS...... 102

3.5.14. FLOW CYTOMETRY...... 104

3.5.15. IMMUNOFLUORESCENCE...... 104

3.5.16. ORGANOID VIABILITY ASSAY...... 105

3.5.17. PDAC XENOGRAFT STUDY...... 106

3.5.18. PRISM SOFTWARE...... 107

3.5.19. IMAGEJ SOFTWARE...... 107

3.5.20. SYNERGYFINDER...... 107

3.5.21. DECISION TREE AND FOREST ANALYSES...... 107

3.5.22. CRISPR ANALYSIS...... 108

x 3.5.23. DSRT CHEMICAL LIBRARY SCREEN ANALYSIS...... 109

3.5.24. RPPA ANALYSIS...... 109

3.5.25. RNA SEQUENCING ANALYSIS...... 109

3.5.26. MIB/MS ANALYSIS...... 110

3.5.27. TUMOR XENOGRAFT STATISTICAL ANALYSIS...... 110

3.5.28. TUMOR XENOGRAFT COMBINATION ANALYSIS METHOD (BLISS INDEPENDENCE)...... 111

CHAPTER 4 – CONCLUSIONS AND FUTURE DIRECTIONS...... 113

APPENDIX A – DEFINING THE KRAS-REGULATED KINOME...... 118

APPENDIX B – LOW-DOSE VERTICAL MAPK INHIBITION...... 137

REFERENCES ...... 154

xi LIST OF TABLES

Table A1 KRAS suppression-induced downregulated kinases and their functions...... 131

Table A2 KRAS suppression-induced upregulated kinases and their functions...... 132

Table A3 RPPA antibodies...... 133

xii LIST OF FIGURES

Figure 1.1 Step-wise development of pancreatic adenocarcinoma (PDAC)...... 3

Figure 1.2 First-line medical management options for metastatic PDAC...... 7

Figure 1.3 Prevalence of RAS mutations in human cancers...... 9

Figure 1.4 KRAS activation-inactivation cycle in normal and tumor cells...... 10

Figure 1.5 RAF-MEK-ERK signaling and feedback loops...... 17

Figure 1.6 Summary of recent RAF-MEK-ERK targeted therapies...... 20

Figure 2.1 KRAS knockdown induces kinome-wide alterations primarily through ERK...... 32

Figure 2.2 DDR1, but not JAK, is a therapeutic vulnerability in PDAC...... 36

Figure 2.3 Loss of KRAS/MAPK signaling contributes to loss of G2/M and DNA-damage response kinases...... 40

Figure 2.4 Inhibition of WEE1 kinase induces growth arrest and apoptosis in PDAC cells...... 42

Figure 2.5 Combined inhibition of WEE1 and ERK induces growth arrest and apoptosis...... 44

Figure 2.6 Inhibition of WEE1+ERK1/2 arrests growth of PDAC organoids...... 46

Figure 3.1 Concurrent inhibition of all RAF isoforms diminishes PDAC growth...... 64

Figure 3.2 Identification of synergistic drug combinations that enhance ERK MAPK inhibitor cytotoxicity...... 69

Figure 3.3 Concurrent RAF and ERKi inhibition disrupts ERK- dependent signaling and cellular processes...... 73

Figure 3.4 Concurrent RAF and ERK inhibition causes apoptosis...... 76

Figure 3.5 Concurrent RAF and ERK inhibition induces mesenchymal-to-epithelial transition...... 79

xiii Figure 3.6 Concurrent inhibition of compensatory signaling enhances RAFi/ERKi growth inhibition...... 82

Figure 3.7 Vertical ERK MAPK inhibition is effective in organoid and rat models of KRAS-mutant cancers...... 86

Figure A1 KRAS-dependent kinome-wide alterations...... 118

Figure A2 Upregulation of DDR1, but not JAK, contributes to PDAC survival upon KRAS knockdown...... 122

Figure A3 KRAS or ERK suppression causes loss of checkpoint and DNA damage response kinases...... 125

Figure A4 Adavosertib treatment of PDAC induces growth arrest and apoptosis...... 128

Figure A5 Combined inhibition of WEE1 and ERK inhibits PDAC organoid growth...... 130

Figure B1 All RAF isoforms are necessary for PDAC growth...... 137

Figure B2 Drug sensitivity resistance testing screen (DSRT)...... 139

Figure B3 Concurrent RAF and ERK inhibition causes synergistic growth suppression...... 141

Figure B4 The pathways altered by the concurrent RAF and ERK inhibition...... 143

Figure B5 Concurrent RAF and ERK inhibition blocks ERK reactivation ...... 144

Figure B6 The alterations of various signaling pathways upon RAF and ERK inhibition...... 146

Figure B7 Concurrent RAF and ERK inhibition induces apoptosis...... 148

Figure B8 Combined RAF and ERK inhibition causes mesenchymal to epithelial transition...... 150

Figure B9 Concurrent PAK or AKT inhibition enhances RAFi/ERKi activity...... 151

Figure B10 Concurrent RAF and ERK inhibition synergistically impairs KRAS mutant cancer growth in vitro and in vivo...... 152

xiv LIST OF ABBREVIATIONS

AML Acute myelogenous leukemia

ASCO American Society of Clinical Oncology

ATP Adenosine triphosphate

BLISS Drug synergy analysis/score

CML Chronic myelogenous leukemia

CRC Colorectal cancer

CRISPR Clustered regularly interspaced short palindromic repeats

CT Computed tomography scan

ΔDSS Change in the drug sensitivity score

DSRT Drug sensitivity and resistance testing

ECM Extracellular matrix

ECOG Eastern Clinical Oncology Group

EMT Epithelial-to-mesenchymal transition

FDA Federal Drug Administration

FOLFIRINOX Folinic acid, 5-fluorouracil, irinotecan, oxaliplatin

FOLFOX Folinic acid, 5-fluorouracil, oxaliplatin

GAP GTPase-activating protein

GDP Guanosine diphosphate

GEF Guanine nucleotide exchange factor

GEMM Genetically-engineered mouse model

GI50 Concentration at which growth is 50% of maximum

Glu Glutamic acid

xv Gly Glycine

GSEA Gene set enrichment analysis

GTP Guanosine triphosphate

HNSCC Head and neck squamous cell carcinoma

IC50 Concentration at which the target is 50% inhibited

KC KrasLSL G12D/+/Pdx-1-Cre mouse model of PDAC

KPC KrasLSL G12D/+/Tp53R172H/R172H/Pdx-1-Cre mouse model of PDAC

LAC Lung adenocarcinoma

MAPK Mitogen-activated protein kinase

MET Mesenchymal-to-epithelial transition

MIB/MS Multiplexed-inhibitor beads coupled to mass spectrometry

MTD Maximum tolerated dose

PanIN Pancreatic intraepithelial neoplasm

PDAC Pancreatic ductal adenocarcinoma

PDX Patient-derived xenograft

RBD RAS-binding domain

RNAi RNA interference

RPPA Reverse phase protein array

RTK Receptor tyrosine kinase

Ser Serine siRNA Small interfering RNA

STRING Biological database of protein-protein interactions

Tyr Tyrosine

xvi WB Western blot

WT Wild-type

xvii

CHAPTER 1 - INTRODUCTION

1.1. IMPORTANCE AND PATHOGENESIS OF PANCREATIC CANCER

Cell proliferation, or expansion of a population of cells, is regulated by an array of intrinsic and extrinsic signals that determine the timing and the frequency of cell division.

In normal somatic cell physiology, the balance of pro- and antiproliferative signals facilitate normal tissue development and cell homeostasis. Cancer develops from cells that gain persistent growth signaling and lose anti-proliferative protective mechanisms.

Advances in genetic, molecular, and bioinformatics techniques since the turn of the century have enabled researchers to pinpoint the driving factors in many cancer types, including pancreatic cancer, though targeted exploitation of these drivers remains under investigation.

1.1.1. PANCREATIC ADENOCARCINOMA

Pancreatic adenocarcinoma (PDAC) diagnoses are estimated to surpass 60,000 new cases in the United States in 2021, continuing a steady upward trend in pancreas cancer incidence (1). PDAC is more common in men (31,950 vs. 26,480 estimated cases) and accounts for ~90% of all neoplasms of the pancreas (2). Less common types of tumors originating in the pancreas include acinar cell carcinoma, pancreatoblastoma, solid-pseudopapillary tumors, and a diversity of functioning and non-functioning endocrine tumors (3). Risk factors for developing PDAC include tobacco use, age (> 50 years), chronic pancreatitis, obesity and diabetes mellitus (4).

1 1.1.2. CLINICAL FEATURES OF PDAC

The clinical presentation of PDAC is often delayed due to the late onset of noticeable symptoms, which typically include jaundice, midepigastric pain and weight loss

(5). Less frequently, a diagnosis of pancreatic cancer may be heralded by recent onset of type II diabetes mellitus (6). Diagnostic workup for patients with this constellation of symptoms includes transabdominal ultrasound or abdominal computed tomography (CT) scan to confirm the presence of a pancreas mass and to assess the surgical resectability of the mass (7,8). Given the insidious course of the disease, however, most patients present with invasive disease that precludes definitive surgical management. PDAC can invade or encase local structures (such as the superior mesenteric artery or the celiac artery) or metastasize distantly to the liver, peritoneum, or extra-abdominal location, any of which would make resection contraindicated (9). Overall 5-year survival for patients diagnosed with any stage of pancreas cancer is 10%, though it decreases dramatically if surgery is not an option (1).

1.1.3. GENOMIC FEATURES OF PDAC AND PATHOPHYSIOLOGY

Pancreatic ductal adenocarcinoma most frequently arises from a precursor lesion, pancreatic intraepithelial neoplasia (PanIN, Fig. 1.1) (10). PanINs first develop through acinar-to-ductal metaplasia concurrent with an activating point mutation in the KRAS oncogene (10). Recent single cell sequencing analysis of these precursor lesions revealed that PanIN lesions can contain different subtypes of metaplastic cells with

Figure 1.1. Step-wise development of pancreatic adenocarcinoma (PDAC). Green gene identifiers denote activating mutations and red gene identifiers denote inactivating mutations or deletions. differing carcinogenic potential (11). Investigation of PanIN lesions has yielded insight into the stepwise accumulation of genomic alterations seen in PDAC. Genomic characterization of PDAC is typified by activating mutations in KRAS (93%) and inactivating mutations or deletions of TP53 (70.5%), CDKN2A (p16, 17.4%), and SMAD4

(24.2%) (12-14). Subsequent analyses revealed similar findings alongside a host of less frequent mutations, including the identification of germline mutations in BRCA2 and ATM that predispose patients to developing PDAC. While most PDAC cases are similar in their dependence on activated KRAS, there is a wide range of co-occurring mutations that contribute to clinical heterogeneity among patients with PDAC. This heterogeneity in genetic drivers among PDAC is likely to blame, in part, for the varied responses to current treatment modalities (9).

3 1.1.4. RESEARCH MODELS FOR PDAC

Preclinical models of pancreatic cancer are essential for the understanding and development of novel therapeutics. Traditional cell line models of human PDAC (e.g., MIA

PaCa-2, PANC-1), maintained in culture for decades, remain the most widely used cell models to dissect the molecular features that drive PDAC behavior (proliferation, avoidance of apoptosis, genomic instability) as well as relationships among signaling pathways in human PDAC (15,16). Recently, low passage cell lines established from patient-derived tumor xenografts (PDX) provide cell models that better represent the heterogeneity of patient tumors.

The recent development of organoid models of PDAC offer much of the same utility that researchers have with adherent cultures while adding an element of three- dimensional growth similar to human tumors (17). Not only can researchers evaluate the proliferative impact of an investigational inhibitor or genetic perturbation, organoids offer the ability to evaluate cell-cell and cell-stroma interactions following treatment (18). What remains lacking in both adherent cultures as well as organoid models of PDAC is capturing the immunosurveillance by the host of the tumor. For this reason, preclinical syngeneic orthotopic mouse models remain a critical tool needed to study the pathophysiology of PDAC and the response to investigational treatments (19). Orthotopic models also model the highly desmoplastic nature of PDAC, where cancer cells comprise only 10-15% of the tumor mass.

Animal models of PDAC can be generally divided into two distinct categories: transplanted tumor animal models and spontaneous tumor animal models (19).

Xenotransplantation tumor models often involve subcutaneous injection of tumor cells

4 into an immunocompromised mouse host. This offers researchers the ability to easily monitor tumor progression of a human cell line-driven tumor. The primary drawback of this model, however, is the lack of a competent immune system in the mouse, which fails to recapitulate the physiological context of most patient’s disease (20). Patient derived tumor xenografts (PDX) are created by injection of dissociated patient-derived tumor cells or implantation of a bulk human tumor section into an immunocompromised mouse host

(21,22). The PDX tumors that develop often retain the histological characteristics of the original tumor as well as retention of tumor supporting cell types such as cancer- associated fibroblasts.

Spontaneous tumor models were developed to better capture the tissue-specific tumorigenesis process in an immunocompetent animal host. Earlier models involved chemical induction of pancreas tumors, though the tumors that formed often varied widely in their histologic subtype and did not mimic the primary PDAC subtype seen in human patients (23,24). Subsequent work permitted the development of genetically-engineered mouse models (GEMMs) that supported pancreas-specific tumor formation and more closely matched the genetic features of PDAC seen in humans (25-29). In particular, the

KPC (KrasG12D/+/Tp53R172H/R172H) GEMM developed by Tuveson and colleagues is considered the gold standard for the field. This model recapitulates key characteristics of the human disease, including highly desmoplastic, poorly vaculularized tumors. The metastatic spread as well as poor response to gemcitabine also mirror the patient cancer.

1.1.5. CURRENT TREATMENT STRATEGIES FOR PDAC

Upon diagnosis of PDAC, only 15 to 20 percent of patients are candidates for surgical resection, which currently offers the only chance of a cure (30). Outside of a

5 clinical trial, there are relatively few options for medical management of a patient with locally advanced or metastatic PDAC. For the majority of patients with unresectable disease, first line systemic therapy depends on a patient’s comorbidities and expected performance status (Fig. 1.2). Guidelines for assessment of comorbidities include evaluation of a patient’s hemoglobin, platelet count, and bilirubin levels. If the patient is capable of tolerating an intensive approach and has favorable blood counts, the recommended treatment is the combination chemotherapy regimen known as

FOLFIRINOX (folinic acid, fluorouracil, irinotecan, and oxaliplatin) (31). In patients that have less favorable, but still adequate, comorbidities, single-agent gemcitabine or gemcitabine plus nab-paclitaxel is often recommended. Some institutions also favor gemcitabine plus nab-paclitaxel versus FOLFIRINOX in any patient with adequate comorbidities. In patients that do not have favorable blood counts, chemotherapy should only be offered on a case-by-case basis with an emphasis on supportive care.

Fluorouracil-based combination regimens, such as FOLFIRINOX and FOLFOX (folinic acid, fluorouracil, oxaliplatin) have been shown to improve all metrics of patient survival in comparison to gemcitabine alone (progression-free survival, objective response rate, overall survival) (32,33).

Given the known systemic toxicity of the majority of chemotherapeutic regimens available for PDAC, there has been a recent emphasis on the identification of cancer- driver targeted therapies (34). The goal for a successful targeted therapy is the preferential disruption of cancer cell proliferation without limiting non-cancerous, somatic cell survival. The EGFR-targeted small molecule inhibitor erlotinib achieved statistical success but clinically insignificant improvement in overall survival (median increase of

6 0.33 months) when combined with gemcitabine (35). Despite FDA approval for PDAC, erlotinib remains a second-line agent for advanced PDAC due to its limited benefit

Figure 1.2. First-line medical management options for metastatic PDAC. Flowchart developed from recent ASCO guidelines (31). Favorable and relatively favorable comorbidity definitions are available in the online publication. ǂ Only offered in a setting where proactive dosing and schedule adjustments can be made to minimize toxicity. and added toxicity profile (36).

While heritable pancreatic cancer is rare, some patients that harbor germline mutations in BRCA1, BRCA2, or PALB2 genes will present with PDAC. Germline or somatic mutations in any of these genes disrupt DNA-damage repair mechanisms in the

PDAC cell. Patients with these tumors will uniquely benefit from platinum-based

7 chemotherapy, which directly disrupt normal DNA structure through the formation of DNA adducts and interstrand crosslinks. Additionally, patients may be amenable to maintenance therapy with a PARP inhibitor such as olaparib which also disrupts DNA- damage repair and has a favorable tolerability profile (37).

1.2. THE KRAS PROTEIN

The KRAS oncogene belongs to the RAS gene family. The identification of HRAS,

NRAS, and KRAS in human cancer cells established RAS-family genes as the first oncogenes discovered (38,39). There are three RAS-gene family members (HRAS,

NRAS, KRAS) which encode four RAS-family proteins (HRAS, NRAS, KRAS4A,

KRAS4B). KRAS has two splice variants which vary in expression in a tissue-dependent manner; KRAS4B is the most abundant splice variant in PDAC and other cancer types, accounting for the focus on KRAS4B rather than KRAS4A in the vast majority of studies

(40). Since their discovery, decades of research has cemented their place among key cancer-promoting proteins in their capacity to initiate and maintain tumor growth in a range of tissue types (41).

RAS-family mutations are found in a multitude of human cancer types (Fig. 1.3).

PDAC is unique among cancer types in its singular dependence on an activating RAS mutation for tumorigenesis; the next most frequently RAS-mutant cancer types are colorectal (52% RAS mutations) and lung adenocarcinoma (32% RAS mutations) (42).

Among RAS family members, KRAS is the most frequently mutated (83% of all RAS mutations in cancer overall), however the distribution of mutant RAS isozymes differs among various cancer types. KRAS is the most frequently mutated in pancreatic and lung

8 adenocarcinoma, as well as colorectal cancer. Melanoma and acute myelogenous leukemia are dominated by activating NRAS mutations, while bladder and head and neck squamous cell carcinoma are most frequently HRAS mutant (42). Why a specific RAS isoform is seen preferentially in different cancer types remains an unresolved issue in the field.

Figure 1.3. Prevalence of RAS mutations in human cancers.

KRAS, as well as other RAS family members, is a GTPase that functions as a molecular switch (43). KRAS cycles between GTP-bound and GDP-bound states which is facilitated by guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs, Fig. 1.4) (44). GEFs interact with KRAS-GDP and promote the exchange of GTP for GDP, functionally activating KRAS. KRAS-GTP is uniquely able to bind and activate effector proteins. GAPs, on the other hand, interact with KRAS-GTP to cause the

9 hydrolysis of GTP to GDP, inactivating KRAS. This activation-inactivation cycling of KRAS is tightly controlled in normal cells, and helps integrate external growth-promoting stimuli with downstream signaling pathways.

Mutational activation of KRAS is the initiating genetic even in PDAC and is found in 95% of cases (14). Missense alterations at Gly-12, Gly-13, and Glu-61 are the most

Figure 1.4. KRAS activation-inactivation cycle in normal and tumor cells. common hotspots locations for mutations in RAS genes, and induce the constitutive activation of KRAS seen in cancer (Fig. 1.4) (42). Mutant KRAS is traditionally considered to be GAP-insensitive, leading to its accumulation in the GTP-bound, or active, state.

Tumorigenesis via constitutive KRAS signaling can also be achieved through loss of

GAPs (NF1), upstream activating mutations in the EGF receptor tyrosine kinase, or mutational activation of downstream effectors (e.g., BRAF).

Upon GTP binding and activation, KRAS is capable of stimulating several known signaling pathways through effector binding (41). The best characterized, and most essential in PDAC, are the RAF serine/threonine kinases (ARAF, BRAF, RAF1/CRAF)

(45). RAF is the first tier of a three-tier protein kinase cascade (RAF-MEK-ERK) that

10 comprise one of three major mitogen-activated protein kinase (MAPK) cascades. KRAS-

GTP binds to RAF through its RAS-binding domain (RBD), promoting normally cytosolic

RAF translation to the plasma membrane, where other protein kinases phosphorylate and activate the kinase activity of RAF. Activated RAF can phosphorylate and activate the highly related MEK1 and MEK2 dual specificity kinases, which in turn phosphorylate and activate the ERK1 and ERK serine/threonine kinases (herein referred to as ERK).

Whereas RAF and MEK exhibit highly restricted substrates, activated ERK has hundreds of substrates, though the critical effectors that facilitate PDAC growth have only been partially described (46). The ERK MAPK cascade is a major focus of my research studies.

The second best characterized KRAS effector pathway involves the class I phosphoinositide 3-kinases (PI3K). KRAS-GTP binds and activates PI3K, which promotes the formation of phosphatidylinositol (3,4,5)-triphosphate; this signaling molecule leads to the activation of the AKT and mTOR protein kinases (47).

Other key KRAS effectors in PDAC include GEFs for other RAS superfamily

GTPases. As with other RAS effectors, these GEFs contain either RBD or RAS association (RA) domains that bind preferentially to GTP-bound RAS. RAS binds TIAM1, leading to activation of the RAC1 RHO family GTPase. RAS binding to a family of

RALGEFs (RALGDS, RGL1, RGL2 and RGL3) leads to activation of the related RALA and RALB RAS family small GTPase (48,49). Like RAS, these GTPases are activated by their respective GEF proteins to form their active GTP-bond states. Altogether, KRAS-

GTP can interact with these as well as other RBD/RA domain containing proteins to regulate a diversity of cytoplasmic signaling networks that in turn regulate a myriad of cellular functions. There is also considerable crosstalk between the individual effector

11 networks to facilitate processes such as cell survival, metabolic activity, cell migration, and proliferation (45).

KRAS is critical to both PDAC tumorigenesis as well as tumor maintenance.

Doxycycline-inducible mouse models of PDAC clearly demonstrated that expression of

KRASG12D was sufficient to induce PDAC formation (50). Additionally, removal of doxycycline and loss of KRASG12D expression caused a regression of the PDAC tumor.

Oncogenic KRAS mediates these effects by binding and activating a complex effector network. Many of these indirect and direct effectors are kinases, which both transmit intracellular signaling and are tractable drug targets (51). The KRAS-regulated kinase effector network in PDAC is directly or indirectly responsible for processes which include cell cycle progression, DNA-damage repair, nucleotide processing, RNA processing and protein translation (51-53). A comprehensive characterization of this effector kinase network is lacking in PDAC and is a focus of this dissertation.

1.3. MAPK SIGNALING

Mitogen-activated protein kinase (MAPK) cascades are critical for transducing extracellular signals to cellular responses. Three families of MAPKs have been well described: classical MAPK (RAF-MEK-ERK), c-Jun N-terminal kinase and stress- activated protein kinase (JNK/SAPK) and the p38 kinase cascade (54). All three cascades can play a role in proliferation, differentiation and development. JNK/SAPK is notable for its role in inflammation and the stress response as well as activation of the transcription factor c-Jun. p38 plays a role in interferon signaling and NF-κB signaling. The best characterized MAPK is the ERK pathway, or RAF-MEK-ERK. In the absence of mutant

12 KRAS, the ERK MAPK cascade is generally activated by upstream growth factor binding to receptor tyrosine kinases (e.g., EGF binding EGFR). The ERK MAPK cascade, particularly in the presence of an activating KRAS mutation, is one primary focus of this dissertation work.

The ERK kinases (MAPK1 and MAPK3; encoding ERK2 and ERK1, respectively) serve as the third tier in the RAF-MEK-ERK MAPK protein kinase cascade. ERK activation has been shown to play a key role in regulating cell cycle division, proliferation, and survival of PDAC cells (Fig. 1.5) (55). Given the importance of ERK signaling in cancer, there are substantial ongoing efforts both to understand the breadth and significance of ERK activity as well as to develop ERK-selective small molecule inhibitors

(56).

1.3.1. ERK-REGULATED PHOSPHORYLATION

ERK1/2 are serine/threonine (S/T) protein kinases with a diverse array of substrates. ERK substrates generally contain either a D-domain and/or a DEF motif, both of which promote ERK binding and phosphorylation (57,58). While some proteins are near-universally identifiable as an ERK substrate (e.g., RSK), a recent meta-analyses of

ERK-substrate literature revealed an expansive list of >2500 direct and indirect ERK phosphosites (46). Multiple research groups have attempted to dissect and characterize the ERK phosphoproteome in various cancer types and cell lines, including colon, cervical and melanoma (59-64). These efforts generally have poor cross-validity, however, in part due to their varied choice of model cell lines (HEK293, NIH3T3), non-human cell lines (rat

IEC-6), and cancer cells with heterogeneous oncogene drivers. Notably, many of the studies that laid the foundation for defining ERK substrates used extracellular growth

13 factor (EGF) stimulation to induce signaling through the EGF receptor (EGFR) and the downstream ERK MAPK pathway (59,65-70). Not only is this treatment non-specific for activating ERK signaling, it also does not accurately recapitulate KRAS-mutant signaling in pancreatic cancer. The dissimilarity in ERK substrate lists between these studies demonstrates the need for additional robust research with an exclusive focus on identifying ERK-interacting partners and key substrates in pancreatic cancer.

ERK has a diverse and incompletely defined array of substrates, some of which are also protein kinases. Known kinase families that are activated by ERK signaling include p90 ribosomal S6 kinases (RSK1-4), MAPK-interacting kinases (MNK2), and mitogen- and stress-activated kinases (MSK1/2) (71). The RSKs play a role in nuclear signaling, cell cycle progression, proliferation, protein synthesis and cell survival. MSK may also play a role in the chromatin landscape in addition to directly activating multiple transcription factors. Notably, activated MSKs can stabilize and activate the transcription factor CREB; phosphorylated CREB helps activate the transcriptional activity of c-Fos,

JunB, and Egr1 (72). MNK family kinases control protein synthesis through regulation of ribosomal initiating factors eIF4E and eIF4G; MNK may also have RNA-binding protein targets (73,74). Dissection of the ERK-regulated kinase landscape, or kinome, is another important aspect of the research in this dissertation.

1.3.2. ERK-REGULATED TRANSCRIPTIONAL ACTIVITY

Once ERK is activated via phosphorylation by MEK1/2, ERK can translocate to the nucleus (75). Many of the known and putative ERK targets are transcription factors, such as MYC, JUN, FOS, FOSL1, and ELK1. Each of these transcription factors regulates a diverse array of transcriptional programs, expanding the list of indirect ERK-regulated

14 genes. MYC, JUN, and FOS have all been identified as oncogenes that can also facilitate cancer growth and maintenance (76-78). Recent work from our lab has also demonstrated that loss of ERK signaling by small molecule inhibition has both an immediate and delayed impact on the transcriptional profile of PDAC cells (79,80). Sears and coauthors demonstrated that MYC is regulated directly by ERK phosphorylation at

Ser-62, and that MYC stabilization is essential for PDAC maintenance (81-83). Therefore,

ERK activation serves to both directly and indirectly activate pro-growth and pro-survival transcriptional programs through key transcription factors.

1.3.3. ERK IS THE KEY KRAS EFFECTOR

Given that 95% of PDAC is driven by a constitutively-activating KRAS mutation, evaluation of driver mutations in KRAS-WT PDAC cases may provide insight into the conserved mechanisms of PDAC tumorigenesis. Activating mutations in BRAF have been shown to both drive PDAC and to be mutually exclusive with KRAS mutations, suggesting that constitutive activation of downstream MAPK nodes can phenocopy mutant KRAS

(84). It is also worth noting that the widely studied BxPC-3 pancreatic cancer cell line, which are frequently used as a KRAS WT counterpart to KRAS-mutant PDAC cell lines to define KRAS-driven activities, harbors an atypical activating BRAF mutation (85).

Taken together, these results suggest that nearly all cases of human PDAC, whether they are KRAS-mutant or KRAS-WT, are driven by hyperactivation of MAPK signaling through

RAF-MEK-ERK.

An additional line of evidence in favor of ERK as the key KRAS effector in PDAC is derived from mouse models of PDAC tumorigenesis. The BRAFV600E activated mutant is sufficient to induce PanIN formation in the pancreas of a mouse (86). When TP53 is

15 also lost, BRAF-mutant PanINs progress to adenocarcinoma of the pancreas. These results recapitulate found between the KC (KrasLSL G12D/+; Pdx-1-Cre) and KPC (KrasLSL

G12D/+;Trp53R172H/+;Pdx-1-Cre) mouse models of PDAC, respectively (87). Thus, activation of the ERK MAPK cascade alone is sufficient to phenocopy KRAS and drive development of fully invasive and metastatic PDAC.

1.3.4. ERK ACTIVITY IN CANCER DEVELOPMENT AND PROGRESSION

KRAS activation through hotspot point mutation is the foundational event in PDAC tumorigenesis, and the ERK MAPK pathway is the primary signaling output for mutant

KRAS (86). Despite the demonstrable importance of ERK MAPK in PDAC and other cancer types, activating mutations in EGFR, KRAS, and downstream effectors, such as

BRAF, are mutually exclusive of one another (84). Additionally, co-mutations in EGFR and KRAS are shown to cause senescence and apoptosis, likely due to hyperactivation of ERK signaling (88,89). A similar detrimental effect has been observed with BRAF and

KRAS activating mutations (90). These findings are indicative of the inhibitory impact of

Figure 1.5. RAF-MEK-ERK signaling and feedback loops. Activation of RAF-MEK- ERK signaling also induces upregulation (transcriptional and post-translational) of feedback inhibitors of MAPK signaling, which in turns promotes a complex signaling environment mediated through ERK.

ERK hyperactivation on cell growth and proliferation, possibly due to increased cellular stress responses.

RAF-MEK-ERK signaling is often portrayed as a linear pathway with singular inputs and outputs. Despite this simplified depiction, the ERK MAPK cascade is subject to feedback inhibition at multiple nodes (91). Multiple families of proteins, particularly dephosphorylases such as DUSPs, are directly regulated by ERK signaling and remove activating phosphate groups from multiple MAPK nodes. Along with other research groups, this thesis research showed that inhibition of ERK alone causes rebound increase

17 in pathway activation. This occurred alongside loss of DUSP4 and other negative regulators of the MAPK cascade. Recently, the development of a covalent KRASG12C inhibitor has shown substantial promise in clinical trials. Despite this success, KRASG12C inhibitors may be similarly limited by rebound activation of ERK and other downstream effectors. Our lab and other groups have shown ERK reactivation within 24 to 72 hours of G12Ci treatment in KRASG12C driven lung, colon, and pancreatic cancer cell lines. ERK

MAPK reactivation in the presence of a direct inhibitor suggests that signaling through this pathway may not be as linear as the field once thought. Additionally, pursuing combination targeted therapies may offer the best solution to counter reactivation of key pathways in the presence of a single drug.

1.3.5. RESISTANCE TO TARGETED THERAPY

PDAC treatment for select patients has evolved from gemcitabine to poly- chemotherapy regimens with moderate improvement in patient survival (31). Despite these advances in chemotherapy for PDAC patients, the prognosis of a patient with unresectable disease remains poor and generates a demand for effective, biologically- driven treatment strategies (34,92). Given the demonstrable dependence of PDAC on

ERK MAPK signaling, there is excitement about the development of selective MAPK node inhibitors, such as EGFR inhibitors (cetuximab, erlotinib), RAF inhibitors (vemurafenib, dabrafenib, sorafenib, LY3009120), MEK inhibitors (trametinib, binimetinib) and ERK inhibitors (SCH772984, LY3214996, ulixertinib, Fig. 1.6) (93). Initially, MEK1/2 selective inhibition with trametinib was first approved for the treatment of melanoma (94). BRAF- mutant selective inhibitors, such as vemurafenib targeting BRAFV600E, were subsequently shown to be effective in a subset of BRAF-mutant melanoma cancers and was also

18 approved (95). ERK inhibitors continue to be studied and developed; ERK may serve as a superior therapeutic target due to its position as the terminal node in the canonical

MAPK pathway and possesses a broad range of targets (55). Both the discovery and rapid development of MEK and BRAF inhibitors generated broad interest in MAPK targeted therapies, and were shown to be effective alone or in combination in melanoma, lung cancer and colon cancer (96-99).

Despite the approval of select MAPK inhibitors in other cancer types, the excitement for MAPK selective inhibitors in KRAS-driven PDAC was tempered by their relative lack of efficacy over standard-of-care chemotherapy (100-105). The only cell- surface receptor target upstream of the MAPK pathway is EGFR, however treatment of

PDAC tumors with cetuximab, a monoclonal anti-EGFR antibody, yielded no significant improvement in patient outcomes (106). KRAS-directed therapies offer promise, but are currently limited to KRASG12C mutant PDAC tumors which constitute only ~2% of all PDAC cases (107). Importantly, direct, small molecule inhibition of KRAS may be subject to similar feedback reactivation as RAF and MEK inhibitors, as previously mentioned.

Downstream of KRAS, multiple kinases in the MAPK pathway offer promising targets for small molecule inhibition in PDAC. The MEK inhibitors trametinib and selumetinib were trialed as monotherapy but did not produce a significant shift in clinical outcomes for advanced PDAC patients (103,108). Treatment with a MEK inhibitor is also complicated by a range of normal tissue dose-limiting toxicities, such as rash, fatigue, diarrhea, peripheral edema, and more rarely, cardiac dysfunction (93). Preclinical identification of dose-limiting toxicities seen in human clinical trials may not have been possible in murine models due to differences in the pharmacokinetics and pharmacodynamics of MEK

Figure 1.6. Summary of recent RAF-MEK-ERK targeted therapies. aApproved for the treatment of BRAF-mutant melanoma. bApproved for the treatment of pediatric patients with NF1 mutant plexiform neurofibromas. cApproved multi-kinase angiogenesis inhibitor. dApproved for compassionate use inhibitors between species (109,110). Small molecule inhibitors of ERK remain under evaluation in clinical trials (e.g., ulixertinib; Fig. 1.6), though rarely in single agent treatment arms; currently there is no FDA approved ERK-selective inhibitor available.

20 1.4. THE PATH FORWARD IN PDAC THERAPY

Perhaps unsurprisingly, single-agent inhibition of various kinases in the MAPK pathway did not yield a meaningful impact on clinical outcomes in PDAC. Additionally, any single-agent targeted therapy is unlikely to significantly prolong survival in the majority of PDAC cases; overcoming this obstacle with combination targeted therapies is a central focus of this dissertation (111). While some cancer types seem to have a singular dependence on signaling through one driver mutation (e.g. Philadelphia chromosome rearrangement in CML), Ozkan-Dagliyan and colleagues identified a therapeutic strategy to counter a mechanism whereby KRAS-driven tumors can survive single-agent inhibition (80). While inhibition of RAF with a pan-RAF inhibitor is sufficient to suppress growth, vertical inhibition of both RAF and ERK synergized to induce apoptosis in a majority of cancer cell lines tested. Vertical inhibition of the MAPK pathway also suppressed growth of organoid cultures as well as xenograft mouse models of

PDAC. Bryant and colleagues sought to better understand the metabolic mechanisms that PDAC relies on for survival (79). In contrast to published literature, suppression of

KRAS signaling through the ERK MAPK pathway increased the cell’s dependency on autophagy for survival. When ERK inhibition was combined with hydroxychloroquine, an inhibitor of autophagy, synergistic induction of apoptosis in PDAC cells was observed.

They extended these analyses both to organoid and mouse models, which showed a promising reduction in tumor growth. Concurrently, Kinsey and colleagues treated a patient with PDAC, who had previously failed all conventional chemotherapies, and achieved a partial response when treated with trametinib plus hydroxychloroquine (112).

21 This preclinical work has served as the foundation from which multiple clinical trials have been launched (NCT04386057, NCT01634893).

Immunotherapy has emerged at the forefront of the next generation of treatment strategies for a multitude of tumor types (113). Therapeutic antibodies that can block

CTLA-4 or PD-1 on lymphocytes or PD-L1 expressed on some tumor cells are capable of preventing tumors from suppressing the immune response mounted against them

(114). In the presence of one of these antibodies, candidate tumors will be targeted and destroyed by the host immune system. These antibody-based therapies have shown efficacy in RAS-driven tumors, such as lung cancer and melanoma (115,116). When tested in patients with PDAC, however, less than five percent of patients showed any clinical benefit from the therapy (117). To better understand the current inefficacy of immunotherapy in PDAC, one group found that the tumor-specific T cell population in

PDAC is relatively low (118). A low abundance of T-cells, or a lack of responsiveness among existing T-cells, could render immuno-checkpoint blockade unable to produce any benefit. Additionally, PDAC tumors harbor far more stroma than other cancer types that have responded to immunotherapy (119,120). It is possible that the dense stroma of

PDAC may prevent access of tumor-specific T-cells to target and destroy cancer cells.

Improving access or reactivity of T-cells in PDAC patients will be a necessary step before achieving success with antibody-based immune checkpoint blockade.

In summary, PDAC remains one of the most difficult types of cancer to treat, with limited effective therapies that are currently available. Work from our lab and many others over the last three decades has substantially advanced the field’s understanding of the genetic and signaling mechanisms that drive PDAC tumorigenesis and tumor

22 maintenance. To date, the majority of developing therapeutic strategies have failed to meaningfully impact outcomes in patients this deadly disease. This dissertation will shed light on previously undescribed signaling dependencies in KRAS-driven PDAC that may lead to the development of novel combination strategies. Additionally, this work will outline a vertical combination inhibition strategy that has direct implications for clinical trials. It is my hope that this body of work will aid our understanding of the fundamental signaling mechanisms underlying PDAC as well as offer therapeutic options moving forward.

CHAPTER 2 – DEFINING THE KRAS-REGULATED KINOME IN KRAS-MUTANT PANCREATIC CANCER

2.1. INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers, with a

5-year survival rate of 10% (1). Despite significant advances in our understanding of the genetic and molecular drivers of PDAC (121), effective targeted therapies are still lacking.

Current standards-of-care are comprised of conventional cytotoxic drugs (122).

Mutational activation of the KRAS oncogene is the initiating genetic event and is found in 95% of PDAC (107). There is now substantial experimental evidence that KRAS is essential for the maintenance of PDAC growth, and consequently, the National Cancer

Institute has identified the development of anti-KRAS therapeutic strategies as one of four major initiatives for the field (123). Recent early-stage clinical findings with direct inhibitors of one KRAS mutant, G12C, support the potential clinical impact of an effective KRAS inhibitor (124,125). However, while KRASG12C mutations are common in lung adenocarcinoma, they comprise only 2% of KRAS mutations in PDAC (45,107).

Therefore, indirect strategies to block aberrant KRAS signaling remain arguably the best approach for the majority of KRAS-mutant PDAC (126).

One key approach is to inhibit downstream effector signaling. Of the multitude of

KRAS effectors, at least four are validated drivers of KRAS-dependent PDAC growth (45).

The best characterized and potentially most crucial is the RAF-MEK-ERK mitogen- activated protein kinase (MAPK) cascade. That activated Braf can phenocopy mutant

Kras and, together with Tp53 mutations, drive full development of metastatic PDAC in

24 mouse models supports the key role of the MAPK cascade in driving Kras-dependent

PDAC growth (86). While the substrates of RAF and MEK kinases are highly restricted,

ERK1/2 serine/threonine kinase activation can cause direct or indirect phosphorylation of a diverse spectrum of more than one thousand proteins (46). Since ERK substrates include other protein kinases (e.g., RSK1-4, MNK2 and MSK1/2), ERK activation can regulate a highly diverse phosphoproteome (71). However, the specific components are context-dependent, and the ERK-regulated phosphoproteome downstream of KRAS in

PDAC is not well delineated.

The second best characterized KRAS effector are phosphoinositide 3-kinases

(PI3K) (47). KRAS activation of PI3K promotes formation of phosphatidylinositol (3,4,5)- triphosphate, leading to activation of the AKT and mTOR serine/threonine kinases.

Activation of PI3K-AKT-mTOR signaling has also been observed as a compensatory response to ERK inhibition, driving resistance to ERK MAPK inhibitors (82,127). Thus, concurrent PI3K inhibition synergistically enhances the anti-tumor activity of ERK MAPK inhibitors (86). Other key KRAS effectors include the RAL and RAC small GTPases, that activate TBK1 and PAK serine/threonine kinases (45). With their high tractability as therapeutic targets, these and other protein kinase components of KRAS effector signaling have been pursued as indirect approaches to KRAS inhibition (128). We propose that still other kinases may also be of interest as targets in KRAS-mutant PDAC.

Additionally, a limitation of essentially all targeted anti-cancer therapies is the onset of treatment-induced acquired resistance (129,130). In response to pharmacologic inhibition of a key cancer driver, cancer cells can induce a complex array of responses that functionally compensate for target inhibition. One major mechanism of resistance is

25 loss of the negative feedback signaling that tempers the strength of growth regulatory signaling networks (131). For example, although aberrant ERK activation can drive cancer growth, excessive ERK activity is deleterious, causing growth cessation through induction of senescence or apoptosis (88,89,132). Therefore, a multitude of ERK- dependent negative feedback loops exist to dampen ERK activity (133). Thus, upon pharmacological inhibition of ERK, loss of ERK-dependent negative feedback leads to

ERK reactivation to overcome inhibitor action. Similar responses are also seen upon inhibition of the PI3K-AKT-mTOR pathway (134).

An important strategy to improve the long-term efficacy of targeted therapies is the elucidation of treatment-induced resistance mechanisms, which can then be used to identify drug combinations capable of blocking or even reversing the onset of resistance.

Unbiased system-wide screening methods applied for this purpose include powerful genetic approaches such as CRISPR- or RNAi-mediated loss-of-function or cDNA overexpression/activation gain-of-function screens (135). For example, a CRISPR-Cas9 screen demonstrated that ERK reactivation is a primary mechanism limiting the efficacy of KRASG12C inhibitors (136). These findings have guided initiation of ongoing clinical trials to evaluate KRASG12C inhibitors in combination with inhibitors that act either upstream

(e.g., on EGFR) or downstream (e.g., on MEK) of KRAS (clinicaltrials.gov). Similarly,

Wood et al. applied an activated signaling expression library to identify both known and novel mechanisms that drive resistance to ERK MAPK inhibitors (137). In a complementary approach, our chemical library screen demonstrated that concurrent inhibition of other ERK MAPK components, to block compensatory ERK reactivation, synergistically enhanced the efficacy of RAF inhibitors in KRAS-mutant PDAC (80).

26 In light of the key role of kinome reprogramming in driving resistance, it is crucial to be able to detect changes in kinase activity and/or expression in response to pharmacologic inhibition of an oncogenic signaling driver. Chemical proteomics is a particularly powerful experimental technique that enables kinome-wide profiling of these changes. One version of this type of assay, MIB/MS, incorporates multiplexed inhibitor beads (MIBs), coupled to mass spectrometry (MS) (138). MIBs, comprised of broad- spectrum kinase inhibitors covalently coupled to Sepharose beads, can be used to monitor protein kinase expression and/or activation, where MIB-associated kinases are identified by MS. In our initial application of MIB/MS, we identified activation of multiple receptor tyrosine kinases (RTKs) in response to MEK inhibitor (MEKi) treatment that may drive MEKi resistance in KRAS-mutant breast cancer (139). Accordingly, concurrent inhibition of RTKs and MEK more effectively impaired transformed and tumor growth in vitro and in vivo than MEKi alone. We have also applied MIB/MS to identify novel inhibitors of chemotherapy-resistant PDAC (140), to pinpoint tumor cell-type specific responses to the clinical kinase inhibitor dasatinib in diffuse large B cell lymphoma (141), and to identify

EGFR/HER2 activation of the MEK5-ERK5 MAPK cascade as compensatory response to ERK1/2 inhibition, which drives resistance to ERK inhibitor treatment by stabilizing the

MYC oncoprotein (81).

In the present study, we hypothesized that a system-wide delineation of KRAS- regulated kinases can identify therapeutic targets that may not have been considered based on our current knowledge of KRAS effector signaling networks. We speculated that

KRAS may require additional kinases beyond the classical RAF-MEK-ERK and PI3K-

AKT-mTOR effector pathways to promote its transforming functions, and that these would

27 be downregulated upon KRAS depletion. In contrast, we anticipated that yet other kinases would be upregulated as compensatory responses to this depletion. Thus, identification of both upregulated as well as downregulated kinases may establish novel targets for anti-KRAS therapies. We therefore applied the kinome-wide MIB/MS assay to elucidate an unbiased profile of the KRAS-dependent kinome in PDAC. Our strategy further elucidates the complex spectrum of protein kinases functionally linked to aberrant KRAS activation and identifies unanticipated signaling vulnerabilities and potential therapeutic approaches for PDAC.

2.2. RESULTS

2.2.1 KRAS SUPPRESSION ALTERS THE ACTIVITY OF DIVERSE KINASES IN

PDAC

Although oncogenic KRAS effector signaling activates the ERK MAPK cascade and other protein kinases, we speculated that the full spectrum of KRAS-regulated protein kinases remained to be elucidated. To gain a full understanding of the KRAS-dependent kinome in KRAS-mutant PDAC, we suppressed KRAS expression in a panel of six KRAS- mutant PDAC cell lines using validated short interfering RNA (siRNA) (Fig. A1 A) (82).

After 72 hours, a time point at which compensatory changes in the kinome have been initiated in response to loss of KRAS (80), cell lysates were processed for MIB/MS label- free proteomics to monitor kinome-wide changes in activity and/or expression (Fig. A1 B).

Our approach detected a total of 227 kinases of sufficient abundance for quantification in one or more PDAC cell lines (Fig. A1 C). As expected, given the genetic heterogeneity of

PDAC tumors, there was significant heterogeneity in the kinase profile of each cell line

28 (Fig. A1 C). Interestingly, the quantified kinases were upregulated and downregulated to the same degree (63 and 62 kinases, respectively; Fig. A1 D). Across all six lines, we identified 15 kinases that were significantly upregulated and 13 that were downregulated in common (Fig. 2.1 A and Fig. A1 E).

To determine if the deregulated kinases represented specific subgroups, we applied the UniProt kinase classification annotation that is based in part on catalytic domain sequence identity and in part on biological function (Fig. A1 F). Notably, although the downregulated kinases did not share much sequence identity (7/13 or 53.8% were

"Other" kinases versus 32/227 or 14.1% of all detected kinases) (Fig. A1, F and G), nearly all have established roles in regulating cell cycle transitions, particularly through mitosis

(e.g., Aurora kinase A (AURKA), CHK1 (CHEK1), WEE1) (table A1).

In contrast, the upregulated kinases were enriched in tyrosine kinases (TK) and tyrosine-like kinases (TKL) (7/15; 46.7%), which comprised 23.3% (53/227) of all detected kinases (Fig. A1, F and H). Strikingly, a driver role in cancer has been attributed to all 15 upregulated kinases (e.g., ERK1 (MAPK3) (table A2), SRC and JAK), supporting the strong likelihood that upregulated kinases serve compensatory growth-promoting roles that attenuate the deleterious consequences of KRAS deficiency. Supporting this possibility, we showed recently that ERK inhibitor treatment of PDAC cell lines caused upregulation of SRC activity and that concurrent SRC inhibition further enhanced ERK inhibitor-mediated growth suppression (81).

To complement these kinome analyses, we used reverse phase protein array

(RPPA) pathway activation mapping (142) to evaluate the signaling consequences of 72 hours of siRNA-mediated KRAS suppression (Fig. A1 I). The RPPA panel included 149

29 phospho-specific and total antibodies to monitor the activation state or expression, respectively, of signaling proteins involved in regulation of cell proliferation, survival, motility, etc. (table A3). As expected, phosphorylated and total ERK proteins were increased, consistent with loss of ERK-dependent negative feedback signaling (Fig. A1

J). Nevertheless, phosphorylation of key ERK substrates important for driving ERK- dependent growth (RSK, ELK1 and MYC) remained strongly suppressed (Fig. A1 J).

Thus, similar to our previous observations with pharmacologic inhibition of ERK (82,127), the level of ERK phosphorylation did not reliably reflect the level of ERK signaling.

Among the decreases in expression and/or activation of multiple proteins involved in cell cycle regulation were a dramatic reduction in the mitotic marker, phosphorylated histone H3 at Ser10, as well as loss of the proliferation markers Ki67 and phosphorylation of RB at Ser780 (Fig. A1 I). RPPA analyses also revealed alterations in expression (e.g.,

BIM) or phosphorylation (e.g., FADD Ser194) of proteins associated with apoptosis, a well- described consequence of loss of mutant KRAS function (143). Finally, reductions were also detected in phosphorylated S6 ribosomal protein, involved in regulation of protein translation (Fig. A1, I and J). Thus, KRAS loss is associated with the activation and inactivation of diverse growth regulatory signaling pathways.

We next wanted to determine the contribution of the ERK MAPK effector pathway to the regulation of the KRAS-dependent kinome. We treated the same panel of six PDAC cell lines for 24 hours with the ERK1/2-selective inhibitor SCH772984 (ERKi) (144), then performed MIB/MS analyses. Quantification of kinases revealed that, of 264 kinases detected, 26 were significantly altered in expression and/or activity across all six cell lines

(Fig. 2.1 B and Fig. A1 K). Of these, seven were upregulated and 19 were downregulated.

30 As expected, ERK1 and ERK2 (MAPK3 and MAPK1) activities were by far the most strongly downregulated upon ERKi treatment (Fig. 2.1 B).

Importantly, many of the same kinases were altered upon pharmacological inhibition of ERK and upon genetic suppression of KRAS. Nine of the kinases downregulated by siKRAS (Fig. 2.1 A) were also downregulated by ERKi (Fig. 2.1 B), including many involved in progression through mitosis. Similarly, DDR1, JAK1 and SRC were among kinases upregulated by both siKRAS and ERKi. Spearman’s rank correlation analysis of siKRAS- and ERKi-mediated kinome changes revealed significant overlap between the 222 kinases detected in both datasets (rho = 0.434, p = 1.77e-11) (Fig. 2.1

C). The significant overlap in kinase signaling changes following either ERK inhibition or

KRAS suppression is consistent with the predominant role of ERK MAPK in supporting

KRAS-dependent PDAC growth (82,86). We conclude that the observed kinome changes conferred by genetic suppression of KRAS were mediated in large part through loss of

ERK1/2 signaling.

2.2.2. INHIBITION OF DDR1, BUT NOT JAK, IMPAIRS PDAC GROWTH

We speculated that upregulated kinases, in particular those with known driver roles in cancer, may represent compensatory activities in response to loss of KRAS. Among the kinases upregulated following KRAS knockdown, JAK1 was notable because the

JAK1-STAT3 signaling axis has been shown to be important for PDAC tumorigenesis and can drive resistance to inhibition of MEK (145,146). Consistent with MIB/MS, RPPA also

32 Fig. 2.1. KRAS knockdown induces kinome-wide alterations primarily through ERK. (A) Heatmap summarizing kinases that were significantly altered (p adjusted < 0.05) after 72 hours of non-specific control siRNA (siNS) or siKRAS treatment, followed by MIB/MS enrichment. Lysates were collected from six KRAS-mutant PDAC cell lines [SW 1990, MIA PaCa-2, PANC-1, HPAC, AsPC-1 and Panc 10.05]. Unsupervised clustering was used to visualize distance metrics of log-transformed fold change values for significant kinases following ANOVA analysis. Kinase class annotations were added from UniProt classification and index of kinases. (B) Volcano plot showing results of significance testing after ANOVA [y-axis] with the log-transformed mean fold change in ERKi (SCH772984) over DMSO control (24 hours). Significant kinases are colored red [increased] or blue [decreased] if p-value is significant (p.adj < 0.05) following Benjamini-Hochberg correction (n = 3). (C) Spearman’s rank correlation analysis of siKRAS/siNS samples versus ERKi/DMSO samples, 222 kinases were shared among dataset.

showed that four of five cell lines displayed significant (p-adjusted < 0.05) activation of

STAT3 following KRAS knockdown (Fig. 2.2 A), as indicated by phosphorylation at the

JAK family site Tyr705 (147). Phosphorylation of STAT3 at Ser727, which is not a JAK site but a possible ERK or JNK target (148), also increased in a cell line dependent manner

(148), as did phosphorylation of STAT1 and STAT5. STAT6 phosphorylation at Tyr641 was the most consistent of all, in agreement with our previous finding of significant upregulation of JAK signaling through this site upon blockade of the MAPK cascade (80).

To determine whether JAK1-STAT3 was similarly affected, we directly inhibited signaling through ERK MAPK. In the presence of the ERK1/2 inhibitor SCH772984, STAT3 phosphorylation (Tyr705) was increased in Pa02C and Pa16C and unchanged in MIA

PaCa-2 and PANC-1 cells (Fig. 2.2 B). We conclude that upregulation of JAK1-STAT signaling induced by KRAS suppression was mediated through loss of ERK effector signaling.

We next determined if upregulation of JAK signaling is a compensatory response that can offset the growth suppression induced by loss of KRAS or ERK. We utilized three different JAK inhibitors, targeting JAK1 (filgotinib), JAK1/2 (ruxolitinib), and JAK1/3

33 (tofacitinib) (149). All three JAK inhibitors reduced STAT3 phosphorylation in a dose- dependent manner, though PANC-1 cells were resistant to filgotinib (Fig. A2 A). JAK inhibitors alone had no effect on anchorage-dependent proliferation of four PDAC cell lines (Fig. 2.2 C), indicating the lack of dependence on JAK when KRAS is present.

Further, co-treatment of four PDAC cell lines with each JAK inhibitor in combination with

ERKi revealed that concurrent JAK inhibition did not further enhance ERKi-mediated growth suppression (Fig. 2.2 D and Fig. A2 B). Thus, while JAK activity was increased upon loss of KRAS, subsequent inhibition of JAK family kinases did not potentiate the antiproliferative effect of direct ERK inhibition in PDAC.

To further investigate whether upregulated kinases could compensate for the loss of KRAS, we next evaluated the discoidin domain receptor 1 (DDR1). DDR1 is a receptor tyrosine kinase that transduces collagen-mediated proliferative signaling from stroma- associated extracellular matrix (150), and pharmacologic inhibition of DDR1 function impaired PDAC growth in vivo in a mouse model (151,152). Given our identification of

DDR1 as a kinase that was upregulated upon KRAS suppression in PDAC cell lines in vitro (Fig. 2.1 A), we sought to determine whether upregulated DDR1 could also serve as an increased pro-proliferative signal in a KRAS-dependent, tumor cell-intrinsic manner.

We first determined that both KRAS knockdown and ERK inhibition increased DDR1 protein levels (Figs. 2.2, E and F, respectively). Additionally, ERK inhibition increased phosphorylation of the DDR1 effector PYK2 (pPYK2, Fig. 2.2 F) at the DDR1 phosphorylation site, Tyr402 (152), in three of four cell lines. Thus, we confirmed that DDR1 signaling is responsive to KRAS-ERK signaling.

34 To evaluate the possibility that DDR1 upregulation is a compensatory response to the growth suppression mediated by loss of KRAS-ERK, we first determined if inhibition of DDR1 alone is deleterious to growth. We treated PDAC cells with 7rh, a potent and selective ATP-competitive DDR1 inhibitor (153) and observed comparable IC50

402 (suppression of PYK2 Tyr ) and GI50 values, supporting on-target growth inhibition (Fig.

2.2 G and Fig. A2, C and D) (153). We found that concurrent inhibition of DDR1 and ERK caused dose-dependent loss of cell viability (Fig. 2.2 H and Fig. A2 E), ranging between additivity and synergy, according to BLISS analysis (Fig. A2 F). Overall, our evaluation of kinases upregulated by siKRAS or ERKi has revealed a novel compensatory mechanism in DDR1 and a surprising dispensability of JAK signaling for PDAC anchorage-dependent cell growth and viability.

2.2.3. LOSS OF KRAS OR MAPK SIGNALING CAUSES LOSS OF G2/M AND DNA-

DAMAGE RESPONSE KINASES

In order to better understand the relationships among downregulated kinases following KRAS genetic suppression, we performed STRING analysis on significantly altered kinases (Fig. 2.3 A). Eleven of the 13 downregulated kinases formed a tight interaction node, with only EPHA2 and TNIK involved in distinctly separate signaling networks. Next, we utilized Panther Gene Ontology to determine the relevant biological processes (Fig. 2.3 B). Given that our queried genes exclusively encode protein kinases, it was expected that “protein phosphorylation” was the top gene set identified.

Additionally, a main function of KRAS signaling through the ERK cascade is to promote cell cycle progression through G1 by transcriptional stimulation of CCND1 (encoding

Figure 2.2. DDR1, but not JAK, is a therapeutic vulnerability in PDAC. (A) Reverse phase protein array (RPPA) of five PDAC cell lines following 72 hour siRNA knockdown of KRAS. Values are log2-transformed fold change with respect to median siNS value (n = 4). (B) Immunoblot of PDAC cell lines following treatment with vehicle or ERKi

36 (SCH772984) for 24 hours (n = 4). Ratios of pSTAT3/STAT3 are reported below the blot. (C) Proliferation of PDAC cells (5 days) following treatment with JAK1i (filgotinib), JAK1/2i (ruxolitinib), or JAK1/3i (tofacitinib) at various concentrations. Cell numbers at endpoint were normalized to vehicle-treated control (100% growth) for each cell line. Curves were fit using a 4-parameter log-logistic function and the drm package in R (n = 3). (D) Proliferation of Pa02C cells (5 days) following combination treatment with JAK1i, JAK1/2i, or JAK1/3i and ERKi (SCH772984) at various concentrations. Cell numbers were normalized and curves were fit as in panel (C) (n = 3). (E) Immunoblot of PDAC cell lines following treatment with non-specific (NS) or KRAS-targeting (K1, K2) siRNA constructs for 72 hours. Relative DDR1 levels are indicated below the blot (total DDR1 normalized for loading differences as determined by β-actin). “Short” and “long” indicate shorter and longer exposures of the same membrane, respectively (n = 3). (F) Immunoblot of PDAC cell lines following treatment with vehicle or ERKi (SCH772984) for 24 hours (n = 4). Relative pPYK2 levels (vs. DMSO) in each cell line are indicated below the blot. (G) Proliferation of PDAC cells (5 days) following treatment with DDR1i (7rh) at various concentrations. Cell numbers were normalized and curves were fit as in panel (C) (n = 3). (H) Proliferation of Pa02C cells (5 days) following combination treatment with DDR1i (7rh) and ERKi (SCH772984) at various concentrations. Cell numbers were normalized and curves were fit as in panel (C) (n = 3).

cyclin D1), leading to CDK4/6-dependent phosphorylation and inactivation of the RB tumor suppressor (154). Thus, it was unexpected that we observed that multiple downregulated kinases are involved in G2/M checkpoint and mitotic functions (table A1).

Consistent with KRAS promotion of G1/S cell cycle progression, flow cytometry analyses in four PDAC cell lines showed that KRAS knockdown caused an increase (14-24%) in cells in G1 and a corresponding decrease (15-22%) in cells in S, albeit no significant change in the G2/M cell population (Fig. A3, A and B).

Interestingly, the 13 downregulated kinases identified in the chemical proteomics screen were also enriched for roles in DNA damage checkpoints and maintaining DNA integrity (Fig. 2.3 B). We therefore queried our RPPA data for genes related to both DNA- damage repair and cell cycle, which revealed a mixed response to KRAS knockdown (Fig.

A3 C). Cyclin D1 levels were reduced whereas cyclin A levels either increased or remained unchanged. Additionally, phosphorylation of ATM at Ser1981 increased whereas

37 phosphorylation of ATR at Ser428 was diminished in all cell lines. Taken together, these changes suggest that loss of KRAS causes a loss of specific cell cycle promoting factors.

Additionally, the changes in ATM and ATR phosphorylation, though divergent, suggest that KRAS knockdown may induce genomic stress that could also contribute to cell cycle factor changes.

While significant literature points to the importance of KRAS and ERK in promoting

G1 progression, fewer studies outline the role of mutant KRAS in maintaining DNA damage checkpoint kinases such as WEE1, PKMYT1, and CHEK1 (Fig. 2.3 A) (155-157).

We confirmed that KRAS knockdown caused loss of WEE1, CHEK1, and PLK1 proteins

(Fig. 2.3 C). A recent transcriptome analysis of PDAC identified WEE1 as a potential therapeutic target (158). Therefore, we focused our further investigations on this DNA- damage checkpoint kinase.

To address a basis for the loss of WEE1 protein caused by KRAS suppression, we first examined WEE1 protein stability. Treatment with the proteasome inhibitor MG132 prevented WEE1 degradation more robustly in KRAS-suppressed samples as compared to control samples. This suggests that the absence of KRAS signaling destabilizes WEE1 protein (Fig. 2.3, D and E, and Fig. A3, D and E). We hypothesized that KRAS, through its extensive transcriptional network, may also regulate WEE1 transcription. Indeed, RT-

PCR analyses demonstrated that loss of KRAS decreased WEE1 transcripts in all five cell lines tested (Fig. 2.3 F). We conclude that WEE1 protein stability and transcription are both reduced upon KRAS suppression.

38 2.2.4. INHIBITION OF WEE1 KINASE INDUCES GROWTH ARREST AND

APOPTOSIS IN PDAC CELLS

The loss of WEE1 observed upon KRAS knockdown-induced growth suppression suggests that WEE1 activity may contribute to KRAS-mutant PDAC proliferation and therefore that direct inhibition of WEE1 may suppress KRAS-mediated cell proliferation.

To address this possibility, we first treated cells with siRNA targeting WEE1 and verified that loss of phosphorylation of the WEE1 substrate, CDK1 (Tyr15; pCDK1) was a reliable biomarker for loss of WEE1 function in PDAC (Fig. 2.4 A). Similarly, treatment with the

WEE1-selective clinical candidate inhibitor adavosertib/AZD1775 (WEE1i) dose- dependently reduced CDK1, with >80% reduction at 100 nM (Fig. 2.4 B and Fig. A4 A).

Additionally, WEE1i treatment inhibited the proliferation of all six PDAC cell lines evaluated, with GI50 values (38 to 168 nM) comparable to the IC50 values (< 100 nM), supporting on-target growth suppression (Fig. 2.4 C and Fig. A4, A to C). Similar growth inhibitory activities of WEE1i were also seen in clonogenic colony formation assays (Fig.

2.4 D). Consistent with previous studies (159), we found that pharmacological inhibition of WEE1 led to accumulation in S- and G2/M-phases of the cell cycle (Fig. 2.4 E, and Fig.

A4, D and E). Additionally, WEE1i caused an approximately fivefold increase in apoptotic cells (Fig. 2.4 F). Taken together, these results support our conclusion that downregulation of WEE1 contributes to the growth suppression induced by loss of KRAS.

2.2.5. COMBINED INHIBITION OF WEE1 AND ERK CAUSES SYNERGISTIC

GROWTH ARREST AND APOPTOSIS

Paradoxical activation of ERK has been reported previously in response to pharmacological inhibition of CHK1 (160,161), another protein kinase involved in

Figure 2.3. Loss of KRAS/MAPK signaling contributes to loss of G2/M and DNA- damage response kinases. (A) Downregulated kinase identifiers from the MIB-MS screen (Fig. 1A) were used as input into the STRING database, and the resulting bitmap was downloaded. Edges (lines between nodes) represent known or predicted protein- protein associations. (B) Downregulated kinase identifiers were used as input for Gene Ontology enrichment analysis of biological processes (162,163). Selected enriched gene

40 sets (p-adjusted < 0.05) are displayed along the y-axis, with the –log10-transformed p- values displayed along the x-axis. (C) Immunoblot analysis of PDAC cell lines following treatment with non-specific (NS) or KRAS-targeting (K1, K2) siRNA constructs for 72 hours (n = 4). (D) Protein stability of WEE1 as determined by 4-hour MG132 proteasomal inhibitor treatment following 26-hour treatment with non-specific (NS) or KRAS-targeted (K1, K2) siRNA constructs (n = 3). (E) Quantification of blot in panel (D). Densitometry estimates were first normalized for loading efficiency using β-actin. Next, each sample value was calculated as a ratio of MG132+/MG132- and paired by siRNA construct. (F) qRT-PCR was performed on five PDAC cell lines following 72-hour treatment with non- specific (NS) or KRAS-targeted (K1, K2) siRNA. Relative expression [y-axis] of WEE1 transcripts was measured (n = 3).

checkpoint inhibition in response to DNA damage. To determine if there is a similar response to WEE1 inhibition, we examined pERK in PDAC cell lines treated with adavosertib for 48 hours. We observed a dose-dependent increase in pERK in all treated lines (Fig. 2.4 B and Fig. A4, A and F). Evaluation at shorter time-points showed that

WEE1 activity (pCDK1) was rapidly suppressed after 6 to 24 hours, whereas increased pERK was seen only after 48 hours (Fig. A4 C). This delayed onset is consistent with compensatory reactivation of ERK to offset WEE1 inhibition-induced growth suppression.

To address this possibility, we determined whether concurrent ERK inhibition enhances

WEE1i growth inhibitory activity. We treated a panel of cell lines with both WEE1i and the

ERK1/2-selective inhibitor SCH772984 (ERKi), which resulted in fewer colonies than treatment with either inhibitor alone (Fig. 2.5 A). BLISS synergy analysis revealed that the combination exhibited synergistic activity in three cell lines (MIA PaCa-2, Pa16C and

Pa02C), and additive activity in three cell lines (Fig. 2.5, B and C). Combining low-dose

ERK inhibition (IC25-IC50) with WEE1 inhibition also synergistically induced apoptosis in four of four cell lines (Fig. 2.5, D and E). We conclude that concurrent inhibition of WEE1 and ERK synergistically induces both growth arrest and apoptosis in PDAC cell lines.

Figure 2.4. Inhibition of WEE1 kinase induces growth arrest and apoptosis in PDAC cells. (A) Immunoblot analysis of PDAC cell lines following treatment with non-specific (NS) or WEE1-targeting (W1-1, W1-2) siRNA for 24 or 48 hours (n = 3). (B) Immunoblot analysis of Pa02C cells following treatment with WEE1i for 48 hours (n = 4). (C)

42 Proliferation of PDAC cells following treatment for 5 days with WEE1i (adavosertib) at various concentrations. Cell numbers at endpoint were normalized to vehicle-treated control (100% growth) for each cell line. Curves were fit using a 4-parameter log-logistic function and the drm package in R (n = 3). (D) PDAC cells were plated at low density to allow for clonogenic growth and various concentrations of WEE1i were added. After 10 days, plates were stained with crystal violet and colonies were imaged (n = 4). (E) Cells were treated with vehicle or adavosertib (100 nM for Pa02C, 200 nM for Pa16C) for 24 hours, then fixed and stained with propidium iodide. Flow cytometry quantification of cell cycle populations is shown for two representative cell lines (n = 3). (F) Annexin V-FITC staining followed by flow cytometry in PDAC cell lines after 72-hour treatment with 500 nM WEE1i (n = 4).

2.2.6. INHIBITION OF WEE1+ERK1/2 ARRESTS GROWTH OF PDAC ORGANOIDS

Three-dimensional (3D) patient-derived PDAC organoids maintained in Matrigel are believed to better model the therapeutic response of PDAC patients than cells grown in two-dimensional (2D) culture (17,164). Therefore, we next evaluated the impact of concurrent WEE1 and ERK inhibition on proliferation of KRAS-mutant PDAC organoid cultures. The combination of inhibitors caused organoids to collapse and shrink more than either inhibitor alone (Fig. 2.6 A, and Fig. A5, A and B). Concurrent WEE1 and ERK inhibition caused additive or synergistic effects on viability, depending on doses (Fig. 2.6,

B and C). Taken together with anchorage-dependent growth assays, we conclude that concurrent ERKi treatment to block compensatory ERK activation can enhance WEE1 inhibition-mediated growth suppression.

2.3. DISCUSSION

It is now well-appreciated that the effectiveness of pharmacologic inhibition of key cancer drivers is offset by the induction of compensatory activities (129,130). Therefore, identification of treatment-induced compensatory mechanisms can guide the development of combination approaches to better achieve long-term and durable clinical

Figure 2.5. Combined inhibition of WEE1 and ERK induces growth arrest and apoptosis. (A) PDAC cells were plated at low density and various concentrations of WEE1i (adavosertib) and/or ERKi (SCH772984) were added to the media. Cells were

44 permitted to grow for 10 days to allow for clonogenic growth. Plates were then stained with crystal violet and imaged (n = 3). (B) Proliferation of PDAC cells following treatment for 5 days with WEE1i (adavosertib) and ERKi (SCH772984) at various concentrations in combination. Cell numbers at endpoint were normalized to vehicle-treated control (100% growth) for each cell line. Curves were fit using a 4-parameter log-logistic function and the drm package in R (n = 4). (C) BLISS Synergy scores were calculated using the proliferation effect sizes from panel (B). Scores < 1 indicate synergy (red), scores = 1 indicate additivity (white), and scores > 1 indicate antagonism (blue) (n = 4). (D) Annexin V-FITC staining followed by flow cytometry in PDAC cell lines after 72-hour treatment with WEE1i. Quantification of apoptosis at each dose combination is displayed for the indicated cell lines (n = 4). (E) BLISS Synergy scores were calculated using the proliferation effect sizes from panel (D). Scores as for panel (C), representative heatmaps are shown (n = 4). responses. The recent development of direct inhibitors of KRAS (124,125) has made it even more critical to understand the compensatory activities that can drive resistance to

KRAS suppression. In the present study, we applied an activity-dependent chemical proteomics strategy, MIB/MS, to profile the KRAS-dependent kinome in KRAS-mutant

PDAC. We identified a diverse spectrum of kinases that were downregulated or upregulated in response to KRAS depletion; many of these had not previously been associated with KRAS effector signaling. We speculated that both groups of kinases may be effective therapeutic targets, with upregulated kinases representing compensatory mechanisms that drive resistance to KRAS suppression. We identified upregulation of

DDR1 as one such mechanism and showed that DDR1 inhibition suppressed the growth of KRAS mutant PDAC lines. Conversely, our characterization of one downregulated kinase, WEE1, showed that this DNA damage checkpoint inhibitor can also serve as a therapeutic target. Our study demonstrates the utility of kinome-wide profiling to identify novel strategies for targeting KRAS-mutant PDAC.

Our application of the MIB/MS proteomics screen identified a spectrum of kinases that were down- or upregulated upon acute KRAS suppression. We detected a total of

Figure 2.6. Inhibition of WEE1+ERK1/2 arrests growth of PDAC organoids. (A) hM1A PDAC organoids were seeded for three days in Matrigel and organoid maintenance factors, followed by treatment for 10 days with ERKi or WEE1i or the combination. Representative images are shown; scale bar is equivalent to 200 µm (n = 4). (B) Quantification of CellTiter-Glo 3D cell viability assay in organoids at single dose combination from (A). (C) Organoids from (A) were quantified using CellTiter-Glo 3D cell viability assay across a range of WEE1i (adavosertib) and ERKi (SCH772984) doses. Percent viability was normalized to DMSO-treated organoids at the assay endpoint.

46 227 kinases in one or more of the six KRAS-mutant PDAC cell lines analyzed. Of these,

125 (55%) were altered in activation and/or expression in one or more lines upon KRAS suppression, underscoring the diverse consequences that aberrant KRAS function confers upon the human kinome. It is likely that even this is a substantial underestimate: the human kinome encodes over 500 protein kinases. Some kinases were not detected here because they are not recognized by any one of the six broad-spectrum kinase inhibitors used in the affinity purification step; others were not detected due to low or lack of expression in pancreatic cancer (165).

Further, although the genetic landscape of PDAC is dominated by the frequent occurrence of alterations in only four genes (KRAS, TP53, CDKN2A and SMAD4), the majority of genetic alterations are found at frequencies of less than 5% (166). Of the 125

KRAS-deregulated kinases, only 28 (22%) were altered across all six KRAS-mutant

PDAC lines. This observed heterogeneity is thus consistent with the genetic heterogeneity of PDAC, and highlights the distinct and significant impact that co-occurring mutations may have on KRAS function, and, consequently, on response to specific therapies.

We observed significant overlap of the kinase activity/expression changes caused by acute genetic suppression of KRAS compared with those caused by pharmacologic inhibition of ERK. Nine of 13 KRAS-downregulated kinases and four of 15 KRAS- upregulated kinases were also altered upon ERK inhibition. That we observed a dominant role of the ERK MAPK cascade in regulating the KRAS-dependent kinome is consistent with the critical role of this protein kinase cascade in driving KRAS-dependent PDAC growth. Nevertheless, many of the 28 KRAS-regulated kinases are not known to be

47 directly associated with ERK signaling. Only eight (CKIIε, CDK1, CHK1, ERK1, EPHA2,

SRPK2, TNIK and TTK) are listed in a recent compilation of direct or indirect ERK substrates from 14 different phosphoproteomic studies (46). However, it is still possible that some of the other 20 KRAS-regulated kinases are ERK substrates. The compilation includes no studies performed in PDAC. In our ongoing phosphoproteomic analyses of

ERK-dependent phosphorylation in PDAC, we have identified numerous potential direct or indirect ERK substrates that have not been identified previously. Finally, none of the

28 kinases identified in the present study are listed in the GSEA KRAS gene signature, indicating that many of the kinases we detected are altered in activity rather than by gene expression.

Finally, some observed alterations may be indirect rather than associated specifically with KRAS signaling. For example, reduced kinase expression may be a consequence of the incomplete G1 arrest caused by KRAS suppression. NEK2 is undetectable during G1, but accumulates progressively throughout S phase, reaching maximal levels in late G2 (167).

Our working hypothesis for this study was that the kinases that are upregulated upon KRAS suppression would be distinct from those typically utilized by KRAS for its transforming activities. Instead, it is now well-established that compensatory kinome reprogramming occurs upon pharmacologic inhibition of RAF and PI3K signaling

(133,134). We hypothesized that loss of KRAS signaling would similarly induce compensatory kinome reprogramming, such that the upregulated kinases would enable maintenance of the transformed phenotype. Consistent with this possibility was the significant enrichment of upregulated tyrosine kinases, a class that includes many

48 oncogenic kinases (e.g., EGFR, HER2, etc.). To evaluate this possibility, we selected two kinases, JAK1 and DDR1, for validation analyses. Since there is evidence that JAK1 can act as a cancer driver in multiple cancer types (168), we were surprised that pharmacologic inhibition of JAK1, either alone or together with ERK inhibition, did not negatively impact PDAC growth. This finding emphasizes that the potential therapeutic value of an upregulated or hyperactivated kinase requires functional validation, not simply evidence of increased expression or activity.

In contrast to JAK1, we verified that pharmacological inhibition of DDR1 alone or in combination with an ERK inhibitor caused additive or synergistic growth suppression of PDAC cell lines. To date, there has been limited effort in the pursuit of DDR1 as a therapeutic target in PDAC and there are no clinically tractable DDR1-specific inhibitors.

However, the DDR1-selective pharmacological inhibitor 7rh, together with chemotherapy, impaired tumorigenic growth of PDAC cell lines (152). In a complementary study, genetic ablation of Ddr1 in the KPC (KrasG12D/+; Tp53R172/+) mouse model of PDAC impaired metastatic tumor growth (151). Finally, a recent kinome profiling screen using a different experimental method than MIB/MS demonstrated upregulation of DDR1 expression in patient-derived PDAC cell lines (169). In agreement, we observed that direct pharmacologic inhibition of DDR1 with the small molecule inhibitor 7rh suppressed PDAC cell proliferation in vitro. Additionally, we observed synergistic reduction in proliferation when we combined the DDR1 inhibitor with the ERK1/2-selective inhibitor SCH772984, supporting further evaluation of this combinatorial therapeutic strategy.

The downregulated kinases were enriched (11 of 13) in kinases that modulate cell cycle progression, particularly processes involved in mitosis. Inhibition of some of these

49 individual kinases are sufficient to impair cancer growth, indicating that KRAS regulates growth through a multitude of mechanisms. For example, polo-like kinase (PLK1) has been identified as a synthetic lethal interactor with mutant RAS (170). We showed previously that pharmacologic inhibition of TTK impaired PDAC growth (171). In agreement with previous studies (166,172), we showed here that inhibition of WEE1 suppresses PDAC growth. Additionally, we found that concurrent ERK inhibition, to counter the compensatory ERK activation associated with WEE1 inhibition, further enhanced WEE1i growth-inhibitory activity. The WEE1i adavosertib is currently being evaluated alone or in combination with other anti-cancer therapies in 20 active or recruiting clinical trials, including one in pancreatic cancer (NCT02194829, accessed Feb.

22, 2021).

In summary, the MIB/MS chemical proteomics strategy provides a powerful experimental approach to delineate a more complete understanding of the KRAS- dependent kinome, implicating kinases not identified by other methods. We additionally showed that both downregulated and upregulated kinases upon loss of KRAS represent potential therapeutic targets for PDAC treatment. Our findings also further establish mechanisms by which the ERK MAPK effector pathway drives KRAS-dependent PDAC growth, through regulation of multiple distinct regulators of cell cycle progression, particularly mitosis. Finally, that KRAS suppression caused upregulation of a diverse spectrum of functionally distinct kinases underscores the need to develop combination inhibitor therapies to thwart treatment-induced kinome reprogramming, that will result in acquired resistance and limit the long-term efficacy of mutant-specific KRAS inhibitors.

50 2.4. MATERIALS AND METHODS

2.4.1. CELL CULTURE

Cell lines were obtained from the American Type Culture Collection (AsPC-1,

Panc10.05, SW-1990, MIA PaCa-2, PANC-1, HPAC and HPAF-II) or were a gift from J.

Fleming at MD Anderson Cancer Center (Pa01C, Pa02C, Pa14C and Pa16C). Cells were maintained in either Dulbecco’s minimum essential medium (DMEM) (Gibco, 11995-065;

MIA PaCa-2, PANC-1, HPAC, HPAF-II, Pa01C, Pa02C, Pa14C and Pa16C) or RPMI-

1640 (Gibco; AsPC-1, Panc 10.05 and SW-1990) supplemented with 10% fetal bovine serum (Sigma-Aldrich), penicillin and streptomycin (Sigma-Aldrich). All cell lines were short-tandem repeat (STR)-profiled to confirm their identity. All cell lines tested negative for mycoplasma contamination.

2.4.2. ANTIBODIES AND REAGENTS

SCH772984 was provided by Merck. The following compounds were purchased from Selleckchem: adavosertib (S1525), filgotinib (S7605), ruxolitinib (S5243), and tofacitinib (S2789); or from Sigma-Aldrich: 7rh (SML1832), MG-132 (M7449). The following antibodies were obtained from Cell Signaling Technology: anti-DDR1 (5583S), anti-PYK2 (3090S), anti-STAT3 (9139S), anti-phosphorylated STAT3 Tyr705 (9145S), anti-WEE1 (13084S), anti-CHK1 (2360S), anti-PLK1 (4513T), anti-ERK (4696S), anti- phosphorylated ERK Thr202/Tyr204 (4370S), anti-phosphorylated CDC2 Tyr15 (4539S), anti-phosphorylated HistoneH3 Ser10 (53348S); from Sigma-Aldrich: anti-vinculin

(V9131), anti-β-actin (A5441) and anti-KRAS (WH0003845M1); from Invitrogen: anti- phosphorylated PYK2 Tyr402 (44-618G); or from Santa Cruz Biotechnology: anti-CDC2

(sc-54).

51 2.4.3. RNAI KNOCKDOWN STUDIES

Short-interfering RNA (siRNA) experiments were performed with 10 nM siRNA and

RNAiMAX Lipofectamine (Invitrogen, 13778150) according to the manufacturer’s protocol. siRNAs were obtained from Thermo Fisher: Non-specific siRNA (4390844), siKRAS-1 (4390825-s7939), siKRAS-2 (4390825-s7940), siWEE1-1 (4390824-s21), siWEE1-2 (AM51331-404), siDDR1-1 (4390824-s2298), and siDDR1-2 (4390824- s2230). On day 1, cells were plated at 2 x 105 cells per well of a 6-well plate. On day 0,

Lipofectamine was warmed to room temperature and added to Opti-MEM (Gibco, 31985-

070) to a final concentration of 20x before low-speed vortex. siRNAs were added to individual tubes of Opti-MEM at 20x concentration. Both Lipofectamine and siRNA tubes were incubated at RT separately for 5 min. After incubation, Lipofectamine in Opti-MEM was combined at a 1:1 ratio with individual siRNAs in Opti-MEM. Tubes were carefully inverted five to seven times to mix Lipofectamine and siRNA in Opti-MEM and were subsequently incubated at RT for 30 min. Two hundred µl of the Lipofectamine with siRNA were added to 1.8 ml of fresh cell culture media per well. Cells were incubated for specified times before collection for immunoblotting, proliferation, clonogenic, cell cycle or apoptosis assays.

For qRT-PCR experiments, the above transfection protocol was modified to combine the day 1 and day 0 steps. Cells (2 x 105) were plated in 1.8 ml cell culture medium with the addition of 200 µl of Lipofectamine with respective siRNA on day 0.

2.4.4. MIB/MS

Samples were prepared according to the RNAi knockdown protocol outlined in

Materials and Methods. Following 72 hours of RNAi knockdown with either non-specific

52 siRNA or siKRAS 1, the cell plates were placed on ice and the cells were washed 5x with large-volume, ice cold phosphate-buffered saline (PBS). The final PBS wash was thoroughly aspirated and MIB/MS lysis buffer (50 mM HEPES (pH 7.5), 0.5% Triton X-

100, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 10 mM sodium fluoride, 2.5 mM sodium orthovanadate, protease inhibitor cocktail (Roche), 1% phosphatase inhibitor cocktail 2

(Sigma-Aldrich), and 1% of phosphatase inhibitor cocktail 3 (Sigma-Aldrich) was added to the adherent cells and permitted to incubate on ice for 5 min. Using a rubber cell scraper, cell lysates were transferred from the plates to 1.7 ml microcentrifuge tubes and placed on ice for 10 min. Cell lysates were subsequently sonicated four times for 15 sec at 50% power, alternating between samples to allow cooling on ice between pulses. Cell lysates were then centrifuged at top speed (13,200 rpm) for 10 min at 4°C. Supernatants were clarified using a 0.2 µm filter, transferred into labeled tubes and stored at -80°C (~5 mg protein per experiment). A small portion of lysate was removed for Bradford assay estimation of protein concentration. The lysates were thawed and gravity-flowed over multiplexed kinase inhibitor beads (MIBs; Sepharose conjugated to VI-16832, CTx-

0294885, PP58, Purvalanol B, UNC8088A, and UNC21474). MIBs were then washed with high salt (1 M NaCl) and low salt (150 mM NaCl + 0.1% SDS) lysis buffers without the inhibitors. The samples were boiled with the elution buffer (100 mM tris-HCl, 0.5%

SDS, and 1% β-mercaptoethanol, pH 6.8) at 100°C for 5 min to elute the bound kinases from MIBs. The eluted kinases (proteins) were concentrated with Amicon Ultra-4 (10K cutoff) spin columns (Millipore), purified by removing the detergent using methanol/chloroform extraction, and digested by sequencing grade trypsin (Promega) overnight at 37°C. Hydrated ethyl acetate extraction was used to remove Triton, and

53 PepClean C18 spin columns (Thermo Scientific) were used to de-salt the digested peptides.

Biological triplicates of the MIB samples were analyzed by LC-MS/MS as we described previously (81). Briefly, each sample was injected onto an EASY-Spray

PepMap C18 column (75 mm id 3 25 cm, 2 mm particle size) (Thermo Scientific) and separated over a 2-hour method. The gradient for separation consisted of 5%–32% mobile phase B at a 250 nl/min flow rate, where mobile phase A was 0.1% formic acid in water and mobile phase B consisted of 0.1% formic acid in ACN. The Thermo QExactive

HF was operated in data-dependent mode where the 15 most intense precursors were selected for subsequent HCD fragmentation (set to 27%).

Following MaxQuant processing of data and generation of Label-free quantification

(LFQ) intensity values, data files were analyzed using R (version 3.5.2). Kinases were excluded if they were not present in >50% of samples or if they contained two or more peptides. For imputation of missing values, a normal distribution was modeled on the non- missing LFQ intensity values of the kinases containing missing intensity values. Imputed values were drawn randomly from this distribution. Following filtering and imputation, LFQ intensity values were log2 transformed and the fold change over the median vehicle value was calculated for each kinase. Kinases significantly different between treatments with siNS and siKRAS, or with DMSO and ERKi, were determined using one-way ANOVA

(Benjamini-Hochberg adjusted p value < 0.05). Euclidean distance and average linkage were utilized for unsupervised hierarchical clustering of log2 fold-change values for significant kinases.

54 2.4.5. IMMUNOBLOTTING

Plates containing the PDAC cell lines were washed with PBS and lysed with Triton

X-100 lysis buffer (25 mM tris buffer, 100 mM NaCl, 1 mM EDTA and 1% Triton X-100), supplemented with cOmplete protease inhibitor (Roche) and Phosphatase Inhibitor

Cocktail (Sigma-Aldrich). Cells were incubated on ice for 10 min before scraping into a microcentrifuge tube and placed back on ice. Sample tubes were centrifuged for 10 min at 13,200 rpm at 4°C. The supernatant was collected and used to determine protein concentration using a Bradford Assay (Bio-Rad), and samples were prepared with 4x

Laemmli Sample Buffer (Bio-Rad). Samples were then boiled and stored at -20°C. Ten percent polyacrylamide gels were used to clarify lysates before transferring onto PVDF membranes (Millipore, IPVH00010) at 90 V for 90 min. Membranes were blocked for 1 hour in 5% BSA solution diluted in TBST (TBS with 0.05% Tween-20) and washed with

TBST. Membranes incubated overnight in primary antibodies diluted in 5% BSA solution supplemented with sodium azide. Secondary antibodies used were goat anti-mouse

(Invitrogen, 31432) and goat anti-rabbit (Invitrogen, 31462). Membranes were washed with TBST before imaging using the ChemiDoc MP Imaging System (Bio-Rad) and ECL reagent.

2.4.6. QUANTITATIVE REAL-TIME PCR

Cells were grown in 6-well plates, transfected with siRNA as indicated, and harvested. RNA was extracted using the RNeasy Isolation Kit (Qiagen) and converted to cDNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems).

RT-PCR was performed using the TaqMan system (Applied Biosystems) in a 384-well format. FAM-labeled target primer and endogenous human ACTB control (beta actin)

55 (Thermo Fisher Scientific) were mixed with master mix and template, and after 40 cycles were analyzed on an QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems).

2.4.7. FLOW CYTOMETRY

For apoptosis assays, TACS Annexin V-FITC Kits (BD Biosciences) were utilized to measure apoptosis. The following protocol is closely adapted from the manufacturer’s specifications. Prior to trysonization, floating cells in the spent culture medium were collected. Cells were then trypsinized cells and collected, mixed, and centrifuged at 300g for 5 min at room temperature. The cells were washed with phosphate buffered saline

(PBS) and centrifuged at 300g for 5 min before incubating the cell pellet in annexin V staining solution (1% Annexin V-FITC, 1x propidium iodide solution, in 1x calcium- containing binding buffer) in the dark for 15 min at room temperature. Cell mixture was subsequently diluted 1:5 in binding buffer. For cell cycle analysis, adherent cells were washed with PBS prior to being trypsinized. After trysponization, cells were centrifuged at 300g for 5 min before washing once in PBS. Cells were pelleted and resuspended in 1 ml PBS before adding 9 ml of 70% ethanol drop-wise to each tube with gentle agitation.

Cells were permitted to fix for a minimum of 18 hours at 4°C. Fixed cells were then pelleted, washed once in PBS, resuspended in 40 μg/ml propidium iodide (PI), 100 μg/ml

RNase A in PBS, and incubated at 37°C for 3 hours.

Apoptosis assays were analyzed using FCS Express. A FSC-A vs. SSC-A dot plot was used to exclude debris and generate a “cells” gate. “Cells” were plotted in a FITC-A

(x) vs. PI-A (y) dot plot and apoptotic cells (FITC+) were analyzed. Cell cycle analyses were performed with FCS Express. After first establishing a "cells" gate, a “singlets” gate was determined using a FSC-A (x) vs. FSC-H (y) dot plot. Singlets were then analyzed in

56 a histogram for PI-A content before employing the Multicycle algorithm to analyze cell cycle.

2.4.8. RPPA

Samples were prepared according to the RNAi knockdown protocol outlined in

Materials and Methods. Following 72 hours of RNAi-mediated knockdown with non- specific siRNA, siKRAS-1, or siKRAS-2, plates were washed three times with PBS. Cell lysates were prepared as previously described (80). Coomassie Protein Assay Reagent kit (Thermo Fisher Scientific) was used to measure protein concentration, following the manufacturer’s instructions. Tris-glycine SDS 2x sample buffer (Life Technologies) with

5% β-mercaptoethanol was used to dilute cell lysates to 0.5 mg/ml. Samples were boiled for 8 min and stored at -20°C until arrayed. An Aushon 2470 automated system (Aushon

BioSystems) (173) was used to immobilize cell lysates and the internal controls and print in technical replicates (n = 3) onto nitrocellulose-coated glass slides (Grace Bio-Labs).

Sypro Ruby Protein Blot Stain (Molecular Probes) was used to quantify protein concentration in each sample, following the manufacturer’s instructions. Reblot Antibody

Stripping solution (Chemicon) was used to pretreat the remaining arrays (15 min at room temperature). The arrays were washed with PBS and incubated in Iblock (Tropix) for 5 hours before antibody staining (142). Arrays were incubated with 3% H2O2, avidin, biotin

57 used to probe arrays (table S3). All antibodies used were validated as described previously (174). Signal amplification was determined by using biotinylated anti-rabbit

(Vector Laboratories) or anti-mouse secondary antibody (DakoCytomation) and a commercially available tyramide-based avidin-biotin amplification system (Catalyzed

Signal Amplification System, DakoCytomation). IRDye 680RD streptavidin (LI-COR

Biosciences) fluorescent detection system was used. TECAN laser scanner was used to scan Sypro Ruby and antibody-stained slides, and the images were analyzed using commercially available software (MicroVigene Version 5.1.0.0, Vigenetech) as previously described (175).

2.4.9. PROLIFERATION ASSAYS

For 96-well-format growth assays, 103 cells per well were plated on Day 1 in 200

µl cell culture media. Following overnight incubation, one plate per cell line was isolated on Day 0 and quantified by counting calcein AM positive (500 nM, 20 min) labeled live cells using the SpectraMax i3x multimode detection platform (Molecular Devices) for the assessment of plating efficiency. Next, we utilized the TECAN 300D dispenser to add inhibitor titrations to the plates. Dose ranges used are indicated in figures and/or figure panels. DMSO was kept at or below 0.01% final concentration. Cells were incubated at

37°C and 5% CO2 for 5 days. Plates were quantified using calcein AM staining (500 nM,

20 min) followed by counting of positively stained cells using the SpectraMax i3x multimode detection platform. Raw cell numbers were minimally adjusted by cell line depending on the plating efficiency determined on Day 0. Next, raw cell counts (positively labeled with calcein AM) were normalized to the untreated wells. One hundred % growth was assigned to an average of the untreated well cell counts, all treated wells were

58 calculated relative to this number and technical replicates were averaged together.

Growth curves were constructed by first modeling the raw data with the drc R package

(version 3.0-1) and the four-parameter log-logistic function LL.4 (4PL). Next, we isolated the GI50 values from the models and drew the growth curves using the ggplot2 package in R (version 3.3.2).

2.4.10. BLISS SYNERGY ANALYSIS

First, quantifications of proliferation assays (2D or 3D cultures) were normalized to untreated controls (100% growth). Next, treatment effect sizes were calculated by subtracting the growth percentage (as a decimal) from 1. Values above 1 were removed from BLISS analysis (corresponding to treatments that stimulated cell growth above untreated control). Expected effect sizes for each treatment combination were calculated according to the BLISS algorithm (176). Expected effect size was then divided by observed effect size and the results were mapped to a color scale (0-1 are shades of red, indicative of “synergy”; 1 is white, indicative of additivity; greater than 1 are shades of blue, indicative of “antagonism”) and plotted as a heatmap using the ComplexHeatmap package in R (version 2.4.3).

2.4.11. PROTEASOME INHIBITOR STUDIES

Knockdown was performed in the manner described in Materials and Methods using either non-specific siRNA, siKRAS 1, or siKRAS 2. Following incubation with respective siRNA for 68 hours, the cell culture medium was replaced with standard cell culture medium supplemented with vehicle or 10 µM MG132. The cells were then incubated at 37°C for 4 hours before using the standard immunoblotting protocol as described in Materials and Methods.

CHAPTER 3 – LOW-DOSE VERTICAL INHIBITION OF THE RAF-MEK-ERK CASCADE CAUSES APOPTOTIC DEATH OF KRAS-MUTANT CANCERS123

3.1. SIGNIFICANCE

The key role of the RAF-MEK-ERK kinase cascade in driving KRAS-dependent pancreatic cancer growth is well-established. However, the most effective pharmacologic approach for inhibition of this three-tier kinase network remains to be determined.

Although our chemical library screens identified diverse functional classes of inhibitors that caused apoptotic anti-tumor activity upon combination with RAF, MEK or ERK inhibitors, concurrent inhibition of RAF and ERK was the most effective combination, synergizing to cause apoptotic death at far lower doses compared with single inhibitor treatment. This combination caused insensitivity to feedback mechanisms of ERK reactivation and induced system-wide disruption of gene transcription to drive cell cycle arrest, apoptosis, and mesenchymal-to-epithelial transition. Concurrent RAF and ERK inhibition can provide a therapeutic approach that enhances tumor cell cytotoxicity and decreases normal cell toxicity.

1 This chapter previously appeared as an article in the journal Cell Reports. The original citation is as follows: I. Ozkan-Dagliyan et al., Low-Dose Vertical Inhibition of the RAF-MEK-ERK Cascade Causes Apoptotic Death of KRAS Mutant Cancers. Cell Rep. 31, 107764 (2020)

2 I contributed the following figure panels to this publication: Fig. 3.1 D, Fig. 3.2 A, Fig. 3.3 A and B, Fig. 3.4 A, Fig. 3.5 D. Additionally, I contributed the following panels to the associated Appendix B: Fig. B1 I, Fig. B2 A to C, Fig. B3 F and G, Fig. B5 A and B, Fig. B6, Fig. B7 A and B, Fig. B7 F, Fig. B8 A, Fig. B10 H and J.

3 Tables S1, S2, and S3 referenced here are available online with the original publication.

60 3.2. INTRODUCTION

The genetic basis of pancreatic ductal adenocarcinoma (PDAC) is well-established

(177), yet current standards of care are comprised of conventional cytotoxic drugs rather than targeted therapies. Among the four major genetic alterations in PDAC, only KRAS functions as an oncogene. Given the >95% KRAS mutation frequency in PDAC, and substantial experimental evidence that KRAS is essential for PDAC maintenance

(50,178), KRAS is the most attractive target for therapeutic intervention in this disease

(45).

Despite significant recent progress in developing direct inhibitors of mutant KRAS

(179,180), with two now under clinical evaluation, these are selective for KRASG12C, a mutant that is found infrequently (only ~2%) of PDAC (42). Inhibitors of KRAS effector signaling remain promising KRAS-targeted therapies (126,181). Of the multitude of effectors, substantial experimental studies and PDAC patient data support the key role of the RAF-MEK-ERK mitogen-activated protein kinase (MAPK) cascade in driving KRAS- dependent PDAC growth. Mutationally activated BRAFV600E can phenocopy mutant KRAS and drive development of invasive and metastatic PDAC (86), and BRAF mutations are found in ~50% of the rare PDAC that are KRAS wild-type (WT) (14). Further, an effector siRNA screen demonstrated that KRAS-dependent cancers are driven largely by RAF

(182). These observations support the RAF-MEK-ERK cascade as the key effector pathway driving KRAS-dependent PDAC. However, to date, therapeutic targeting of MEK in KRAS-mutant lung cancer demonstrated limited to no efficacy in patients (183,184).

Challenges to effective use of inhibitors of ERK MAPK signaling include toxicity in normal

61 cells (185) and adaptive responses to inhibitor treatment, resulting in ERK reactivation and bypass of inhibitor action (139).

Another challenge in targeting the ERK MAPK cascade is determining which level of the three-tiered kinase cascade will provide the most effective and long-term therapeutic response. At the top of the pathway are the three highly related RAF isoforms,

ARAF, BRAF and CRAF/RAF1, that exhibit distinct roles in RAS-driven cancers (186).

BRAF-selective inhibitors caused paradoxical activation of ERK signaling in RAS-mutant cancers (100,102). Pan-RAF inhibitors overcome paradoxical activation and showed greater activity in KRAS-mutant cancers (187,188). However, genetic deletion studies in

Kras-driven mouse models argue that pan-RAF inhibition may be limited by normal cell toxicity and that a CRAF-selective strategy may provide a tumor-selective therapy

(185,189). In contrast, Craf deficiency in a KrasG12D-driven PDAC model had no inhibitory effect on tumor development or progression (190). Thus, the role of specific RAF isoforms in KRAS-driven oncogenesis may be highly tissue-selective.

Recently, we addressed the limitations of RAF and MEK inhibitors by using

ERK1/2-selective inhibitors in KRAS-mutant PDAC (82). We found that ERK1/2 inhibition suppressed PDAC growth, and that sensitivity correlated with ERK inhibition-mediated loss of MYC protein. In search of combinations to enhance the anti-tumor efficacy of inhibitors of the RAF-MEK-ERK cascade, we identified inhibitors that target a spectrum of functionally diverse proteins that synergized with a pan-RAFi to cause apoptotic death.

Surprisingly, the strongest synergistic and apoptotic activity resulted from concurrent inhibition of RAF and ERK, which was more effective than concurrent inhibition of either

RAF and MEK, or MEK and ERK, and was particularly striking at low doses. We have

62 delineated a multi-faceted mechanistic basis for the anti-tumor potency of this vertical inhibition of the ERK MAPK cascade.

3.3. RESULTS

3.3.1. ARAF, BRAF AND CRAF CONTRIBUTE TO GROWTH OF KRAS-MUTANT

PANCREATIC CANCER

Previous studies evaluating the three RAF serine/threonine kinase isoforms suggested distinct tissue-specific dependencies in the development of KRAS-driven cancers, with Craf critical for Kras-driven lung cancer (185,189) but not pancreatic cancer

(190). We previously determined that a panel of nine conventional KRAS-mutant PDAC lines exhibited KRAS-dependent growth (82). To further address the role of RAF isoforms in the growth of KRAS-mutant PDAC, we additionally verified the KRAS-dependent growth of four KRAS-mutant cell lines established from patient-derived xenograft (PDX)

PDAC tumors (Fig. 3.1 A, and Fig. B1, A and B). Applying previously characterized shRNA vectors for selective suppression of ARAF, BRAF or CRAF (191), we found that suppression of any RAF isoform alone was sufficient to partially impair growth of all six

KRAS-mutant PDX PDAC cell lines (Fig. 3.1 A and Fig. B1, C and D), demonstrating that each RAF gene contributes to KRAS-dependent PDAC growth, with the general hierarchy of significance CRAF>BRAF>ARAF. This finding is similar to that made by McCormick and colleagues, where concurrent siRNA suppression of all three RAF genes were required to cause an equivalent suppression of growth of KRAS-mutant cell lines as seen with KRAS suppression (182).

64 Figure 3.1. Concurrent Inhibition of All RAF Isoforms Diminishes PDAC Growth (A) PDAC cell lines were infected by lentivirus vectors encoding nonspecific (NS) control or distinct shRNAs targeting ARAF, BRAF, CRAF or KRAS sequences. Colonies were stained by crystal violet ~10 days after plating. Data are presented as median. All p values shown are in comparison to the vehicle control for individual graph. Adjusted p values are from Dunnett’s multiple comparison test. Adjusted p values = Pa01C (ARAF-sh1 = 0.6792, ARAF-sh2 = 0.6726, BRAF-sh1 = 0.8883, BRAF-sh2 = 0.0592, CRAF-sh1 = *, 0.0024, CRAF-sh2 = 0.6350), (KRAS-sh = ***, 0.0010). Pa02C (ARAF-sh1 = 0.7153, ARAF-sh2 = 0.0692, BRAF-sh1 = 0.8788, BRAF-sh2 = 0.1990, CRAF-sh1 = 0.0539, CRAF-sh2 = 0.2738), (KRAS-sh = **, 0.0068). Pa14C (ARAF-sh1 = ****, <0.0001, ARAF- sh2 = ****, <0.0001, BRAF-sh1 = *, 0.0103, BRAF-sh2 = ****, <0.0001, CRAF-sh1 = ****, <0.0001, CRAF-sh2 = ****, <0.0001, KRAS-sh = ****, <0.0001). Pa16C (ARAF-sh1 = 0.9995, ARAF-sh2 = 0.7500, BRAF-sh1 = 0.9977, BRAF-sh2 = ****, <0.0001, CRAF-sh1 = ****, <0.0001, CRAF-sh2 = ****, <0.0001, KRAS-sh = ****, <0.0001). MIA PaCa-2 (ARAF-sh1 = 0.5520, ARAF-sh2 = **, 0.0051, BRAF-sh1 = 0.2316, BRAF-sh2 = **, 0.0076, CRAF-sh1 = **, 0.0086, CRAF-sh2= **, 0.0098). PANC-1 (ARAF-sh1= 0.3213, ARAF-sh2 = ****, <0.0001, BRAF-sh1 = 0.0586, BRAF-sh2 =**, 0.0025, CRAF-sh1 = ****, <0.0001, CRAF-sh2 = ****, <0.0001). (B) PDAC cell lines were treated with RAFi (0.04- 10 M, 72 hr). Cell lysates were immunoblotted to determine levels of the indicated proteins. Data are representative of three independent experiments. (C) PDAC cell lines were treated with RAFi (0.01-2.5 M, 72 hr). Proliferation was measured by Calcein AM cell viability assay. Data are the mean average of three independent experiments. Error bars are shown as ± S.E.M. (D) CRISPR screen. PDAC cell lines were infected with the CRISPR library and treated with vehicle control or RAFi (w2: 2 weeks, w4: 4 weeks; a and b indicate replicate samples). The enrichment score indicates either enrichment (red) and or depletion (blue) of the indicated genes in cells treated with RAFi relative to vehicle control. (E) Cell lines were infected by lentivirus vectors encoding NS or two distinct ARAF shRNAs (72 hr) and treated with RAFi (0.01-2.5 M, 120 hr). Proliferation was measured by Calcein AM cell viability assay. Data are the mean average of three technical replicates. Error bars are shown as ± S.E.M. Summary of GI50 values.

This finding suggested that optimal inhibition of RAF in KRAS-mutant PDAC will require a pan-RAF inhibitor. We utilized the pan-RAF inhibitor LY3009120 (RAFi), which displays potent nanomolar inhibition of all three RAF proteins in vitro (187). As observed previously

(100,102), the mutant BRAF-selective inhibitor vemurafenib caused dose-dependent paradoxical activation rather than inactivation of ERK in KRAS-mutant PDAC cells (Fig.

B1 E). In contrast, treatment with RAFi caused dose-dependent inhibition of ERK, with

IC50 values ranging from 0.20 to 2.37 M (Fig. 3.1 B and Fig. B1 F, Table S1 A).

65 We next determined the effects of RAFi treatment on the growth of a panel of

KRAS-mutant PDAC cell lines (Fig. 3.1 C; Tables S1, A, C and D). Defining sensitivity as a GI50 of <2.5 M, a concentration where pERK is suppressed, we observed that four cell lines were sensitive (GI50 0.20-2.41 μM), and five lines were resistant (GI50 > 2.5 μM) after three days of treatment. However, after a 5-day treatment, only two lines remained resistant. Interestingly, sensitivity to RAFi did not correspond to sensitivity to either the

MEK1/2-selective inhibitor selumetinib or the ERK1/2-selective inhibitor SCH772984 (82)

(Table S1 A). These distinct sensitivities indicate that inhibition of the ERK MAPK cascade at different levels may not have equivalent consequences, although off-target activities of each inhibitor and other factors (e.g., mode of action, binding affinity) may contribute to these differences.

To identify a genetic basis for sensitivity and resistance to RAFi, we performed a

CRISPR screen targeting the druggable genome in the presence of a sublethal (GI30) concentration of RAFi in a sensitive (Pa02C) and a resistant (Pa01C) PDAC line. We identified genes whose loss increased or decreased sensitivity to RAFi (Fig. B1, G to I).

Shown in the heatmap are 45 genes where three or more sgRNAs scored in the top 25% of ranked hits on average for each condition; for the majority of these, their loss increased

RAFi sensitivity (Fig. B1 I). Among the 37 genes in that category are some identified previously to regulate sensitivity to MEK/ERK inhibitors (e.g., HDAC7, PIK3CB, PDGFRB)

(192,193). Surprisingly, ARAF was among the top 10% of hits (Fig. 3.1 D), with stable suppression of ARAF causing up to a 4-fold shift in GI50 in both RAFi-sensitive and - resistant PDAC cell lines (Fig. 3.1 E and Fig. B1 J). Suppression of BRAF or CRAF also

66 increased sensitivity to RAFi (Fig. B1 K). These results may reflect non-kinase functions are not blocked by an ATP-competitive inhibitor of kinase activity.

3.3.2. CHEMICAL LIBRARY SCREEN IDENTIFIES SYNERGISTIC ERK MAPK

VERTICAL INHIBITION COMBINATIONS

To determine if co-treatment with other signaling inhibitors could overcome RAFi de novo resistance, we utilized a 525-compound chemical library comprised of approved or clinical candidate oncology inhibitors (194) (Table S1 E) in a panel of 20 KRAS-mutant human or mouse PDAC cell lines (Fig. B2 D). We applied both viability (CellTiter-Glo) and cytotoxicity (CellTox Green) assays to identify drug combinations that enhanced RAFi growth inhibitory or cytotoxic activity, respectively. Combinations were identified as synergistic when the deltaDSS score [DSS (drug+RAFi)-DSS (drug alone)] was >5 or antagonistic when deltaDSS was <-5 (194). Synergy or antagonism identified in two or more cell lines are shown for cytotoxicity and viability assays (Fig. 3.2 A and Fig. B2 A).

Reflecting the genetic heterogeneity of PDAC (45), we observed significant cell line variability in drug sensitivity. The screens identified multiple chemically distinct inhibitors of the same functional class of proteins.

We focused on the RAFi combinations identified in the cytotoxic assay (Fig. 3.2

A). As expected, we identified multiple inhibitors of PI3K-AKT-mTOR signaling in the viability screen, and to a significantly lesser degree in the cytotoxicity screen (Fig. 3.2 A and Fig. B2 A). Multiple inhibitors of EGFR/HER2 receptor tyrosine kinases, HSP90, histone deacetylases, and microtubule organization were also able to cause cell death when combined with RAFi.

67 Unexpectedly, we found that cytotoxic RAFi combinations included MEK or ERK, but not RAF inhibitors. Similarly, combinations with the MEK inhibitor trametinib (MEKi) or the ERK inhibitor SCH772984 (ERKi) were with EGFR/HER2, pan-RAF and ERK, but not other MEK inhibitors (Fig. B2 B), or with EGFR/HER2, pan-RAF and MEK inhibitors, but not other ERKi (Fig. B2 C), respectively. These findings suggest that, for a vertical combination to be synthetic lethal, each inhibitor must target a distinct node of the RAF-

MEK-ERK pathway (Fig. B2 E).

We performed Bliss analyses to determine whether the inhibitory activities of each combination were additive or synergistic; Bliss scores greater than 1.0 indicate synergy.

Concurrent inhibition of RAF and ERK (designated RAFi/ERKi hereafter) not only caused cytotoxicity in RAFi-sensitive KRAS-mutant PDAC lines in a dose-dependent manner but was also able to sensitize RAFi-resistant cells (Fig. 3.2 B and Fig. B3 A). This combination was highly synergistic, with average synergy scores of 12.5 and 15.5 for Pa02C and

Pa16C cell lines, respectively (Table S1 F), particularly at lower concentrations of each inhibitor. In ERKi-resistant Pa16C cells, the GI50 for RAFi alone was 839 nM, whereas it was only 5 nM when combined with a low dose of ERKi (80 nM) (Table S1 D). Thus, 168- fold less RAFi was able to produce similar efficacy when used as a component of vertical pathway inhibition.

Interestingly, the ERKi screen did not identify BRAF-selective inhibitors (Fig. B2 C) and BRAFi in combination with ERKi resulted in no significant enhancement in activity

(Fig. B3 B and Table S1 I). This result supports the requirement to inhibit all RAF isoforms to disrupt KRAS signaling to ERK (Fig. 3.1 A and Fig. B1, C and D).

Figure 3.2. Identification of Synergistic Drug Combinations that Enhance ERK MAPK Inhibitor Cytotoxicity (A) Cell lines were treated with a 525-inhibitor library with or without RAFi (2 M, 72 hr). Cell death was measured by CellTox Green. Drug sensitivity score (ΔDSS) was used to quantify inhibitor responses and plotted as red (> additive), blue (< additive) or white (no effect). (B) Pa02C and Pa16C were treated with RAFi (0.01- 2.5 M) alone or in combination with ERKi (0.08-1.25 M), (C) MEKi (0.25-4 nM), or (D)

69 EGFRi (0.08-1.25 M) for 120 hr. Proliferation was measured by Calcein AM cell viability assay. Representative bliss synergy score heatmap for 3 independent experiments is shown (left). Red (synergy), green (antagonism), white (no effect). Averaged dose response curves of three independent experiments (with 3 technical replicates) are shown (right). Error bars are ± S.E.M. Synergyavg = average bliss synergy score.

Consistent with the requirement to target distinct nodes, ERKi did not synergize with the mechanistically distinct ERK inhibitor ulixertinib/BVD-523 (Fig. B3 C and Table

S1 J). On the other hand, a second pan-RAF inhibitor, lifirafenib/BGB-283, also synergized with ERKi (Fig. B3 D and Table S1, K and L). Similarly, RAFi also synergized with another ERK inhibitor, LY3214996 (Fig. B3 E and Table S1, M and N). Finally, leveraging additional combinations with RAFi targeting different nodes of the MAPK pathway, we found that RAFi, in combination with either erlotinib (EGFRi) or trametinib

(MEKi), also synergistically suppressed growth (Fig. 3.2, C and D; Table S1, G and H).

In summary, synergistic cytotoxic growth suppression was seen only when the combinations involve inhibitors of distinct nodes of the pathway.

Since a common mechanism for resistance to MEK inhibitors involves reactivation of ERK (133,195,196), we speculated that the RAFi/ERKi combination is more effective than the RAFi/MEKi combination, in part, due to resistance to ERK reactivation. To address this possibility, we applied MIB/MS kinome profiling, a kinome-wide unbiased method that has been used to monitor drug-induced compensatory signaling activities

(139). The kinase activity/expression changes caused by 72 hr RAFi/MEKi or RAFi/MEKi showed near-identical profiles (Fig. B3, F and G). However, RAFi/ERKi but not

RAFi/MEKi showed strong suppression of ERK1/2 activities. Immunoblot analyses also showed that the RAFi/ERKi combination caused greater suppression of ERK signaling

70 and phosphorylation of the ERK substrate RSK (Fig. B3 H). Thus, the RAFi/MEKi combination showed greater resistance to ERK reactivation.

3.3.3. RAFI/ERKI TREATMENT SUPPRESSES ERK SIGNALING DESPITE LOSS OF

NEGATIVE FEEDBACK INHIBITION

Immunoblot analyses of six PDAC cell lines verified that RAFi/ERKi synergistically reduced pERK levels (Fig. 3.3C and Fig. B5, C and D), and that RAFi similarly synergized with the chemically distinct ERK1/2-selective inhibitor, LY3214996 (Fig. B5 E). To identify changes in signaling pathways and the resulting alterations in gene transcription that were quantitatively or qualitatively different upon combination treatment versus each inhibitor alone, we performed reverse phase protein array (RPPA) (142) pathway activation mapping (Fig. 3.3 A) and RNA-Seq analyses (Fig. 3.3 B). Together, these datasets show how RAFi/ERKi disrupted multiple signaling networks that in turn disrupted multiple transcription factor-driven transcriptomes (Fig. B4).

RPPA-based pathway mapping revealed dynamic changes in protein phosphorylation and total protein components of signaling networks (Fig. 3.3 A and Table

S2, A and B). These changes were much more robust upon concurrent treatment with

RAFi/ERKi compared to the limited time-dependent alterations caused by either inhibitor alone. The combination suppressed ERK signaling (pRSK, pMYC) more strongly than did single agents (Fig. 3.3 A). Additionally, RAFi/ERKi treatment strongly reduced SRC family kinase (SFK) phosphorylation and activation. SFK phosphorylation of ARAF and CRAF is a critical RAS-mediated step in activation of RAF kinase activity (195). Decision tree analysis confirmed the significant alterations of these proteins (Table S2 C). Finally,

71 consistent with the growth inhibitory consequences, the RAFi/ERKi combination also reduced phosphorylation and activation of mitotic kinases, Aurora and PLK1 (Fig. 3.3 A).

We applied MIB/MS and confirmed that 3-day treatment with RAFi/ERKi caused additive or synergistic alterations in kinase activity and/or expression compared to RAFi or ERKi alone (Fig. B6 A). While RAFi alone reduced activity/expression of BRAF but not

ERK1 or ERK2, and ERKi alone reduced ERK1 and ERK2, but not BRAF, the combination reduced both BRAF and ERK. Consistent with our previous observation that loss of MYC protein correlated with ERK inhibitor sensitivity, RPPA and MIB/MS revealed that the combination much more effectively reduced MYC protein levels (Fig. 3.3 A and Fig. B5,

C to E). Finally, MIB/MS also indicated that the combination reduced the activity/expression of Aurora and PLK1 (Fig. B6 A).

To compare the resulting gene transcription changes induced by RAFi/ERKi or each agent alone, we performed RNA-Seq analyses after 4 or 24 h of treatment (Figure

3B). The strongest induction of gene transcription was of the interferon (IFN)-α/γ-related gene sets (Fig. 3.3 B and Table S2, D to I). This is consistent with a study of Kras-driven colon cancer, where Kras strongly downregulated IFN-α/γ gene sets, suppressing a T- cell immune response (197). Consistent with the major role of the ERK MAPK effector pathway in driving KRAS-regulated gene expression, Gene Set Enrichment Analysis

(GSEA) revealed that RAFi/ERKi suppressed genes upregulated by KRAS and increased expression of genes downregulated by KRAS (Fig. 3.3 B and Fig. B5, A and B). In particular, RAFi/ERKi strongly suppressed MYC transcription (Fig. B6 B) as well as potently silenced the expression of MYC-regulated genes (Fig. 3.3 B and Fig. B6 C).

RAFi/ERKi also robustly suppressed the expression of a second oncogenic transcription

73 Figure 3.3. Concurrent RAF and ERKi Inhibition Disrupts ERK-dependent Signaling and Cellular Processes (A) RPPA analyses of PDAC cell lines treated with vehicle control, RAFi (0.3 M), ERKi (0.04 M) or the combination for multiple time points (0.25, 1, 8, 24 and 72 hr). RAFi/ERKi treated PDAC cells were normalized to their respective vehicle control. Proteins with significant phosphorylation or expression changes at 72 hr time point are plotted as fold changes. Red (increased fold change), blue (decreased fold change), white (no change). (B) GSEA of the cell lines shown in Fig. 3.3 A. Enriched or depleted gene sets treated with RAFi/ERKi compared to RAFi (upper graph) or ERKi (below graph) are shown (24 hr). (C) Pa16C cells were treated with vehicle control, RAFi (0.3 M) and ERKi (0.04 M) alone or the combination (120 hr). Cell lysates were immunoblotted to determine levels of pERK, total ERK, total MYC and vinculin. Data are representative of three independent experiments. factor, FOSL1. Both MYC and FOSL1 are well-validated drivers of KRAS-dependent

PDAC growth (81,198).

Previous studies showed that single agent inhibition of any node of the RAF-MEK-

ERK cascade is limited by loss of negative feedback on the pathway, causing ERK reactivation (133,195,196). Given its potent suppression of ERK signaling, RAFi/ERKi also robustly decreased inhibitory feedback on ERK. RPPA pathway mapping demonstrated that RAFi/ERKi increased phosphorylation at PAK phosphorylation sites on ARAF and CRAF that enhance RAF kinase activity (199) (Fig. 3.3 A). PAK signaling drives resistance to combined BRAF and MEK inhibition in BRAF-mutant melanoma by causing ERK reactivation (200). Similarly, there was strong suppression of genes encoding diverse negative regulators of ERK signaling. These include proteins (e.g.,

DUSP4-7) that directly dephosphorylate ERK as well as proteins (e.g., SPRY2/4,

SPRED1/2) that inhibit RTK activation of RAS via its exchange factor SOS1 or that inactivate RAS via its negative regulator NF1, respectively (Fig. B6 B). To determine how

RAFi/ERKi treatment was able to retain strong suppression of ERK signaling despite the extensive loss of negative feedback on ERK, we examined SHP2, essential for RTK activation of RAS and ERK (201). RAFi/ERKi treatment reduced SHP2 phosphorylation

74 (Fig. 3.3 A) and also suppressed transcription of the gene (PTPN11) encoding SHP2 (Fig.

B6 D). RAFi/ERKi treatment also reduced phosphorylation of the SHP2 docking site on

FRSα (Y436), an adaptor protein that links the FGFR RTK to SOS1 and RAS activation.

FRSα is phosphorylated and inactivated by ERK feedback inhibition (202). Thus, downregulation of SHP2 contributes to the effectiveness of the RAFi/ERKi combination, by creating insensitivity to the loss of ERK negative feedback.

3.3.4. RAFI/ERKI TREATMENT DECREASES CELL CYCLE PROGRESSION,

SUPPRESSES PROTEIN TRANSLATION SIGNALING AND INCREASES

APOPTOSIS

Pathway mapping at the levels of transcriptional control (RNA-seq), protein abundance (WB, RPPA, MIB/MS) and protein activity (RPPA, MIB/MS), all showed that the biological consequences of RAFi/ERKi treatment are due to multiple distinct mechanisms (Fig. B4). RAFi/ERKi resulted in numerous changes that decreased the

ERK-dependent events that facilitate G1 progression, including increased phosphorylation of p27KIP1, decreased expression of CCND1 (encoding cyclin D1) (Fig.

B6 B), decreased phosphorylation of the RB tumor suppressor (Fig. 3.3 A), loss of E2F1 gene transcription (Fig. B6 D), and decreased transcription of E2F target genes (Fig. B6

E). RAFi/ERKi suppressed genes normally upregulated during G2/M checkpoint progression more strongly than did either RAFi or ERKi alone (Fig. B6 E), and suppressed the activities/expression of mitotic kinases that are critical regulators of the G2 to M transition (Fig. 3.3 A and Fig. B6 E). Reflecting these activities was a time-dependent decrease in the proliferation marker Ki67 (Fig. 3.3 A).

76 Figure 3.4. Concurrent RAF and ERK Inhibition Causes Apoptosis (A) Fold changes (log2) of RNA expression of pro-apoptosis and pro-survival genes as the averaged values of MIA PaCa-2, Pa02C, Pa14C and Pa16C cell lines treated with RAFi/ERKi (0.3 M and 0.04 M, respectively) and compared to RAFi (0.3 M) (24 hr) (left). Error bars are shown as standard error. All p values shown are in comparison to the vehicle control for individual graph. p values are from Wald test. Adjusted p values = BAX (0.2971), BAK1 (0.7226), BAD (*, 0.0332), BID (0.3363), BIK (0.2635), HRK (**, 0.003), BCL2L11 (****, 1.49E-05), PUMA/BBC3 (*, 0.0221), CASP1 (0.3642), CASP2 (**, 0.0034), CASP3 (0.6314), CASP4 (0.1526), CASP6 (0.5876), CASP7 (0.5114), CASP8 (0.9215), CASP9 (0.2887), BCL2 (***, 0.0014), BCL2L1 (0.1569), MCL1 (0.9338), BCL2L2 (0.9080). Fold changes (log2) of the transcripts of the cells treated with RAFi/ERKi (0.3 M and 0.04 M, respectively) and compared to ERKi (0.04 M) (24 hr) (right). Error bars are shown as standard error. All p values shown are in comparison to the vehicle control for individual graph. p values are determined from the Wald test. Adjusted p values = BAX (***, 0.0003), BAK1 (0.4834), BAD (0.6736), BID (0.1958), BIK (0.2171), HRK (***, 0.0011), BCL2L11 (****, 7.12E-06), PUMA/BBC3 (*, 0.0148), CASP1 (0.3406), CASP2 (*, 0.0250), CASP3 (0.7561), CASP4 (*, 0.0229), CASP6 (*, 0.0615), CASP7 (0.1599), CASP8 (0.4119), CASP9 (0.2582), BCL2 (****, 0.0001), BCL2L1 (**, 0.0032), MCL1 (0.7493), BCL2L2 (0.9250). (B) Pa01C, Pa02C or Pa14C cells were treated with vehicle, RAFi (0.3 M), ERKi (0.04 M) or the combination (72 hr). Cell lysates were immunoblotted to determine levels of the indicated proteins. (C) Percent apoptosis of Pa02C and Pa16C cells treated with the vehicle control, RAFi (0.3 or 0.6 M), ERKi (0.04 or 0.08 M) or the combinations (120 hr). Error bars are shown as ± S.E.M. (D) Representative images of % apoptosis of the cell lines in Fig. 3.4 C. FACS analysis was used to measure apoptosis.

Pathway activation mapping also indicated that RAFi/ERKi synergistically suppressed mTORC1 signaling, that promotes protein translation, as indicated by reduced phosphorylation of mTORC1 substrates 4EBP1 and S6K, and of the S6K substrate ribosomal protein S6 (Fig. 3.3 A). Strong suppression of mTORC1-stimulated gene expression was also seen with RAFi/ERKi (Fig. 3.3 B and Fig. B7 F).

Further, RAFi/ERKi suppressed essentially all genes in the glycolytic pathway (Fig.

B6 E), consistent with studies demonstrating that mutant KRAS drives increased transcription of glycolytic genes through ERK and MYC (50,79). RAFi/ERKi also strongly suppressed genes involved in mitochondrial biogenesis, mitophagy and impaired mitochondrial function (Fig. B6 E). Thus, RAFi/ERKi-mediated growth suppression involves suppression of key metabolic processes.

77 Finally, pathway activation mapping also identified increases in markers of apoptosis caused by RAFi/ERKi, increased cleaved caspases 3, 6 and 7 and PARP, and increased expression of the pro-apoptotic proteins BIM and BAD (Fig. 3.3 A). RAFi/ERKi also strongly increased transcription of genes encoding pro-apoptotic proteins (e.g., HRK,

BCL2L11 [encoding BIM], PUMA, CASP1), and reduced transcription of genes encoding pro-survival proteins (BCL2L1 [encoding BCLXL], MCL1) than either single agent (Fig.

3.4 A and Fig. B7, A and B). RAFi/ERKi treatment-mediated increases in BIM and BAD were further verified by immunoblotting (Fig. 3.4 B). Induction of apoptosis was verified by flow cytometry analysis. Whereas RAFi or ERKi treatment alone did not significantly increase apoptosis over vehicle control (<10%), the percentage of apoptotic cells was more than tripled with RAFi/ERKi (Fig. 3.4, C and D; Fig. B7, C to E). Thus, RAFi/ERKi treatment impairs processes that promote cell proliferation and enhances processes that lead to cell death.

3.3.5. COMBINATION RAFI/ERKI TREATMENT STIMULATES MESENCHYMAL-TO-

EPITHELIAL TRANSITION

We noted from the RPPA analyses that combined RAFi/ERKi treatment increased

E-cadherin protein levels (Fig. 3.3 A and Fig. B8 A), suggesting induction of the mesenchymal-to-epithelial transition (MET) program. RAFi/ERKi treatment substantially increased E-cadherin expression (2.4- to 7.3-fold) in four of six cell lines evaluated (Fig.

3.5, A and B; Fig. B8, A and B; Table S3, A and B), whereas RAFi or ERKi treatment alone did so to a lesser degree (Fig. 3.5 B). RAFi/MEKi or MEKi/ERKi treatment also increased

E-cadherin expression. In contrast, BRAFi in combination with either ERKi or MEKi did not significantly increase E-cadherin expression. The limited change in vimentin

Figure 3.5. Concurrent RAF and ERK Inhibition Induces Mesenchymal-to-Epithelial Transition (A) Pa02C and Pa16C cells were treated with RAFi (0.3 M), BRAFi (1 M), MEKi (0.5 nM) or ERKi (0.04 M) alone or in combination as indicated (120 hr). Cell lysates were immunoblotted to determine the levels of the indicated proteins. (B) Expression levels of the proteins in Fig. 3.5 A are plotted as fold changes. All the proteins

79 are normalized to loading control and their respective vehicle control. Error bars are shown as ± S.E.M. (C) Representative immunofluorescence images of Pa02C cells treated with vehicle, RAFi (0.3 M), ERKi (0.04 M) or the combination to visualize E- cadherin expression and distribution (72 hr). Scale bar, 20 m. (D) Fold changes in RNA expression of epithelial and mesenchymal markers are plotted for the mean average of MIA PaCa-2, Pa02C, Pa14C and Pa16C cell lines. Error bars are shown as standard error. p values shown are from Wald test and are in comparison to the vehicle control. Adjusted p values = CDH1 (*, 0.0252), CLDN1 (0.8818), TJP1 (0.7301), VIM (0.8465), CDH2 (0.3881), CTNNB1 (0.2445), SNAI1 (0.2542), SNAI2 (0.5672), ZEB1 (*, 0.0262). transcription and protein levels upon RAFi/ERKi treatment (Fig. 3.3 B; Fig. 3.5, A and B;

Fig. B8 B) is consistent with a partial MET program. The degree of E-cadherin increase correlated with the degree of pERK reduction, which was caused most effectively by combination RAFi/ERKi compared to treatment with any single inhibitor or other combinations. Increases in E-cadherin also correlated with induction of apoptosis (Fig.

3.4, B to D; Fig. B7, C to E). In the lines in which E-cadherin was upregulated, RAFi/ERKi also synergistically induced apoptosis. In contrast, in Pa01C cells, RAFi/ERKi did not alter

E-cadherin levels (Fig. B8 B) and caused only weak induction of apoptosis (Fig. 3.4 B and Fig. B7, C to E). Finally, immunofluorescence analyses determined that RAFi but not

ERKi treatment alone enhanced E-cadherin staining at cell peripheries and at cell-cell junctions, and combined RAFi/ERKi treatment caused the strongest upregulation of E- cadherin (Fig. 3.5 C and Fig. B8 C).

We used RNA-Seq and GSEA to identify the mechanistic underpinnings of MET induced by RAFi/ERKi. Expression of CDH1, which encodes E-cadherin, was increased by the combination (Fig. 3.5 D), consistent with reduced levels of several transcription factors that suppress CDH1 transcription (SNAI1, SNAI2 and ZEB1) (203) (Fig. 3.5 D).

Conversely, RPPA analyses showed that TGFβ/SMAD2, a well-established signaling pathway that drives EMT, was also reduced by RAFi/ERKi (Fig. 3.3 A). RAFi/ERKi caused

80 significant reduction in PLK1 activity (Fig. 3.3 A and Fig. B6 A), a driver of EMT through

ERK and FRA1 (204), with FRA1 also significantly reduced transcriptionally (FOSL1) by this combination (Fig. B6 B). Thus induction of MET-like reprogramming, synergistically induced by RAFi/ERKi treatment, correlated with induction of apoptosis.

3.3.6. CONCURRENT INHIBITION OF COMPENSATORY SIGNALING ENHANCES

RAFI/ERKI CYTOTOXICITY

We next addressed two additional potential improvements in response to the combination. First, we determined if sequential treatment could be more effective than concurrent treatment. We compared low-dose sequential inhibitor treatment to low-dose concurrent treatment. For sequential treatment, cells were treated with the initial RAFi or

ERKi for 3 days and then the other inhibitor [(RAFi + ERKi) or (ERKi + RAFi)] was added.

In Pa02C cells, RAFi/ERKi resulted in 70% fewer cells than vehicle or either inhibitor alone (Fig. 3.6 A). In contrast, sequential treatment in either order caused much more limited decrease (~20-25%). In Pa14C cells, whereas treatment with either RAFi or ERKi alone caused ~25% decrease, concurrent RAFi/ERKi treatment caused near-complete suppression of proliferation (~95%). By comparison, RAFi followed by ERKi, or ERKi followed by RAFi, caused ~75% or ~50% reduction, respectively. Thus concurrent treatment was more effective than sequential inhibitor treatment.

Second, we determined if further concurrent inhibition of additional feedback mechanisms can further enhance RAFi/ERKi treatment in Pa02C cells, where there was room for improvement over the RAFi/ERKi combination (Fig. 3.6 A). We observed that

RAFi/ERKi was associated with increased PAK activity. For example, RPPA demonstrated increased phosphorylation at the PAK phosphorylation site in CRAF (S338)

Figure 3.6. Concurrent Inhibition of Compensatory Signaling Enhances RAFi/ERKi Growth Inhibition (A) Pa02C and Pa14C cells were treated with RAFi (0.3 M), ERKi (0.04 M), FRAX597 (PAKi, 1 M) or MK2206 (AKTi, 0.6 M) alone or in combination (‘’/‘‘ concurrent inhibition; ‘‘+‘‘ sequential inhibition, inhibitor addition after 72 hr). Remaining cells were stained with crystal violet after a total of 5 days. Data are the mean average of two independent experiments. Error bars are shown as ± S.E.M. (B) Pa02C and Pa14C cells were treated as in (A) for a total of 5 days. Cell lysates were immunoblotted to determine the levels of the indicated proteins. Data are representative of two independent experiments.

82 (199) (Fig. 3.3 A and Fig. B4), and this was verified by immunoblotting (Fig. 3.6 B).

Increased LIMK phosphorylation and activation, supported by increased phosphorylation at S3 of the actin-severing LIMK substrate Cofilin (Fig. 3.3 A and 3.6 B), was also consistent with upregulation of PAK activity. S3 phosphorylation inactivates Cofilin, stimulating actin polymerization (205), which is also consistent with the MET-associated changes (203) that we observed. Immunofluorescence images revealed F-actin rearrangement upon RAFi and/or ERKi treatment (Fig. B8 C). Thus, RAFi/ERKi was associated with increased PAK activation, likely through upregulation of RTK signaling

(206). Therefore, we determined if adding the PAK inhibitor FRAX597 (PAKi) would further enhance RAFi/ERKi growth inhibition. PAKi treatment alone reduced pCRAF without affecting proliferation. However, the triple combination of RAFi/ERKi/PAKi was able to nearly ablate proliferation (Fig. 3.6 A and Fig. B9, A to C).

Another potential compensatory activity is PI3K-AKT activation, which has been described in response to MEK inhibitor treatment. This also explains how concurrent

PI3Ki can enhance ERK MAPK inhibitory activity (Fig. 3.2 A). Accordingly, RAFi/ERKi treatment of Pa02C cells increased AKT pS473, which was blocked by the AKT inhibitor

MK2206 (AKTi) (Fig. 3.6 B). Like PAKi, AKTi alone did not affect proliferation, but concurrent or sequential AKTi treatment caused more growth suppression than

RAFi/ERKi treatment alone (Fig. 3.6 A and Fig. B9, A to C). Thus, as with PAKi, concurrent

AKTi treatment blocked a compensatory activation mechanism and further enhanced

RAFi/ERKi growth suppression.

83 3.3.7. LOW-DOSE RAFI/ERKI VERTICAL INHIBITION IS EFFECTIVE IN KRAS-

MUTANT ORGANOIDS AND TUMOR-BEARING RATS

We next evaluated the activity of the RAFi/ERKi combination in other KRAS- mutant cancer cell lines, and in PDAC organoids and tumor-bearing animals. RAFi/ERKi also caused synergistic growth suppression in KRAS-mutant colon and lung cancer cell lines (Fig. 3.7 A and Fig. B10 A). Extending these analyses to models that may better reflect patient tumor response, we observed strong synergistic growth inhibition due to the RAFi/ERKi combination in patient-derived KRAS-mutant PDAC and CRC organoid models (Fig. 3.7, B and C; Fig. B10, B and C).

Finally, we extended the analyses of the RAFi/ERKi combination to evaluate anti- tumor activity. Since RAFi has poor pharmacokinetics in mice, our analyses were limited to analyses of PDAC cell line-induced tumors in immune-compromised rats. For these analyses, we utilized three PDAC lines that showed different degrees of response to vertical inhibition combinations. HPAF-II cells exhibit strong synergistic growth suppression with RAFi in combination with ERKi, MEKi and EGFRi (Fig. B10, E to G;

Table S1 D; Table S1, F to H). CFPAC1 cells were also responsive to vertical inhibition combinations, but to a lesser degree, and SW1990 cells showed very limited increased growth suppression with the combinations. Whereas ERKi and RAFi alone simply reduced the rate of tumor growth in HPAF-II PDAC xenografts, the RAFi/ERKi vertical inhibition combination was able to induce tumor regression even at low doses, and did so without statistically significant toxicity (Fig. 3.7 D). The target-based mechanism of tumor regression is supported by immunoblot analyses of remaining tumor tissue isolated 4 hr after the last treatment (day 21), where the combination reduced ERK signaling more

84 potently than either inhibitor alone (Fig. 3.7 E and Fig. B10 D). Analyses of CFPAC-1 also determined that RAFi/ERKi showed greater activity than each inhibitor alone (Fig. B10, H and I). In contrast, RAFi alone did not significantly reduce SW1990 tumor growth, whereas ERKi alone or in combination with RAFi show comparable limited tumor reduction (Fig. B10 J). No significant toxicity was observed for both models, as indicated by maintained body weight (Fig. B10, H and J). Thus, the in vivo responses closely mirrored the different sensitivities of each cell line when evaluated in cell culture.

3.4. DISCUSSION

Despite the essential role of the RAF-MEK-ERK cascade in KRAS-dependent

PDAC growth, single agent pharmacologic inhibition of this cascade has been ineffective, due to loss of ERK-dependent negative feedback inhibitory mechanisms that then cause

ERK reactivation and drug resistance in cancer cells. Normal tissue toxicity has also been a limitation. To address these limitations, we applied a chemical screen to identify combinations that enhance the apoptotic activity of RAF-MEK-ERK inhibitors. While we identified multiple mechanistically distinct apoptotic combinations, surprisingly, the most potent combination was concurrent inhibition of two distinct nodes of the three-tiered ERK

MAPK cascade, where treatment with a pan-RAF inhibitor together with an ERK-selective inhibitor (RAFi/ERKi) exhibited the strongest synergistic activity. Despite strong induction of compensatory signaling activities that can drive ERK reactivation, the RAFi/ERKi combination was able to cause synergistic suppression of ERK activation and system- wide disruption of ERK-dependent cellular processes, causing cell cycle arrest and

86 Figure 3.7. Vertical ERK MAPK Inhibition is Effective in Organoid and Rat Models of KRAS-mutant Cancers (A) KRAS mutant cancer cell lines were treated with RAFi (0.01-2.5 M) and ERKi (0.08-1.25 M) alone or in combination for 120 hr. Proliferation was measured by Calcein AM cell viability assay. Representative bliss synergy score heatmaps for 3 independent experiments is shown (left). Red (synergy), green (antagonism), white (no effect). The averaged dose response curves of 3 independent experiments are shown (right). Error bars are ± S.E.M. Synergyavg = average bliss synergy score. (B) KRAS mutant PDAC (10 days) and CRC organoids (5 days) were treated with RAFi (0.01-2.5 M) and ERKi (0.04-0.63 M) alone or in combination. Proliferation was measured by CellTiter-Glo 3D cell viability assay. Dose response curves and bliss synergy scores were calculated and represented as in Fig. 3.7 A. (C) Representative images of PDAC organoid hM1A treated with the vehicle control DMSO, RAFi (0.16 M), ERKi (0.04 M) alone or in combination (left). Scale bar, 200 m. Representative images of CRC organoid OT238 treated with DMSO, RAFi (0.31 M), ERKi (0.04 M) alone or in combination (right). Scale bar, 100 m. (D) Relative tumor volume of the NIH nude rats with implanted HPAF-II cells were treated with RAFi (20 mpk, BID) alone or in combination with the ERK inhibitor LY3214996 (LY ERKi, 10 mpk, QD) for 36 days (left). Body weight changes are shown (right). Error bars are shown as ± S.E.M. (E) Quantitation of blot analysis to determine levels of pRSK of tumor lysates (n = 5 animals per group). Error bars are shown as ± S.E.M. apoptosis, loss of MYC-, E2F- and FRA1-dependent transcriptomes, impaired metabolism and induction of MET. That the synergistic apoptotic growth suppression was strongest at lower inhibitor concentrations suggests that combined vertical inhibition of the ERK MAPK signaling circuitry can overcome the limitations seen with single agent inhibition and lead to tumor regression rather than stasis at doses that can reduce normal tissue toxicity.

The concept of vertical inhibition of ERK MAPK was first demonstrated in BRAF- mutant melanoma, where BRAFi/MEKi combinations are now approved. This combination strategy delays onset of resistance and reduces toxicity compared with

BRAF inhibitor treatment alone, albeit without any reduction in the dosing compared with

BRAFi treatment alone (207-209). Extending this concept further, it was shown that a triple RAFi/MEKi/ERKi combination, with each used in the combination at the maximum tolerated dose, further delayed onset of resistance and exhibited stronger suppression of

87 BRAF-mutant tumors, and further reduced toxicity compared with the double combination

MEKi/ERKi (210).

Similar to our RAFi/ERKi vertical inhibition strategy, two recent studies showed that concurrent RAFi/MEKi treatment blocked ERK reactivation caused by MEKi treatment alone in KRAS-mutant or wild-type cancer cell lines (188,211). A third study showed that concurrent MEKi/ERKi treatment exhibited stronger inhibition of ERK and increased anti-tumor activity compared with either inhibitor alone (212). While our study found that combined inhibition of any two distinct nodes of the EGFR-RAF-MEK-ERK cascade is superior to any single node treatment, our analyses support the RAFi/ERKi combination as the optimal combination. MEK and ERK inhibitors are limited by CRAF reactivation, whereas RAF and MEK inhibitors are limited by ERK reactivation – therefore, the RAFi/ERKi combination targets the two key reactivation nodes. We also determined that concurrent rather than sequential inhibitor treatment allowed more effective ERK inhibition and growth suppression. Finally, we demonstrated that triple combinations with a third inhibitor that targets additional compensatory mechanisms (PAK or AKT activation) further enhance RAFi/ERKi activity.

To address a mechanistic basis for the synergistic and apoptotic growth suppression of the RAFi/ERKi combination, we applied RPPA and RNA-Seq analyses to identify activities seen with RAFi/ERKi but not with each inhibitor alone. Together, these analyses identified a spectrum of ERK-dependent signaling activities and cellular processes driven more potently by RAFi/ERKi than RAFi or ERKi alone, where any one perturbation alone would be expected to significantly impair cancer cell proliferation.

Perhaps most significant is the synergistic loss of a key ERK substrate, MYC, and

88 suppression of MYC-regulated gene transcription. We showed recently that loss of MYC is a key basis for ERK inhibitor sensitivity in PDAC and that MYC suppression alone impairs PDAC tumorigenic growth (81,82).

RAFi/ERKi induced more robust suppression of ERK signaling than was achievable even by high dose treatment with each inhibitor alone. Along with this, it also more robustly induced changes associated with loss of ERK negative feedback mechanisms (195,196), such as suppressing transcription of multiple DUSP family genes encoding protein phosphatases that dephosphorylate and inactivate ERK, and activating

RTKs that promote PAK-dependent phosphorylation and activation of CRAF. However, although these changes would be expected to reactivate ERK signaling, the combination nevertheless retained the ability to strongly suppress ERK signaling. Thus, another basis for the synergistic activity of RAFi/ERKi is insensitivity to the compensatory activation mechanisms that limit the effectiveness of RAF, MEK or ERK inhibitors when used as single agent therapies. An additional unexpected consequence of RAFi/ERKi was inhibition of SHP2, a key relay mechanism that connects RTK activation with downstream activation of RAS and ERK signaling. Recent studies showed that concurrent treatment with a SHP2 inhibitor can negate the RTK-mediated compensatory activities that are stimulated by ERK MAPK inhibition and can synergistically enhance MEK inhibitor activity

(201,213-216).

Concurrent RAFi/MEKi treatment also caused apoptotic cell death not seen with each inhibitor alone. A mechanistic basis for this was identified, where the combination showed stronger promotion of pro-apoptotic and suppression of pro-survival activities.

Similarly, RAFi/ERKi synergistically caused G1 arrest through RB activation of loss of

89 transcription of E2F-mediated gene expression. Together, these activities provide a basis for the ability of RAFi/ERKi to suppress PDAC growth at lower concentrations of each inhibitor. Thus, vertical inhibition of the ERK MAPK cascade may reduce the normal tissue toxicity seen with single agent therapy and promote cytotoxic rather than cytostatic inhibition of cancer cell proliferation.

We also observed that RAFi/ERKi induced a partial MET program by transcriptional suppression of SNAI1/2 and ZEB1, the latter of which encodes a transcription factor suppressor of CDH1 transcription (203). With enhanced CDH1 expression promoting enhanced E-cadherin expression, a key driver of MET, RAFi/ERKi- treated PDAC cells exhibit a transition from a mesenchymal to an epithelial state. Further, we observed a trend in which MET was the strongest in the PDAC cell lines where

RAFi/ERKi caused the strongest induction of apoptosis. This relationship mirrors the earlier findings, where KRAS-mutant cancer cell lines with a mesenchymal phenotype were those that escaped KRAS addiction, whereas KRAS-mutant cell lines with an epithelial phenotype were susceptible to KRAS suppression-induced apoptosis (143).

Similarly, McCormick and colleagues found that KRAS-mutant cancer cells with an epithelial but not mesenchymal phenotype exhibited ERK-dependency (182).

Our determination that the RAFi/ERKi combination caused both G1 arrest and apoptosis in cancer cells shows the ability of this combination to block tumorigenic growth and cause tumor regression. Additionally, we found that RAFi/ERKi may enhance an anti- tumor immune response. Whereas EPHA2 has been shown to suppress anti-tumor T-cell immunity (217), kinome profiling upon RAFi/ERKi treatment revealed reduction of EPHA2 expression/activity. Further, one of the strongest consequences of RAFi/ERKi was

90 stimulation of interferon-α/γ genes, which would also stimulate a T-cell immune response

(197). Thus, RAFi/ERKi may cause potent anti-tumor activity both by suppressing tumor cell growth as well as by stimulating a host immune response. Our analyses showed that

RAFi/ERKi caused tumor regression in immune-suppressed rats. We speculate that the

RAFi/ERKi combination will elicit robust tumor regression in syngeneic PDAC mouse models where there is an intact immune system.

During the course of our studies, a clinical trial evaluating LY3009120 in patients with advanced or metastatic cancer was terminated early based on the lack of sufficient clinical efficacy observed (NCT02014116), emphasizing the need to consider vertical inhibition combination approaches. One ongoing clinical trial is evaluating the pan-RAF inhibitor LXH254 in combination with either an ERK or MEK inhibitor in patients with advanced or metastatic KRAS-mutant lung cancer or NRAS-mutant melanoma

(NCT02974725). This study will provide a clinical comparison of a RAFi/ERKi versus

RAFi/MEKi combination.

In summary, while we identified inhibitors of diverse cellular components that enhanced the anti-tumor activity of a pan-RAF inhibitor, the most potent combination involved vertical inhibition of the RAF-MEK-ERK cascade that was effective at low doses.

Our evidence shows that RAFi/ERKi, despite stimulating robust compensatory mechanisms that can drive ERK reactivation, is refractory to these mechanisms and consequently achieves pathway suppression at a level not achievable with each inhibitor alone. This concept is further supported by our triple combinations with inhibitors of PAK- or AKT-dependent compensatory mechanisms. This causes loss of a spectrum of ERK- dependent cellular processes driven by aberrant gene transcription (G1 progression, pro-

91 survival, EMT) that then promotes cancer cell death and tumor regression. Finally, since we found that RAFi/ERKi was effective in KRAS-mutant pancreatic, lung and colorectal cancer cell lines, this combination may serve as a pan-KRAS mutant cancer therapy.

3.5. MATERIALS AND METHODS

3.5.1. DATA AND CODE AVAILABILITY

Binary sequence alignment/map (BAM) files of RNA-seq data of cell lines MIA

PaCA-2, Pa02C, Pa14C and Pa16C are available from the EMBL-EBI European

Nucleotide Archive (ENA) database - http://www.ebi.ac.uk/ena/ with accession number

PRJEB38063 are accessible via http://www.ebi.ac.uk/ena/data/view/PRJEB38063. The file names containing "R" indicate RAF inhibitor treated samples, "E" indicate ERK inhibitor treated samples, "C" indicate combination treated samples and "V" indicate vehicle control. The file names containing "4" and "24" indicate treatment time of 4 hours and 24 hours respectively."

3.5.2. CELL CULTURE

The patient-derived xenograft (PDX) human pancreatic cancer cell lines Pa01C,

Pa02C, Pa03C, Pa04C, Pa14C and Pa16C were gifted by Dr. Anirban Maitra (MD

Anderson Cancer Center). Conventional human pancreatic cancer cell lines (MIA PaCa-

2, PANC-1, HPAF-II, CFPAC-1 and SW 1990), lung cancer cell lines (A549, NCI-H358 and SW900) and colorectal cancer cell line (SW620) were obtained from American Type

Culture Collection (ATCC). PDX pancreatic and colorectal cancer cell lines were maintained in Dulbecco’s Modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS). HPAF-II, CFPAC-1 and SW 1990 pancreatic cancer cell lines were maintained in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS),

92 respectively. Lung cancer cell lines were maintained in RPMI medium supplemented with

10% FBS. Cell lines were tested for mycoplasma. Pancreatic cancer cell line identities were verified by Short Tandem Repeat (STR) analysis.

3.5.3. PATIENT-DERIVED ORGANOIDS

The human pancreatic cancer organoids were provided by Dr. David Tuveson

(Cold Spring Harbor Laboratory). The patient-derived PDAC organoids hM1A KRASG12D

G12R and hT2 KRAS were cultured at 37°C in 5% CO2. Cells were seeded in growth factor reduced Matrigel (Corning) domes and fed with complete human feeding medium: advanced DMEM/F12 based WRN condition media (L-WRN (ATCC CRL-3276)), 1x B27 supplement, 10 mM HEPES, 0.01 µM GlutaMAX, 10 mM nicotinamide, 1.25 mM N- acetylcysteine, 50 ng/mL hEGF, 100 ng/mL hFGF10, 0.01 µM hGastrin I, 500 nM A83-

01, 1 µM PGE2 and additionally 10.5 µM Y27632 (17). Organoids were tested for mycoplasma. The patient-derived colorectal organoids OT227 KRASG13D, OT238

KRASG12D and OT302 KRASG12D were previously fully characterized in terms of genomic alterations by (218). CRC organoids were cultured in crypt culture medium (CCM) containing advanced DMEM/F12 (Gibco) supplemented with 1x GlutaMAX™ (Gibco), 10 mM HEPES buffer (Gibco), Penicillin/Streptomycin (100 U/ml/100 μg/ml), 1 mM N- acetylcysteine (Sigma), 1x N2 Supplement (Gibco), 1x B27 Supplement (Gibco) and prepared with freshly added hFGF basic/FGF2 (20 ng/ml) (Sigma) and hEGF (50 ng/ml)

(Sigma).

3.5.4. RATS

All in vivo studies were performed in accordance with the American Association for Laboratory Animal Care institutional guidelines and approved by The Eli Lilly and

93 Company Animal Care and Use Committee. HPAF-II, CFPAC-1 and SW1990 pancreatic cancer cells (ATCC cat#CRL-1997)) were cultured in MEM supplemented with 10% heat inactivated fetal bovine serum, sodium pyruvate, and nonessential amino acids. Cell lines were tested for mycoplasma and identity was confirmed by STR-based DNA finger printing and multiplex PCR (IDEXX-Radil). Logarithmically growing cells with < 7 passage from the thaw were used for implantation.

In all three studies (HPAF II, CFPAC1 and SW1990), 7-8 weeks old (120-145 grams) female NIH Nude (NIHRNU-IVI) rats from Taconic Farms Inc were used. Animal maintenance and care:12 hr light/dark cycle, Temp: 68-75oC, Humidity: 30-70% relative,

3-5 animals /cage, Bedding: Bed o cob, Filtered water, Feeding: Ad libitum, Feed:

Standard chow

3.5.5. SHRNA AND PLASMID TRANSFECTIONS

The shRNAs targeting ARAF (TRCN0000000567, TRCN0000000568), BRAF

(TRCN0000006290, TRCN0000006291), and CRAF (TRCN0000001065,

TRCN0000001066) were provided Deborah K. Morrison (NCI), and shRNA targeting

KRAS (TRCN0000010369) was provided by J. Settleman (Genentech). 0.9 x 106

HEK293T cells were seeded on T25 flasks and incubated overnight. To generate lentiviral particles, HEK293T cells were transfected with plasmids by using FuGENE6 (Roche) protocol. Vector (4 g), pSPAX2 (3 g) and pMD2.G (1 g) plasmids were diluted in 400

l Opti-MEM medium. 24 Transfection reagent FuGENE6 (24 l) was added into the diluted plasmid mixture and incubated for 15 min at room temperature. Transfection mixture was added onto HEK293T cells dropwise and incubated overnight. Transfection medium was replaced with DMEM with 20% FBS and incubated for 48 hr. Virus particles

94 were collected. 106 cells were infected by 0.5 l virus combined with polybrene (final concentration of 8 g/l) in 2 ml medium. The medium was replaced with complete medium (DMEM with 10% FBS) after 8 hr. Antibiotic selection was started after 12-16 hr of incubation in complete medium. Upon 72 or 120 hr of antibiotic selection, cells were collected for immunoblotting, anchorage-independent colony formation, or proliferation assays.

To quantify anchorage-independent colony formation, 400-100 cells per well

(depending on the cell line) were seeded on 6-well plates and incubated for ≥ 10 days at

37°C until colonies were formed. Colonies were stained by crystal violet and quantified by ImageJ (version 2.0.0). Briefly, the 6-well plate images were converted to 8-bit images.

The same thresholding was applied to all the wells to subtract background. The mean intensities were normalized by dividing each value to the average intensity of its respective control well.

To quantify proliferation, 2x105 cells per well were seeded and incubated overnight at 37°C. The next day cells were treated with LY3009120 (0.3 M), SCH772984 (0.04

M), the PAK1 inhibitor FRAX-597 (0.5 M), or the pan-AKT1/2/3 inhibitor MK-2206 (0.6

M) alone or in various combinations as indicated in the figures for a total of 10 days.

Cells were stained by crystal violet and quantified by ImageJ as described above.

3.5.6. IMMUNOBLOTTING

The human pancreatic cancer cells were washed with ice-cold PBS, lysed with ice- cold 1% RIPA buffer (50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 0.01% SDS, 0.5% sodium deoxycholate, 1% NP-40) supplemented with protease (Roche) and phosphatase

(Sigma) inhibitors, scraped and incubated in cold tubes for 10 min on ice. Cell lysates

95 were centrifuged at 18,213 x g (12,700 rpm) at 4°C for 10 min, and the supernatant was used for determining protein concentration by Bradford assay (Bio-Rad). Standard immunoblotting procedures were followed. Membranes were blocked in 5% BSA diluted in TBST (TBS with 0.05% Tween 20) for 1 hr.

3.5.7. PROLIFERATION ASSAYS

The cancer cells (103 per well) were seeded in 96-well plates, incubated overnight, and treated with small molecule inhibitors. Cells were treated with a pan-RAF inhibitor

LY3009120 (0.01-2.5 M) alone or in combination with ERK1/2 inhibitor SCH772984

(0.04-1.25 M, 72 or 120 hr), ERK1/2 inhibitor LY3214996 (0.04-1.25 M, 120 hr),

MEK1/2 inhibitor trametinib (Mekinist) (0.125-4.0 nM, 120 hr), or EGFR inhibitor erlotinib

(Tarceva) (0.01-2.5 M, 120 hr). Cells were treated with ERK1/2 inhibitor SCH772984

(0.04-0.16 M) alone or in combination with mutant BRAF-selective inhibitor vemurafenib

(Zelboraf) (0.01-2.5 M), ERK1/2 inhibitor ulixertinib (BVD-523) (0.01-2.5 M), or RAF family kinases and EGFR inhibitor BGB-283 (lifirafenib) (0.01-2.5 M) for 120 hr. Vehicle control DMSO was kept ≤ 0.01%. Cells were incubated at 37°C 72 or 120 hr. Proliferation was measured by counting calcein AM (500 nM, 20 min) (Invitrogen) labeled live cells by using a SpectraMax i3x multimode detection platform (Molecular Devices). Bliss synergy scores were calculated using SynergyFinder (version 1.6.1) package of R (version 3.5.1) environment. An R script was used to convert the 96-well plate format data into

SynergyFinder data table format. SynergyFinder data table includes the identifiers for inhbitiors, row and column numbers, the name of the inhibitors in a dose-response matrix, the concentration and its unit for each well, and the response, which is % inhibition in cell growth. SynergyFinder calculates a synergy score based on Bliss model (219).

96 3.5.8. DESIGN AND CLONING OF THE CRISPR LIBRARY

In order to assemble a library of druggable, cancer-relevant genes, we manually curated 2,240 genes from six broad domains of interest: chromatin modifiers (including epigenetic readers, writers, erasers), the full kinome, pathways responsible for mediating and repairing DNA damage, genes/proteins that represent the target of FDA-approved drugs for any indication, genes that are frequently mutated in cancer, and genes that comprise the pathways most frequently dysregulated in tumorigenesis, tumor maintenance, and drug resistance. Beyond these genes of interest, we also selected 150 genes, chosen for their demonstrated dispensability or non-dispensability across a series of essentiality studies (220), to be used as control genes. We selected five constructs to represent each of these 2,390 genes, producing a subtotal of 11,950 short guide RNA

(sgRNA) constructs. Finally, we included 50 non-targeting control constructs for a total of 12,000 sgRNAs. All of the CRISPR constructs used in this library were selected from a previously characterized and published library (221). sgRNA inserts corresponding to the entire 12,000 sgRNA library were synthesized by CustomArray, Inc in the form:

GGAAAGGACGAAACACCGXXXXXXXXXXXXXXXXXXXXGTTTTAGAGCTAGAAATA

GCAAGTTAAAATAAGGC. Where "X" denotes variable 20mer sgRNA sequence unique to each construct. The resulting library was cloned using previously published methods

(222). In brief, the oligo pool was diluted 1:100 in molecular biology grade water and amplified using NEB Phusion Hotstart Flex with the following primers and PCR protocol:

Array F:

TAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACA

CCG

97 Array R:

ACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAA

PCR protocol: 98°C/30 s, 18 × [98°C/10 s, 63°C/10 s, 72°C/15 s], 72°C/3 min. Resulting inserts were processed with Axygen PCR clean-up beads (ratio of 1.8 x starting volume;

Fisher Scientific) and reconstituted in half the volume of molecular biology grade water.

In parallel, the lentiCRISPRv2 vector (Addgene ID 52961) was digested with BsmBI

(Thermo Fisher) at 37°C for 2 hr. The product was run out for size-selection on a 1% agarose gel and the ~13 kB band was gel-extracted. Using 100 ng of BsmBI-cleaved lentiCRISPRv2 and 40 ng of sgRNA oligo insert as substrates, a Gibson assembly reaction was performed (total volume of 20 μl, for 30 min, at 50°C). Following Gibson assembly, 1 μl of the product was electroporated into electrocompetent Lucigen 10G-elite cells, spread onto LB-ampicillin plates and incubated at 37°C for 16 hr. The efficiency of transformation was estimated by plating limiting dilutions on LB-ampicillin plates. Multiple electroporations were performed, producing an estimated 500,000 total colonies, sufficient to cover the entire library of 12,000 constructs more than 40-fold. The colonies were collected in LB and plasmid extra was performed using a plasmid maxiprep kit

(QIAGEN). DNA was used to make lentivirus.

3.5.9. CRISPR/CAS9 LIBRARY LENTIVIRUS GENERATION AND INFECTION

Lentivirus generation was performed as described previously (137). HEK 293T cells were seeded in 15 cm dishes and grown up to 50% confluency. Library plasmid

(6.25 μg), psPAX2 (5.6 μg), pVSVg (0.625 μg) and transfection reagent FuGENE6

(Roche) was incubated for 30 min. The transfection mixture was added to the cells and

98 incubated overnight. Harvest media (DMEM with 30% FBS) was added the next day and incubated for 48 hr. Virus particles were collected and filtered through a 0.45 μm filter.

Virus tittering was performed as described previously (137).

Human pancreatic cancer cell lines (5x105 per well) were seeded in 6-well plates and incubated overnight. Next day, virus was added at an MOI of 0.3. Upon puromycin selection, a day 2 sample is taken to ensure library representation and the remaining cells were seeded in 500 cm dishes. The cells were maintained in puromycin media for 10 days to achieve 1,000x coverage of the library. The cells were either treated with vehicle control DMSO (≤ 0.01%) or LY3009120 RAF inhibitor (GI20-30). The cells were collected after 2 and 4 weeks, DNA was extracted with DNeasy Blood & Tissue Kit (QIAGEN) and prepared for sequencing.

3.5.10. DRUG SENSITIVITY RESISTANCE TESTING (DSRT) CHEMICAL LIBRARY

SCREEN

The DSRT platform that has been described previously (194,223) was adapted for the PDAC cell lines. 525 different oncological compounds were utilized in this study

(Supplementary Table S2J). The compounds were plated to white clear bottom 384-well plates (Corning #3712) in 5 concentrations in 10-fold dilution steps, thus covering an individually optimized 10,000-fold concentration range for each compound using an Echo

550 Liquid Handler (Labcyte). Cell killing 100 μM benzethonium chloride (BzCl2) and 0.1% dimethyl sulfoxide (DMSO) vehicle were used as positive and negative controls respectively. For the combination screenings, single concentration of RAFi (LY3009120),

MEKi (trametinib), ERKi (SCH772984) in final concentrations of 2 M, 25 nM, or 100 nM respectively, were added on top of 525 compound 5-concentrations plates using Echo

99 550. Pa01C, Pa02C, Pa03C, Pa04C, Pa14C, Pa18C and MDA-PATC53 were screened in combination with ravoxertinib whereas the remaining cell lines were screened in combination with SCH772984. All subsequent liquid handling was performed using a

MultiDrop dispenser (Thermo Scientific). The pre-dispensed compounds were dissolved in 5 l of culture media, containing viability and cytotoxicity measurement reagents,

RealTime-Glo and CellTox Green (Promega), respectively and left on a plate shaker at room temperature for 30 min. Twenty l cell suspension containing optimized number of cells per well were seeded in the drugged plates. After 72 hr incubation, the multiplexed cell viability (luminescence) and cytotoxicity (fluorescence) was recorded using a

PheraStar plate reader (BMG Labtech). The raw luminescence and fluorescence data were analyzed in Breeze software, an in-house developed data analysis pipeline at

Institute for molecular Medicine Finland (FIMM), to calculate the drug sensitivity scores

(DSS) (224). The drug combination selective effect was calculated as combination DSS minus single agent DSS, termed as “Delta DSS”.

3.5.11. REVERSE PHASE PROTEIN ARRAY (RPPA)

Human pancreatic cancer cell lines MIA PaCA-2, Pa02C, Pa14C and Pa16C were seeded (2x105 cells per well) onto 6-well plates. The next day cells were treated with vehicle control DMSO (≤ 0.01%), LY3009120 RAFi (0.3 M), SCH772984 ERKi (0.04

M), or the combination of the two inhibitors for 15 min, and 1, 8, 24 or 72 hr. Cells were lysed and processed as previously described (142). Coomassie Protein Assay Reagent kit (Thermo Fisher Scientific) was used to measure protein concentration, following the manufacturer’s instructions. 2X Tris-glycine SDS sample buffer (Life Technologies) with

5% β-mercaptoethanol was used to dilute cell lysates to 0.5 mg/ml. Samples were boiled

100 for 8 min and stored at -20°C until arrayed. An Aushon 2470 automated system (Aushon

BioSystems) (173) was used to immobilize cell lysates and the internal controls and print in technical replicates (n = 3) onto nitrocellulose-coated glass slides (Grace Bio-Labs).

Sypro Ruby Protein Blot Stain (Molecular Probes) was used to quantify protein concentration in each sample, following the manufacturer’s instructions. Reblot Antibody

Stripping solution (Chemicon) was used to pretreat the remaining arrays (15 min at room temperature). The arrays were washed with PBS and incubated in I-block (Tropix) for 5 hr before antibody staining (142). Arrays were incubated with 3% H2O2, Avidin, Biotin

(DakoCytomation), and an additional serum-free protein block (DakoCytomation) to reduce nonspecific binding of endogenous proteins. Staining was performed using an automated system (DakoCytomation) was used. Each slide was probed for 30 min with one antibody targeting the protein of interest, with 157 antibodies that target proteins involved in signaling networks that regulate cell growth, survival and metabolism were used to probe arrays (Supplementary Table S2J). All antibodies used were validated as described previously (174). Signal amplification was determined by using biotinylated anti-rabbit (Vector Laboratories) or anti-mouse secondary antibody (DakoCytomation) and a commercially available tyramide-based avidin-biotin amplification system

(Catalyzed Signal Amplification System, DakoCytomation). IRDye 680RD streptavidin

(LI-COR Biosciences) fluorescent detection system was used. TECAN laser scanner was used to scan Sypro Ruby and antibody stained slides, and the images were analyzed using commercially available software (MicroVigene Version 5.1.0.0, Vigenetech) as previously described (175).

101 3.5.12. RNA SEQUENCING

The human pancreatic cancer cell lines MIA PaCA-2, Pa02C, Pa14C and Pa16C were seeded at ~50% confluency. The next day the cells were treated with the vehicle control DMSO (≤ 0.01%), LY3009120 (RAFi, 0.3 M), SCH772984 (ERKi, 0.04 M) or the combination of the two inhibitors for 4 or 24 hr. The cells were washed twice with ice- cold PBS and before scrape removal in ice-cold PBS. The cells were collected by centrifugation at 326 x g (500 rpm) at 4°C for 10 min, and the cell pellets were flash frozen by liquid nitrogen. Whole transcriptome libraries were generated from total RNA (50 ng) of the human pancreatic cancer cell lines by using Illumina’s Truseq RNA Sample Prep to perform RNA sequencing. Oligo(dT) magnetic beads were used to select Poly(A) mRNA, and TruSeq PCR Master Mix and primer cocktail were used to enrich the libraries.

The Agilent Bioanalyzer and Invitrogen Qubit were used to clean and quantify the amplified products. The Illumina HiSeq 2500 was used to sequence the clustered flowcell for paired 100-bp reads by using Illumina’s TruSeq SBS Kit V3. Lane level fastq files were appended together if they were sequenced across multiple lanes. These fastq files were then aligned with STAR 2.3.1 to GRCh37.62 using ensembl.74.genes.gtf as GTF files.

Transcript abundances were quantified by HTSeq in total read counts per transcript.

3.5.13. MIB/MS

The human pancreatic cancer cell line MIA PaCA-2 was treated with the vehicle control DMSO (≤ 0.01%), LY3009120 (RAFi; 0.3 M), trametinib (MEKi; 0.5 nM),

SCH772984 ERKi (0.04 M), or the combinations of RAFi/MEKi and RAFi/ERKi for 72 hr.

The samples were prepared as described previously (139). Briefly, the cells were processed on ice by using MIB lysis buffer [50 mM HEPES (pH 7.5), 0.5% Triton X-100,

102 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 10 mM sodium fluoride, 2.5 mM sodium orthovanadate, 1X protease inhibitor cocktail (Roche), 1% phosphatase inhibitor cocktail

2 (Sigma-Aldrich), and 1% of phosphatase inhibitor cocktail 3 (Sigma-Aldrich)]. The cell lysates were sonicated (3 x 10 sec) on ice and were collected by centrifugation (10,000 x g) at 4°C for 10 min. The supernatant was filtered through a 0.2 mm SFCA membrane.

The lysates (~5 mg protein per experiment) were gravity-flowed over multiplexed kinase inhibitor beads (MIBs) (Sepharose conjugated to VI-16832, CTx-0294885, PP58,

Purvalanol B, UNC8088A, UNC21474). MIBs were washed with high (1 M NaCl) and low salt (150 mM NaCl + 0.1% SDS) lysis buffers without the inhibitors. The samples were boiled with the elution buffer (100 mM Tris-HCl, 0.5% SDS, and 1% -mercaptoethanol, pH 6.8) at 100°C for 5 min to elute the bound kinases from MIBs. The eluted kinases

(proteins) were concentrated with Amicon Ultra-4 (10K cutoff) spin columns (Millipore), purified by removing the detergent using methanol/chloroform extraction, and digested by sequencing grade Trypsin (Promega) overnight at 37°C. Hydrated ethyl acetate extraction was used to remove triton, and PepClean C-18 spin columns (Pierce, Thermo

Scientific) were used to de-salt the digested peptides.

Biological triplicates of the MIB samples were analyzed by LC-MS/MS as described previously (81). Briefly, each sample was injected onto an Easy Spray PepMap

C18 column (75 μm id × 25 cm, 2 μm particle size) (Thermo Scientific) and separated over a 2 hr method. The gradient for separation consisted of 5–32% mobile phase B at a

250 nl/min flow rate, where mobile phase A was 0.1% formic acid in water and mobile phase B consisted of 0.1% formic acid in ACN. The Thermo QExactive HF was operated

103 in data-dependent mode where the 15 most intense precursors were selected for subsequent HCD fragmentation (set to 27%).

3.5.14. FLOW CYTOMETRY

TACS® Annexin V-FITC Kit (BD Biosciences) was used to measure apoptosis according to the manufacturer’s instructions. Detached cells in the culture medium and the trypsinized cells were collected, mixed and centrifuged at 300 x g for 5 min at room temperature. The cells were washed with ice-cold PBS and incubated in Annexin V

Incubation Reagent (1% Annexin V-FITC, 1X propidium iodide solution, in 1X calcium- containing binding buffer) in the dark for 15 min at room temperature. Cell mixture was diluted 1:5 in 1X binding buffer. BD LSRFortessa flow cytometer was used to analyze the cells. FACSDiva v8.0.1 was used to collect and export 30,000 cells to be analyzed with

Cytobank. A “cells“ gate was generated to avoid small and large debris in the bottom right corner or off-scale on either axis by using a side scatter area (SSC-A) (y) versus forward scatter area (FSC-A) (x) dot plot. Propidium iodide area (PI-A) (y) versus fluorescein isothiocyanate area (FITC-A) (x) dot plot was used to measure apoptosis. Vehicle control

DMSO (≤ 0.01%) treated cells were assigned as the healthy cell population by a quadrant gate (PI-A negative, FITC-A negative).

3.5.15. IMMUNOFLUORESCENCE

Human pancreatic cancer cells Pa01C and Pa02C were plated on 10 g/ml fibronectin-coated glass coverslips and treated with DMSO (≤ 0.01%), LY3009120 RAFi

(0.3 M), SCH772984 ERKi (0.04 M), or the combination of the two inhibitors for 72 hr.

The cells were washed twice with PBS, fixed in 3.7% formaldehyde (Fisher Scientific) for

10 min at room temperature, washed with PBS and permeabilized with 0.1% Triton-X100

104 (Sigma) for 5 min at room temperature. Non-specific signals were blocked using 2% BSA

(Sigma) in PBS for 30 min at room temperature. Cells were incubated with the primary antibody (diluted in 2% BSA-PBS) for 40 min at room temperature, followed by three washes with PBS and incubation with the secondary antibody (diluted in 2% BSA-PBS) for 45 min at room temperature. After washing three times with PBS, cells were mounted with Prolong Diamond antifade mounting media (Invitrogen). E-cadherin was visualized by anti-rabbit E-cadherin (clone 24E10; Cell Signaling, 1:100) followed by a goat-anti- rabbit Alexa-Fluor-568 conjugated secondary antibody (Invitrogen, 1:200). Phalloidin conjugated with an Alexa-488 fluorophore (Invitrogen, 1:200) was used to visualize F- actin and DAPI (Invitrogen, 1:10000) was used to label the nucleus. Images were acquired in five random fields on an Olympus FV1000 confocal microscope using a 40x objective (1.2 zoom) and a maximally opened pinhole.

3.5.16. ORGANOID VIABILITY ASSAY

PDAC organoids were dissociated and 3000 single cells per well were seeded in

150 µl of 10% growth factor reduced Matrigel (Corning) and 90% human organoid feeding media + 10.5 µM Y27632 (Selleckchem) into Poly(2-hydroxyethyl methacrylate) (SIGMA) coated 96-well clear flat bottom plates (Corning Ref. 3903). On the second day after seeding organoids were drugged with SCH772984 (0.04 μM to 0.63 μM) and LY3009120

(0.01 μM to 2.5 μM). Ten days after drugging, organoids were imaged with the Molecular

Devices SpectraMax i3x MiniMax 300 imaging cytometer. After image acquisition organoid viability was accessed with CellTiter-Glo 3D Cell Viability Assay (Promega) according to manufactures protocol on the SpectraMax i3x plate reader. Colorectal organoid cultures were immersed in 90% Matrigel (BD, Cat# 356231), plated onto 24-well

105 plates in 20 µl drops per well and upon solidification overlaid with 500 µl CCM. For inhibitor experiments the organoids were harvested and trypsinized until single cell suspension was available for plating onto flat bottom 96-well format (Corning, Cat. No.

3603) (outer wells filled with PBS only). Per well 1000-1500 single cell organoids were plated in a 6 µl Matrigel drop and 150 µl CCM was added to culture PD3D’s for two days prior inhibitor addition. The inhibitors were added to the CRC PD3D’s as specified using the Tecan 300D dispenser and incubated for 5 days prior monitoring of proliferation by microscopy (Exp1-3, Tecan Spark, Exp 4 and 5, Keyence BZ-X710) and CellTiter-Glo

3D measurement.

Bliss synergy scores were calculated using SynergyFinder as described in “Proliferation assays”.

3.5.17. PDAC XENOGRAFT STUDY

Five x 106 HPAF II (ATCC# CRL-1997), CFPAC-1 (ATCC# CRL-1918) or SW1990

(ATCC# CRL-2172) pancreatic cancer cells in a 1:1 Matrigel mix (HPAF-II and SW1990;

0.2 ml total volume) were injected subcutaneously into the right hind flank of 7- to 8-week old (125-150 gm) female athymic NIH (NIHRNU-IVI) nude rats (Taconic Farms). After tumors reached 200-300 mm3, animals were randomized into groups of six. Both

LY3214996/ERKi and LY3009120/RAFi were administered orally (gavage) in 0.2 ml volume of vehicle (1%HEC/0.25% Tween 80/0.05% Antifoam) for 21 days. Tumor volume and body weight are measured twice weekly. Tumor volume is estimated by using the formula: v = l x w2 x 0.536 where l = larger of measured diameter and w = smaller of perpendicular diameter.

106 3.5.18. PRISM SOFTWARE

GraphPad Prism (versions 7.0.4 and 8.0.2) built-in tests were used to analyze the data provided. For the box plots and growth inhibition curves, data are the mean average of 3 independent experiments and error bars are represented as ±S.E.M. (unless otherwise noted). p values are indicated on the graphs and the absolute values are denoted in the figure legends. All data presented are normalized to their respective vehicle control. Technical and biological replicates are as indicated in the figure legends.

3.5.19. IMAGEJ SOFTWARE

ImageJ (version 2.0.0) software was used to analyze images (225).

3.5.20. SYNERGYFINDER

Bliss synergy scores were calculated using SynergyFinder (version 1.6.1) R

(version 3.5.1) package (226) as described in “Proliferation assays”.

3.5.21. DECISION TREE AND FOREST ANALYSES

Decision tree and forest analyses were performed by using Sci-Kit Learn (version

0.20.3) in Python (version 3.6.3). RPPA data set was grouped into five categories: vehicle control versus RAFi, vehicle control versus ERKi, vehicle control versus RAFi + ERKi,

RAFi versus RAFi + ERKi, and ERKi versus RAFi + ERKi. RPPA data set was further grouped into two categories as training and test data. Training (70% of RPPA data set) and test data (remining 30% of RPPA data set) comprise equal distribution of samples from the five categories described above. Decision trees were generated by using these training and test data. Accuracy for decision trees was above 80%.

For decision forest analysis, 70% of the data from each of the five categories

(described above) were fit to a decision forest by a Random Grid Search to optimize for

107 the best fitting forest based on F1 macro score (number of estimators between 300 and

500 and max features between 30 and 150). Once a model was obtained, accuracy on the test set was accessed (greater than 90% for all categories) and feature importance’s from the model were used to rank the most critical proteins.

3.5.22. CRISPR ANALYSIS

CRISPR sequencing was performed at UNC (High-Throughput Sequencing

Facility, Chapel Hill, NC) and Hudson Alpha (HudsonAlpha Genomic Services Laboratory,

Huntsville, AL). Single-end sequencing (75 bp) was performed by using Illumina NextSeq

500 with ~10% PhiX (varied depending on the run). Read counts for individual samples were quantified using the protocol outlined in (222). Briefly, unaligned reads in FASTQ format were used to generate a Burrows-Wheeler index using the Bowtie build-index function. Reads were then aligned to the index using the Bowtie aligner, followed by the quantification of the number of reads per sgRNA. For each pair of samples (vehicle control and drug treated), guide constructs were removed if they were not identified in the vehicle control. Missing values in the treated samples were imputed with the mean value of all guide constructs for that gene in the treated sample. Reads were then normalized between samples by converting them to a percentage of total reads per sample. The ratio of vehicle/treated for each construct yielded an “enrichment ratio” for the pair of samples per guide construct. To summarize results per gene, the average enrichment ratio (AER) was calculated for each gene among all constructs. Genes with a standard deviation in enrichment ratio greater than three were removed. Genes were rank ordered by their AER and assigned to bins according to their position in the ranked list. Genes were then given a value according to their bin assignment: top 10% sensitizers (bin value = 2), top 25%

108 sensitizer, excluding top 10% (bin value = 1), top 10% antagonizer (bin value = -2), top

25% antagonizer, excluding top 10% (bin value = -1), neither sensitizer nor antagonizer

(bin value = 0). Given the variability of genetic CRISPR-Cas9 screens (between samples and replicates), this bin placement method permitted greater comparability between sample pairs.

3.5.23. DSRT CHEMICAL LIBRARY SCREEN ANALYSIS

Inhibitors included in the screen were categorized by their known primary protein target (194,223,224). Cell lines and drugs were hierarchically clustered using average linkage and Euclidean distance; DSS values are displayed.

3.5.24. RPPA ANALYSIS

Antibody intensity values were imported into R (version 3.5.2) and missing values were imputed with the median intensity value for the respective antibody. Data were log2 transformed and the fold change over the median vehicle value was calculated for each antibody, factored by cell line and time point. Euclidean distance and average linkage were utilized for semi-supervised hierarchical clustering of log2 fold-change values for antibodies.

3.5.25. RNA SEQUENCING ANALYSIS

Counts data were imported into R (version 3.5.2) and differential expression analysis was performed with the DESeq2 package (version 1.22.2) (227-231). Significant hits were defined by an adjusted p-value < 0.05. Gene set enrichment analysis (GSEA) was performed using the GSEA Desktop application (version 3.0, available at http://software.broadinstitute.org/gsea/index.jsp) and curated gene sets were obtained from MSigDB (v6.2, available at

109 http://software.broadinstitute.org/gsea/msigdb/index.jsp). Specific gene sets are referenced in figure legends. GSEAPreranked was run using the DESeq2 test statistic as the weighted ranking factor for each comparison.

3.5.26. MIB/MS ANALYSIS

Following MaxQuant processing of data and generation of Label-free quantification

(LFQ) intensity values, data files were analyzed using R (version 3.5.2). A total of 207 kinases were identified with 191 present in >50% of samples and containing two or more peptides. 27/191 kinases were missing one or more values after this filtering and these values were imputed. A normal distribution was modeled on the non-missing LFQ intensity values of the kinases containing missing intensity values. Imputed values were drawn randomly from this distribution. Following filtering and imputation, LFQ intensity values were log2 transformed and the fold change over the median vehicle value was calculated for each kinase. significant kinases between DMSO, ERKi, RAFi, and

ERKi+RAFi treatments were determined using one-way ANOVA (Benjamini-Hochberg adjusted p-value < 0.05). Euclidean distance and average linkage were utilized for unsupervised hierarchical clustering of log2 fold-change values for significant kinases.

3.5.27. TUMOR XENOGRAFT STATISTICAL ANALYSIS

The statistical analysis of the tumor volume data begins with a data transformation to a log scale to equalize variance across time and treatment groups. The log volume data are analyzed with a two-way repeated measures analysis of variance by time and treatment using the MIXED procedures in SAS software (Version 9.3). The correlation model for the repeated measures is Spatial Power. Treated groups are compared to the control group at each time point. The MIXED procedure is also used separately for each

110 treatment group to calculate adjusted means and standard errors at each time point. Both analyses account for the autocorrelation within each animal and the loss of data that occurs when animals with large tumors are removed from the study early. The adjusted means and standard errors (s.e.) are plotted for each treatment group versus time.

Analysis for tumor volume is based on log10 and spatial power covariance structure. P value is based on the comparison between two specific groups.

3.5.28. TUMOR XENOGRAFT COMBINATION ANALYSIS METHOD (BLISS

INDEPENDENCE)

First, the usual repeated measures model is fit to log volume versus group and time. Then contrast statements are used to test for an interaction effect at each time point using the 2 specific treatments that are combined. This is equivalent to the Bliss

Independence method and assumes that tumor volumes can, in theory, reach zero, i.e., complete regression. The expected additive response (EAR) for the combination is calculated on the tumor volume scale as: response (EAR) EAR volume = V1 * V2/V0, where V0, V1, and V2 are the estimated mean tumor volumes for the vehicle control, treatment 1 alone, and treatment 2 alone, respectively. If the interaction test is significant, the combination effect is declared statistically more than additive or less than additive depending on the observed combination mean volume being less than or more than the

EAR volume, respectively. Otherwise, the statistical conclusion is additive. In addition, a biologically relevant range of additivity can be defined as X% above and below the EAR volume. Typically, X would be 25 to 40%. Then a biological conclusion can be made for the combination as synergistic (greater than additive), additive, or less than additive if the observed combination mean volume is below, in, or above the interval of additivity.

111 There may be situations were stasis is the best expected response. In those situations, the Bliss method can be applied directly to the % delta T/C values to obtain an

EAR percent response: EAR % delta T/C = Y1 * Y2/100, where Y1 and Y2 are the percent delta T/C values for the single-agent treatments. Currently, there is no statistical test to compare the observed % delta T/C in the combination group versus the EAR, but the biological criterion described above can be applied.

112

CHAPTER 4: CONCLUSIONS AND FUTURE DIRECTIONS

The work presented in this dissertation supplements our understanding of the signaling mechanisms that drive PDAC as well as the tumor cell response to targeted therapeutics. As the third-leading cause of cancer mortality in the US (1), it is critically important to develop new strategies to suppress growth or to kill pancreatic cancer cells in patients. Here, we demonstrate the importance of a number of kinase signaling pathways, including the MAPK cascade, in the maintenance of tumor cell proliferation.

Additionally, in an effort to combat tumor resistance to single-agent MAPK cascade inhibition, we show that vertical inhibition of the RAF and ERK nodes provides a viable strategy to suppress growth and prevent feedback pathway reactivation.

In Chapter 2, we took a broad approach to better understand KRAS-regulated kinase signaling in pancreatic cancer. We hypothesized that KRAS suppression would alter kinase activity to support continued PDAC growth in the absence of the key pro- proliferative oncogene. Our data support multiple known and previously undescribed mechanisms by which PDAC compensates for loss of KRAS, including upregulation of

SRC and DDR1. Both SRC and DDR1 have previously been shown to play a role in PDAC tumor maintenance, but our findings support the additional pro-survival role of DDR1 in the absence of mutant KRAS (151,232). The DDR1 kinase is also a therapeutic target, though currently available small molecule inhibitors are only in the early stages of development (153). In the present study, we also show that WEE1 kinase is a viable therapeutic target in PDAC. We provide evidence that WEE1 kinase activity is important

113 for preventing apoptosis and maintaining cell proliferation in PDAC. Loss of WEE1 kinase, either by genetic suppression or small molecule inhibition, renders PDAC cells unable to survive the stress of cell division and traps cells in S/G2/M-phase of the cell cycle.

Additionally, we show a synergistic reduction in proliferation and induction of apoptosis when cells are co-treated with an ERK inhibitor and a WEE1 inhibitor.

PDAC is a highly stromal cancer; one known function of DDR1 is to transmit contextual clues from the surrounding extracellular matrix to intracellular signaling cascades. The tumor stroma in PDAC has also been shown to modulate the response of the tumor to chemotherapy, typically by reducing the efficacy of a drug (233). Future work on DDR1 might include the evaluation of the tumor and the stroma response to DDR1 inhibition in organoid or mouse models of pancreatic cancer. If DDR1 is important in stromal maintenance in PDAC, DDR1 inhibition or genetic suppression may potentiate or synergize with current chemotherapies or other targeted therapies. The next steps in the evaluation of DDR1 as a target need to be carried out in in vivo models of PDAC, such as the KPC mouse model described in Chapter 1. Looking beyond inhibitors of DDR1, characterization of the interaction between tumor and stroma should be an emphasis during the preclinical evaluation of novel combination therapies. Small molecule therapies administered systemically are likely to have an impact on tumor, stromal cells and the immune system alike. We need to improve our understanding of the impact of potential treatments on the tumor microenvironment in addition to their growth inhibitory effects on

2D PDAC cultures.

Our data also supports inhibition of the DNA-damage response kinase WEE1 as a therapeutic option in PDAC. This observation has been made previously and is currently

114 under evaluation in clinical trials; synergistic growth reduction in PDAC cells upon co- treatment with an ERK inhibitor and WEE1 inhibitor has never been shown (234).

Additional evaluation of this combination is needed in order to understand the mechanistic basis for the success of tumor inhibition. Inhibition of WEE1 or other DNA-damage response kinases, such as CHK1, causes an S-phase and G2/M-phase arrest and in some cases mitotic catastrophe (235,236). Our data also shows that KRAS knockdown or ERK inhibition causes an incomplete G1-phase arrest. It is possible that an in vitro cell population harbors sufficient genetic or phenotypic heterogeneity that can be exploited by utilizing inhibitors that cause divergent cell cycle responses. Further, to definitively evaluate the preclinical efficacy of WEE1 inhibition combined with ERK inhibition, future work must be carried out in tumors harbored in mice. In this context, we could better measure both the efficacy and the potential toxicity of this combination.

In Chapter 3 of this work, we set out to address the limitations observed with single agent inhibition of the MAPK pathway. We first applied a chemical inhibitor screen to identify combinations of inhibitors that enhanced apoptotic activity of MAPK inhibitors. To our surprise, the most effective combinations were those that included two inhibitors of distinct nodes of the RAF-MEK-ERK cascade. Further evaluation of these combinations revealed that vertical inhibition of the MAPK cascade blocked feedback regulatory signaling that would normally overcome single-node inhibition. Additionally, we showed that combination inhibition of RAF and ERK caused a system-wide collapse in ERK- dependent signaling, including transcriptional processes regulated by the oncogene

MYC. We also observed the strongest synergy in induction of apoptosis at the lowest

115 doses of either the RAF or the ERK inhibitor, which could be leveraged to help prevent normal tissue toxicity.

Despite the clear link between KRAS mutation status and successful PDAC growth suppression by RAF-MEK-ERK vertical inhibition, heterogeneity in responses between

PDAC cell lines remains present in vitro and in vivo. This heterogeneity does not appear to correlate with the mutational status in driver mutations typically found in PDAC.

Investigation of the basis of this heterogeneity could help us identify biomarkers that could predict success of a vertical RAF-MEK-ERK inhibition strategy in patients. Additionally, we describe here a strategy for complete or near-complete suppression of MAPK signaling. It is possible that resistance or compensatory signaling mechanisms will arise that do not depend on the RAF-MEK-ERK cascade, circumventing the utility of the proposed RAFi + ERKi treatment strategy. Expanding the cohort of PDAC cell line and organoid models in which we evaluated this combination may help us identify and investigate non-responding cell populations before trialing this combination in human patients.

The concept of vertical inhibition of the RAF-MEK-ERK cascade was first shown in melanoma using BRAF-mutant selective inhibitors with MEK inhibitors (207,208,210).

Extensive work has now demonstrated that the vertical inhibition strategy presented in this work can be utilized to delay the onset of resistance to MAPK inhibitors. Given the potential for success of vertical inhibition of the MAPK pathway in KRAS mutant cancers, future work could focus on vertical inhibition of other critical pathways. For example, vertical inhibition of the PI3K/AKT pathway is synergistic in breast cancer (237), and dual inhibition of DNA-damage response components has also been met with success in

116 neuroblastoma, ovarian cancer, AML, and melanoma (238-241). We also showed that the highest synergy scores for growth inhibition and apoptosis induction were observed at some of the lowest doses of both ERK inhibitor and RAF inhibitor. This combination of inhibitors holds promise for patients with KRAS-mutant cancer, however the “low-dose” framework will be difficult to evaluate in a typical clinical trial framework. Generally, drugs are dosed to their maximum tolerated dose (MTD) where side-effects are typically present but within a tolerable limit. The driving motivation for this strategy is that more target inhibition portends a better clinical response. The data presented here may provide a rationale for a different dosing strategy in clinical trials, one in which a minimum effective dose is prioritized rather versus a MTD. Future work should focus on evaluating the efficacy of drug combinations utilized at lower than maximum tolerated doses, particularly when normal tissue toxicity is a concern.

117 APPENDIX A: DEFINING THE KRAS-REGULATED KINOME

118

119

120 Figure A1. KRAS-dependent kinome-wide alterations. (A) Immunoblot analysis of siNS- and siKRAS-treated samples submitted for MIB/MS analysis. Prior to submitting sample lysates to the UNC Proteomics Core, a small portion of the lysate was retained to confirm KRAS knockdown. Replicate 2 of PANC-1 was lost during processing. (B) Schematic of MIB/MS method performed. Cells were transfected with respective siRNA oligonucleotides for 72 hours prior to cell lysis and kinase enrichment. (C) Heatmap summarizing all 227 kinases detected among six KRAS mutant, KRAS-dependent PDAC cell lines (SW-1990, MIA PaCa-2, PANC-1, HPAC, AsPC-1 and Panc10.05). Log2(fold- change) values were calculated by comparing siKRAS and siNS for each cell line. (D) Kinase activity/expression changes identified from siKRAS MIB/MS (Fig. S1C) in individual cell lines were compared. All 125 kinases significantly (p adjusted < 0.05) altered in at least one cell line are displayed here. (E) Volcano plot showing results of significance testing after ANOVA [y-axis] with the log-transformed mean fold change in siKRAS over siNS (72 hours). Significant kinases are colored red (increased) or blue (decreased) if p-value was significant following Benjamini-Hochberg correction (n = 3). (F) Pie charts indicating the proportion of kinase class representation in all detected kinases (227), upregulated kinases (15), and downregulated kinases (13). (G-H) Tree diagrams generated with KinMap (242) showing kinase family relationships of upregulated (J) or downregulated (K) kinases detected in 72-hour siKRAS samples from Fig 1A. (I) Volcano plot highlighting significantly altered proteins/phosphoproteins identified by RPPA analysis (p adjusted < 0.05). Five PDAC cell lines (HPAF-II, Pa02C, Pa14C, Pa16C and PANC-1) were transfected with siNS or siKRAS for 72 hours prior to sample collection. Significant kinases are colored gold [increased] or violet [decreased]. Data are representative of five independent biological replicates. (J) Selected results from RPPA analysis are shown for samples from (I). ANOVA was used to determine significant alterations among all cell lines (n.s. indicates non-significant; * p value < 0.05; ** p value < 0.005; *** p value < 0.0005). (K) Heatmap summarizing all kinases detected among six PDAC cell lines in response to ERK inhibition or DMSO treatment. Values were calculated by comparing ERKi treated samples to DMSO treated samples within each cell line group.

121

122

123 Figure A2. Upregulation of DDR1, but not JAK, contributes to PDAC survival upon KRAS knockdown. (A) Immunoblot analysis of two PDAC cell lines treated for 24 hours with JAK1i (filgotinib), JAK1/2i (ruxolitinib), or JAK1/3i (tofacitinib) at increasing concentrations. Data are representative of three independent experiments. (B) Proliferation of PDAC cells following treatment for 5 days with JAK1i (filgotinib), JAK1/2i (ruxolitinib), or JAK1/3i (tofacitinib) at various concentrations. Cell numbers at endpoint were normalized to vehicle-treated control (100% growth) for each cell line. Curves were fit using a 4-parameter log-logistic function and the drm package in R (n=3). (C) GI50 values for the DDR1 inhibitor 7rh (5 days of treatment) were calculated in four PDAC cell lines (n = 3). (D) Immunoblot analysis of PDAC cell lines treated for 24 hours with increasing doses of the DDR1 inhibitor 7rh (n = 3). (E) Two dimensional growth of PDAC cells following treatment for 5 days with DDR1i (7rh) and ERKi at various concentrations in combination. Quantification and analysis is the same as in panel (A) (n = 3). (F) BLISS Synergy scores were calculated using the proliferation effect sizes from Fig. 2D. Scores < 1 indicate synergy (red), scores = 1 are additive (white), and scores > 1 are antagonistic (blue) (n = 3).

124

125

126 Figure A3. KRAS or ERK suppression causes loss of checkpoint and DNA damage response kinases. (A) Flow cytometry cell cycle analysis was performed following 72- hour siNS, siKRAS 1 and siKRAS 2 treatment of four PDAC cell lines. Quantification was performed in FCS Express (n = 3). (B) Tabular summary of results from panel (A). Student’s t-test was used to identify significant differences between KRAS knockdown and control. (C) Reverse phase protein array (RPPA) analysis of 5 PDAC cell lines following 72-hour siRNA knockdown of KRAS. Relative color of heatmap indicates the log2-transformed fold change of siKRAS over siNS, with red indicating a greater value in siKRAS compared to siNS and blue indicating a lesser value in siKRAS compared to siNS. All 4 replicates for both siKRAS constructs (K1, K2) are shown as individual columns (n = 4). (D) Protein stability of WEE1 as determined by 4-hour MG132 proteasomal inhibitor treatment following 26-hour treatment with non-specific (NS) or KRAS-targeted (K1, K2) siRNA (n = 3). (E) Quantification of blot analysis from panel (D). Densitometry estimates were first normalized for loading efficiency using β-actin. Next, each sample was calculated as a ratio of MG132+/MG132- and paired by siRNA construct.

127

128

Figure A4. Adavosertib treatment of PDAC induces growth arrest and apoptosis. (A) Immunoblot analysis of PDAC cells following treatment with WEE1i (adavosertib) for 48 hours (n = 4). (B) GI50 values were calculated in six PDAC cell lines for the WEE1 inhibitor adavosertib after five days of treatment. Data are representative of three independent experiments. (C) Immunoblot analysis of Pa16C cells following treatment with WEE1i (adavosertib) at various time points (n = 4). (D) Flow cytometry cell cycle analysis was performed following 48 hour DMSO or WEE1i (Pa02C 100 nM adavosertib; Pa16C, PANC-1, MIA PaCa-2 200 nM adavosertib) treatment of four PDAC cell lines. Quantification was performed in FCS Express (n = 4). (E) Tabular summary of results from panel (A). Student’s t-test was used to identify significant differences between adavosertib treatment and DMSO treated samples. (F) Quantification of relative pERK over ERK ratios using densitometry with 100 nM (Pa02C) or 200 nM WEE1i (Pa16C, MIA PaCa-2 and PANC-1) corresponding to (Fig. 4B and Fig. S4A) (n = 4).

129

Figure A5. Combined inhibition of WEE1 and ERK inhibits PDAC organoid growth. (A) hM1A PDAC organoids were seeded for three days in Matrigel and organoid maintenance factors, followed by treatment for 10 days with ERKi or WEE1i alone or in combination across a range of doses. Representative images are shown; scale bar is equivalent to 100 µm (n = 4). (B) hT2 PDAC organoids were seeded and treated according to the same parameters as hM1A organoids in (A).

130 Table A1. KRAS suppression-induced downregulated kinases and their functions. Kinase functions are adapted from UniProtKB (https://www.uniprot.org/help/uniprotkb) and GeneCards (https://www.genecards.org/)

Gene Protein Function NEK2 NEK2 NIMA related kinase 2 Ser/Thr protein kinase involved in mitotic regulation by controlling centrosome separation and bipolar spindle formation, and chromatin condensation in meiotic cells WEE1 WEE1 Ser/Thr protein kinase acts as a negative regulator of entry into mitosis (G2 to M transition) by protecting the nucleus from cytoplasmically activated cyclin B1-complexed CDK1 before the onset of mitosis by mediating phosphorylation of CDK1 TTK TTK Tyr/Ser/Thr protein kinase essential for chromosome alignment at the centromere during mitosis and is required for centrosome duplication PLK1 PLK1 Polo-like kinase 1 Ser/Thr protein kinase performs several important functions throughout M phase of the cell cycle, including the regulation of centrosome maturation and spindle assembly, the removal of cohesins from chromosome arms, the inactivation of anaphase-promoting complex/cyclosome (APC/C) inhibitors, and the regulation of mitotic exit and cytokinesis MELK MELK Maternal embryonic leucine zipper Ser/Thr protein kinase involved in cell cycle regulation, mitotic spindle pole assembly, apoptosis and splicing regulation AURKA Aurora A Ser/Thr protein kinase plays a critical role in microtubule formation and stabilization during chromosome segregation in mitosis CHEK1 CHK1 Ser/Thr protein kinase is required for checkpoint-mediated cell cycle arrest in response to DNA damage. Integrates signals from ATM/ATR protein kinases to facilitate cell cycle inhibition PKMYT1 PKMYT1 Protein kinase, membrane-associated tyrosine/threonine 1 Ser/Thr protein kinase is membrane-associated and negatively regulates the G2/M transition of the cell cycle by phosphorylating CDK1 EPHA2 EPHA2 Ephrin type-A receptor 2 Tyr kinase binds membrane-bound Ephrin-A family ligands, regulating cell adhesion and differentiation; overexpressed in cancer, with tumor promoting role TNIK TNIK TRAF2 and NKC interacting Ser/Thr protein kinase activates the Wnt- - catenin signaling pathway, with roles in gene transcription, cytoskeletal rearrangements and cell spreading CDK1 CDK1 Cyclin dependent kinase 1 Ser/Thr protein kinase plays a key role in the control of the eukaryotic cell cycle by modulating the centrosome cycle as well as mitotic onset; promotes G2/M transition and regulates G1 progress and G1/S transition via association with multiple interphase cyclins; required in higher cells for entry into S-phase and mitosis CDK7 CDK7 Cyclin dependent kinase 7 Ser/Thr protein kinase involved in cell cycle control and in RNA polymerase II-mediated RNA transcription; required for activation and complex formation of CDK1/cyclin-B during G2/M transition CSNK2A1 CKIIε Casein kinase 2 alpha 1 Ser/Thr protein kinase involved in various cell cycle control and apoptosis. During mitosis, functions as a component of the p53- dependent spindle assembly checkpoint (SAC) that maintains cyclin-B-CDK1 activity and G2 arrest in response to spindle damage

131 Table A2. KRAS suppression-induced upregulated kinases and their functions. Kinase functions are adapted from UniProtKB (https://www.uniprot.org/help/uniprotkb) and GeneCards (https://www.genecards.org/)

Gene Protein Function TGFBR1 Transforming growth factor beta receptor 1 Ser/Thr protein kinase is a TGFβR1 transmembrane protein that integrates extracellular TGFβ ligand to regulate a diverse array of processes including mesenchymal cell proliferation, extracellular matrix production, and carcinogenesis; mediates its effects through SMAD-family transcription factors MAPK3 ERK1 Extracellular signal-regulated kinase 1 Ser/Thr protein kinase is an essential component of the RAS-RAF-MEK-ERK MAPK signal transduction cascade; mediates diverse biological functions such as cell growth, adhesion, survival and differentiation through the regulation of transcription, translation, and cytoskeletal rearrangements SRPK2 SRSF2 SRSF protein kinase 2 Ser protein kinase regulates mRNA splicing; promotes neuronal apoptosis by upregulating cyclin D1 expression TK2 TK2 Thymidine kinase 2 deoxyribonucleoside kinase phosphorylates thymidine, deoxycytidine, and deoxyuridine in the mitochondrial matrix. mtDNA synthesis in non-cycling cells depends solely on TK2 and DGUOK CAMKK2 CAMKK2 Calcium/calmodulin-dependent protein kinase kinase 2 Ser/Thr protein kinase plays a role in Ca2+/calmodulin-dependent signaling cascade and participates in the phosphorylation of AMPK; regulates cell proliferation and migration, overexpressed in cancer BMPR2 BMPR2 Bone morphogenic protein receptor type 2 Ser/Thr protein kinase is activated by ligands from the TGFβ superfamily; involved in bone growth and embryogenesis ILK ILK Integin-linked Ser/Thr receptor-proximal protein kinase regulates integrin- mediated signal transduction; important in the epithelial to mesenchymal transition, and overexpression implicated in tumor growth and metastasis INSR INSR Insulin receptor Tyr protein kinase, regulates glucose uptake and release; mediates many of its effects through PI3K-AKT signaling cascade; paralog of IGF1R YES1 YES1 YES proto-oncogene 1, SRC family Tyr protein kinase is involved in the regulation of cell growth and survival, cell-cell adhesion, cytoskeleton remodeling. YES1 is recruited to activated receptor tyrosine kinases including EGFR, PDGFR, and FGFR SRC SRC SRC proto-oncogene non-receptor Tyr protein kinase participates in signaling pathways that control gene transcription, cell adhesion, cell cycle progression, migration, and transformation. There is significant redundancy among SRC- family kinases JAK1 JAK1 Janus kinase 1 Tyr protein kinase plays a role in interferon and interleukin signaling through STAT family transcription factors DDR1 DDR1 Receptor Tyr protein kinase integrates signals from the extracellular matrix to facilitate cell attachment, extracellular matrix remodeling, cell migration, survival and cell proliferation; can facilitate signaling through SRC and MAP kinases PI4KA PI4Kα Phosphatidylinositol 4-kinase lipid kinase catalyzes the biosynthesis of phosphatidylinositol 4,5-bisphosphate, a lipid component of the plasma membrane and the substrate of many important signaling proteins, including as an intermediate in the inositol triphosphate/diacylglcerol second messenger signaling pathway

132 STK38 STK38 Ser/Thr kinase 38 protein kinase functions in the cell cycle and during apoptosis; regulates MYC protein stability CAMK2G CAMK2G Calcium/calmodulin-dependent protein kinase type II gamma Ser/Thr protein kinase that is regulated by the presence of calcium and calmodulin to regulate sarcosplasmic reticulum calcium transport as well as dendritic spine and synapse formation in neurons

Table A3. RPPA Antibodies.

Catalog # Antibody Company 3661 Acetyl-CoA Carboxylase (S79) CellSig 05-736 Akt1/PKB alpha (S473) (SK703) Upstate 9271 Akt (S473) CellSig 9275 Akt (T308) CellSig 4184 AMPKalpha1 (S485) CellSig 4181 AMPKBeta1 (S108) CellSig ab47563 Androgen Receptor (S650) Abcam 07-1375 Androgen Receptor (S81) Millipore 2416 Arrestin1 (Beta) (S412) (6-24) CellSig 3761 ASK1 (S83) CellSig 2630 Atg5 (part of Autophagy Ab Sampler CellSig #4445) 5883 ATM (S1981) (D6H9) CellSig 2853 ATR (S428) CellSig 2914 Aurora A (T288)/B (T232)/C (T198) CellSig (D13A11) 2696 B-Raf (S445) CellSig 9291 Bad (S112) CellSig 9295 Bad (S136) CellSig 9297 Bad (S155) CellSig 9292 Bad CellSig 3814 Bak CellSig 2772 Bax CellSig 2872 Bcl-2 CellSig 2762 Bcl-xL CellSig 3901 Bcr (Y177) CellSig PRS3613 Beclin-1 Sigma 2933 BIM CellSig H00000645- BLVRB (biliverdin reductase B) (2F4) Abnova M09 2864 c-Abl (T735) CellSig 3391 c-Kit (Y719) CellSig 9402 c-Myc CellSig 9427 c-Raf (S338) (56A6) CellSig 2861 c-Abl (Y245) CellSig 9662 Caspase-3 CellSig 9661 Caspase-3, cleaved (D175) CellSig 9761 Caspase-6, cleaved (D162) CellSig 9491 Caspase-7, cleaved (D198) CellSig 9505 Caspase-9, cleaved (D315) CellSig 9501 Caspase-9, cleaved (D330) CellSig

133 9561 Catenin (beta) (S33/37/T41) CellSig 9565 Catenin (beta) (T41/S45) CellSig 2267-1 Caveolin-1 (Y14) (EPR2288Y) Epitomics 2341 Chk1 (S345) CellSig 2665 Chk2 (S33/35) CellSig 3313 Cofilin (S3) (77G2) CellSig 9191 CREB (S133) CellSig 3491 CrkII (Y221) CellSig 3181 CrkL (Y207) CellSig 2926 Cyclin D1 (DCS6) CellSig 4656 Cyclin A2 (BF683) CellSig 4065 E-Cadherin CellSig 44-794 EGFR (Y1173) BioSource 3597 eIF2alpha (S51) (119A11) CellSig 9741 eIF4E (S209) CellSig 9181 Elk-1 (S383) CellSig 9571 eNOS (S1177) CellSig 07-357 eNOS/NOS III (S116) Upstate IMG-90189 ErbB2/HER2 (Y1248) Imgenex 4791 ErbB3/HER3 (Y1289) (21D3) CellSig 9101 ERK 1/2 (T202/Y204) CellSig 9102 ERK 1/2 CellSig 2515 Estrogen Receptor alpha (S118) CellSig 3144 Ezrin (Y353) CellSig 2781 FADD (S194) CellSig 3281 FAK (Y576/577) CellSig ab12327 FHL2 Abcam 9461 FKHR/FOX01 (S256) CellSig 06-953 FKHRL1/FOX03 (S253) Upstate 9464 FKHR-FOX01 (T24)/FKHRL1-FOX03a CellSig (T32) 14655 FOXM1 (T600) CellSig 3861 FRS2-alpha (Y436) CellSig D16H11 GAPDH (D16H1) CellSig 9331 GSK-3alpha/beta (S21/9) CellSig 9336 GSK-3beta (S9) CellSig sc-11420 Histone Deacetylase 6 (HDAC6) SantaCruz 06-570 Histone H3 (S10) Mitosis Marker Upstate 07-145 Histone H3 (S28) Upstate 3488 HSP90a (T5/7) CellSig 3021 IGF-1 Rec (Y1131)/Insulin Rec (Y1146) CellSig 9246 IkappaB-alpha (S32/36) (5A5) CellSig 4406 Jak2 (Y1007) (D15E2) CellSig M7240 Ki67 (MIB-1) DAKO 3868 LC3B (D11) CellSig 2752 Lck CellSig 3055 LKB1 (S334) CellSig 3051 LKB1 (S428) CellSig sc-819 Mcl-1 (S-19) SantaCruz 3521 MDM2 (S166) CellSig 9121 MEK1/2 (S217/221) CellSig 9128 MEK1 (S298) CellSig 3126 Met (Y1234/1235) CellSig 3852 MMP-9 CellSig

134 2971 mTOR (S2448) CellSig 3033 NF-kappaB p65 (S536) (93H1) CellSig MABE1328 p16 INK4a (13H4.1) Millipore 71-7700 p27 (T187) Zymed 610241 Kip1/p27 (57) BD 9211 p38 MAP Kinase (T180/Y182) CellSig 9284 p53 (S15) CellSig 8025 p62/SQSTM1 (D5E2) CellSig 9202 p70 S6 Kinase CellSig 9208 p70 S6 Kinase (S371) CellSig 9205 p70 S6 Kinase (T389) CellSig 9341 p90RSK (S380) CellSig 9541 PARP, cleaved (D214) CellSig 07-021 PDGF Receptor beta (Y716) Upstate 3061 PDK1 (S241) CellSig 9375 PKC alpha/beta II (T638/641) CellSig 9374 PKC delta (T505) CellSig 9377 PKC theta (T538) CellSig 9378 PKC zeta/lambda (T410/403) CellSig 44-1100 PRAS40 (T246) BioSource 9551 PTEN (S380) CellSig 9421 Raf (S259) CellSig 3321 Ras-GRF1 (S916) CellSig 3322 Ras-GRF1 CellSig 9309 Rb (4H1) CellSig 3590 Rb (S780) CellSig 5176-1 Ron (Y1353) Epitomics 9348 RSK3 (T356/S360) CellSig 4856 S6 Ribosomal Protein (S235/236) (2F9) CellSig 2215 S6 Ribosomal Protein (S240/244) CellSig 9251 SAPK/JNK (T183/Y185) CellSig 9155 SEK1/MKK4 (S80) CellSig 07-206 Shc (Y317) Upstate 44-558 SHP2 (Y580) Biosource 3101 Smad2 (S465/467) CellSig 3104 Smad2 (S245/250/255) CellSig 2101 Src Family (Y416) CellSig 2105 Src (Y527) CellSig 9171 Stat1 (Y701) CellSig 9134 Stat3 (S727) CellSig 9145 Stat3 (Y705) (D3A7) CellSig 9351 Stat5 (Y694) CellSig 9361 Stat6 (Y641) CellSig 2711 Syk (Y525/526) CellSig 8155 TAB2 (S372) (D5A4) CellSig 3614 Tuberin/TSC2 (Y1571) CellSig 3111 VASP (S157) CellSig 44-488 Vav3 (Y173) Biosource 2478 VEGFR 2 (Y1175) (19A10) CellSig 3295 Vimentin CellSig 9451 4E-BP1 (S65) CellSig 13008 YAP (S127) (D9W2I) CellSig 2701 Zap-70 (Y319)/Syk (Y352) CellSig

135 2546 CDK2 (78B2) CellSig 13748 c-MYC (S62) CellSig 2406 HSP27 (S82) CellSig 06-822 PKC alpha (S657) Upstate 3221 Ret (Y905) CellSig

136 APPENDIX B: LOW-DOSE VERTICAL MAPK INHIBITION

137 Figure B1. All RAF isoforms are necessary for PDAC growth (A) KRAS mutant PDAC cell lines were infected by lentivirus vectors encoding nonspecific (NS) control or KRAS shRNA (72 hr). Cell lysates were immunoblotted to determine levels of KRAS and vinculin (control for total protein). (B) PDAC cell lines were infected by lentivirus vectors encoding NS control or KRAS shRNAs (72 hr) and colonies were stained with crystal violet ~10 days after plating. Representative images are shown. (C) PDAC cell lines were infected by lentivirus vectors encoding NS control or two shRNAs targeting ARAF, BRAF or CRAF (72 hr). Cell lysates were immunoblotted to determine levels of ARAF, BRAF, CRAF and vinculin. (D) PDAC cell lines were infected by lentivirus vectors encoding NS control, ARAF, BRAF or CRAF shRNAs (72 hr). Colonies were stained by crystal violet ~10 days after plating. Representative images are shown. (E) PDAC cell lines were treated with mutant BRAF-specific inhibitor vemurafenib (BRAFi, 0.04-10 M, 72 hr). Cell lysates were immunoblotted to determine levels of phosphorylated ERK, total ERK and vinculin. (F) PDAC cell lines were treated with the pan-RAF inhibitor LY3009120 (RAFi, 0.04-10 M, 72 hr). Cell lysates were immunoblotted to determine levels of phosphorylated ERK, total ERK, phosphorylated AKT, total AKT and vinculin. (G) The composition of the loss-of- function CRISPR/Cas9 druggable genome (2,500 genes) library screen. (H) CRISPR/Cas9 druggable genome library screen schematic. (I) CRISPR screen. PDAC cell lines were infected with a pooled CRISPR library and treated with vehicle control or RAFi (w2: 2 weeks, w4: 4 weeks; a and b indicate replicate samples). The enrichment score indicates either enrichment (red) or depletion (blue) of barcoded sequences corresponding to the indicated genes in cells treated with RAFi relative to vehicle control. (J) Pa16C cells were infected by lentivirus vectors encoding NS control or two distinct shRNAs for ARAF, BRAF or CRAF for 72 hr, and treated with LY3009120 (RAFi, 0.3 M) for additional 72 or 120 hr (related to Figure 1E). Cell lysates were immunoblotted to determine levels of ARAF, BRAF, CRAF, phosphorylated ERK, total ERK, phosphorylated MEK (Ser 298), total MEK and vinculin. (K) Related to Figure 1E. Pa02C and Pa16C cell lines were infected by lentivirus vectors encoding NS or two distinct BRAF or CRAF shRNAs (72 hr) and treated with LY3009120 (RAFi, 0.01-2.5 M) for additional 120 hr. Proliferation was measured by Calcein AM cell viability assay. Data are the mean average of three technical replicates. Error bars are shown as ± S.E.M.

138

139 Figure B2. Drug sensitivity resistance testing screen (DSRT) (A) Human or mouse KRAS mutant PDAC cell lines were treated with a 525-inhibitor library with or without LY3009120 (RAFi, 2 M, 72 hr). Proliferation was measured by Calcein AM viability assay. Drug sensitivity score (δDSS) was used to quantify inhibitor responses. Red (> additive), blue (< additive) or white (no effect). (B) Human or mouse KRAS mutant PDAC cell lines were treated with a 525-inhibitor library with or without trametinib (MEKi, 0.025 M, 72 hr). Proliferation was measured by Calcein AM viability assay (left), and cell death was measured by CellTox Green cytotoxicity assay (right). Inhibitor responses were plotted as δDSS. Red (> additive), blue (< additive) or white (no effect). (C) Human or mouse KRAS mutant PDAC cell lines were treated as described in Figure S2B with SCH772984 (ERKi, 0.1 M, 72 hr). Proliferation and cell death measured as described in Figure S2B. (D) The composition of the DSRT screen. (E) Top hits summarized for the RAFi, MEKi or ERKi DSRT viability and cytotoxicity screens.

140

141 Figure B3. Concurrent RAF and ERK inhibition causes synergistic growth suppression (A) Indicated KRAS mutant PDAC cell lines were treated with the vehicle control (lower left of each graph), LY3009120 (RAFi, 0.01-2.5 M, 1:2 dilution) and SCH772984 (ERKi, 0.08-1.25 M, 1:2 dilution) alone or in combination for 120 hr. Proliferation was measured by Calcein AM cell viability assay. Representative bliss synergy score heatmaps of three biological replicates (up). Red (synergy), green (antagonism), white (no effect). Averaged synergy scores presented on the synergy heatmaps. Averaged dose response curves of three biological replicates (down). Error bars are shown as ±S.E.M. (B) PDAC cell lines Pa02C and Pa16C were treated with the vehicle control (lower left of each graph), vemurafenib (BRAFi, 0.01-2.5 M, 1:2 dilution) and SCH772984 (ERKi, 0.08-1.25 M, 1:2 dilution) alone or in combination for 120 hr. Bliss synergy scores and dose response curves are calculated as in Figure S2F. (C) Pa02C and Pa16C were treated with the vehicle control (lower left of each graph), the ERK1/2 inhibitor ulixertinib (BVD-523, 0.01-2.5 M, 1:2 dilution) and SCH772984 (ERKi, 0.08-1.25 M, 1:2 dilution) alone or in combination for 120 hr. Bliss synergy scores and dose response curves are calculated as in Figure S2F. (D) Pa02C, Pa16C and MIA PaCa- 2 cell lines were treated with the vehicle control (lower left of each graph), the pan-RAF inhibitor lifirafenib/BGB-283 (0.01-2.5 M, 1:2 dilution) and SCH772984 (ERKi, 0.08-1.25 M,1:2 dilution) alone or in combination for 120 hr. Bliss synergy scores and dose response curves are calculated as in Figure S2F. (E) KRAS mutant PDAC cell lines Pa02C and Pa16C were treated with the vehicle control (lower left of each graph), LY3009120 (RAFi, 0.01-2.5 M, 1:2 dilution) and the ERK1/2 inhibitor LY3214996 (0.08- 1.25 M, 1:2 dilution) for 120 hr. Bliss synergy scores and dose response curves are shown as in Figure S2F. (F) Kinome-wide chemical proteomics screen by multiplexed kinase inhibitor beads and mass spectrometry (MIB/MS). MIA PaCa-2 cells were treated with the vehicle control DMSO, LY3009120 (RAFi, 0.3 M), trametinib (MEKi, 0.5 nM), SCH772984 (ERKi, 0.04 M) alone or in combination for 72 hr. Fold changes of significantly altered kinases (p value ≤ 0.05) are plotted. Each treatment is normalized to its respective vehicle control. (G) Average log2 fold-change values (over vehicle control) for each kinase identified in MIB/MS were plotted along the x-axis (RAFi+ERKi) or y-axis (RAFi+MEKi). The dashed lines indicate differences in log2 fold-change values of 1 or -1. (H) Pa16C and PANC-1 cells were treated with LY3009120 (RAFi, 0.3 M, trametinib (MEKi, 0.5 nM) or SCH772984 (ERKi, 0.04 M) alone or in combination as indicated (120 hr). Cell lysates were immunoblotted to determine the levels of phosphorylated and total MEK, ERK, RSK proteins and the loading control vinculin.

142 Figure B4. The pathways altered by the concurrent RAF and ERK inhibition. The processes altered by LY3009120 (RAFi, 0.3 M) and SCH772984 (ERKi, 0.04 M) combination treatment revealed by reverse phase protein array (RPPA) and RNA sequencing.

143

144 Figure B5. Concurrent RAF and ERK inhibition blocks ERK reactivation (A) Gene set enrichment analysis (GSEA) of RNA transcripts of MIA PaCa-2, Pa02C, Pa14C and Pa16C cell lines. Enriched or depleted gene sets treated with RAFi + ERKi combination compared to the vehicle control DMSO are shown (24 hr). (B) Gene set enrichment analysis (GSEA) of hallmark KRAS signaling. Altered gene expressions are plotted as enrichment scores (E.S.). PDAC cells were treated with LY3009120 (RAFi, 0.3 M) and SCH772984 (ERKi, 0.04 M) for 24 hr and normalized to the vehicle control DMSO (left), RAFi (middle), or ERKi (right). Data shown are the average of four PDAC cell lines MIA PaCa-2, Pa02C, Pa14C and Pa16C. (C) Indicated KRAS mutant PDAC cell lines were treated with the vehicle control DMSO, LY3009120 (RAFi, 0.3 or 0.6 M), SCH772984 (ERKi, 0.04 or 0.08 μM) alone or in combination for 72 hr or (D) 120 hr. Cell lysates were immunoblotted to determine levels of phosphorylated ERK, total ERK, MYC and vinculin. (E) Pa02C and Pa16C cells were treated with the vehicle control DMSO, LY3009120 (RAFi, 0.3 or 0.6 μM), the ERK1/2 inhibitor LY3214996 (0.04, 0.08 or 0.16 μM) alone or in combination for 72 or 120 hr. Cell lysates were immunoblotted to determine levels of phosphorylated and total ERK, MYC, phosphorylated RSK, phosphorylated AKT and vinculin.

145

146 Figure B6. The alterations of various signaling pathways upon RAF and ERK inhibition (A) Kinome-wide chemical proteomics screen by multiplexed kinase inhibitor beads and mass spectrometry (MIB/MS). MIA PaCa-2 cells were treated with the vehicle control DMSO, LY3009120 (RAFi, 0.3 μM), SCH772984 (ERKi, 0.04 μM) alone or in combination for 72 hr. Fold changes of significantly altered kinases (p value ≤ 0.05) are plotted. Each treatment is normalized to its respective vehicle control. (B) Fold changes (log2) of RNA expression of ERK gene targets that drive cancer and ERK feedback mechanism regulators. MIA PaCa-2, Pa02C, Pa14C and Pa16C cell lines treated with RAFi + ERKi (0.3 μM and 0.04 μM, respectively) and compared to RAFi (0.3 μM), ERKi (0.04 μM) or the vehicle control (24 hr). Error bars are shown as standard error. p values are from Wald test. Adjusted p values for RAFi + ERKi compared to RAFi = MYC (*, 0.0161), FOSL1 (**, 0.0018), DUSP4 (****, 1.51E-13), DUSP5 (*, 0.0547), DUSP6 (****, 8.79E-17), DUSP7 (****, 8.90E-07), SPRY2 (***, 0.0002), SPRY4 (****, 5.81E-20), SPRED1 (****, 2.17E-29), SPRED2 (****, 3.05E-06), CCND1 (***, 0.0013), LIF (***, 0.0003). Adjusted p values for RAFi + ERKi compared to ERKi = MYC (**, 0.0112), FOSL1 (****, 2.74E-11), DUSP4 (****, 6.39E-19), DUSP5 (**, 0.0019), DUSP6 (****, 3.09E-17), DUSP7 (****, 5.35E-07), SPRY2 (****, 1.80E-12), SPRY4 (****, 1.40E-22), SPRED1 (****, 1.12E-29), SPRED2 (****, 2.67E-07), CCND1 (***, 0.002), LIF (****, 1.96E-05). Adjusted p values for RAFi + ERKi compared to the vehicle = MYC (****, 1.13E-05), FOSL1 (****, 5.46E-09), DUSP4 (****, 2.91E-36), DUSP5 (****, 6.44E-19), DUSP6 (****, 1.93E-41), DUSP7 (****, 9.59E-20), SPRY2 (***, 0.0001), SPRY4 (****, 4.05E-52), SPRED1 (****, 2.57E-15), SPRED2 (****, 5.69E-05), CCND1 (**, 0.0039), LIF (****, 6.08E-07). (C) Gene set enrichment analysis (GSEA) of hallmark MYC targets. Altered gene expressions are plotted as enrichment scores (E.S.). PDAC cells were treated with LY3009120 (RAFi, 0.3 μM) and SCH772984 (ERKi, 0.04 μM) for 24 hr and normalized to the vehicle control DMSO (left), RAFi (middle), or ERKi (right). Data shown are the average of four PDAC cell lines MIA PaCa-2, Pa02C, Pa14C and Pa16C. (D) Fold changes (log2) of RNA expression of IGF1R, PTPN11 (SHP2) and E2F1. MIA PaCa-2, Pa02C, Pa14C and Pa16C cell lines treated with RAFi + ERKi (0.3 μM and 0.04 μM, respectively) and compared to RAFi (0.3 μM), ERKi (0.04 μM) or the vehicle control (24 hr). Error bars are shown as standard error. p values are from Wald test. Adjusted p values for RAFi + ERKi compared to RAFi = IGF1R (n.s., 0.9064), PTPN11 (*, 0.0491), E2F1 (***, 0.0005). Adjusted p values for RAFi + ERKi compared to ERKi = IGF1R (n.s., 0.8150), PTPN11 (n.s., 0.3391), E2F1 (****, 2.49E-08). Adjusted p values for RAFi + ERKi compared to the vehicle = IGF1R (n.s., 0.9106), PTPN11 (*, 0.0538), E2F1 (***, 0.0001). Adjusted p values for RAFi = IGF1R (n.s., 0.9828), PTPN11 (n.s., 0.2844), E2F1 (n.s., 0.0713). Adjusted p values for ERKi = IGF1R (n.s., 0.9916), PTPN11 (n.s., 0.0608), E2F1 (n.s., 0.1325). (E) Gene set enrichment analysis (GSEA) of hallmark E2F targets, G2M checkpoint, glycolysis, autophagy, mitochondrial biogenesis, and lysosome. Altered gene expressions are plotted as enrichment scores (E.S.). PDAC cells were treated with LY3009120 (RAFi, 0.3 μM) and SCH772984 (ERKi, 0.04 μM) for 24 hr and normalized to the vehicle control DMSO (left), RAFi (middle), or ERKi (right). Data shown are the average of four PDAC cell lines MIA PaCa-2, Pa02C, Pa14C and Pa16C.

147

148 Figure B7. Concurrent RAF and ERK inhibition induces apoptosis (A) Fold changes (log2) of RNA expression of pro-apoptosis and pro-survival genes as the averaged values of MIA PaCa-2, Pa02C, Pa14C and Pa16C cell lines treated with RAFi + ERKi (0.3 μM and 0.04 μM, respectively) and compared to the vehicle control DMSO (24 hr). Error bars are shown as standard error. All p values shown are in comparison to the vehicle control. p values are from Wald test. Adjusted p values = BAX (0.1030), BAK1 (0.6372), BAD (*, 0.0195), BID (0.4139), BIK (0.0835), HRK (**, 0.0016), BCL2L11 (0.1258), PUMA/BBC3 (****, 5.78E-05), CASP1 (0.4693), CASP2 (*, 0.0170), CASP3 (***, 0.0008), CASP4 (0.9750), CASP6 (**, 0.0050), CASP7 (0.2896), CASP8 (0.8912), CASP9 (*, 0.0158), BCL2 (****, 2.83E-07), BCL2L1 (*, 0.0171), MCL1 (****, 1.13E-05), BCL2L2 (0.8906). (B) GSEA of hallmark apoptosis signaling. Altered gene expressions are plotted as E.S. (enrichment score) PDAC cells were treated with LY3009120 (RAFi, 0.3 μM) and SCH772984 (ERKi, 0.04 μM) for 24 hr and normalized to the vehicle control (left), RAFi (middle), or ERKi (right). Data are the average of four PDAC cell lines MIA PaCa-2, Pa02C, Pa14C and Pa16C. (C) Related to Figure 4D. Representative images of percent apoptosis induced by the vehicle control, LY3009120 (RAFi, 0.3 or 0.6 μM), SCH772984 (ERKi, 0.04 or 0.08 μM) or the combinations (120 hr). FACS analysis of Annexin- V/propidium iodide labeled cells was used to measure apoptosis. (D) Quantitation of percent apoptosis of the indicated KRAS mutant PDAC cells treated with the vehicle control, LY3009120 (RAFi, 0.3 or 0.6 μM), SCH772984 (ERKi, 0.04 or 0.08 μM) or the combinations for 72 hr or (E) 120 hr. FACS analysis of Annexin V-FITC/propidium iodide- labeled cells was used to measure apoptosis. (F) Gene set enrichment analysis (GSEA) of hallmark mTORC1 signaling. Altered gene expressions are plotted as enrichment scores (E.S.). PDAC cells were treated with LY3009120 (RAFi, 0.3 μM) and SCH772984 (ERKi, 0.04 μM) for 24 hr and normalized to the vehicle control DMSO (upper graph), RAFi (middle graph), or ERKi (lower graph). Data are the average of four PDAC cell lines MIA PaCa-2, Pa02C, Pa14C and Pa16C.

149

Figure B8. Combined RAF and ERK inhibition causes mesenchymal to epithelial transition (A) RPPA. PDAC cells were treated with the vehicle control, LY3009120 (RAFi, 0.3 μM), SCH772984 (ERKi, 0.04 μM) or the combination for 15 min, 1, 8, 24 and 72 hr. The alterations of protein expression of E-cadherin (epithelial marker), vimentin (mesenchymal marker) and phosphorylated cofilin (actin polymerization) were plotted over time. Data are the mean average of five independent experiments. Error bars are shown as ±S.E.M. FC: fold change. (B) Pa01C, Pa14C, MIA PaCa-2 and PANC-1 cells were treated with LY3009120 (RAFi, 0.3 μM), vemurafenib (BRAFi, 1 μM), trametinib (MEKi, 0.5 nM) or SCH772984 (ERKi, 0.04 μM) alone or in combination as indicated (120 hr). Cell lysates were immunoblotted to determine the levels of E-cadherin, vimentin, phosphorylated ERK and vinculin. (C) Related to Figure 5C. Representative images (the same representative image shown in Figure 5C as E-cadherin and nucleus merge) of Pa02C cells treated with vehicle control, LY3009120 (RAFi, 0.3 μM), SCH772984 (ERKi, 0.04 μM) or the combination to visualize F-actin (shown alone or merged with E-cadherin and DAPI) expression and distribution (72 hr). Scale bar, 20 μm.

150

Figure B9. Concurrent PAK or AKT inhibition enhances RAFi/ERKi activity (A) Pa02C and Pa14C cell lines were treated with LY3009120 (RAFi, 0.01-2.5 μM), SCH772984 (ERKi, 0.08-1.25 μM), FRAX597 (PAKi, 0.01-2.5 μM) alone or the combination with a constant concentration of a second or third inhibitor as indicated. Proliferation was measured after 120 hr by Calcein AM cell viability assay. Data are the mean average of two independent experiments. Error bars are shown as ±S.E.M. (B) Pa02C and Pa14C cell lines were treated with LY3009120 (RAFi, 0.01-2.5 μM), SCH772984 (ERKi, 0.08-1.25 μM), MK2206 (AKTi, 0.01-2.5 μM) alone or the combination with a constant concentration of a second or third inhibitor as indicated. Proliferation was measured after 120 hr by Calcein AM cell viability assay. Data are the mean average of two independent experiments. Error bars are shown as ±S.E.M. (C) Average GI50 values for the data described in Figures S9A and S9B.

151

152 Figure B10. Concurrent RAF and ERK inhibition synergistically impairs KRAS mutant cancer growth in vitro and in vivo (A) KRAS mutant lung cancer cell lines NCI- H358 and SW 900 were treated with the vehicle control (lower left of each graph), LY3009120 (RAFi, 0.01-2.5 μM) and SCH772984 (ERKi, 0.08-1.25 μM) alone or in combination for 120 hr. Proliferation was measured by Calcein AM cell viability assay. Representative bliss synergy score heatmaps for three independent experiments is shown (left). Red (synergy), green (antagonism), white (no effect). Averaged synergy scores presented on the synergy heatmaps. Averaged dose response curves of three independent experiments are shown (right). Error bars are ±S.E.M. (B) KRAS mutant CRC organoids OT227 were treated with the vehicle control (lower left of each graph), LY3009120 (RAFi, 0.01-2.5 μM) and SCH772984 (ERKi, 0.04-0.63 μM) alone or in combination for 5 days. Proliferation was measured by CellTiter-Glo 3D cell viability assay. Dose response curves and bliss synergy scores were calculated and represented as in Figure S10A. (C) Representative images of PDAC organoid hT2 treated with the vehicle control DMSO, LY3009120 (RAFi, 0.16 μM), SCH772984 (ERKi, 0.04 μM) alone or in combination. Scale bar = 200 μm. (D) Tumors from HPAF-II PDAC rat (n = 5 for each condition) were harvested at day 21. Tumor lysates were immunoblotted to determine levels of phosphorylated and total RSK, phosphorylated and total ERK, phosphorylated ribosomal protein S6 (S240/244), total ribosomal protein S6, MYC and B- actin (control for total protein). (E) The indicated PDAC cell lines were treated with LY3009120 (RAFi, 0.01-2.5 μM) and SCH772984 (ERKi, 0.08-1.25 μM) alone or in combination for 120 hr. Proliferation was measured by Calcein AM cell viability assay. Averaged dose response curves of three independent experiments are shown. Error bars are ±S.E.M. (F) The indicated PDAC cell lines were treated with LY3009120 (RAFi, 0.01- 2.5 μM) and trametinib (MEKi, 0.25 - 4.00 μM) alone or in combination for 120 hr. Proliferation was measured by Calcein AM cell viability assay. Averaged dose response curves of three independent experiments are shown. Error bars are ±S.E.M. (G) The indicated PDAC cell lines were treated with LY3009120 (RAFi, 0.01-2.5 μM) and erlotinib (EGFRi, 0.08-1.25 μM) alone or in combination for 120 hr. Proliferation was measured by Calcein AM cell viability assay. Averaged dose response curves of three independent experiments are shown. Error bars are ±S.E.M. (H) Relative tumor volume of the NIH nude rats with implanted CFPAC-1 cells were treated with LY3009120 (RAFi, 20 mpk, BID) alone or in combination with ERK1/2 inhibitor LY3214996 (LY ERKi, 10 mpk, QD) for 36 days (left). Body weight changes are shown (right). (I) Quantitation of the western blot analysis to determine the levels of phosphorylated RSK of tumor lysates (n = 5 animals per group). (J) Relative tumor volume of the NIH nude rats with implanted SW 1990 cells were treated with LY3009120 (RAFi, 20 mpk, BID) alone or in combination with ERK1/2 inhibitor LY3214996 (LY ERKi, 10 mpk, QD) for 36 days (left). Body weight changes are shown (right).

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