Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer

Research Article

Hervé Tiriac1, Pascal Belleau1, Dannielle D. Engle1, Dennis Plenker1, Astrid Deschênes1, Tim D. D. Somerville1, Fieke E. M. Froeling1, Richard A. Burkhart2, Robert E. Denroche3, Gun-Ho Jang3, Koji Miyabayashi1, C. Megan Young1,4, Hardik Patel1, Michelle Ma1, Joseph F. LaComb5, Randze Lerie D. Palmaira6, Ammar A. Javed2, Jasmine C. Huynh7, Molly Johnson8, Kanika Arora8, Nicolas Robine8, Minita Shah8, Rashesh Sanghvi8, Austin B. Goetz9, Cinthya Y. Lowder9, Laura Martello10, Else Driehuis11,12, Nicolas LeComte6, Gokce Askan6, Christine A. Iacobuzio-Donahue6, Hans Clevers11,12,13, Laura D. Wood14, Ralph H. Hruban14, Elizabeth Thompson14, Andrew J. Aguirre15, Brian M. Wolpin15, Aaron Sasson16, Joseph Kim16, Maoxin Wu17, Juan Carlos Bucobo5, Peter Allen6, Divyesh V. Sejpal18, William Nealon19, James D. Sullivan19, Jordan M. Winter9, Phyllis A. Gimotty20, Jean L. Grem21, Dominick J. DiMaio22, Jonathan M. Buscaglia5, Paul M. Grandgenett23, Jonathan R. Brody9, Michael A. Hollingsworth23, Grainne M. O’Kane24, Faiyaz Notta3, Edward Kim7, James M. Crawford25, Craig Devoe26, Allyson Ocean27, Christopher L. Wolfgang2, Kenneth H. Yu6, Ellen Li5, Christopher R. Vakoc1, Benjamin Hubert8, Sandra E. Fischer28,29, Julie M. Wilson3, Richard Moffitt16,30, Jennifer Knox24, Alexander Krasnitz1, Steven Gallinger3,24,31,32, and David A. Tuveson1

abstract Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective, and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient–derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver onco- genes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we found that PDO therapeutic profiles paralleled patient outcomes and that PDOs enabled longitudinal assessment of chemosensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemosensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemorefractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection.

SIGNIFICANCE: New approaches to prioritize treatment strategies are urgently needed to improve survival and quality of life for patients with pancreatic cancer. Combined genomic, transcriptomic, and therapeutic profiling of PDOs can identify molecular and functional subtypes of pancreatic cancer, predict therapeutic responses, and facilitate precision medicine for patients with pancreatic cancer. Cancer Discov; 8(9); 1–18. ©2018 AACR.

1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York. 2Johns Philadelphia, Pennsylvania. 10SUNY Downstate Medical Center, Depart- Hopkins University, Division of Hepatobiliary and Pancreatic Surgery, ment of Medicine, New York, New York. 11Hubrecht Institute, Royal Neth- Baltimore, Maryland. 3PanCuRx Translational Research Initiative, Ontario erlands Academy of Arts and Sciences (KNAW), Utrecht, the Netherlands. Institute for Cancer Research, Toronto, Ontario, Canada. 4Swiss Fed- 12University Medical Center (UMC), Utrecht, the Netherlands. 13Princess eral Institute of Technology Lausanne (EPFL), School of Life Sciences, Maxime Center (PMC), Utrecht, the Netherlands. 14Department of Pathol- Swiss Institute for Experimental Cancer Research (ISREC), Laboratory ogy, Johns Hopkins University, Baltimore, Maryland. 15Dana-Farber Can- of Tumor Heterogeneity and Stemness in Cancer, Lausanne, Switzerland. cer Institute, Broad Institute, Boston, Massachusetts. 16Department of 5Department of Medicine, Stony Brook University, Stony Brook, New York. Surgery, Stony Brook University, Stony Brook, New York. 17Department 6Memorial Sloan Kettering Cancer Center, New York, New York. 7University of Pathology, Stony Brook University, Stony Brook, New York. 18Donald of California, Davis, Comprehensive Cancer Center, Division of Hematol- and Barbara Zucker School of Medicine at Hofstra/Northwell, Division ogy and Oncology, Sacramento, California. 8New York Genome Center, New of Gastroenterology, Hempstead, New York. 19Department of Surgery, York, New York. 9Department of Surgery, Thomas Jefferson University, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell,

OF1 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

is used in conjunction with oncologic resection but adds INTRODUCTION only a modest benefit in survival (4, 5). Most patients are not Pancreatic ductal adenocarcinoma (PDAC) is a deadly surgical candidates and are diagnosed with locally advanced malignancy often diagnosed at advanced stages. Fifteen to or metastatic disease. Therapeutic options for these patients thirty percent of patients with PDAC are diagnosed with include the combination chemotherapy regimens gemcit- clinically localized disease that is amenable to potentially abine/nab-paclitaxel (6) or FOLFIRINOX (5-fluorouracil, curative surgical resection (1, 2). Following surgical resec- leucovorin, irinotecan, and oxaliplatin; ref. 7). Despite thera- tion, the majority of patients will have local or distant recur- peutic intervention, the median overall survival (OS) is 6.7 rence (3) and succumb to the disease. Systemic treatment, in to 11.1 months [progression-free survival (PFS) = 3.3–6.4] the form of neoadjuvant or adjuvant cytotoxic chemotherapy, for advanced disease (6, 7), compared with 25 to 28 months

Hempstead, New York. 20Department of Biostatistics, Epidemiology and 30Department of Biomedical Informatics, Stony Brook University, Stony Informatics, University of Pennsylvania, Philadelphia, Pennsylvania. Brook, New York. 31Lunenfeld-Tanenbaum Research Institute, Mount 21Department of Medicine, University of Nebraska Medical Center, Omaha, Sinai Hospital, Toronto, Ontario, Canada. 32Hepatobiliary/Pancreatic Nebraska. 22Department of Pathology and Microbiology, University of Surgical Oncology Program, University Health Network, Toronto, Ontario, Nebraska Medical Center, Omaha, Nebraska. 23University of Nebraska Canada. Medical Center, Eppley Institute for Research in Cancer and Allied Dis- Note: Supplementary data for this article are available at Cancer Discovery eases, Fred & Pamela Buffet Cancer Center, Omaha, Nebraska. 24Wallace Online (http://cancerdiscovery.aacrjournals.org/). McCain Centre for Pancreatic Cancer, Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Univer- P. Belleau and D.D. Engle contributed equally to this article. 25 sity of Toronto, Toronto, Ontario, Canada. Department of Pathology and Corresponding Authors: David A. Tuveson, Cold Spring Harbor Laboratory, Laboratory Medicine, Donald and Barbara Zucker School of Medicine at 1 Bungtown Road, Cold Spring Harbor, NY 11724. Phone: 516-367-5246; 26 Hofstra/Northwell, Hempstead, New York. Division of Medical Oncology, Fax: 516-367-8353; E-mail: [email protected]; and Steven Gallinger, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Toronto General Hospital, 200 Elizabeth Street, 10EN, Room 206, Toronto, 27 Hempstead, New York. Weill Cornell Medical College, New York, New Ontario M5G 2C4, Canada. E-mail: [email protected] York. 28Department of Pathology, University Health Network, University doi: 10.1158/2159-8290.CD-18-0349 of Toronto, Toronto, Ontario, Canada. 29Department of Laboratory Medi- cine and Pathobiology, University of Toronto, Toronto, Ontario, Canada. ©2018 American Association for Cancer Research.

SEPTEMBER 2018 CANCER DISCOVERY | OF2 RESEARCH ARTICLE Tiriac et al.

(PFS = 13.1–13.9) in surgically resected patients (4). Many tome, we identify the expected hallmarks of PDAC. In addi- patients with PDAC have chemorefractory disease, but a tion, we find high concordance between the primary tumor smaller subset exhibits significant response to chemotherapy. and paired PDO samples when sufficient neoplastic cellularity Current therapeutic selection for patients with both local was observed in the patient specimen. We establish a PDAC- and metastatic pancreatic cancer is often based on patient specific PDO drug-testing pipeline, termed “pharmacotyp- performance status and comorbidities. Altogether, this high- ing,” and demonstrate that drug-sensitivity profiles can be lights the unmet clinical need to define responsive subgroups generated for each PDO within a clinically meaningful time- to inform treatment selection and to nominate alternative frame. In a retrospective analysis of a small subset of patients treatment options for patients who are resistant to currently with advanced PDAC from whom the PDOs were generated, approved treatment regimens. Therefore, approaches that the PDO chemotherapy sensitivity profile reflected patient predict the most effective chemotherapeutic regimen should response to therapy. These studies suggest that drug testing in improve patient care. To date, PDAC driver mutations have PDO cultures may be used to inform treatment selection. In been hard to target in the clinical setting, with the exception addition, longitudinal sampling in a single patient predicted of microsatellite instability (8), BRCA2 mutations (9), and acquisition of resistance to chemotherapy that paralleled clin- potentially targetable, uncommon KRASG12C mutations (10). ical disease progression. Furthermore, several PDO cultures Furthermore, there are a considerable number of patients resistant to all available chemotherapeutic options exhibited without these particular genetic alterations who would still exceptional sensitivity to targeted agents, providing alterna- benefit from alternative treatment strategies. tive treatment options for chemorefractory disease. Finally, to PDAC molecular subtypes have been described and vali- identify patients most likely to benefit from chemotherapy, we dated in several independent patient cohorts (11–13). By generated PDO-derived gene signatures predictive of chemo- growing consensus, two major subtypes of PDAC exist. The therapy sensitivity. We demonstrate that these chemosensi- basal-like, squamous or quasimesenchymal subtype identifies tivity signatures can retrospectively identify large groups of patients with PDAC with poor prognosis and is characterized patients with PDAC who are more likely to respond to several by basal markers such as cytokeratins. The classic or pan- chemotherapeutics in either the adjuvant or advanced-disease creatic progenitor subtype is characterized by differentiated settings. These chemosensitivity signatures may enable more ductal markers and identifies patients with a better progno- rapid treatment stratification of patients with PDAC into sis. Moffitt and colleagues (13) found that the classic subtype those who may benefit from currently available chemothera- of PDAC is significantly underrepresented in current PDAC peutic interventions and those who should instead be consid- cell culture models. Additional subtypes including aberrantly ered for rationally targeted and investigational agents. differentiated endocrine, exocrine, and immunogenic subtypes have been reported (11), but The Cancer Genome RESULTS Atlas pancreas cancer project recently demonstrated their association with tumors exhibiting low neoplastic cellular- Assembling a Pancreatic Cancer PDO Library ity, suggesting that stroma and normal pancreas contribute Previously, tumor models of patients with metastatic markedly to these subtype signatures (14). Regardless of the PDAC were often difficult to generate because of the limited subtype, the low neoplastic cellularity of primary tumors material available from diagnostic biopsies. To comprehen- makes it difficult to access molecular details regarding a sively model the full clinical spectrum of PDAC, we obtained particular profile of genetic alterations and gene expression 159 human samples from primary tumors (hT) and metas- changes in the neoplastic compartment. tases (hM) in 138 patients for PDO generation (refs. 15, 17; Until recently, the phenotypic study of early and late PDAC Supplementary Fig. S1). Seventy-eight specimens were iso- has been hampered by a lack of tractable patient-derived lated from surgical resections, 60 from FNBs of primary or models that encompass the full spectrum of disease, which metastatic lesions (hF), 20 from metastatic disease following would enable rapid evaluation of predictive biomarkers of rapid autopsies, and 1 from a video-assisted thoracoscopic treatment response. Using advances in organoid culture tech- surgical (VATS) resection of a lung metastasis. Organoid nology, we established the methodology to culture PDAC culture conditions do not enable the survival or outgrowth patient–derived organoids (PDO) from both surgical resec- of nonepithelial cells (15). We successfully generated PDO tion specimens and fine-needle biopsies (FNB), with a high cultures that expanded for at least 5 passages. Using these success rate (15, 16). These cultures exhibit mutation allele metrics, the PDO generation efficiency was 75% (72% for frequencies indicative of pure neoplastic cultures. Seino and FNBs, hF; 78% for tumor resections, hT), resulting in a total colleagues have generated and characterized a library of 39 of 114 PDO cultures from 101 patients (73% of patients; PDO cultures (17), which recapitulate the expected DNA sig- Fig. 1A). As previously reported by Seino and colleagues, nature of PDAC and exhibit a differential WNT dependence addition of serum to the culture media was detrimental to that was inversely correlated with the classic subtype. How- the isolation and propagation efficiency (17). The pancreatic ever, the utility of pancreatic cancer PDO cultures for defin- cancer PDOs exhibited mixed morphology consisting of hol- ing predictive biomarkers of treatment response remains to low epithelial lined cystic structures with differing degrees of be explored. filled lumens (Fig. 1B). In parallel, 11 human normal (hN) Herein, we describe a library of 66 PDO cultures obtained pancreatic ductal organoids were established from healthy from FNB, surgical resection, and rapid autopsy PDAC speci- normal pancreata obtained from islet transplant centers (refs. mens collected from multiple clinical institutions. Using deep 15, 17; Supplementary Table S1A), all of which exhibited a molecular characterization of the PDO genome and transcrip- hollow epithelial cystic architecture.

OF3 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

A Total Biopsy (hF) Resected (hT) Success Figure 1. Genomic landscape of Failure pancreatic cancer PDO. A, Isolation efficiency rate of PDOs from total samples, biopsies (hF), and resected 75% 72% 78% surgical specimens (hT). B, PDO morphology in brightfield microscopy. Scale bars, 1,000 or 500 μm as indicated. C, Single-nucleotide variants in the PDO library. Mutation frequency indicated in both cancer and normal n = 138 n = 60 n = 78 organoids [cancer % (left)/normal % (right)]. Only mutations reported in B hN hT hF the Catalogue of Somatic Mutations in Cancer (COSMIC) were included. Patient staging and type of mutation are denoted by a color-coded key. FS, frameshift; del., deletion; ins., inser- tion; IF, in frame; NA, not available. D, Copy-number alterations (−2.0

through −0.235 and 0.235 through 1,000 µm 1,000 µm 1,000 µm

2.0 log2 copy-number ratio color key) in the PDO library. The cancer stages of the patients are indicated.

1,000 µm 500 µm 500 µm C Stage KRAS (96/0%) Normal Stage 1/2 TP53 (88/0%) Stage 3/4 CDKN2A (24/0%) No info. SMAD4 (20/0%) Mutation ARID1A (17/0%) FS del. RNF43 (6/9%) FS ins. IF del. GNAS (5/0%) Missense ATM (5/0%) Nonsense MYC (3/0%) Splice Site PIK3CA (1.5/0%) PBRM1 (1.5/0%) PALB2 (1.5/0%) MAP2K1 (1.5/0%) KDM6A (1.5/0%) ERBB2 (1.5/0%) BRCA1 (1.5/0%) AKT2 (1.5/0%) 7 11 hF3 hT1 hT3 hF2 hM8 hF23 hF28 hT93 hT96 hT85 hF81 hF32 hF77 hT60 hT82 hT98 hF27 hF50 hT30 hT81 hF54 hF82 hT44 hF45 hF24 hF70 hF78 hF31 hF34 hF44 hF57 hF68 hF71 hF72 hT25 hT48 hT64 hT89 hT91 hT58 hT87 hT88 hF74 hT83 hF43 hF39 hN39 hN30 hN31 hN32 hN33 hN34 hN35 hN36 hN37 hN38 hN40 hM1F hM1E hM1A hT123 hT101 hT125 hT hT108 hT134 hT127 hT103 hT105 hT106 hT133 hT102 hM19B hM19A hM17D hM19C hM19D D KRAS – Normal TP53 – CDKN2A – Stage 1/2 SMAD4 – Stage 3/4 ARID1A – NA RNF43 – GNAS – Duplication ATM – Neutral MYC – Deletion PIL3CA – No information PBRM1 – PALB2 – Log copy ratio 2 MAP2K1 – 2 ERBB2 – 1 BRCA1 – 0 AKT2 – –1 BRCA2 – –2 EGFR – RREB1 – NF1 – PRSS1 – GATA6 – FGFR1 – TGFBR2 – 7 A 11 hF3 hT1 hT3 hF2 hM8 hF23 hF28 hF81 hF32 hF77 hF27 hF50 hF54 hF82 hF45 hF24 hF70 hF78 hF31 hF34 hF44 hF57 hF68 hF71 hF72 hT25 hT48 hT64 hT89 hT91 hT58 hT87 hT88 hF74 hT83 hF43 hF39 hT93 hT96 hT85 hT60 hT82 hT98 hT30 hT81 hT44 hN39 hN30 hN31 hN32 hN33 hN34 hN35 hN36 hN37 hN38 hN40 hM1F hM1E hM1A hT103 hT105 hT106 hT133 hT102 hT123 hT101 hT125 hT hT108 hT134 hT127 hM19B hM19 hM17D hM19C hM19D

PDOs Recapitulate Genetic Hallmarks of plementary Fig. S1). The criteria to be classified as a con- Pancreatic Cancer and Reveal New Characteristics firmed tumor PDO culture required the presence of known pathogenic mutations. Eighty-eight PDO cultures have thus We developed a precision medicine pipeline that first far been subjected to Sanger sequencing (KRAS only) or focuses on molecular characterization of the PDO library whole-exome sequencing (WES), and 69 (78%) of the PDO as the organoid cultures pass quality control criteria (Sup- cultures harbored genetic alterations consistent with PDAC

SEPTEMBER 2018 CANCER DISCOVERY | OF4 RESEARCH ARTICLE Tiriac et al.

(Fig. 1C and D; Supplementary Fig. S1; Supplementary Table tary Fig. S2A). For these cultures, hT83 harbored a KRASG12R S1A–S1D). Nineteen (22%) PDOs exhibited diploid genomes and TP53 mutation, but did not exhibit TP53 LOH or inacti- without discernable genetic hallmarks of pancreatic cancer, vation of other canonical tumor suppressor genes, whereas suggesting the outgrowth of normal ductal epithelial cells hF43 had features of mismatch repair deficiency, including as previously reported (17), and were not further analyzed. MSH6 mutation, complete loss of MLH1, and a frequency The 11 hN organoid cultures isolated from the exocrine com- of insertions and deletions (per megabase) more than 8-fold partment obtained from normal healthy donors for islet cell higher than the cohort mean (11.54 vs. 1.303 indels/MB; Sup- transplantation were also subjected to WES and maintained plementary Fig. S2B; ref. 22). a diploid genome without bona fide pathogenic mutations Whole-genome sequencing (WGS) was performed on a (Fig. 1C and D). Deidentified patient clinical data were avail- subset of PDAC-confirmed PDOs derived from surgical resec- able for the 69 confirmed PDAC PDO cultures. Twelve PDAC tions and their matched primary tumor (bulk), and both PDO cultures were generated from 5 pretreated patients were germline corrected using normal tissue (n = 13; Fig. 2A; with metastatic disease whereas the remaining 57 organoids Supplementary Fig. S2C). 82.49% to 99.96% (mean, 97.43%) of were isolated from 55 patients who were treatment-naïve at the mutations detected in the primary tumor specimen were the time of PDO generation. Given that many patients who also detected in the PDO culture. The four most commonly present with resectable disease typically receive neoadjuvant altered genes in PDAC (KRAS, TP53, CDKN2A, and SMAD4) therapy prior to surgical resection, this PDO library is a unique were also examined for their overlap between matched pri- resource. For 66 of the patients with PDAC with clinical-stage mary and PDO specimens. In 11 of the 13 cases, the PDO data, PDO cultures were generated from patients with stage cultures completely recapitulated the PDAC core mutation 1 (n = 1), 2 (n = 34), 3 (n = 7), and 4 (n = 24) disease (Supple- profile found in the patient, although in the primary tumor mentary Table S1A). specimens there were often low numbers of reads and the KRAS was mutated in 66 of 69 PDOs (96%), whereas 3 of mutation was not confidently called (Supplementary Table 69 organoids presented with wild-type KRAS. The expected S1D). The primary tumor specimens from the two sam- prevalence of KRAS mutations was observed in the PDO cul- ple pairs that did not exhibit overlap had extremely low tures, with 31 (45%) cases exhibiting G12V, 29 (42%) G12D, purity (<15%), and no alterations in PDAC core genes were 4 (6%) G12R, 2 (3%) Q61H, and 3 (4%) wild-type KRAS. In 2 detected in the primary tumor specimens. High concordance (3%) cases, multiple KRAS mutations were detected within of somatic mutations was achieved between the primary a single PDO culture (Supplementary Table S1A), with one tumor and PDO in most cases (6 > 80%; 11 > 59%), with case (hF50) exhibiting a biallelic KRASG12V and KRASG12R more somatic mutations detected in the PDO cultures due mutation and the other (hF70) exhibiting an amplifiedKRAS to the paucicellular nature of the primary tumors and high allele likely harboring a compound KRASG12D,G179S mutation. neoplastic purity of the organoids. In 2 cases where there Of the KRAS wild-type PDO cultures, hF43 harbored an was low tumor purity (<15%), low concordance (<10%) was oncogenic PIK3CAE110del allele (18), hF39 exhibited an activat- observed, likely due to the limited ability to detect somatic ing MAP2K1Q58_E62del allele associated with MEK1 inhibitor mutations in the paucicellular primary tumors compared resistance (19, 20), and hT102 harbored a hyperactivating with the increased ability to extract genetic alterations from mutation of ERBB2S310F (21) concomitant with a copy-number the purely neoplastic PDO cultures. Copy-number analyses gain of the wild-type ERBB2 allele. TP53 mutations were of the paired primary tumors and PDOs also showed con- detected in 58 of 66 (88%) of the organoids subjected to WES cordance in the primary specimen with high purity (purity (excluding the KRAS-only Sanger-sequenced organoids) and > 40%, hT98; Fig. 2B); however, most primary tumor specimens were concomitant with loss of heterozygosity (LOH) in 56 had insufficient purity to reveal copy-number alterations of 58 cases (97%; Supplementary Table S1B). In addition, we (CNA), whereas CNA and gross chromosomal rearrange- observed a high rate of deep copy-number loss (log2 < −3) or ments were readily discernable in the PDO cultures (Fig. 2C; homozygous, inactivating mutation of CDKN2A (n = 32% Supplementary Figs. S2D and S3). In addition to the hT PDO and 24%, respectively) and SMAD4 (n = 8% and 20%, respec- and primary tumor pairings, WGS with germline correction tively). Thirty-five percent ofKRAS -mutant PDO cultures was also performed on 8 hF PDO cultures. Due to the small exhibited inactivation of all three commonly altered PDAC amount of tissue obtained from these biopsies, the entire tumor suppressor genes (TP53, SMAD4, and CDKN2A), whereas specimen was directed toward PDO generation such that pri- 45% exhibited inactivation of two of these tumor suppres- mary tumor tissue from the hF PDO cultures was unavailable sors (Supplementary Table S1C). A small fraction (14%) of for comparison. Complex genomic rearrangements were also the KRAS-mutant PDOs harbored inactivation of only one observed in several of the PDO hF cultures (Supplementary tumor suppressor. Sixty-four of the 66 PDAC PDO cultures Fig. S3). Although genetic assessment of PDAC primary tis- subjected to WES were aneuploid, whereas two cultures, hT83 sue specimens is often challenging due to their low neoplastic and hF43, maintained a largely diploid genome (Supplemen- cellularity, these genomic analyses revealed the high depth

Figure 2. Deep molecular clarity obtained from PDO genetic analyses. A, Purity, ploidy, concordance, and percentage of the primary tumor mutations found in the PDO cultures using whole genome SNVs of the PDO and matched primary tumor specimens following germline variant removal. Representa- tive Venn diagrams are shown of PDO and primary tumor SNVs. B, CNA in representative matched primary tumor specimens and corresponding PDO. Two representative cases with differing degrees of primary tumor purity are shown. C, Circos plots demonstrating CNA (red and blue CNA inner circles) and gross chromosomal rearrangements (connecting lines) in representative, matched primary tumor, and PDOs following germline variant removal.

OF5 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

A B 1 hT81 hT85 Primary tumor 123 % Primary Sample Germline Purity Ploidy Concordance found in PDO hT30-T hT30-N 0.23 NA 59.50% 99.39% hT30-O hT30-N 1.00 3.01 hT81-T hT81-N 0.18 2.96 312 4,893 32,058 3,466 10

93.95% 99.94% –1 hT81-O hT81-N 0.99 3.16 hT82-T hT82-N 0.24 3.65 65.38% 96.81% hT82-O hT82-N 0.99 3.44 Organoid hT123 ry hT85-T hT85-N <0.15 NA 62.63% 99.71% hT85-O hT85-N 0.96 2.89 ima hT87 hT98 00 hT87-T hT87-N 0.27 2.13 95.80% 99.96% hT87-O hT87-N 0.85 2.12 Pr hT91-T hT91-N 0.17 2.03 85.86% 99.92%

hT91-O hT91-N 0.98 2.02 –4 ratio

hT93-T hT93-N 0.18 2.14 2 90.83% 98.18% 220 5,076 2 1,175 3,844 193 hT93-O hT93-N 0.92 2.00 1 Primary tumor 98

hT98-T hT98-N 0.44 1.93 Log 73.75% 95.22% hT98-O hT98-N 0.99 1.90 hT101-T hT101-N 0.26 1.98 95.98% 98.81% hT101-O hT101-N 1.00 2.04 hT102-T hT102-N <0.15 NA PDO hT108 hT123 8.66% 99.42% hT102-O hT102-N 1.00 2.99 –1 hT108-T hT108-N 0.19 1.99 60.75% 82.49% hT108-O hT108-N 1.00 2.99 44 Organoid hT98 hT117-T hT117-N <0.15 NA 9.67% 98.95% 923 2,129 452 637 3,731 11 hT117-O hT117-N 0.99 2.03

hT123-T hT123-N <0.15 NA 00 85.19% 99.71% hT123-O hT123-N 0.98 3.03

–4 1234567891011121416182022X Chromosome

C Patient tumor 123 Patient tumor 102 y y 1 1 x x

22 22

21 21 TT A NKRD20 C 2 B 2 28 N T C 0 0 D TC 2 2 - A11P 49 0E 21 .1 2 C Y 9ZN P 9 1 2A 1 F54 6 6 ZN C F57 EP 3 89 T LE 18 18 2

3 3

1 84F7. 1-3 P1 17 17 R

TPRG1

G 16 16 NAO1

ANTXR2 4 4 SLC39A8

15 15

LINC00221

14 14 CTC- 254B4. 5 5 1

13 13

3 20.

11-749H P R

6 6

12 12 PMS2

P 4 C D NTNAP2 NAJB

PRKD

6 UBAP1 NPR2 7 7 11 C 11

10 8 10 8 9 9

Organoid hT123 Organoid hT102

92 -

AP1 y 5 y 1 L WDR R 1

1 3 A P1

L 5 L R x x FBLIM1

5.1 11.4 1

T GR SMG A17 N1

M T 1 N

2 A M5

H HSD 4

A 552M 4Z 7 SP

MG WI P 22 22 EK A4

LL ZC S L PH Y 11- 5 11-343 2 17 T Z

PO P E PP C H A T 4 4 C2 B RP RP D H2 2 C P2 G 1 10 T B SL T H 21 Y 21 A 6 HA AH Y A R G 2 T T 3 TTC S F 2 GP I 2 T B DN 2 EN 2 C28 K 2 2 6L E P 8 3 B FCG 0 P C 3 0 2 K P N 2 NR FA R A BP ZF K2 R M P E IP1 G 7 2 E C 1E2 H 1 S DH C IF3 YCE IL HL S P1 MA F M S B K ZNF5 11 CT 2 CT CR LI LA P1 9ZNF5 D 9 D- C R 1 -2 1 2 O L 46 P 2 1 D2 73 AK7 6 0 L 5O 5 S 2 E1 M14A 1 3. CEP8 .7 6 D S N P M 9 ASE LB A D D4 2 H 18 RP11- 18 729L2.2 3 3 T RP AF 11 4B -14 A3 P20 .1 EPH AT 17 MBT W P6V0 D BP ATP6V1A 17 A1 1 2 AOC TM DC L 3 E AK 4 NAA SM M132ED A1 11-5 UR LC7 30N F2 7 S 7.3 P1 MD AC0 SA RP11-134F2.2 2776 3.2 DGKG 2.5 WWOX TA HYDI AT NGO6 N SMPD G 3 16 N 16 AO1 D ATP10 R4 ZC3H7A RBFOX1 11-420N3.2 8E5.1 HNRM 43 4 4 ADCY9 RP11- BMP3 ARHGAP24 GRID2 PDLIM5 AD WDR93 COL25A1 AM PRSS12 TBCK EXOSC9 TRPC3 FG RP11-775H9.2 15 15

SPOCK3 GALNTL6 CHEK2P2

EML1 4P21.8 RP11-40 KRB1 J PAPLN RHO L PYG FA 14 14 3 M1 20. SV 69A -218E 2C IQ C GAP RNP N RP11 A 2 HN REP PC 5 N 5 CTC REP NM -554D6 -AS1 E5 .1 P CD HB 15 GMD LECT1 S RP1 -8 13 13 GPR110 0B PKH 9.2 EY MYL R S D T 2 P1-23 1 N K4 2 C6 RP FR CD M 4 RP S C MS -6 2L24. F P2A LAC 7 1 21 T .1 PKI 6 1- A AHI 1R 3 22 J1 406O16. IP A E1 L 3 3 PHA 1 H H2 UTRNHI B PR .2 R TMEM181 1 N S P 14 T TR YNCRIP11 C 6 -1 C ULP4 K DM 6 - 1 TR 1 E MT11AIN2 5 1 Z1 A 6 PM2A 1 54 EE 2 D o RP D C rf A 1 F ZC RP L A 1 R SL 1 5.1 1A P P 2 I C C3 1- NT5E 1 12 U 2 12 A5 1 0 RPS6KA2D1 P EP 4 TR H 2 2 DE 5 36D C 70.16. ST C B 8 F1 N 1 O S 7 5L A B EKH X1 A B T 2 AC0 007 8B C Y M PL G 3 PL KMT2EO XL .1 361I14. 70 D1 9 D N 5 P0 MMP20 4 R L26A1 5 M 024 H 1 RP11 AP7 CNTNAP A P H 11- E BP UNC AC .2 11-306G20 E P N AD 5 RP11- 5 PIWIL2 C R LI -5 073871.2 4 3 F GB A J1 1 9 .1 K K5 N 1 7 BP2 3 .1 1 A 4O SI 1 T R C D R LINC00536 00589 1 L 2 12 -8 C G A1 C B CNTLN 2. P1 U C 1 AD A 3 T A8 7 7 7 4 2 1 1 N1 .1 BAP1 DKN2A P 11 0 11 G 6 P I PD L 1

R R CIZ1 M -2 -2 IT RP5-982E9.1 O Z DNAJB6 L A UNE2 M1

G NFIL3 C3HC1 C 55B2 RBP 11 R N T P FA SL PR R EU M 1 N RP 3 S .4

A 1 RP 1 2 1 8 0 1-32 8 0

1 RNF XD

1 P -1 9 9 6

I I3 3

SEPTEMBER 2018 CANCER DISCOVERY | OF6 RESEARCH ARTICLE Tiriac et al. and clarity in which PDAC genetics can be evaluated in PDO macotyping was stable over multiple passages, with minor cultures, providing alternative means of identifying action- variation only occasionally observed (Supplementary Fig. S5). able genetic alterations in patients with PDAC. For each chemotherapeutic agent, we divided the PDO library into 3 subgroups: the least responsive (resistant; top 34% Transcriptomic Profiling and Subtyping AUC), the most responsive (sensitive; lowest 33% AUC), and of Pancreatic Cancer PDO Cultures those exhibiting intermediate response (middle; 33% AUC). RNA sequencing (RNA-seq) was performed on 44 PDAC- To determine whether this subgrouping was informative for confirmed PDOs and 11 hN organoid cultures. The hN cul- individual patients, we obtained retrospective clinical follow- tures clustered separately from the PDAC PDOs in principal up from 9 patients with advanced PDAC who were treated component analysis (PCA; Fig. 3A). The hM19A-D series of with these 5 agents (Supplementary Fig. S6A). Of the 6 organoids were isolated from different metastatic sites of the patients with a PFS longer than the published median PFS (6, same patient following rapid autopsy and represent a distinct 7), 5 were treated with at least one drug to which the matched cluster relative to the other stage 4 PDO cultures. Gene set PDO culture was particularly sensitive and no drug to which enrichment analysis (GSEA) of the differentially expressed the matched PDO culture was resistant. These 6 patients had genes in PDAC relative to hN organoids indicated an enrich- a mean PFS of 332 days compared with the expected PFS of ment in MYC and E2F targets, the G2-M checkpoint, as well 180 days (6, 7). Two of the 3 patients who rapidly progressed as pathways involved in metabolism that include glutathione were treated with a chemotherapeutic agent to which their metabolism, steroid biosynthesis, and biosynthesis of unsat- PDO was markedly resistant. One of the 9 patients exhibited urated fatty acids (Supplementary Table S2). These PDO an outcome inconsistent with the matched PDO (hF50) RNA-seq data were used to identify the classic and basal- pharmacotyping profile. Altogether, these data suggest the like subtype signatures previously derived from bulk tissues potential relevance of this approach. following virtual microdissection (ref. 13; Fig. 3B). Seventy For 1 patient, corresponding with PDO hF2, extensive percent of the PDO cultures are the classic subtype (31/44) retrospective data were available following the generation of and 30% are basal-like (13/44)—a notable finding, as there are the PDO. The patient with PDAC from whom the hF2 PDO very few available cell line models of the classic PDAC subtype was generated was first treated with a four-drug combination, (ref. 13; Supplementary Table S3A). Therefore, in addition to including two drugs with an intermediate PDO response pro- being able to efficiently culture organoids from every stage file (oxaliplatin and 5-FU) and one drug with a resistant PDO of pancreatic cancer, including previously difficult-to-access response (paclitaxel; Supplementary Fig. S6B and S6C). This metastatic disease, this culture method enables the propaga- patient exhibited early progression in both the primary and tion and study of PDO cultures from both classic and basal- metastatic sites (Supplementary Fig. S6C) and was switched like PDAC subtypes. to a second-line regimen that contained two drugs to which The PDO transcriptomes were also independently classi- the PDO was sensitive (gemcitabine and SN-38). Follow- fied using nonnegative matrix factorization (NMF) clustering the change in regimen, the patient exhibited a partial ing, revealing two stable clusters in PDAC PDO cultures response for 388 days before adopting third- and fourth-line (Fig. 3C; Supplementary Fig. S4A and S4B; Supplementary therapeutic strategies until ultimately succumbing to disease Table S3B). Cluster C1 was enriched for TGFβ signaling and 1,020 days following diagnosis (Supplementary Fig. S6C). For epithelial–mesenchymal transition by GSEA (Fig. 3D; Supple- this single-patient case study, the retrospective clinical data mentary Table S2). In contrast, cluster C2 exhibited enrich- paralleled the PDO chemosensitivity profile. ment for xenobiotic metabolism, fatty-acid metabolism, and oxidative phosphorylation by GSEA. Although the genes Temporal Evolution of PDO Chemosensitivity comprising the C1/C2 signatures did not overlap with those In an analogous case-study manner, we found that longitu- defining basal and classic subtypes (1 gene overlap isMYO1A ), dinal PDO generation reflected the clinical course for an indi- the classifications were largely concordant with 83% of the vidual patient (Fig. 5A; ref. 23). In the hM1 series, the hM1A basal-like PDO cultures falling in the C1 classifier and 93% of PDO was isolated from a VATS resection of a lung metastasis, the classic PDO cultures falling in the C2 cluster. Therefore, and following resection the patient was found to respond well PDO cultures revealed unique gene expression programs that to both the FOLFIRINOX and gemcitabine/nab-paclitaxel reg- divide PDAC into two distinct molecular classes. imens. Indeed, the hM1A PDO was sensitive to gemcitabine, paclitaxel, 5-FU, and oxaliplatin and exhibited an intermediate PDO Pharmacotyping Corresponds with Individual SN-38 response within our cohort. Approximately 2 years later, Patient Treatment Responses the patient presented with progressive disease that histologi- Therapeutic profiling or “pharmacotyping” was performed cally exhibited neuroendocrine/small cell–like characteristics. on 66 PDAC-confirmed PDOs using the 5 chemotherapeutic A repeat organoid culture, hM1E, was established from a agents most commonly used to treat patients with PDAC: percutaneous core biopsy of a lung metastasis. The patient suc- gemcitabine, nab-paclitaxel (paclitaxel used in PDOs), irinote- cumbed to the disease shortly afterward, and a rapid autopsy can (SN-38; active metabolite used in PDOs), 5-fluorouracil was performed, leading to the generation of the final hM1F (5-FU), and oxaliplatin. PDO pharmacotyping revealed organoid. Intriguingly, the hM1E and hM1F PDO cultures marked interpatient variability in the PDO response to sin- showed amplification of theKRAS allele (Fig. 1D) and were gle chemotherapy agents as evaluated using dose–response resistant to gemcitabine, paclitaxel, and SN-38 while hM1F curves and the corresponding area under the curves (AUC; gained additional resistance to oxaliplatin and switched to a Fig. 4A–E; Supplementary Table S4A). The PDO culture phar- more basal-like subtype (Fig. 3B). This case suggests the utility

OF7 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

A B Row Z-score Normal 20 Stage 1/2 –3 03 10 Stage 3 PCA3 Stage 4 Classic expression score 5.68% 0 No info. Basal-like expression score –10 Stage

–20 Basal/classic subtype –30 hM19A-D hT85 NMF clustering –40 –50 –60–80 hF57 –40 –20 –20 0 0 PCA2 20 20 PCA1 9.65% 40 25.27%

C Color key & histogram NMF C1 300 NMF C2 Unstable NMF C1 200 Count Not classified 100 0 –3 –1 13 Value Basal Classic

Stage 1/2 Stage 3/4 No stage info. NMF clustering Basal/classic subtype Stage 17 hF3 hF2 hT1 hT3 hM8 hT82 hT60 hT30 hF28 hF43 hT98 hF27 hT81 hF57 hF44 hF39 hF34 hF32 hF31 hF24 hF23 hT93 hT91 hT89 hT85 hT64 hT25 hT23 hT48 hF50 hM1F hM1A hM1E hT127 hT105 hT108 hT1 hT101 hT102 hM19B hM19A hM19D hM19C hM17D op 25 T Classic score Basal-like score Stage 1/2 Subtype 5.97 6.7 Stage 3/4 Basal 7.36 7.84 No info. Classic 8.75 8.97 10.1 10.1 NMF C1 11.5 11.2 NMF C2

D NMF cluster C1 enriched Enrichment plot: Enrichment plot: HALLMARK_APICAL_JUNCTION Enrichment plot: HALLMARK_TGF_BETA_SIGNALING 0.40 HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION 0.35 0.5 0.30 NES 1.47 0.4 NES 1.60 0.25 0.4 0.20 P = 0.004 0.3 NES 1.46 P = 0.005 0.15 0.3 0.10 q = 0.127 0.2 0.05 P = 0.009 0.2 q = 0.072 0.1 0.00 –0.05 0.0 0.1 q = 0.106 Enrichment score (ES) –0.10 –0.15 –0.1 Enrichment score (ES) 0.0 –0.20 Enrichment score (ES) –0.2

7.5 ‘na_pos’ (positively correlated) 7.5 ‘na_pos’ (positively correlated) 7.5 ‘na_pos’ (positively correlated) 5.0 5.0 5.0 2.5 2.5 2.5 Zero cross at 17597 Zero cross at 17597 Zero cross at 17597 0.0 0.0 0.0

–2.5 –2.5 –2.5

–5.0 ‘na_neg’ (negatively correlated) –5.0 ‘na_neg’ (negatively correlated) –5.0 ‘na_neg’ (negatively correlated) 05,000 10,000 15,000 20,000 25,000 30,000 05,000 10,000 15,000 20,000 25,000 30,000 05,000 10,000 15,000 20,000 25,000 30,000 Ranked list metric (PreRanked) Ranked list metric (PreRanked) Ranked list metric (PreRanked) Rank in ordered dataset Rank in ordered dataset Rank in ordered dataset Enrichment profile HitsRanking metric scores Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores NMF cluster C2 enriched Enrichment plot: Enrichment plot: Enrichment plot: HALLMARK_XENOBIOTIC_METABOLISM HALLMARK_FATTY_ACID_METABOLISM HALLMARK_OXIDATIVE_PHOSPHORYLATION 0.2 0.00 0.05 NES −1.54 0.1 NES −1.66 –0.05 NES −1.62 0.00 –0.10 –0.05 0.0 P = 0.000 P = 0.000 –0.15 P = 0.000 –0.10 –0.15 –0.1 –0.20 q = 0.033 –0.20 –0.2 q = 0.027 –0.25 q = 0.022 –0.25 –0.30 –0.3 –0.30

Enrichment score (ES) Enrichment score (ES) –0.35 Enrichment score (ES) –0.35 –0.4 –0.40

7.5 ‘na_pos’ (positively correlated) 7.5 ‘na_pos’ (positively correlated) 7.5 ‘na_pos’ (positively correlated) 5.0 5.0 5.0

2.5 2.5 2.5 Zero cross at 17597 Zero cross at 17597 Zero cross at 17597 0.0 0.0 0.0

–2.5 –2.5 –2.5

–5.0 ‘na_neg’ (negatively correlated) –5.0 ‘na_neg’ (negatively correlated) –5.0 ‘na_neg’ (negatively correlated) 17 hT3 hT1 hF2 hF3

hM8 0 5,000 10,000 15,000 20,000 25,000 30,000 015,000 0,000 15,000 20,000 25,000 30,000 015,000 0,000 15,000 20,000 25,000 30,000 hT64 hT25 hT81 hT89 hT48 hT93 hT85 hT91 hT30 hT60 hT82 hF43 hF50 hT98 hF32 hF31 hF44 hF34 hF28 hF57 hF39 hF27 hF23 hF24 hM1F Ranked list metric (PreRanked) Ranked list metric (PreRanked) Ranked list metric (PreRanked) hM1E hM1A hT1 hT101 hT102 hT127 hT105 hT123 hT108 Rank in ordered dataset Rank in ordered dataset Rank in ordered dataset hM19B hM19A hM19D hM19C hM17D Enrichment profile HitsRanking metric scores Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores

Figure 3. Transcriptomic profiling of PDOs reveals distinct subtypes. A, PCA of organoids isolated from different cancer stages and normal healthy controls. B, Clustering of PDO culture RNA-seq data reveals concordance with classic and basal-like subtypes. Patient staging and subtype are indicated. C, Clustering using NMF defines two distinct clusters of PDO cultures, C1 and C2. Patient staging and subtype are indicated. D, GSEA of differentially expressed genes between C1 and C2. Three hallmark pathways are shown to be enriched in C1 compared with C2 (top), and three are enriched in C2 (bottom; negative enrichment C1/C2).

SEPTEMBER 2018 CANCER DISCOVERY | OF8 RESEARCH ARTICLE Tiriac et al.

A B Gemcitabine Paclitaxel 0.95 0.95 100 100 C

C 0.85 0.85 AU AU 0.75 0.75 ed ed Viability Viability 50 50

% 0.65 % 0.65 maliz maliz Nor Nor 0.55 0.55

0 0.45 0 0.45 −12 −10 −8 −6 −12 −10 −8 −6 n = 66 n = 66 Log drug Log drug

C SN-38 D 5-FU 0.95 0.95 100 100 0.85 C

C 0.85 AU AU 0.75 0.75 ed ed Viability Viability 50 0.65 50 0.65 maliz maliz % % Nor 0.55 Nor 0.55

0.45 0 0 0.45 −8 −6 −4 −12 −10 −8 −6 n = 66 n = 64 Log drug Log drug

E Oxaliplatin 0.95 100

C 0.85 AU 0.75 ed

Viability 50 Figure 4. Pharmacotyping of PDOs reveals heterogeneity of 0.65 % maliz chemotherapy response. A–E, Dose–response curves and normalized AUC distribution for gemcitabine (A), paclitaxel (B), SN-38 Nor 0.55 (C), 5-FU (D), and oxaliplatin (E) on PDO cultures (n = 63–66). The 0.45 blue portion represents the 33% most-sensitive samples, the 0 red portion the 34% most-resistant samples, and the middle −8 −6 −4 n = 63 portion intermediate drug responses. Drug concentration is Log Log drug transformed mol/L. of longitudinal PDO sampling following repeat biopsies to cancer subclones that will require novel therapeutic regimens evaluate the acquisition of resistance mechanisms to first-line to achieve the best clinical response. chemotherapeutic regimens. At the same time, this longitudinal case series revealed resistance to all commonly used chemo- Nomination of Alternative Treatment Strategies therapeutics for pancreatic cancer, a common issue observed in for Chemorefractory PDO Cultures several PDO cultures and encountered in the clinic. To ascertain alternative treatment strategies for PDO cultures, pharmacotyping was performed using a panel of tar- Spatial Intrapatient Heterogeneity geted agents (n = 21) on 66 PDAC-confirmed PDO cultures of Chemosensitivity (Supplementary Fig. S7A; Supplementary Table S4B). Among We also examined the therapeutic sensitivity of four differ- the PDO cultures lacking sensitivity to any of the five chemo- ent PDO cultures generated from two liver (hM19A, B) and therapeutic agents (22 of 66, 33%; Supplementary Table S4C), one diaphragmatic (hM19C) metastases, as well as ascites alternative treatment strategies were evaluated for 21 of these (hM19D) from the same patient following a rapid autopsy PDO cultures. We were able to identify targeted agents with (Fig. 5B). We found that these four hM19 cultures exhib- extreme PDO sensitivity (10% most sensitive) for half (n = ited similar therapeutic profiles to three chemotherapeutic 11) of these chemotherapy-insensitive PDO cultures. For agents, but different sensitivities to 5-FU. Although these example, hT89 was resistant to four chemotherapeutic regi- four PDOs harbored similar DNA mutations by exomic mens, but sensitive to the broad-spectrum kinase inhibitor sequencing (Fig. 1C), they possessed small differences in CNA sunitinib (Supplementary Table S4C). Targeted agent sensi- (Fig. 1D) and mRNA expression (Fig. 3A–C). Whether these tivities were also evaluated for chemosensitive PDO cultures. molecular differences underlie this therapeutic profile het- For instance, hT105, which was sensitive to oxaliplatin and erogeneity remains to be determined, and this case highlights paclitaxel, was also sensitive to several targeted agents includ- the possibility that metastatic patients may possess different ing selumetinib, afatinib, everolimus, and LY2874455 (FGFR

OF9 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

A Longitudinal series 1.0 1.0 1.0 1.0 1.0

0.8 0.8 0.8 0.8 0.8 AUC AUC AUC AUC AUC

0.6 0.6 0.6 0.6 0.6 Normalized Normalized Normalized Normalized Normalized

hM1A hM1E hM1F 0.4 0.4 0.4 0.4 0.4 Gemcitabine Paclitaxel SN-38 5-Fluorouracil Oxaliplatin B Synchronous metastases series

1.0 1.0 1.0 1.0 1.0

0.8 0.8 0.8 0.8 0.8 AUC AUC AUC AUC AUC

0.6 0.6 0.6 0.6 0.6 Normalized Normalized Normalized Normalized Normalized hM19A hM19B hM19C hM19D 0.4 0.4 0.4 0.4 0.4 Gemcitabine Paclitaxel SN-38 5-Fluorouracil Oxaliplatin CDE 1.0 1.0 1.0

0.8 0.8 0.8 AUC AUC AUC

hM1E hM1A

Normalized 0.6 Normalized 0.6 Normalized 0.6 hM1F hF43

hT102

Mutations > 0.8 CNA < −0.8 0.4 0.4 0.4 Afatinib Olaparib Everolimus ERBB2 amp. BRCA2* BRCA2 loss EGFR & ERBB2 amp. ATM loss PALB2 loss ERBB2, EGFR, AKT2 amp. ATM frameshift & ERBB2S310F

Figure 5. Longitudinal, spatial, and genetic influences on PDO response. A, AUC distribution of hM1A, E , and F PDO longitudinal series. B, AUC distribution of hM19 A, B, C, and D PDOs from the same patient but different metastatic sites. C–E, AUC distribution and genotype correlation of afatinib (C), olaparib (D), and everolimus (E) responders. Asterisk in D indicates mutation.

SEPTEMBER 2018 CANCER DISCOVERY | OF10 RESEARCH ARTICLE Tiriac et al. inhibitor). In line with previous findings, the PDO hF39 determined that 50% of patients were enriched (Supplemen- that harbors the oncogenic MEK1 allele MAP2K1Q58_E62del tary Fig. S11A). We used this signature to evaluate patient was not sensitive to the MEK inhibitor selumetinib (20). response in the subgroup of 55 patients who received gemcit- The ERBB-directed agent afatinib showed increased activity abine monotherapy and found that patients with enrichment toward PDOs harboring ERBB2 amplification, with the most for the gemcitabine-sensitivity signature had a significantly sensitive PDO being the KRAS wild-type PDO hT102 that better PFS (772 vs. 373 days, HR = 0.54, P = 0.04; Fig. 6B and harbors the hyperactivating ERBB2S310F allele in the setting C), and a trend toward improved OS (Supplementary Fig. of amplifications inEGFR, ERBB2, and AKT2 (Fig. 5C). Evalu- S11B). Interestingly, in this cohort of 55 patients, the basal- ation of other genes involved with homologous repair defi- like subtype was similarly represented in the gemcitabine- ciency revealed that although there are many haploid losses sensitive and nonsensitive groups (Fig. 6B). Application of in the copy number of these genes, these single-copy losses this gemcitabine-sensitivity transcriptomic signature to a do not correspond with olaparib sensitivity (Figs. 1D and 5D; larger subgroup of 91 patients who received either gemcit- Supplementary Fig. S7B). Deleterious BRCA1/2 mutations abine monotherapy or gemcitabine in combination with were not present in this PDO library. Nonetheless, a trend 5-FU or cisplatin also identified patients with a significantly was observed between olaparib sensitivity and complete loss better PFS, but not OS (Supplementary Fig. S11C and S11D). of PALB2 (Fig. 1C and D). The only organoid harboring a In the small cohort of 30 untreated patients, the gemcitabine- PIK3CA mutation, the KRAS wild-type PDO hF43 that carried sensitivity signature did not identify patients with improved the oncogenic PIK3CAE110del allele (18), was highly sensitive to PFS or OS, demonstrating that this signature is treatment the rapamycin analogue everolimus (Fig. 5E). Finally, the pre- dependent (Fig. 6D; Supplementary Fig. S11E and S11F). viously mentioned hM1 longitudinal series includes hM1A, Finally, the chemosensitivity signatures were applied to an which was isolated from a lung metastasis and exhibited a independent transcriptomic data set obtained from tumors PDAC pathology, whereas the two PDO cultures, hM1E and of patients with PDAC on the COMPASS trial (26). Patients hM1F, were isolated after the lung metastases switched to a on the COMPASS trial had advanced pancreatic cancer and small cell-like (neuroendocrine) phenotype. Neuroendocrine underwent core needle biopsy prior to treatment with combi- tumors are often responsive to mTOR inhibition (24), which nation chemotherapy. The biopsies were of sufficient size to is potentially paralleled by the switch of the hM1 series from perform laser capture microdissection for mRNA isolation and an average to a sensitive everolimus therapeutic profile (Fig. transcriptomic analysis. Thirty percent of the tumors (22/73) 5E). These results suggest that targeted therapy sensitivities were the basal-like subtype of PDAC. We found that 44, 37, empirically identified in PDO pharmacotyping may supple- 31, 29, and 36 (60%, 51%, 42%, 40%, and 49%) patients exhib- ment precision medicine approaches for patients with PDAC. ited enrichment for the PDO-derived sensitivity signatures for oxaliplatin, 5-FU, SN-38, gemcitabine and paclitaxel, respec- PDO Pharmacotranscriptomic Signature Reflects tively (Fig. 7A; Supplementary Fig. S12A–S12D). The basal-like Treatment Response in Patients with Pancreatic patients were equally distributed between the chemosensitive Cancer and nonsensitive signatures with the exception of oxalipl- To investigate whether PDO pharmacotyping could be atin, which exhibited an enrichment of the basal-like subtype applied to patients with advanced pancreatic cancer, we gen- in the nonsensitive patient group (Fig. 7A; Supplementary erated drug-sensitivity signatures by correlating PDO tran- Fig. S12A–S12D). RECIST measurements were available for scriptional profiles with the pharmacotyping results. For most patients 8 weeks following the initiation of therapy. We each chemotherapeutic agent, we computed the Spearman found that the oxaliplatin signature significantly correlated correlation between PDO gene expression and the AUC for with response (r = −0.396, P = 0.0078) in patients receiving each drug and thereby defined distinct transcriptional sig- FOLFIRINOX (n = 47). Patients who had an enrichment for natures (Fig. 6A; Supplementary Figs. S8, S9A–S9E, S10A the oxaliplatin signature exhibited better tumor responses to and S10B; Supplementary Table S5A–S5E). We refined the FOLFIRINOX than their nonsensitive counterparts, but the signatures to include genes that increased in expression when 5-FU and SN-38 signatures did not provide additional infor- AUC decreased (negative rho value), which is indicative of mation (Fig. 7B). There was also a trend for increased OS in the increased drug sensitivity. By clustering the PDO cultures oxaliplatin-sensitive patients (Fig. 7C), and notably there is a using the individual drug-response signatures, the PDOs larger number of patients still alive in the oxaliplatin-sensitive could be grouped into sensitive or nonsensitive classes for (n = 13) versus nonsensitive cohorts (n = 5). Intriguingly, of the each individual chemotherapeutic signature. To determine 6 basal-like patients who lacked enrichment of the oxaliplatin whether the PDO-derived pharmacotranscriptomic signa- chemosensitivity signature and progressed on FOLFIRINOX, tures reflected treatment responses in patients, we obtained 5 exhibited enrichment for the gemcitabine chemosensitivity neoplastic cell-enriched gene-expression data and associated signature (Fig. 7B). clinical details from 126 patients who underwent resection Nineteen tumor biopsies from patients with advanced of their pancreatic tumor and then received either adjuvant PDAC were obtained prior to treatment with the combina- treatment (n = 95) or no treatment (n = 31; ICGC-CA; ref. tion chemotherapy regimen gemcitabine and nab-paclitaxel, 25). In this sample set, 43% of the patient tumors (55/126) and RECIST criteria were again measured at 8 weeks. In this were the basal-like subtype of PDAC. Treated patients received smaller subset of patients, 7 patients harbored the gemcit- gemcitabine either alone or in combination with other chemo abine-sensitivity signature and 7 patients also exhibited the therapeutic agents. Therefore, we applied the gemcitabine- paclitaxel-sensitivity signature (Supplementary Figs. S12A specific PDO-sensitivity signature to this patient cohort and and S12B; S13). The 4 patients who were sensitive to both

OF11 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

A C PFS-GEM UP gene signature patients receiving gem 100 Gem. sensitive (n = 32) −20 2 Gem. nonsensitive (n = 33) −20 2 P = 0.0406

0.05 hF3 0.06 hF50 0.07 hT64 0.28 hM19A 0.34 hM1F 0.36 hT127 0.37 hM1A 0.37 hM8 0.39 hF28 0.40 hM19B 0.45 hM19D 0.52 hF27 0.54 hM1E 0.60 hM19C 0.64 hT81 0.68 hT93 0.74 hF57 0.76 hT89 0.78 hT91 0.86 hF39 0.88 hF30 HR: 0.5499 (0.3007–1.006) Mean 1.11 hF23 0.99 hF44 0.97 hF31 0.86 hT102 0.86 hT108 0.71 hF32 0.62 hT48 0.54 hT82 0.50 hF24 0.46 hT105 0.40 hT1 0.39 hT98 0.33 hT101 0.29 hT25 0.24 hT117 0.18 hM17D 0.16 hF2 0.16 hT85 0.15 hT60 0.13 hT3 0.06 hF43 0.03 hT123 − − − − − − − − − − − − − − − − − − − − − 1.818 (0.9942–3.326) Gem. profile Median PFS: 772 vs. 373 days Stage NMF cluster atients 50 Subtype % P Gem. profile Sensitive Nonsensitive 0 Stage 0 1,000 2,000 3,000 Stage 1/2 Days Stage 3/4 No info. D PFS-GEM UP gene signature NMF cluster patients receiving no adjuvant NMF C1 NMF C2 100 Gem. sensitive (n = 14) Unstable C2 Gem. nonsensitive (n = 16) No cluster P = 0.4795 HR: 1.359 (0.5346–3.452) Subtype 0.7361 (0.2897–1.871) Basal Median PFS: 163 vs. 154 days Classic

atients 50 Gemcitabine Paclitaxel % P SN-38 5-FU Oxaliplatin 0 0 500 1,000 1,500 2,000 2,500 Days B 0.01 PCSI0073 0.01 PCSI0653 0.01 PCSI0467 0.01 PCSI0105 0.02 PCSI0355 0.04 PCSI0590 0.05 PCSI0075 0.07 PCSI0085 0.08 PCSI0208 0.08 PCSI0639 0.10 PCSI0344 0.13 PCSI0019 0.14 PCSI0463 0.16 PCSI0081 0.17 PCSI0625 0.19 PCSI0142 0.23 PCSI0302 0.24 PCSI0044 0.29 PCSI0468 0.30 PCSI0080 0.31 PCSI0101 0.32 PCSI0890 0.34 PCSI0645 0.38 PCSI0356 0.39 PCSI0612 0.44 PCSI0449 0.46 PCSI0511 0.47 PCSI0358 0.52 PCSI0592 0.53 PCSI0610 0.57 PCSI0474 0.57 PCSI0627 0.66 PCSI0218

Mean 0.96 aPCSI0292 0.93 PCSI0145 0.72 PCSI0608 0.70 PCSI0530 0.40 PCSI0077 0.37 PCSI0173 0.36 PCSI0046 0.35 PCSI0309 0.31 PCSI0103 0.30 PCSI0628 0.30 PCSI0347 0.28 PCSI0602 0.25 PCSI0529 0.21 PCSI0616 0.18 PCSI0623 0.18 PCSI0593 0.18 PCSI0473 0.18 PCSI0228 0.17 PCSI0102 0.17 PCSI0104 0.16 PCSI0311 0.09 PCSI0084 0.08 PCSI0609 0.08 PCSI0466 0.07 PCSI0109 0.07 PCSI0642 0.06 PCSI0531 0.05 PCSI0655 0.04 PCSI0357 0.03 PCSI0475 0.02 PCSI0108 0.02 PCSI0107 0.01 PCSI0096 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − Gem. profile Stage NMF cluster Subtype −20 2

Gem. profile Sensitive Nonsensitive Stage Stage 1B Stage 2A Stage 2B NMF cluster NMF C1 NMF C2 Subtype Basal Classic

Figure 6. PDO-derived gemcitabine-sensitivity signature stratifies patients with pancreatic cancer with improved response to adjuvant gemcitabine. A, The gemcitabine-sensitivity prediction signature was used to cluster the PDO RNA-seq data. Additional data regarding the pharmacotyping AUC response (log2-transformed z-score), C1/C2 subtype, basal/classic subtype, and stage are shown. B, The gemcitabine-sensitivity prediction signature was applied to RNA-seq data from patients who received single-agent gemcitabine (ICGC-CA). Additional data regarding the pharmacotyping AUC response

(log2-transformed z-score), C1/C2 subtype, basal/classic subtype, and stage are shown. C, Kaplan–Meier analysis of PFS of gemcitabine-sensitive and nonsensitive patients as identified inB . D, Kaplan–Meier analysis of PFS of gemcitabine-sensitive and nonsensitive untreated patients. Log-rank (Mantel–Cox) test P value and log-rank hazard ratio are shown. gemcitabine and paclitaxel had reduced tumor sizes by their cer stem cells (17), redox metabolism (27), and intermediary 8-week radiologic evaluation and many patients sensitive to metabolism (28, 29); and to non–cell-autonomous properties either gemcitabine or paclitaxel also responded, although the such as limited drug delivery (30–32), impaired intratumoral sample size is limited and the analysis interim (Supplemen- immunity (33), and fibroblast- and microbial-mediated drug tary Fig. S13). The ability of the PDO chemosensitivity sig- metabolism (34, 35). Although the influence of different natures to expediently identify patients with better response matrix components or cancer-associated fibroblasts to thera- in both the ICGC-CA and COMPASS studies suggests that peutic response is worthy of future examination, in this these signatures may have potential clinical utility following study, we use PDOs as a well-defined model system and evaluation in prospective clinical trials. demonstrate a broad range of intrinsic neoplastic cell drug sensitivities to conventional chemotherapeutic agents. These data reveal the additional impact of interpatient diversity DISCUSSION to chemotherapeutic drug responses that may supersede or The poor response of patients with PDAC to therapies has modify other potential causes of drug resistance. The biologi- been attributed to neoplastic cell characteristics such as can- cal basis of this interpatient drug responsiveness is currently

SEPTEMBER 2018 CANCER DISCOVERY | OF12 RESEARCH ARTICLE Tiriac et al.

A COMP-0044 COMP-0073-G COMP-0047 COMP-0042 COMP-0088-G COMP-0084-G COMP-0029 COMP-0080 COMP-0092-G COMP-0087-G COMP-0004 COMP-0069 COMP-0037 COMP-0007 COMP-0102-G COMP-0098 COMP-0005 COMP-0085 COMP-0019 COMP-0090 COMP-0071 COMP-0006 COMP-0045 COMP-0091-G COMP-0017 COMP-0104 COMP-0050 COMP-0036 COMP-0106 COMP-0089-G COMP-0072 COMP-0078 COMP-0030 COMP-0009 COMP-0059 COMP-0043 COMP-0096-G COMP-0063 COMP-0001 COMP-0057 COMP-0023 COMP-0028 COMP-0097 COMP-0014 COMP-0035 COMP-0081 COMP-0067-G COMP-0077-G COMP-0108 COMP-0049 COMP-0002 COMP-0026 COMP-0034 COMP-0055 COMP-0032 COMP-0020 COMP-0025 COMP-0048 COMP-0015 COMP-0013 COMP-0010 COMP-0074 COMP-0018 COMP-0021 COMP-0056 COMP-0075 COMP-0038 COMP-0033 COMP-0041 COMP-0008 COMP-0093 COMP-0058 COMP-0046 Treatment Subtype NMF cluster 1 1 1 1 0.01 0.03 0.04 0.04 0.04 0.06 0.08 0.10 0.1 0.16 0.16 0.23 0.24 0.30 0.33 0.33 0.42 0.42 0.42 0.43 0.43 0.43 0.46 0.56 0.58 0.60 0.64 0.71 0.74 0.56 MEAN 0.46 0.45 0.41 0.39 0.36 0.32 0.31 0.31 0.31 0.26 0.24 0.23 0.22 0.22 0.21 0.20 0.19 0.19 0.19 0.17 0.16 0.16 0.14 0.14 0.14 0.13 0.12 0.12 0.12 0.12 0.1 0.1 0.1 0.09 0.07 0.07 0.06 0.06 0.05 0.02 0.02 0.01 0.01 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − Oxa profile

−20 2

Oxa profile Sensitive Nonsensitive Treatment GA FFX NMF cluster NMF C1 NMF C2 Subtype Basal Classic

B FOLFIRINOX COMPASS cohort C COMPASS FOLFIRINOX patients 100 5-FU & SN-38 sensitive 100 Oxaliplatin nonsensitive Oxaliplatin sensitive

Basal Classic P = 0.1195 Patients 50 NMF C1 expired NMF C2 prior to 8 weeks Percent survival

0 0 050 100 Weeks

8 weeks % tumor change Oxa sensitive n = 25 (13 alive) Oxa nonsensitive n = 22 (5 alive) −1.0−0.5 0 0.5 1.0

RECIST Oxaliplatin 5-FU SN-38 Paclitaxel −100 Gemcitabine C1/C2 Basal-like/classic G G G G G G G COMP-0075 COMP-0056 COMP-0047 COMP-0037 COMP-0021 COMP-0019 COMP-0043 COMP-0036 COMP-0042 COMP-0023 COMP-0069 COMP-0029 COMP-0001 COMP-0009 COMP-0035 COMP-0046 COMP-0026 COMP-0057 COMP-0050 COMP-0014 COMP-0030 COMP-0006 COMP-0005 COMP-0049 COMP-0044 COMP-0108 COMP-0055 COMP-0038 COMP-0017 COMP-0028 COMP-0020 COMP-0034 COMP-0015 COMP-0045 COMP-0018 COMP-0013 COMP-0097 COMP-0085 COMP-0048 COMP-0025 COMP-0067- COMP-0091- COMP-0092- COMP-0089- COMP-0088- COMP-0102- COMP-0077-

Figure 7. PDO-derived oxaliplatin (Oxa) sensitivity signature stratifies patients with advanced pancreatic cancer with improved response to FOLFIRINOX. A, The PDO-derived sensitivity signatures were applied to the RNA-seq data from 73 patients enrolled on the COMPASS trial who received either m-FOLFIRINOX or gemcitabine with nab-paclitaxel. B, A waterfall plot of the patients with RECIST criteria at 8 weeks after baseline who received FOLFIRINOX. Oxaliplatin signature significantly correlated with response (r = −0.396, P = 0.0078). Additional data regarding the mean chemotherapeutic signature scores, C1/C2 subtype, and basal/classic subtype are shown. C, The OS of patients receiving m-FOLFIRINOX segregated by their enrichment of the oxaliplatin signature. Log-rank (Mantel–Cox) test P value. under investigation and may involve drug transport, metabo- common responders to first-line chemotherapy agents and lism, and/or response to cell damage. Importantly, such ques- enable stratification of patients such that they may rap- tions may be addressed with PDOs, as they are representative idly achieve clinical benefits while more tailored treatments of the various features of PDAC observed across a large can be developed for each patient. Interestingly, there are a population, including a similar distribution of the basal-like number of patients who exhibit enrichment for the chemo- and classic PDAC subtypes: 30% compared with 70%, respec- sensitivity signatures in both the adjuvant and the advanced tively. By considering individual drug sensitivities in orga- disease setting who are continuing to respond. Whether these noids, transcriptional signatures were derived that mirrored long-term surviving, chemosensitive patients represent excep- patient outcomes in two separate clinical cohorts following tional responders to either gemcitabine or oxaliplatin will the adjuvant treatment of patients with gemcitabine, or the require additional investigation. The 5-FU–sensitivity signa- palliative treatment of patients with modified FOLFIRINOX ture, which contained a relatively small number of genes, did or gemcitabine/nab-paclitaxel. These signatures may identify not perform well when evaluated in patients and will require

OF13 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE further laboratory assessment and optimization. Addition- cotyping was completed in less than 6 weeks, demonstrating ally, although promising in PDOs, the SN-38 signature did the ability of the PDO pharmacotyping to produce recom- not clarify the impact of the oxaliplatin signature in the mendations within a clinically meaningful timeframe for COMPASS trial patients. This may reflect the reduced irinote- both early- and late-stage pancreatic cancer. Complemen- can dosing on the modified FOLFIRINOX regimen and/or tary genomic and transcriptomic profiling has recently been the need to further refine the SN-38 signature. On the other shown to be feasible for patients with advanced pancreatic hand, the gemcitabine, oxaliplatin, and paclitaxel signatures cancer (26), thus providing further capacity to validate PDO show concordance with patient responses in our preliminary pharmacotyping and pharmacotranscriptomic signatures in studies. Cases that lack concordance with the oxaliplatin, a prospective manner, even when first-line therapy is being gemcitabine, and paclitaxel chemosensitivity signatures may selected. The technology of generating and analyzing PDOs represent intratumoral heterogeneity that existed at the initi- will continue to iteratively improve as the methodology is not ation of therapy or evolved quickly. Methods that utilize non- uniformly successful for all patients. Altogether, these early invasive biomarkers as surrogates for disease response may results suggest that chemosensitivity signatures may stratify facilitate rapid adjustment to a more effective therapeutic and thereby improve the initial care of patients with pancre- regimen for patients. Additionally, although both subtypes of atic cancer. Furthermore, when coupled with longitudinal pancreatic cancer were found in the chemosensitive and non- PDO molecular and pharmacologic profiling, this approach sensitive transcriptomic subgroups, there was enrichment for can be tailored to optimize the care of individual patients. the basal-like subtype in the oxaliplatin-nonsensitive group. This strategy should not be limited to pancreatic cancer. Of note, some of these oxaliplatin-nonsensitive patients demonstrated enrichment for other chemosensitivity signatures, suggesting that alternative chemotherapies might be CONCLUSION beneficial to those patients. Going forward, these pharma- We generated a pancreatic cancer PDO library that encom- cotranscriptomic signatures will need to be refined and pro- passes a broad spectrum of disease stage, uncommon genetic spectively evaluated on larger cohorts of patients from whom events as well as the previously established subtypes of pan- high-quality PDAC transcriptomes can be obtained. creatic cancer. PDO cultures facilitate in-depth molecular Although the pharmacotranscriptomic signatures can con- characterization that has been traditionally challenging in ceivably immediately benefit many patients with PDAC, an the unique paucicellular state of primary pancreatic tumors. additional group of patients may also benefit from organoid PDO profiling using next-generation sequencing of DNA and profiling with investigational agents that are available in a RNA combined with pharmacotyping may predict responses clinical trial setting. Indeed, approximately one third of the in patients with pancreatic cancer and provide a rationale PDAC PDOs lacked sensitivity to any of the five chemothera- for prioritizing therapeutic regimens. This approach merits pies evaluated. For these chemotherapy-nonsensitive PDOs, further evaluation in prospective clinical trials for patients 52% (11 of 21) of the PDO cultures demonstrated sensitivity with pancreatic cancer. to one or several targeted agents on the small panel we used. Whether these PDO sensitivities will translate into clinical METHODS responses in patients has yet to be determined in prospective clinical trials. Additionally, our study has focused on assess- Human Specimens ing single-agent activity, and it is likely that drug combina- Normal pancreatic tissues were obtained from the islet transplant tions may yield more clinical opportunities in the future. program at the University of Miami Miller School of Medicine as Low cellularity is a common problem in primary pancreatic described previously (15). Pancreatic cancer tissue was obtained from patients undergoing surgical resection or tissue biopsy at Memo- cancer specimens, often making it difficult to discern molecular rial Sloan Kettering, Stony Brook University (GI Cancer Clinical characteristics with high clarity and depth. Indeed, we found few Resource Core), Johns Hopkins University, Northwell Health, Weill genetic alterations when assessing primary tumor specimens Cornell University, University of California, Davis, Thomas Jefferson in all but 1 of 13 cases that were analyzed by WGS. In contrast, University Hospital, MD Anderson Cancer Center, Washington Uni- the PDO cultures yielded mutations with the expected allele versity St. Louis, and St. Francis Hospital. Autopsy specimens from frequency for pure, neoplastic cultures in addition to complex metastatic sites were obtained from the Rapid Autopsy Program at genetic rearrangements. These analyses and the high concordance University of Nebraska Medical Center and Washington University between primary tumor specimens and their associated PDO cul- St. Louis. All tissue donations and experiments were reviewed and tures demonstrate the added benefit of performing deep genetic approved by the Institutional Review Board of Cold Spring Harbor analyses on PDO cultures. In addition to thoroughly character- Laboratory and all clinical institutions. Written informed consent was obtained prior to acquisition of tissue from all patients. The izing the canonical genomic hallmarks of pancreatic cancer, three studies were conducted in accordance with recognized ethical guide- cases of KRAS wild-type pancreatic cancer were identified that lines (Declaration of Helsinki). Samples were confirmed to be tumor harbored uncommon oncogenic drivers such as the oncogenic or normal based on pathologist assessment. alleles ERBB2S310F, MAP2K1Q58-E62del, and PIK3CAE110del. In two of these cases, exquisite sensitivities to afatinib (ERBB2S310F; hT102) Organoids, Cell Cultures, and Culture Conditions E110del and everolimus (PIK3CA ; hF43) were observed, suggesting For human samples, tissues were minced and incubated in diges- that these are actionable genetic alterations. tion media (1 mg/mL Collagenase XI, 10 μg/mL DNAse I, 10.5 Precision medicine approaches for pancreatic cancer are μmol/L Y-27632 in Human Complete Medium) at 37°C with mild challenging due to the short median survival of patients with agitation for up to 1 hour. Cells were plated with Matrigel and metastatic pancreatic cancer. In some cases, PDO pharma- grown in Human Complete Feeding Medium: advanced DMEM/F12,

SEPTEMBER 2018 CANCER DISCOVERY | OF14 RESEARCH ARTICLE Tiriac et al.

HEPES 10 mmol/L, Glutamax 1×, A83-01 500 nmol/L, hEGF 50 Organoid–Tumor SNV Concordance ng/mL, mNoggin 100 ng/mL, hFGF10 100 ng/mL, hGastrin I 0.01 SNV concordance between tumor–organoid pairs was determined μmol/L, N-acetylcysteine 1.25 mmol/L, nicotinamide 10 mmol/L, from the overlap of variant calls and variant allelic fractions. For each PGE2 1 μmol/L, B27 supplement 1× final, R-spondin1 conditioned SNV called in the tumor or organoid, we ran Samtools Pileup (with media 10% final, and afamin/Wnt3A conditioned media 50% final minimum base quality and minimum mapping quality of 10) at this (17, 36). Organoid nomenclature is as follows: human normal (hN), position for both samples to compute the variant allele fractions. If human tumor obtained from resections (hT), human FNBs obtained read evidence for the SNV was present in both samples (and therefore by either fine-needle aspiration or by core biopsy (hF), and human VAF > 0), the SNV was considered concordant. To add confidence to metastasis obtained from direct resection of metastases following this analysis, we included only SNVs that were called by two or more rapid autopsy or VATS resection (hM). All organoid models were variant callers in at least one of the samples. isolated, cultured, and routinely tested for Mycoplasma at Cold Spring Harbor Laboratory. Organoid models were characterized by DNA Organoid–Tumor CNV Comparison sequencing, and no additional authentication was performed. CNVs were compared between organoids and tumors by plotting WGS Library Preparation and Sequencing the CNV log2 values across chromosomes. A threshold of −0.235 and 0.2 was used to delineate the cutoff for deletions and amplifications, WGS libraries were prepared using the TruSeq DNA PCR-free respectively. This threshold is based off of a diploid sample with 30% Library Preparation Kit in accordance with the manufacturer’s purity. Neutral segments are colored in green and deletions/amplifi- instructions. Briefly, 1μ g of DNA was sheared using a Covaris LE220 cations in red. The y-axis range is smaller for the tumor samples so sonicator (adaptive focused acoustics). DNA fragments underwent that possible CNVs can be more easily identified. bead-based size selection and were subsequently end-repaired, adenylated, and ligated to Illumina sequencing adapters. Final libraries WGS Purity/Ploidy Estimates were evaluated using fluorescent-based assays, including qPCR, with the Universal KAPA Library Quantification Kit and Fragment Ana- Purity and ploidy for each sample were manually calculated by com- lyzer (Advanced Analytics) or BioAnalyzer (Agilent 2100). Libraries paring the VAF of somatic SNV, BAF, and CNV log2 values of multiple were sequenced on an Illumina HiSeq X sequencer (v2.5 chemistry) variants within each sample. The final average ploidy/purity was taken using 2 × 150 bp cycles aiming for 40, 60, and 80× coverage for nor- from the Titan (49) or ABSOLUTE (50) estimate that most closely mal germline, PDO, and primary tumor specimens. matched the manual calculation. No exact purity estimates were made for tumor samples that seem to have extremely low purity (<15%). Exome Panel Library Preparation and Sequencing SNV and CNV Landscape Visualization WES libraries were prepared using the KAPA Hyper Prep (Roche) and xGen Research Exome v1 Panel probes (Integrated DNA Tech- The SNV landscape is displayed using the Bioconductor package nologies) in accordance with the manufacturer’s instructions. Briefly, GenVisR v1.8.0 (51). The CNV per gene heat map was generated using 200 ng of DNA was sheared using a Covaris LE220 sonicator (adaptive the Bioconductor package ComplexHeatmap v1.17.1 (52). A log2 focused acoustics). DNA fragments were end-repaired, adenylated, value was assigned to each gene using the CNV region that covered ligated to Illumina sequencing adapters, and amplified 7 cycles. The at least 50% of the gene via a custom R script. When no CNV region libraries were normalized and pooled equal molar in 12-plex ponds. respected that threshold, the gene was assigned a “No information” A total mass of 2,500 ng of the precapture ponds was blocked and label. The CNV landscape per chromosome was illustrated using hybridized for 16 hours with the probes following the manufacturer’s Bioconductor package gtrellis v1.11.1 (53). recommendations. Resulting captured libraries were then amplified for 10 cycles. Final libraries were evaluated using fluorescent-based RNA-seq Library Construction assays including PicoGreen (Life Technologies) and Fragment Ana- For RNA-seq experiments, organoids in Matrigel were lysed lyzer (Advanced Analytics), and they were sequenced on an Illumina directly with 1 mL of TRIzol reagent (Thermo Fisher), and total RNA HiSeq2500 sequencer (v4 chemistry) using 2 × 125 bp cycles aiming was extracted according to the manufacturer’s instructions. RNA- for 50× coverage. seq libraries were constructed using the TruSeq sample Prep Kit V2 (Illumina) according to the manufacturer’s instructions. Briefly, 2μ g Sequencing Preprocessing and Variant Calling of purified RNA was poly-A selected and fragmented with fragmenta- WGS and WES data for the tumor, organoid, and matched normal tion enzyme. cDNA was synthesized with Super Script II master mix, samples were processed by the NYGC somatic preprocessing pipe- followed by end repair, A-tailing, and PCR amplification. RNA-seq line, which includes aligning reads to the GRCh37 human reference libraries were sequenced using an Illumina HiSeq2500 or NextSeq genome using the Burrows-Wheeler Aligner (37), marking of dupli- platform with paired-end reads of 125 bases (Cold Spring Harbor cate reads by the use of NovoSort (a multithreaded bam sort/merge Genome Center, Woodbury). tool by Novocraft technologies; http://www.novocraft.com), joint indel realignment for matched samples, and base recalibration via RNA-seq Analysis the Genome Analysis Toolkit (38). The exome study used the Hap- RNA-seq reads quality was first quantified using FastQC v0.11.5 Map NA12878 sample in place of a matched normal sample, which (https://www.bioinformatics.babraham.ac.uk/projects/fastqc). Reads was processed using the same protocol as the organoid samples. were then trimmed using Trimmomatic v0.36 (54) and aligned using Somatic single-nucleotide variant (SNV) calling is performed using STAR v2.5.2b (55) on the transcripts corresponding to the human muTect v1.1.7 (39), LoFreq v2.1.3a (40), and Strelka v1.0.14 (41), and genome (GRCh38.p10 assembly) and obtained from GENCODE indel calling was performed using Pindel v0.2.5 (42), Scalpel v0.5.3 (release 27; ref. 56). RSEM v1.3.0 (57) was used to extract counts (43), and Strelka v1.0.14. SNVs were detected by the use of Crest v1.0 per gene. The counts per gene were normalized using Bioconductor (44), Delly v0.6.1 (45), and BreakDancer v1.4.0 (46). Copy-number package DESeq2 v1.14.1 (58). For further analysis, genes without at variants (CNV) were detected using NBIC-seq v0.7 (47) for WGS, and least one count in 10% of the organoids were discarded as well as FACETS v0.5.2 (48) for exome, resulting in segmented profiles where genes not assigned as protein coding according to VEGA gene and the copy number is approximated by a piecewise-constant function transcript annotation from Ensembl human (release 91). An average of the genomic position. of 50 million reads per sample were aligned to the reference genome.

OF15 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

PCA Visualization highest sum of the squared differences between triplicate measure- A PCA was performed on all organoids (normal and tumor orga- ments and fitted curve. The AUC was calculated using CRAN package noids) using the normalized and filtered counts per gene (see “RNA- Bolstad2 v1.0-28 (https://cran.r-project.org/web/packages/Bolstad2/). seq Analysis” above). A variance stabilization transformation was per- Normalized AUC was obtained by dividing the AUC value by the maxi- formed using Bioconductor package DESeq2 v1.14.1 (58). The 2,000 mum area for the concentration range measured for each drug. The most variable genes were retained and used as input for the PCA range of the normalized AUC is between 0 and 1. analysis which was performed using R stats package (59). K-means Pharmacotranscriptomic Analysis clustering was performed on the PDO expression data reduced to three principal components, also using R stats package with the For each drug, the Spearman rank correlation coefficient was cal- parameter for the number of clusters fixed to 2. The result of the culated between the organoid drug-specific AUC and the 10,000 most PCA-based clustering was displayed using the CRAN package plotly variably expressed genes (normalized counts) obtained from the RNA- v4.7.1 (https://CRAN.R-project.org/package=plotly). seq analysis. The Spearman rank correlation was selected to test for monotonic, but not necessarily linear, dependence between the AUC Differential Gene Expression Analysis Normal versus and the gene expression. For derivation of drug-specific transcriptional Tumor Organoids signatures, genes were filtered using theP values for the Spearman coefficient calculation as threshold P( < 0.01). Genes positively cor- A differential gene expression (DGE) analysis was performed with related with sensitivity (r < −0.38) to a chemotherapeutic drug were Bioconductor package DESeq2 v1.14.1 (58) using the normalized selected for validation in patient-derived RNA-seq data (Supplemen- and filtered counts per gene from the RNA-seq analysis. The DGE tary Table S5). Each filtered gene list was clustered separately using the analysis was performed between normal and tumor organoids using Spearman rank correlation coefficients. To rank sensitivity of PDOs DESeq2 likelihood ratio test. and patients using RNA-seq data, mean z-score was computed for the individual drug-sensitivity signature and ranked from high mean Clustering of Tumor Organoids expression to low mean expression. A flow chart of the pharmacotran- Using only tumor organoids, an NMF clustering was performed on scriptomic analysis pipeline is presented in Supplementary Fig. S8. the 2,000 most variable genes, as determined in the RNA-seq analysis, to identify stable tumor organoid clusters using CRAN package NMF Pathway Analysis v0.20.6 (60). The NMF parameters were Brunet factorization method, Pathway analysis was performed with GSEA2 version 2.2.4 (62). rank of 2 through 7, 500 iterations. The best-performing clustering result was selected using the observed cophenetic correlation Accession Numbers between clusters and the average silhouette width of the consensus Data have been deposited in the NCBI dbGaP archive under acces- matrices. The NMF clustering generated 2 stable tumor organoid sion number phs001611.v1.p1. clusters, labeled C1 and C2. Only the individuals with the higher consensus were included in the C1 and C2 clusters. Three PDO cul- Disclosure of Potential Conflicts of Interest tures with poor cluster identity consensus were excluded from the subsequent differential expression analysis. The NMF clustering was L.D. Wood is a consultant/advisory board member for Personal refined to 245 genes (Fig. 3; Supplementary Table S3). Genome Diagnostics. R.H. Hruban is a board member for MyDiag- nostics and reports receiving royalties from UpToDate. B.M. Wolpin reports receiving a commercial research grant from Celgene. A. DGE Analysis Tumor Organoids Sasson has ownership interest (including patents) in Sanguine Diag- A DGE analysis was performed on the tumor organoids only. A nostics and Therapeutics. C.R. Vakoc reports receiving a commercial DGE analysis was performed between C1 and C2 tumor organoid research grant from Boehringer Ingelheim and is a consultant/advi- clusters using DESeq2 with default parameters. The CRAN package sory board member for KSQ Therapeutics. No potential conflicts of gplots v3.0.1 (https://CRAN.R-project.org/package=gplots) was used interest were disclosed by the other authors. to generate a heat map of the 250 genes most significantly differentially expressed between C1 and C2. Of these, 5 genes with a median Authors’ Contributions expression count of zero were removed. Conception and design: H. Tiriac, R.A. Burkhart, N. Robine, C.Y. Lowder, A. Sasson, P. Allen, K.H. Yu, E. Li, B. Hubert, D.A. Tuveson Pharmacotyping of Organoids Development of methodology: H. Tiriac, P. Belleau, D.D. Engle, Organoids were dissociated into single cells. Five hundred viable cells R.A. Burkhart, K. Miyabayashi, C.M. Young, N. Robine, C.Y. Lowder, were plated per well in 20 μL 10% Matrigel/ human complete organoid H. Clevers, M. Wu, P. Allen, K.H. Yu, B. Hubert, A. Krasnitz media. Therapeutic compounds were added 24 hours after plating, Acquisition of data (provided animals, acquired and managed after the reformation of organoids was visually verified. Chemothera- patients, provided facilities, etc.): H. Tiriac, D.D. Engle, D. Plen- peutics were tested in triplicate: gemcitabine, paclitaxel, SN38 range ker, T.D.D. Somerville, R.A. Burkhart, K. Miyabayashi, C.M. Young, from 8.1 × 10−12 mol/L to 2.0 × 10−6 mol/L, and 5-FU and oxaliplatin H. Patel, J.F. LaComb, R.L.D. Palmaira, A.A. Javed, J.C. Huynh, range from 1.0 × 10−8 to 5.0 × 10−5 mol/L. Targeted drugs were tested N. Robine, A.B. Goetz, C.Y. Lowder, C.A. Iacobuzio-Donahue, L.D. Wood, in singlicate (range from 1.0 × 10−8 to 1.0 × 10−5 mol/L). Compounds R.H. Hruban, E. Thompson, A. Sasson, J. Kim, M. Wu, J.C. Bucobo, were dissolved in DMSO and all treatment wells were normalized to P. Allen, D.V. Sejpal, W. Nealon, J.D. Sullivan, J.M. Winter, J.L. Grem, 0.5% DMSO content. After 5 days, cell viability was assessed using J.M. Buscaglia, P.M. Grandgenett, J.R. Brody, M.A. Hollingsworth, CellTiter-Glo as per the manufacturer’s instruction (Promega) on a G.M. O’Kane, F. Notta, E. Kim, J.M. Crawford, C. Devoe, A. Ocean, SpectraMax I3 (Molecular Devices) plate reader. A three-parameter K.H. Yu, E. Li, B. Hubert, S.E. Fischer, J. Knox, S. Gallinger log-logistic function with upper limit equal to the mean of the DMSO Analysis and interpretation of data (e.g., statistical analysis, bio- values was fit to the pharmacotyping data (viability vs. dose) with statistics, computational analysis): H. Tiriac, P. Belleau, D. Plenker, CRAN package drc v3.0-1 (61). Quality control was performed on the A. Deschênes, F.E.M. Froeling, R.A. Burkhart, C.M. Young, H. Patel, curve fitness: rejection of the curve if 100% plateau is located beyond 2 M. Johnson, K. Arora, N. Robine, M. Shah, R. Sanghvi, G. Askan, P.A. Gimotty, standard deviation of the mean DMSO control and visual inspection, P.M. Grandgenett, F. Notta, C.L. Wolfgang, K.H. Yu, E. Li, B. Hubert, leading to possible rejection, of the top 5% curves ranked with the R. Moffitt, J. Knox, A. Krasnitz, S. Gallinger, D.A. Tuveson

SEPTEMBER 2018 CANCER DISCOVERY | OF16 RESEARCH ARTICLE Tiriac et al.

Writing, review, and/or revision of the manuscript: H. Tiriac, Translational Research Initiative) through funding provided by the P. Belleau, D.D. Engle, D. Plenker, A. Deschênes, F.E.M. Froeling, Government of Ontario. R.A. Burkhart, C.M. Young, J.F. LaComb, A.A. Javed, J.C. Huynh, M. Johnson, K. Arora, N. Robine, M. Shah, R. Sanghvi, A.B. Goetz, Received March 30, 2018; revised May 3, 2018; accepted May 25, L. Martello, E. Driehuis, N. LeComte, L.D. Wood, R.H. Hruban, 2018; published first May 31, 2018. A.J. Aguirre, B.M. Wolpin, A. Sasson, P. Allen, D.V. Sejpal, W. Nealon, J.D. Sullivan, J.M. Winter, P.A. Gimotty, J.M. Buscaglia, J.R. Brody, M.A. Hollingsworth, G.M. O’Kane, E. Kim, J.M. Crawford, C. Devoe, References A. Ocean, C.L. Wolfgang, K.H. Yu, E. Li, B. Hubert, J. Knox, A. Krasnitz, 1. Khorana AA, Mangu PB, Berlin J, Engebretson A, Hong TS, Maitra A, S. Gallinger, D.A. Tuveson et al. Potentially curable pancreatic cancer: American Society of Clinical Administrative, technical, or material support (i.e., reporting or Oncology clinical practice guideline. J Clin Oncol 2016;34:2541–56. organizing data, constructing databases): H. Tiriac, D.D. Engle, 2. Winter JM, Cameron JL, Campbell KA, Arnold MA, Chang DC, Coleman R.A. Burkhart, R.E. Denroche, H. Patel, M. Ma, J.F. LaComb, R.L.D. J, et al. 1423 pancreaticoduodenectomies for pancreatic cancer: a Palmaira, N. Robine, J. Kim, D.J. DiMaio, G.M. O’Kane, B. Hubert, single-institution experience. J Gastrointest Surg 2006;10:1199–210; J.M. Wilson, S. Gallinger, D.A. Tuveson discussion 210-1. Study supervision: H. Tiriac, C.L. Wolfgang, K.H. Yu, C.R. Vakoc, 3. Groot VP, Rezaee N, Wu W, Cameron JL, Fishman EK, Hruban RH, et al. Patterns, timing, and predictors of recurrence following pancreatectomy B. Hubert, A. Krasnitz, D.A. Tuveson for pancreatic ductal adenocarcinoma. Ann Surg 2018;267:936–45. Other (processed low-level data to intermediate level in some 4. Neoptolemos JP, Palmer DH, Ghaneh P, Psarelli EE, Valle JW, Halloran RNA-seq data): G.-H. Jang CM, et al. Comparison of adjuvant gemcitabine and capecitabine with Other (patient enrollment): A. Ocean gemcitabine monotherapy in patients with resected pancreatic cancer Acknowledgments (ESPAC-4): a multicentre, open-label, randomised, phase 3 trial. Lancet 2017;389:1011–24. We would like to thank the patients, families, and clinicians who 5. Sinn M, Bahra M, Liersch T, Gellert K, Messmann H, Bechstein W, contributed to this work. This work was supported by the Lustgarten et al. CONKO-005: adjuvant chemotherapy with gemcitabine plus Foundation (D.A. Tuveson). D.A. Tuveson is a distinguished scholar erlotinib versus gemcitabine alone in patients after R0 resection of of the Lustgarten Foundation and Director of the Lustgarten Founda- pancreatic cancer: a multicenter randomized phase III trial. J Clin tion Dedicated Laboratory of Pancreatic Cancer Research. S. Gallinger Oncol 2017;35:3330–7. is supported by the Ontario Institute for Cancer Research, charitable 6. Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, donations from the Canadian Friends of the Hebrew University (Alex et al. Increased survival in pancreatic cancer with nab-paclitaxel plus U. Soyka) and the Pancreatic Cancer Canada Foundation, and the Leb- gemcitabine. N Engl J Med 2013;369:1691–703. ovich Chair in Hepatobiliary/Pancreatic Surgical Oncology. This pro- 7. Conroy T, Desseigne F, Ychou M, Bouche O, Guimbaud R, Becouarn ject was also supported in part by the NCI/LEIDOS Human Cancer Y, et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic Models Initiative (HHSN26100008; D.A. Tuveson, H. Clevers, and J.M. cancer. N Engl J Med 2011;364:1817–25. Crawford), NIH 5P50CA101955-07 (D.A. Tuveson), P20CA192996-03 8. Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, et al. (D.A. Tuveson), 1U10CA180944-04 (D.A. Tuveson), 5U01CA168409-5 Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017;357:409–13. (D.A. Tuveson), 1R01CA188134-01 (D.A. Tuveson), 1R01CA190092- 9. Waddell N, Pajic M, Patch AM, Chang DK, Kassahn KS, Bailey P, et al. 04 (D.A. Tuveson), 5T32CA148056 (D.D. Engle), 5K99CA204725 Whole genomes redefine the mutational landscape of pancreatic can- (D.D. Engle), 5P01CA013106 (C.R. Vakoc), 5P30CA45508 (D.A. Tuve- cer. Nature 2015;518:495–501. son and P.A. Gilmoty), 1R01CA212600-01 (J.R. Brody and J.M. Win- 10. Ostrem JM, Peters U, Sos ML, Wells JA, Shokat KM. K-Ras(G12C) ter), P20CA192994 (E. Li), R01CA202762 (K.H. Yu), P50CA127297 inhibitors allosterically control GTP affinity and effector interac- (M.A. Hollingsworth; UNMC RAP), U01CA210240 (M. A. Hollings- tions. Nature 2013;503:548–51. worth, D.A. Tuveson; UNMC RAP), and 5R50CA211462 (M.A. Hol- 11. Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, et al. lingsworth; UNMC RAP), V Foundation Translational Grant (K.H. Genomic analyses identify molecular subtypes of pancreatic cancer. Yu and D.A. Tuveson), Cold Spring Harbor Laboratory Association Nature 2016;531:47–52. (D.A. Tuveson), Stand Up To Cancer/AACRPS09 (D.A. Tuveson), 12. Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, et al. Precision Medicine Research Associates (D.A. Tuveson), SWOG ITSC Subtypes of pancreatic ductal adenocarcinoma and their differing 5U10CA180944-04 (D.A. Tuveson, H. Tiriac, and E.J. Kim), Pancreatic responses to therapy. Nat Med 2011;17:500–3. Cancer Action Network-AACR 16-20-25-VAKO (C.R.Vakoc), State of 13. Moffitt RA, Marayati R, Flate EL, Volmar KE, Loeza SG, Hoadley KA, et al. New York C150158 (T.D.D. Somerville; the opinions, results, find- Virtual microdissection identifies distinct tumor- and stroma-specific sub- ings, and/or interpretation of data contained herein are the respon- types of pancreatic ductal adenocarcinoma. Nat Genet 2015;47:1168–78. sibility of the authors and do not necessarily represent the opinions, 14. Cancer Genome Atlas Research Network. Integrated genomic char- interpretations or policy of New York State), Concetta Greenberg in acterization of pancreatic ductal adenocarcinoma. Cancer Cell 2017; memory of Marvin S. Greenberg, MD (J.R. Brody and J.M. Winter), 32:185–203 e13. RAN grant from the AACR Pancreatic Cancer Action Network (J.R. 15. Boj SF, Hwang CI, Baker LA, Chio II, Engle DD, Corbo V, et al. Orga- noid models of human and mouse ductal pancreatic cancer. Cell Brody and J.M. Winter), ASGE Endoscopic Research Award 71040 2015;160:324–38. (J.M. Buscaglia), Simons Foundation Award 415604 (E. Li), and 16. Tiriac H, Bucobo JC, Tzimas D, Grewel S, Lacomb JF, Rowehl LM, Canadian Cancer Society Research Institute grant 702316 (S. Gall- et al. Successful creation of pancreatic cancer organoids by means inger). We acknowledge the Cold Spring Harbor Laboratory Tissue of EUS-guided fine-needle biopsy sampling for personalized cancer Culture and Next Generation Sequencing Shared Resources, which treatment. Gastrointest Endosc 2018;87:1474–80. are funded by NIH Cancer Center Support Grant 5P30CA045508. 17. Seino T, Kawasaki S, Shimokawa M, Tamagawa H, Toshimitsu K, We also acknowledge the contributions of Production Sequenc- Fujii M, et al. Human pancreatic tumor organoids reveal loss of stem ing, Genome Sequencing Informatics and Tissue Portal (Diagnostic cell niche factor dependence during disease progression. Cell Stem Development) at the Ontario Institute for Cancer Research, as well Cell 2018;22:454–67 e6. as the University Health Network BioBank. The pancreatic can- 18. Zhang Y, Kwok-Shing Ng P, Kucherlapati M, Chen F, Liu Y, Tsang cer patient study (ICGC-CA, COMPASS) was conducted with the YH, et al. A pan-cancer proteogenomic atlas of PI3K/AKT/mTOR support of the Ontario Institute for Cancer Research (PanCuRx pathway alterations. Cancer Cell 2017;31:820–32 e3.

OF17 | CANCER DISCOVERY SEPTEMBER 2018 www.aacrjournals.org Pancreatic Cancer Organoids Parallel Patient Response RESEARCH ARTICLE

19. Diamond EL, Durham BH, Haroche J, Yao Z, Ma J, Parikh SA, et al. 39. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez Diverse and targetable kinase alterations drive histiocytic neoplasms. C, et al. Sensitive detection of somatic point mutations in impure and Cancer Discov 2016;6:154–65. heterogeneous cancer samples. Nat Biotechnol 2013;31:213–9. 20. Donovan KF, Hegde M, Sullender M, Vaimberg EW, Johannessen 40. Wilm A, Aw PP, Bertrand D, Yeo GH, Ong SH, Wong CH, et al. CM, Root DE, et al. Creation of novel protein variants with CRISPR/ LoFreq: a sequence-quality aware, ultra-sensitive variant caller for Cas9-mediated mutagenesis: turning a screening by-product into a uncovering cell-population heterogeneity from high-throughput discovery tool. PLoS One 2017;12:e0170445. sequencing datasets. Nucleic Acids Res 2012;40:11189–201. 21. Greulich H, Kaplan B, Mertins P, Chen TH, Tanaka KE, Yun CH, 41. Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham et al. Functional analysis of receptor tyrosine kinase mutations in RK. Strelka: accurate somatic small-variant calling from sequenced lung cancer identifies oncogenic extracellular domain mutations of tumor-normal sample pairs. Bioinformatics 2012;28:1811–7. ERBB2. Proc Natl Acad Sci U S A 2012;109:14476–81. 42. Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pat- 22. Pritchard CC, Smith C, Salipante SJ, Lee MK, Thornton AM, Nord tern growth approach to detect break points of large deletions and AS, et al. ColoSeq provides comprehensive lynch and polyposis syn- medium sized insertions from paired-end short reads. Bioinformatics drome mutational analysis using massively parallel sequencing. J Mol 2009;25:2865–71. Diag 2012;14:357–66. 43. Narzisi G, O’Rawe JA, Iossifov I, Fang H, Lee YH, Wang Z, et al. Accu- 23. Wolff RA, Wang-Gillam A, Alvarez H, Tiriac H, Engle D, Hou S, et al. rate de novo and transmitted indel detection in exome-capture data Dynamic changes during the treatment of pancreatic cancer. Onco- using microassembly. Nat Methods 2014;11:1033–6. target 2018;9:14764–90. 44. Wang J, Mullighan CG, Easton J, Roberts S, Heatley SL, Ma J, et al. 24. Beuvink I, Boulay A, Fumagalli S, Zilbermann F, Ruetz S, O’Reilly T, CREST maps somatic structural variation in cancer genomes with et al. The mTOR inhibitor RAD001 sensitizes tumor cells to DNA- base-pair resolution. Nat Methods 2011;8:652–4. damaged induced apoptosis through inhibition of p21 translation. 45. Rausch T, Zichner T, Schlattl A, Stutz AM, Benes V, Korbel JO. Cell 2005;120:747–59. DELLY: structural variant discovery by integrated paired-end and 25. Connor AA, Denroche RE, Jang GH, Timms L, Kalimuthu SN, split-read analysis. Bioinformatics 2012;28:i333–i9. Selander I, et al. Association of distinct mutational signatures with 46. Chen K, Wallis JW, McLellan MD, Larson DE, Kalicki JM, Pohl CS, correlates of increased immune activity in pancreatic ductal adeno- et al. BreakDancer: an algorithm for high-resolution mapping of carcinoma. JAMA Oncol 2017;3:774–83. genomic structural variation. Nat Methods 2009;6:677–81. 26. Aung KL, Fischer SE, Denroche RE, Jang G-H, Dodd A, Creighton S, et al. 47. Xi R, Lee S, Xia Y, Kim TM, Park PJ. Copy number analysis of whole- Genomics-driven precision medicine for advanced pancreatic cancer: genome data using BIC-seq2 and its application to detection of can- early results from the COMPASS trial. Clin Cancer Res 2018;24:1344. cer susceptibility variants. Nucleic Acids Res 2016;44:6274–86. 27. Chio IIC, Jafarnejad SM, Ponz-Sarvise M, Park Y, Rivera K, Palm W, 48. Shen R, Seshan VE. FACETS: allele-specific copy number and clonal et al. NRF2 promotes tumor maintenance by modulating mRNA heterogeneity analysis tool for high-throughput DNA sequencing. translation in pancreatic cancer. Cell 2016;166:963–76. Nucleic Acids Res 2016;44:e131. 28. Biancur DE, Paulo JA, Małachowska B, Quiles Del Rey M, Sousa CM, Wang 49. Ha G, Roth A, Khattra J, Ho J, Yap D, Prentice LM, et al. TITAN: infer- X, et al. Compensatory metabolic networks in pancreatic cancers upon per- ence of copy number architectures in clonal cell populations from turbation of glutamine metabolism. Nat Commun 2017;8:15965. tumor whole-genome sequence data. Genome Res 2014;24:1881–93. 29. Commisso C, Davidson SM, Soydaner-Azeloglu RG, Parker SJ, Kam- 50. Carter SL, Cibulskis K, Helman E, McKenna A, Shen H, Zack T, et al. phorst JJ, Hackett S, et al. Macropinocytosis of protein is an amino Absolute quantification of somatic DNA alterations in human can- acid supply route in Ras-transformed cells. Nature 2013;497:633. cer. Nat Biotechnol 2012;30:413–21. 30. Jacobetz MA, Chan DS, Neesse A, Bapiro TE, Cook N, Frese KK, et al. 51. Skidmore ZL, Wagner AH, Lesurf R, Campbell KM, Kunisaki J, Grif- Hyaluronan impairs vascular function and drug delivery in a mouse fith OL, et al. GenVisR: genomic visualizations in R. Bioinformatics model of pancreatic cancer. Gut 2013;62:112–20. 2016;32:3012–4. 31. Olive KP, Jacobetz MA, Davidson CJ, Gopinathan A, McIntyre D, 52. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and Honess D, et al. Inhibition of Hedgehog signaling enhances delivery correlations in multidimensional genomic data. Bioinformatics of chemotherapy in a mouse model of pancreatic cancer. Science 2016;32:2847–9. 2009;324:1457–61. 53. Gu Z, Eils R, Schlesner M. gtrellis: an R/Bioconductor package for mak- 32. Provenzano PP, Cuevas C, Chang AE, Goel VK, Von Hoff DD, Hin- ing genome-level Trellis graphics. BMC Bioinformatics 2016;17:169. gorani SR. Enzymatic targeting of the stroma ablates physical bar- 54. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for riers to treatment of pancreatic ductal adenocarcinoma. Cancer Cell Illumina sequence data. Bioinformatics 2014;30:2114–20. 2012;21:418–29. 55. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. 33. Balli D, Rech AJ, Stanger BZ, Vonderheide RH. Immune cytolytic STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013; activity stratifies molecular subsets of human pancreatic cancer. Clin 29:15–21. Cancer Res 2017;23:3129. 56. Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, 34. Geller LT, Barzily-Rokni M, Danino T, Jonas OH, Shental N, Nej- Kokocinski F, et al. GENCODE: the reference human genome anno- man D, et al. Potential role of intratumor bacteria in mediating tation for The ENCODE Project. Genome Res 2012;22:1760–74. tumor resistance to the chemotherapeutic drug gemcitabine. Science 57. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA- 2017;357:1156–60. Seq data with or without a reference genome. BMC Bioinformatics 35. Hessmann E, Patzak MS, Klein L, Chen N, Kari V, Ramu I, et al. Fibro- 2011;12:323. blast drug scavenging increases intratumoural gemcitabine accumu- 58. Love MI, Huber W, Anders S. Moderated estimation of fold change and lation in murine pancreas cancer. Gut 2018;67:497. dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550. 36. Mihara E, Hirai H, Yamamoto H, Tamura-Kawakami K, Matano M, 59. Team RC. R: A language and environment for statistical computing. Kikuchi A, et al. Active and water-soluble form of lipidated Wnt pro- R Foundation for Statistical Computing 2014. tein is maintained by a serum glycoprotein afamin/alpha-albumin. 60. Gaujoux R, Seoighe C. A flexible R package for nonnegative matrix Elife 2016;5. pii: e11621. doi: 10.7554/eLife.11621. factorization. BMC Bioinformatics 2010;11:367. 37. Li H, Durbin R. Fast and accurate short read alignment with Burrows- 61. Ritz C, Baty F, Streibig JC, Gerhard D. Dose-response analysis using Wheeler transform. Bioinformatics 2009;25:1754–60. R. PLoS One 2015;10:e0146021. 38. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky 62. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, A, et al. The Genome Analysis Toolkit: a MapReduce framework Gillette MA, et al. Gene set enrichment analysis: a knowledge-based for analyzing next-generation DNA sequencing data. Genome Res approach for interpreting genome-wide expression profiles. Proc Natl 2010;20:1297–303. Acad Sci U S A 2005;102:15545–50.

SEPTEMBER 2018 CANCER DISCOVERY | OF18