Distinct Transcriptional Programming Drive Response to MAPK Inhibition in BRAF -Mutant Melanoma Patient-Derived Xenografts

Published OnlineFirst September 16, 2019; DOI: 10.1158/1535-7163.MCT-19-0028

Cancer Biology and Translational Studies Molecular Cancer Therapeutics Distinct Transcriptional Programming Drive Response to MAPK Inhibition in BRAFV600-Mutant Melanoma Patient-Derived Xenografts Tianshu Feng, Javad Golji, Ailing Li, Xiamei Zhang, David A. Ruddy, Daniel P. Rakiec, Felipe C. Geyer, Jane Gu, Hui Gao, Juliet A.Williams, Darrin D. Stuart, and Matthew J. Meyer

Abstract

Inhibitors targeting BRAF and its downstream kinase MEK are preferentially sensitive to MAPK inhibition. These gene- produce robust response in patients with advanced BRAFV600- expression programs are somewhat similar to the MITF-high mutant melanoma. However, the duration and depth of and -low phenotypes described in cancer cell lines, but response vary significantly between patients; therefore, pre- demonstrate an inverse relationship with drug response. dicting response a priori remains a significant challenge. Here, This suggests a discrepancy between in vitro and in vivo we utilized the Novartis collection of patient-derived xeno- experimental systems that warrants future investigations. grafts to characterize transcriptional alterations elicited by Finally, BRAFV600-mutant melanoma relies on either MAPK BRAF and MEK inhibitors in vivo, in an effort to identify or alternative pathways for survival under BRAF and MEK mechanisms governing differential response to MAPK inhibi- inhibition in vivo, which in turn predicts their response to tion. We show that the expression of an MITF-high, "epithelial- further pathway suppression using a combination of BRAF, like" transcriptional program is associated with reduced MEK, and ERK inhibitors. Our findings highlight the inter- sensitivity and adaptive response to BRAF and MEK inhibitor tumor heterogeneity in BRAFV600-mutant melanoma, and treatment. On the other hand, xenograft models that express the need for precision medicine strategies to target this an MAPK-driven "mesenchymal-like" transcriptional program aggressive cancer.

Introduction A subset of BRAFV600-mutant melanoma patients progress rapidly upon treatment using targeted therapy (3–6). In contrast, Approximately 50% of cutaneous melanoma harbor oncogenic approximately 20% of patients benefit from durable response to mutations of BRAF. The majority of BRAF mutations occur in the BRAF and MEK inhibition, as measured by progression-free V600 position, resulting in constitutive activation of the MAPK survival at 3 years (10, 11). The disparity between these 2 patient signaling pathway (1, 2). The inhibition of BRAF, either alone or groups is not well understood. Clinical factors related to disease in combination with MEK, provides remarkable clinical benefits burden, including lactate dehydrogenase levels and the number for patients with advanced BRAFV600-mutant melanoma (3–6). of metastases, are important prognostic markers (10, 11). At the Nevertheless, despite the robust initial response, most patients molecular level, melanoma cell line sensitivity to BRAF-targeted eventually develop resistance. Acquired resistance to MAPK inhi- therapy is linked to the melanoma cell state (12, 13). bition involves heterogeneous genetic and nongenetic mechan- Gene-expression profiling revealed that melanoma falls into isms that activate MAPK or other compensatory signaling two distinct transcriptional states irrespective of their mutation pathways (7–9). As such, there is a need for patient stratification status, characterized by proliferative or invasive features and and treatment strategies in order to improve response and delay MITF expression levels (14–16). The invasive, or MITF-low and the onset of disease progression. AXL-high phenotype, confers intrinsic resistance to MAPK inhibition in BRAFV600-mutant melanoma in vitro (12, 13). However, Oncology Drug Discovery, Novartis Institutes for BioMedical Research (NIBR), both high and low MITF expression levels are implicated in Cambridge, Massachusetts. therapeutic resistance against MAPK inhibition in melano- Note: Supplementary data for this article are available at Molecular Cancer ma (17). It remains to be determined whether the melanoma Therapeutics Online (http://mct.aacrjournals.org/). cell state predicts response to BRAF inhibitor–based therapy in the Current address for T. Feng: Tango Therapeutics, Cambridge, MA; current clinical setting. address for A. Li and M.J. Meyer: Discovery Oncology, Bristol-Myers Squibb, In this study, we sought to elucidate mechanisms governing Cambridge, MA; and current address for H. Gao: AstraZeneca, Waltham, MA. differential sensitivity to BRAF and MEK inhibition, using our Corresponding Authors: Matthew J. Meyer, Bristol-Myers Squibb, 100 Binney internally established patient-derived xenograft (PDX) collec- Street, Cambridge, MA 02142. Phone: 617-494-7466; E-mail: tion (18). Work by our group and others demonstrated the [email protected]; and Darrin D. Stuart, Novartis Institutes for Bio- prognostic value of PDXs in modeling clinical response to tar- Medical Research, 250 Massachusetts Avenue, Cambridge, MA 02139. – Phone: 617-871-5311; E-mail: [email protected] geted therapies (18 20). In comparison with other preclinical experimental systems, PDX models conserve tumor architecture Mol Cancer Ther 2019;18:1–12 and heterogeneity, and faithfully recapitulate patient response to doi: 10.1158/1535-7163.MCT-19-0028 chemo- and targeted therapies (18, 19, 21–23). Moreover, 2019 American Association for Cancer Research. because obtaining high-quality research biopsies from the clinic

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is challenging, PDXs enable profiling of treatment and time amplified RNA-seq library products were quantified using the matched tumors with statistically powered replicates. Here, we Fragment Analyzer Standard Sensitivity NGS Fragment Analysis characterized on-treatment PDX tumor samples to compare the Kit (Advanced Analytical Technologies). Samples were diluted to pharmacodynamic effects of BRAF and MEK inhibitors in 10 nmol/L in Elution Buffer (QIAGEN), denatured, and loaded at models that achieved complete regression versus partial a range of 2.5 to 4.0 pmol/L on an Illumina cBOT using the HiSeq response and stable disease. Transcriptome profiling revealed 4000 PE Cluster Kit (Illumina). RNA-seq libraries were sequenced that in vivo,aMITF-high "epithelial-like" phenotype is associ- on a HiSeq 4000 at 75 base pair paired-end with 8 base pair dual ated with adaptive and intrinsic resistance, whereas a MAPK- indexes using the HiSeq 4000 SBS Kit, 150 cycles (Illumina). The high "mesenchymal-like" phenotype is linked to sensitivity to sequence intensity files were generated on instrument using the BRAF and MEK inhibitor treatment. This is different from Illumina Real-Time Analysis software and resulting intensity files findings using in vitro cancer cell lines (12, 13, 17) and suggests were demultiplexed with the bcl2fastq2. an in vitro–in vivo discrepancy that warrants additional inves- tigation. In less sensitive models, the lack of tumor regression Gene-expression analysis in response to treatment was model dependent and mediated Gene-level read counts were normalized and voom transfor- by either MAPK-dependent or -independent mechanisms. Our mated (24) for differential expression using the limma frame- results suggested that response of BRAFV600-mutant melanoma work (25), and P values were corrected for multiple testing xenografts to BRAF and MEK inhibitors is heterogeneous, and using the Benjamini–Hochberg method. Genes were considered precision medicine–based combination strategies are required differentially expressed if the log2-fold change > 1.5 in absolute to target this aggressive cancer. Importantly, this study provided value and the adjusted P value < 0.05. Log-transformed and insights into determinants of therapeutic response in BRAFV600 TMM-normalized (26) expression values were used for cluster- melanoma in vivo. ing analysis with NbClust (27). K ¼ 5 was selected as the most informative partition. A hypergeometric distribution function was used to determine enrichment P values for overlap of each Materials and Methods cluster membership with gene sets from mSigDB (28), in Mouse xenograft studies addition to transcription factor and canonical gene sets Mice were maintained and handled in accordance with the exported from MetaCore (Clarivate Analytics). Gene set enrich- Novartis Institutes for BioMedical Research Animal Care and Use ment P values were adjusted using the Benjamini–Hochberg Committee protocols and regulations. PDX models were pro- method. Mutation and copy-number calls were made as cured and established as previously described (18). Surgical described in Gao et al. (18). The RNA-seq data have been tumor tissues were obtained from treatment-na€ve cancer submitted to the Sequence Read Archive database; submission patients, all of whom provided informed written consent for the number PRJNA562481. samples procured by Novartis Inc. PDX fragments were implanted subcutaneously into the right Clinical data set flanks of female nude mice (Charles River) using a trocar. Once Normalized and background corrected microarray data were the tumor volumes reached 300 to 500 mm3 in size, mice were downloaded from Gene-Expression Omnibus public database, randomized into respective groups. For tissue collection, mice accession number GSE99898 (29). The data set was derived from were dosed twice daily by oral gavage with encorafenib and 17 patients treated with dabrafenib, vemurafenib, or dabrafenib binimetinib at 20 and 3.5 mg per kg, respectively. These doses and trametinib. Seventeen untreated, 8 on-treatment, and were selected as they achieved exposures in mice comparable with 13 resistant samples were collected in total. Patient response that in humans (4). For efficacy studies, encorafenib and bini- data were obtained from Table 1 in the reference publica- metinib were administered via diet supplementation (200 mg tion (29). Gene signature scores were calculated as sum of the encorafenib and 35 mg binimetinib per kg of chow; Bio-Serv). The normalized and z-transformed gene-expression values for all supplemented diet was designed to achieve similar drug exposure genes of interest. to twice-daily oral gavage. The ERK inhibitor VX-11e was administered orally at 50 mg per kg daily. Body weight and tumor size Western blot analysis were monitored twice per week. Tumor volume was measured Snap-frozen tumor fragments were suspended in RIPA lysis using a caliper and calculated as (length width width)/2. Best buffer with protease (Roche) and phosphatase (Sigma) inhibitor average response and response calls were determined as previ- cocktails, and mechanically homogenized with tungsten beads ously described (18). All studies were performed using PDXs using the TissueLyser (QIAGEN). Protein concentrations were between passages 4 and 9. quantified using the Pierce BCA protein assay kit (Thermo Fisher Scientific). Western blotting was performed using standard pro- RNA extraction and sequencing cedures. Antibodies against MEK1/2, pMEK1/2 (S217/S222), Total RNA were extracted from snap-frozen tumor fragments, ERK1/2, pERK1/2 (T202/Y204), FRA1, pFRA1 (S265), RSK1/2/3, using the RNeasy kit with the QIAcube (QIAGEN) automated pRSK3 (T356/S360), FOSB, EGR1, AKT, pAKT (S473), S6, pS6 sample preparation platform. The RNA concentration and integ- (S240/S244), Cyclin D1, pRB (S780), N-cadherin, E-cadherin, rity were measured using the RNA 6000 nano kit (Agilent Tech- vimentin, BIM, MITF, and GAPDH were purchased from Cell nologies) on the Agilent 2100 BioAnalyzer. Signaling Technology. Antibodies against ERF and pERF (T526) Two hundred nanograms of high-purity RNA was used as input were purchased from Invitrogen. Signals were developed using to generate sample libraries using the TruSeq Stranded mRNA SuperSignal West Femto, Dura, or Pico chemiluminescent sub- library prep kit (Illumina), following the manufacturer's instruc- strates (Thermo Fisher Scientific), and visualized using the Chemi- tions on the Hamilton STAR robotics platform. The PCR- Doc Imaging System (Bio-Rad).

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RT-PCR formed unsupervised clustering of treatment-matched samples RT-PCR reactions were performed using TaqMan Gene- from all 6 PDX models using DEGs from the aforementioned Expression master mix (Applied Biosystems), FAM-labeled probe analyses (Supplementary Table S1). Only the top 1,000 most for DUSP6, and Beta-2-macroglobulin (B2M) as a normalization variable DEGs across all samples were included in the clustering control. Samples were run on a 7900HT Real-Time PCR machine analysis, which may enrich for model- and time-dependent gene- (Applied Biosystems), and data were analyzed and normalized expression differences in addition to posttreatment transcriptome D according to the manufacturer's instructions (2 Ct method). alterations. Five major clusters were identified (Fig. 2). To elucidate the biological functions of each cluster, gene set enrichment was performed using the Broad Institutes' Molecular Signatures Results Database (MSigDB) Hallmark (31) and MetaCore transcription Characteristics of the BRAFV600-mutant melanoma PDXs factor gene sets (GeneGo; Fig. 2; Supplementary Table S2). Genes Of all the cutaneous melanoma models in the Novartis PDX in clusters 3 and 5 exhibited potentially interesting model and encyclopedia, 13 models had BRAFV600 mutations (18). Figure 1A treatment-dependent patterns, although we were unable to find shows the common genetic lesions identified in these models. any functionally meaningful gene sets enriched in these two CDKN2A loss-of-function occurred at a higher frequency in these clusters. PDX models relative to human tumors (2), consistent with results The expression of cluster one genes was reduced with treat- from a previous study (30). MITF copy-number alterations and ment across all PDX models (Fig. 2). Multiple gene sets were mutations were found in five PDX models with potential onco- enriched in cluster one, including NF-kB signaling, cell cycle genic effects (1), but no trends associated with drug response were and proliferation, and the activation of several transcription observed. factors downstream of MAPK signaling (32). In addition, The Novartis mouse clinical trial (18) demonstrated that cluster 1 contained known ERK target genes including DUSP4, response of the BRAFV600-mutant PDX models to continuous DUSP6, SPRY4,andCCND1 (ref. 32; Supplementary Table S3). treatment with encorafenib and binimetinib ranged from com- The expression pattern, in addition to gene set enrichment plete regression (CR), to partial regression (PR) and stable disease results, indicated that these genes were downstream of MAPK (SD; Supplementary Fig. S1). Consistent with the phase III signaling. COLUMBUS trial, where the disease control rate was In contrast, the expression of cluster two genes increased with >90% (4), none of the PDX models displayed outright resistance BRAF and MEK inhibition. Cluster two was enriched for the MITF (ref. 18; Supplementary Fig. S1). In addition to the PDXs that were activation gene set, which contained known MITF target genes enrolled in the mouse clinical trial (18), one additional model, such as MLANA, TYRP1, and BCL2A1 (ref. 12; Fig. 2; Supplemen- X-20767, was also included in subsequent experiments (Fig. 1B). tary Table S3). In addition, epithelial markers such as CDH1, SNAI2, and ZEB2 were also upregulated. This is consistent with BRAF and MEK inhibitor treatment elicits distinct the enrichment of a differentiated melanocytic transcriptional transcriptional regulation in vivo state during the early phase of drug treatment. Although this To identify factors associated with differential drug response in cluster was associated with MITF activity, many of the activated BRAFV600-mutant melanoma in vivo, we characterized immediate genes were not found in the in vitro–derived gene signature and longer term transcriptional changes in response to encora- describing the high MITF, proliferative melanoma phenotype fenib and binimetinib treatment in our PDX models. RNA sensitive to MAPK inhibition (refs. 15, 33; Supplementary sequencing (RNA-seq) was performed on tumor fragments har- Fig. S3A). The upregulation of MITF transcriptional programming vested prior to treatment, 8 hours post a single dose, or 8 hours was described as a mechanism of drug tolerance, which protects post the final dose on the ninth day of continuous treatment melanoma against MAPK inhibitor treatment (17, 34). Based on (Fig. 1C). Three biological replicates were collected for each time known biology of MITF and the expression pattern of cluster 2, point, with the exception of X-1906, which only had two RNA-seq these genes appeared to be linked to adaptive response to MAPK samples for day 9 due to limitation in tumor size. In total, two CR inhibition in melanoma. and four PR/SD models were profiled. The day 9 samples for Genes in cluster 4 were highly expressed in CR PDX models at X-2613 were excluded from the analysis, because they were made baseline, and downregulated with treatment. Gene set enrich- up of >80% mouse RNA. ment identified genes involved in epithelial–mesenchymal tran- We applied limma-voom, a well-established method for RNA- sition, NF-kB signaling, and the activation of multiple transcrip- seq differential expression analysis (24, 25), to identify differen- tion factors known to be downstream of ERK (32). Although AXL tially expressed genes (DEG) between treated and untreated was identified in this gene set, there was very little overlap between conditions for each model (Supplementary Table S1). For all six cluster 4 and the in vitro–derived gene signature for the invasive PDX models, more transcriptome reprogramming occurred after melanoma phenotype associated with drugresistance (refs.15, 33; 9 days of treatment in comparison with a single dose (Fig. 1D). Supplementary Fig. S3B). Consistent with gene set enrich- There was significant overlap in differential gene expression ment results, cluster 4 contained genes representing a more between early and late time points, suggesting that the early "mesenchymal-like" phenotype, in addition to ERK target transcriptome changes were retained after prolonged treatment. genes such as FOSL1 (FRA1), EGR2,andDUSP5 (32). These Furthermore, only a small subset of DEGs were shared across results suggested that the genes in cluster 4 defined a MAPK- multiple models, whereas a significant number of DEGs were dependent, "mesenchymal-like" phenotype associated with unique to each model (Supplementary Fig. S2), indicating het- sensitivity to BRAF and MEK inhibition. erogeneity among PDXs. Taken together, hierarchical clustering revealed distinct pat- In an attempt to better understand patterns of transcriptional terns of gene expression that were dependent on MAPK inhibitor reprogramming by BRAF and MEK inhibitors in vivo, we per- treatment, drug sensitivity, and baseline model variability.

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Sensitive Resistant A CR PR SD D X-1906 (CR) BRAF Known COSMIC gain-of-function mutation Late vs. untreated NRAS Amplification (>5 copies)

NF1 Heterozygous deletion PTEN 81 366 1,176 Homozygous deletion PIK3CA Novel mutation Early vs. untreated MITF Heterozygous truncation, frameshift, or known COSMIC loss-of-function mutation CDKN2A Homozygous truncation, frameshift, TP53 or known COSMIC loss-of-function mutation

X-1906X-3483X-4644X-2613X-3746X-3211X-4538X-2723X-4530X-2602X-3676X-4668X-4849 X-20767 (SD) X-4530 (PR)

Late vs. untreated Late vs. untreated B Complete response Stable disease X-2613 X-20767 1,500 2,000 43 113 1,216 9 55 341

) Untreated 3 BRAFi + MEKi (chow) 1,500 Early vs. untreated Early vs. untreated 1,000

1,000

500 500 Tumor volume (mm X-4668 (SD) X-4849 (SD) 0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 Late vs. untreated Late vs. untreated Days after first dose Days after first dose C 77 695 40 778 11 5 Early vs. untreated Early vs. untreated BRAFi + MEKi BRAFi + MEKi

Untreated Early treatment (single dose) Late treatment (day 9)

Figure 1. Description and transcriptome analysis of BRAFV600-mutant melanoma PDXs. A, Genetic features of BRAFV600-mutant melanoma models in the Novartis PDX collection, arranged in order of their best average response to encorafenib and binimetinib as published in Gao et al. (18). B, Representative efﬁcacy studies showing examples of complete response (X-2613) and SD (X-20767), upon continuous treatment with encorafenib and binimetinib for 28 days. C, Schematic of sample collection from mice treated with encorafenib and binimetinib. Samples were harvested from tumor-bearing mice (n ¼ 3) at baseline, 8 hours post the ﬁrst dose, and 8 hours post the last dose after 9 days of continuous dosing. D, Venn diagrams showing genes that were differentially expressed after a single dose or 9 days of treatment, relative to the untreated controls for each PDX model. The cutoff for differential gene-expression analysis was log2 fold change > |1.5| and P value < 1.05.

Unsupervised clustering using the top 1,000 most variable DEGs drug-sensitivity genes in comparison with the PR/SD models demonstrated that there are three major sets of genes related to (Fig. 3C and D). There were no significant differences in the canonical MAPK signaling, drug tolerance, and drug sensitivity, expression of canonical MAPK signaling genes between CR and respectively. Whereas an MITF-dependent, differentiated mela- PR/SD PDXs (Supplementary Fig. S4A; Fig. 3E). nocyte transcription program was induced upon drug treat- To validate our findings, we assessed relevant markers and ment, the expression of a subset of MAPK-dependent genes pathways at the protein level (Fig. 3F). Several posttransla- featuring "mesenchymal-like" markers was enriched in mela- tional and transcriptional targets of ERK were highly expressed noma PDXs that achieve complete response upon BRAF and and/or phosphorylated in CR models. These include FRA1 MEK inhibition. (FOSL1), fosB, and EGR1 (32), consistent with the enrichment of relevant gene sets in the drug-sensitivity cluster Identification of transcriptional programs associated with (Fig. 2). Furthermore, the complete responders exhibited sensitivity and resistance to BRAF and MEK inhibitors in increased mesenchymal protein markers, whereas the epithe- PDXs lial markers were highly expressed at the protein level in the Using RNA-seq data from untreated tumors, we asked wheth- less sensitive PDX models (Fig. 3F). The AKT–mTOR pathway er baseline expression of the identified clusters from Fig. 2 was was also profiled given its known role in BRAFV600-mutant correlated with response to encorafenib and binimetinib across melanoma drug response (35, 36), though a clear trend was all 13 BRAFV600-mutant melanoma PDXs (18). Genes associ- not observed. ated with the MITF-high drug tolerance phenotype were more We showed that in BRAFV600-mutant melanoma PDXs, the highly expressed in PR/SD models relative to CR models at MITF-high, "epithelial-like" phenotype is not only associated baseline (Fig. 3A). The calculated signature scores were statis- with adaptive response, but also reduced sensitivity to tically significantly different between the two groups (Fig. 3B). MAPK inhibition. Inversely, the expression of a MAPK-high, On the other hand, the CR models expressed elevated levels of "mesenchymal-like" transcriptional program is linked to

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Complete response Stable disease

X-1906 X-2613 X-20767 X-4530 X-4668 X-4849 Untreated Early treatment Late treatment Untreated Early treatment Untreated Early treatment Cluster Late treatment Untreated Early treatment Late treatment Untreated Early treatment Late treatment Untreated Early treatment Late treatment

TNFα signaling via NF-κB G2M checkpoint CREB1 activation SP1 activation E2F targets E2F activation 1 FOXM1 activation p53 inhibition Oct-3/4 activation TFIIB activation

101214 02468

TFE3 activation MITF activation 2

101214 02468

Epithelial–mesenchymal transition TNFα signaling via NF-κB SP1 activation Hypoxia AP-2A activation c-Jun activation 4 SMAD3 activation IL2 STAT5 signaling p53 activation EGR1 activation KRAS signaling up

101214 02468 − P Log10 adjusted value 5

Avg log2 TPM (scaled)

3 0 −3 1

Cluster summary score 5

−200 −100 0 100 200

Figure 2. Unsupervised clustering of pre- and on-treatment melanoma PDX models. Heatmap displays unsupervised clustering results using 1,000 most variable DEGs across all treatment-matched samples. Left, gene set enrichment results. Bottom, summary scores for the clusters, calculated as the sum of z-transformed, normalized expression values. sensitivity to BRAF and MEK inhibitors. This ﬁnding is dis- Gene signatures derived from PDXs do not predict clinical tinct from the MITF-high (proliferative) and -low (invasive) outcome phenotypes described in melanoma cell lines, which high- Next, we evaluated the ability of our gene sets to predict clinical lights important differences between in vivo and in vitro outcome using a published cohort of 17 patients (29). Using a experimental systems. combined signature score made up of both drug tolerance and

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A Drug-tolerance genes (cluster 2) B C Drug-sensitivity genes (cluster 4) Sensitive Resistant Sensitive Resistant Drug-tolerance CR PR SD genes (cluster 2) CR PR SD

150 ***

100

-50

Signature score -100

-150

CR models PR/SDmodels X-1906 X-3483 X-4644 X-2613 X-3746 X-3211 X-4538 X-2723 X-4530 X-2602 X-3676 X-4668 X-4849 X-3211 X-2723 X-4530 X-2602 X-3676 X-3483 X-2613 X-3746 X-4538 X-4668 X-4849 X-1906 X-4644

Avg log2 TPM (scaled) Avg log2 TPM (scaled)

3 0 −3 3 0 −3 D E F Sensitive Resistant Drug-sensitivity Canonical MAPK genes (cluster 4) genes (cluster 1) CR PR SD

n.s. 300 *** 100 X-4849 X-4644 X-2613 X-4538 X-2723 X-3676 X-4668 X-1906 X-3483 X-3746 X-3211 X-4530 X-2602 200 50 pERK 100 tERK 0 0 pRSK3 tRSK -50 Signature score -

100 Signature score pFRA RAF−MEK−ERK -200 -100 tFRA

CR CR models PR/SDmodels models PR/SDmodels FosB

EGR1 pAKT tAKT − − pS6 PI3K AKT mTOR tS6

MITF

EMT/MITF E-Cadherin N-Cadherin GAPDH

Figure 3. Distinct transcriptional programs are associated with sensitivity to MAPK inhibition in melanoma in vivo. Heatmaps showing baseline expression of genes belonging to the drug-tolerance (A) and drug-sensitivity (C) clusters in BRAFV600-mutant melanoma models from the Novartis PDX collection, arranged in order of previously published best average response to encorafenib and binimetinib (18). Expression of drug-tolerance (B), drug-sensitivity (D), and canonical MAPK (E) gene clusters were quantiﬁed as signature scores and compared between models that achieved either CR or PR/SD in response to drug treatment. Signature score for each PDX model was calculated as the sum of normalized, z-transformed gene-expression values. Statistical analyses were performed using Student t test (, P < 0.001; n.s., not signiﬁcant). F, Western blot analysis of relevant protein markers in tumor fragments collected from untreated mice.

sensitivity genes, there were no differences in best response or treatment (Fig. 4C). The expression of drug-sensitivity genes progression-free survival between patients predicted to be sensi- during the course of treatment underwent heterogeneous changes tive and vice versa (Fig. 4A and B). However, analysis using on- among patients (Fig. 4D). The induction of the MITF-high, treatment patient samples revealed that the expression of drug "epithelial-like" phenotype in all patients suggested that selection tolerance genes was uniformly increased during the course of toward a more drug-resistant phenotype occurs during the course

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A GSE99898 B GSE99898 C Drug-tolerance genes D Drug-sensitivity genes (cluster 2) (cluster 4) 20 n.s. Predicted sensitive 60 100 100 Predicted insensitive 0 40 50 20 -20 50 0 0 -40 -20 Signature score by RECIST (%) -50 -60 -40 Change in tumor burden Progression-free survival -80 0 -60 -100 0 5 10 15 20 25 Time (months) Pre- On- Pre- On- Predicted Predicted treatment treatment treatment treatment sensitive insensitive

Figure 4. Gene signatures derived from PDXs do not predict drug response in the clinic. Signature scores were calculated using a data set of 17 patients (26) by combining the drug-tolerance and drug-sensitivity genes, where drug-tolerance genes were assigned negative values and drug-sensitivity genes were given positive values. There were no signiﬁcant differences in best response measured by RECIST (A; Student t test; n.s., not signiﬁcant) or progression-free survival (B; Cox proportional hazards) between patients who were predicted to be sensitive or insensitive based on overall median of the signature scores. Drug-tolerance (C) and drug-sensitivity (D) signature scores are shown in pre- and on-treatment–matched tumor samples.

of treatment, consistent with our findings from PDXs described identify DEGs posttreatment only in the CR, but not PR/SD PDX herein. Although the baseline expression of these genes was not models (ref. 25; Supplementary Fig. S4B; Fig. 5A). More DEGs associated with patient outcome, it could be due to numerous specific to the CR models were identified after 9 days of treatment factors such as intratumor heterogeneity, small sample size, in comparison with after a single dose. Gene set enrichment was heterogeneous treatment regimen, or tumor microenvironment. performed using MSigDB Hallmark gene sets (31). Genes preferentially downregulated in the CR models relative to the PR/SD Tumor regression in response to MAPK inhibition is models were enriched for epithelial mesenchymal transition concomitant with downregulation of multiple pathways (EMT), cell cycle and proliferation, and MAPK signaling To further characterize gene-expression changes associated (Fig. 5B). Western blot revealed that these pathways were also with tumor regression in vivo, we constructed linear models to inhibited in models that showed PR/SD in response to BRAF and

Late treatment (day 9) A C CR SD

14 X-1906 X-20767 X-4668 X-4849 BRAFi + MEKi - - - + + + - - - + + + - - - + + + - - - + + + (day 9) 12 pERK tERK 10 pRSK3 RAF–MEK–ERK 8 tRSK pFRA

adj P -value 6

10 tFRA 4 pAKT − Log tAKT 2 PI3K–AKT–mTOR pS6

0 tS6 − − − 6 4 20 246 MITF B Log2 fold change Downregulated EMT/MITF Hallmark gene set SIZE ES NES NOM P-val FDR q-val FWER P-val E-Cadherin G2M checkpoint 198 −0.748 −1.996 0 0 0 N-Cadherin Epithelial-mesenchymal transition 189 −0.726 −1.920 0 0 0 E2F targets 199 −0.726 −1.899 0 0 0 Cyclin D1 KRAS signaling (up) 164 −0.702 −1.829 0 0 0 Cell cycle/ TNFα signaling via NFκB 189 −0.683 −1.807 0 0 0 pRb (S780) apoptosis Upregulated BIMEL Hallmark gene set SIZE ES NES NOM P-val FDR q-val FWER P-val GAPDH Interferon α response 93 0.636 1.662 0 0.009 0.018

Figure 5. Tumor regression in response to BRAF and MEK inhibition is concomitant with the downregulation of cell cycle and proliferation, MAPK signaling, and EMT pathways in vivo. A, Volcano plot shows DEGs in complete response, but not SD PDX models after 9 days of treatment relative to untreated tumors. B, Gene set enrichment of the complete response models only, DEGs using MSigDB Hallmark gene sets. C, Western blot analysis of relevant protein markers from gene set enrichment analyses, using untreated and day 9 treatment tumor samples in triplicates.

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MEK inhibition, but the changes were most significant in the X-4849, which did not demonstrate MAPK reactivation following complete responder X-1906 (Fig. 5C). Consistent with earlier BRAF and MEK inhibitor treatment (Fig. 6E). This was despite the gene-expression analyses, BRAF and MEK inhibitor treatment triple combination yielding deeper suppression of the MAPK resulted in a shift toward a more "epithelial-like" phenotype, pathway, as evidenced by reduced RSK3 phosphorylation relative whereas two less sensitive models already exhibited high level of to BRAF and MEK inhibitor double combination (Fig. 6F). Con- E-cadherin protein expression prior to treatment. Furthermore, sistent with the lack of tumor regression, we did not observe any the CR model X-1906 exhibited the highest level of BIM upre- BIM upregulation upon treatment. To identify additional com- gulation upon treatment, consistent with apoptotic induction binations that may be efficacious for MAPK-inactivated PDX and the observed tumor regression (37). Models that showed SD models, we assessed efficacy results from the Novartis mouse also upregulated BIM upon BRAF and MEK inhibitor treatment, clinical trial (18). For model X-4849, combination of encorafenib indicating that this is not sufficient to yield tumor regression. and the PI3K inhibitor burparlisib resulted in markedly more Taken together, encorafenib and binimetinib treatment in tumor regression in contrast to treatment using encorafenib and BRAFV600-mutant melanoma PDXs resulted in inhibition of binimetinib (Fig. 6G). This suggests that targeting parallel path- MAPK signaling, cell cycle and proliferation, and transition ways rather than vertical combination could improve the efficacy toward a more "epithelial-like" cell state. CR in response to for a subset of BRAFV600-mutant melanomas that do not reactivate treatment is associated with strong inhibition of these pathways the MAPK pathway during treatment. and upregulation of the proapoptotic protein BIM. As shown, these data support the notion that some BRAFV600- mutant melanomas do not depend on reactivation of the MAPK BRAFV600-mutant melanoma PDXs exhibit varied dependence pathway to survive under BRAF and MEK inhibitor therapy. on MAPK signaling Inhibition of the MAPK pathway could only achieve limited The gene expression and signaling studies suggested that MAPK efficacy in these tumors, and additional work is required to pathway suppression was more robust in the CR versus PR/SD identify treatment strategies with curative benefit. On the other models. To further extend this observation, MAPK pathway hand, triple combination with BRAF, MEK, and ERK inhibitors suppression was measured in untreated and on-treatment tumor presents as a viable treatment strategy to improve response in fragments (Fig. 1C) using ERK phosphorylation and DUSP6 BRAFV600-mutant melanoma that depend on the MAPK pathway mRNA (Fig. 6A; Supplementary Fig. S5). As an ERK phosphatase for survival, where reactivation of the pathway occurs as early as tightly regulated by MAPK output, DUSP6 expression is highly 9 days following treatment in vivo. sensitive to MAPK pathway inhibition (32). Unsurprisingly, the complete responder X-1906 displayed markedly durable pathway suppression. In comparison, the PR/SD models exhibited varied Discussion levels of MAPK inhibition. Three of the five PDX models displayed Here, we demonstrated that distinct transcriptional programs either sustained MAPK signaling or signaling recovery after 9 days are associated with differential sensitivity to MAPK inhibition of treatment, whereas the remaining two PDXs showed durable in BRAFV600-mutant melanoma PDXs. Melanoma PDXs with MAPK pathway suppression. This finding was consistent with on- MITF-high, "epithelial-like" gene expression were less sensitive treatment patient samples (29), where MAPK inhibition as mea- to BRAF and MEK inhibition. PDX models with increased levels of sured by DUSP6 mRNA was greater in responding tumors but MAPK signaling and expressing a "mesenchymal-like" transcrip- more variable in patients with less tumor shrinkage (Fig. 6B). In tional program were preferentially sensitive to MAPK inhibition. addition, AKT phosphorylation exhibited variable changes upon In addition, the lack of tumor regression in response to BRAF and treatment, highlighting intertumor heterogeneity in adaptive MEK inhibition was associated with adaptive responses consisting response to MAPK inhibition (Supplementary Fig. S5A). These of MAPK reactivation or alternative survival mechanisms, which findings indicated that BRAFV600-mutant melanoma elicited both in turn dictated their response to further suppression of the MAPK MAPK-dependent and -independent adaptive mechanisms to pathway. This study highlighted the intertumor heterogeneity in survive under MAPK inhibitor therapy. BRAFV600-mutant melanoma, and the relevant challenges in To evaluate the hypothesis that BRAFV600-mutant melanoma optimizing therapeutic strategies for individual tumors. varies in their dependence on the MAPK pathway for survival, we In this study, we observed that elevated MITF activity and the assessed the efficacy of triple combination with encorafenib, "epithelial-like" transcriptional program were associated with binimetinib, and the ERK inhibitor Vx-11e (38). The three- therapeutic resistance rather than sensitivity in vivo. This finding drug combination was well tolerated in vivo (Supplementary contradicts in vitro studies, where the "MITF-low" invasive Fig. S6). We evaluated the triple combination in two PDX models cell state is linked to intrinsic resistance to BRAF inhibitors and that exhibited either MAPK signaling rebound (X-20767) or vice versa (12, 13). The reasons for such in vitro-in vivo discrepancies continual pathway suppression (X-4849; Fig. 6A). In the remain unclear. Although tumor samples from melanoma MAPK-activated model, combination with the ERK inhibitor patients could be classified into proliferative and invasive resulted in further tumor regression in comparison with BRAF subtypes (39), there were no obvious correlations between mela- and MEK inhibitors alone (Fig. 6C and D). The ERK inhibitor Vx- nocyte lineage transcriptional programming with clinical 11e stabilizes the phosphorylated form of ERK; therefore, MAPK response (40). Intratumor heterogeneity could affect biological pathway inhibition was assessed by the phosphorylation of ERK interpretations in vivo. Melanoma cells switch between prolifer- substrate RSK3 (32). Tumor samples collected after 9 days of ative and invasive phenotypes in vivo (41), and these two cell types treatment showed that the triple combination resulted in coexist in patient tumors (42). Moreover, the transcriptional and increased suppression of MAPK signaling, in addition to the posttranslational regulation of MITF is highly complex and can be upregulation of BIM (Fig. 6D). In contrast, triple combination modulated by the in vivo tumor microenvironment (17). MITF with an ERK inhibitor did not improve antitumor activity in plays pleiotropic roles under diverse cellular contexts (17),

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Transcriptional Programs Predict Response to MAPK Inhibition

MAPK Activated MAPK Inactivated A B DUSP6 logFC CR PR/SD RECIST 20 0

X-1906 X-20767 X-3676 X-4530 X-4668 X-4849 (treatment vs. baseline) 150 150 150 150 150 0 -1 DUSP6 logFC

100 100 100 100 100 -20 -2

50 50 50 50 50 50 -

by RECIST (%) 40 -3 relative to untreated Change in tumor burden % ERK phosphorylation 0 0 0 0 0 0 -60 -4 0 0 0 0 0 0 12 24 24 24 24 12 24 12 12 24 12 12 7 2 0 1 7 6 3 192 204 216 204 216 216 192 216 192 216 192 216 192 192 204 204 204 204 1 1 1 1 1 Time after first drug adminstration (hour) Patient

X-20767 - MAPK activated BRAFi C D BRAFi + MEKi Untreated Untreated + MEKi + ERKi ERKi 2,000 BRAFi + MEKi

) pERK 3 BRAFi + MEKi + ERKi tERK 1,500 ERKi pRSK3

1,000 tRSK pAKT 500 tAKT

Tumor volume (mm **** BIMEL 0 0 5 10 15 20 25 30 GAPDH Days after treatment

EFX-4849 - MAPK inactivated BRAFi G BRAFi + MEKi Gao et al., 2015 Untreated Untreated + MEKi + ERKi ERKi 20 BRAFi + MEKi 1,500 BRAFi + MEKi pERK BRAFi + PI3Ki )

3 BRAFi + CDK4/6i BRAFi + MEKi + ERKi tERK 0 1,000 ERKi pRSK3 tRSK −20 pAKT 500 −40 n.s. tAKT BIM Tumor volume (mm EL −60 0 GAPDH 015 015202530 Days after treatment X-4849

Figure 6. A subset of PR/SD models do not depend on the MAPK pathway for survival. A, Quantified phosphorylated ERK protein level in tumor samples harvested pretreatment, 8 hours after a single dose, or 8 hours after the final dose on the ninth day of continuous treatment. Based on ERK phosphorylation levels, the PR/SD models were classified into MAPK-activated or -inactivated groups. B, The degree of MAPK pathway suppression in patient samples as measured by DUSP6 log2 fold change, relative to patient response to BRAF inhibitors with or without an MEK inhibitor. Efficacy results of the MAPK-activated model X-20767 (C) and the MAPK-inactivated model X-4849 (E), treated with BRAF and MEK inhibitors with or without an ERK inhibitor, or ERK inhibitor alone. Statistical analysis was performed on the endpoint measurements using Student t test (, P < 0.0001; n.s., not significant). Western blot analysis was performed on tumors harvested after 9 days of treatment with drug combinations as shown in X-20767 (D) and X-4849 (F). G, Best average response in X-4849 to various drug combinations (18), extracted from Supplementary Tables in the reference publication. sometimes demonstrating opposing effects on melanoma resis- resistance across a variety of cancer types and therapeutic modal- tance to targeted therapy (13, 43). In addition, downregulation of ities (46), melanocytes originate from the neural crest and there- AXL expression was identified in BRAF inhibitor–resistant patient fore do not undergo true "EMT." Oncogenic BRAF was shown to tumors (44). It is conceivable that both extremely high and low modulate NF-kB activity (47) and elicit EMT transcription factor levels of MITF could protect against the cytotoxic effects exerted by reprogramming in melanoma (48). In an in vivo murine mela- MAPK pathway inhibition. noma model, treatment with MEK inhibitor resulted in transition Low levels of MITF and increased "EMT"-like transcription toward a more epithelial phenotype (49). Furthermore, the signature in the CR PDX models could also be a direct conse- induction of EMT by the MAPK pathway and its effector FRA1 quence of MAPK pathway output. ERK and its substrate p90RSK1 was described in several epithelial cancers (50–52). In support of posttranslationally phosphorylate MITF (45). Increased MITF this interpretation, we observed that elevated baseline MAPK expression and activity occurs shortly after BRAF inhibition, signaling, in addition to high levels of FRA1 expression and which induces a drug-tolerant state preceding disease progres- phosphorylation, occurred in association with elevated transcrip- sion (34). Although EMT has long been implicated in therapeutic tional programs such as EMT and NF-kB. BRAF and MEK inhibitor

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Feng et al.

treatment resulted in a shift toward more "epithelial" melanocytic for different BRAF and MEK inhibitor–based therapies. Ongoing program in PDXs, which could either be the consequence of MAPK clinical trials of BRAF and MEK inhibitor–based triple combina- pathway inhibition or selection toward more resistant popula- tions will help identify additional therapeutic options for this tions. In patient samples, the increased expression of melanocytic heterogeneous and aggressive cancer. antigens in response to MAPK inhibition is concomitant with In summary, we presented a detailed pharmacodynamic char- elevated CD8þ T-cell infiltration (53). Immune evasion is fre- acterization of BRAFV600-mutant melanoma PDXs treated with quently associated with acquired resistance to BRAF inhibitor– BRAF and MEK inhibitors. The in vivo model system revealed based therapy, as immune modulation plays a significant part in patterns of therapeutic sensitivity distinct from in vitro studies, BRAF inhibitor–mediated efficacy in vivo (44). The potential syn- which underscores the necessity for diverse preclinical experimen- ergy and robust single-agent activity make the combination of tal systems. Results from this study demonstrated the intertumor targeted therapy with immune-checkpoint inhibitors an incredibly heterogeneity in metastatic melanoma, and the need for promising therapeutic strategy for BRAFV600-mutant melano- improved precision medicine approach to ultimately cure this ma (54). To this end, a major limitation of PDXs is the inability aggressive disease. to model the tumor immune microenvironment. Current efforts in developing humanized and syngeneic mouse models will help Disclosure of Potential Conflicts of Interest address this gap in recapitulating human tumor biology. T. Feng is a postdoctoral scholar at Novartis Institutes for Biomedical Interestingly, analysis using a curated ten-gene MAPK signature Research. J. Golji is an investigator at Novartis Institutes of Biomedical Research. demonstrated that high MAPK pathway activity was correlated with F.C. Geyer is an investigator at Novartis Institutes of Biomedical Research. BRAF BRAFV600 H. Gao is a senior investigator at Novartis Institutes of Biomedical Research. J.A. improved response to inhibition in -mutant mel- Williams is an executive director at, reports receiving commercial research grant fi anoma patients (55). This is consistent with our nding that PDXs from, and has ownership interest (including patents) in Novartis Institutes of with elevated MAPK pathway activity responded better to BRAF and Biomedical Research. D.D. Stuart is an executive director at, and has ownership MEK inhibitors. However, we were unable to demonstrate the interest (including patents) in Novartis Institutes of Biomedical Research. M.J. predictive value of our gene sets using a publicly available clinical Meyer is a senior investigator at Novartis Institutes of Biomedical Research, is a In Vivo data set. This could be due to intrinsic variability in small clinical Head, Discovery Pharmacology and Biology at BMS and has ownership interest (including patents) at Novartis Institutes of Biomedical Research and data sets, or additional factors such as the immune system or BMS. No potential conflicts of interest were disclosed by the other authors. disease severity that were not captured by the PDX system. Larger preclinical and clinical data sets will be required to further elucidate Authors' Contributions MITF the links between basal MAPK pathway output, transcrip- Conception and design: T. Feng, D.A. Ruddy, D.D. Stuart, M.J. Meyer tional activity, and clinical response to BRAF and MEK inhibitors. Development of methodology: T. Feng, D.A. Ruddy, D.D. Stuart Analysis of on-treatment PDX tumors revealed that response to Acquisition of data (provided animals, acquired and managed patients, BRAF and MEK inhibition involved significant downregulation of provided facilities, etc.): T. Feng, A. Li, X. Zhang, J. Gu, J.A. Williams, M.J. Meyer cell-cycle and proliferation genes. This is expected as the gene- Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): T. Feng, J. Golji, D.A. Ruddy, F.C. Geyer, J. Gu, expression changes corresponded to sustained inhibition of D.D. Stuart, M.J. Meyer MAPK signaling. However, robust MAPK inhibition was also Writing, review, and/or revision of the manuscript: T. Feng, J. Golji, observed in a subset of PR/SD PDX models, indicating codepen- J.A. Williams, D.D. Stuart, M.J. Meyer dence on alternative signaling pathways and necessity to target Administrative, technical, or material support (i.e., reporting or organizing these parallel pathways to achieve tumor regression. PTEN defi- data, constructing databases): T. Feng, X. Zhang, D.A. Ruddy, D.P. Rakiec ciency has long been associated with resistance against MAPK Study supervision: H. Gao, D.D. Stuart inhibitors in BRAFV600-mutant melanoma (35, 36, 56), although Acknowledgments PTEN loss-of-function alone was insufficient to predict MAPK We thank the PDX team, especially J. Green and A. Loo, for establishing and dependency in the PDX models. On the other hand, three-drug maintaining the Novartis PDX collection; L. Fan and O. Iartchouk for perform- combination of BRAF, MEK, and ERK inhibitors achieved tumor ing RNA sequencing; J. Korn and A. Johnson for bioinformatic support; and regression in PDX models that reactivated the MAPK pathway in G. Caponigro, M. Niederst, V. Cooke, and J. Engelman for helpful discussions. response to BRAF and MEK inhibitors alone. It was shown that the triple vertical combination could overcome the emergence of The costs of publication of this article were defrayed in part by the payment of MAPK-reactivated therapeutic resistance (57). Our data suggest page charges. This article must therefore be hereby marked advertisement in that early adaptive response to MAPK inhibition may be an accordance with 18 U.S.C. Section 1734 solely to indicate this fact. important predictor of response to triple combination of BRAF, MEK, and ERK inhibitors. To this end, profiling of early on- Received January 8, 2019; revised June 26, 2019; accepted September 10, treatment biopsies may serve as a patient stratification strategy 2019; published first September 16, 2019.

References 1. Tsao H, Chin L, Garraway LA, Fisher DE. Melanoma: from mutations to in patients with BRAF-mutant melanoma (COLUMBUS): a multicentre, medicine. Genes Dev 2012;26:1131–55. open-label, randomised phase 3 trial. Lancet Oncol 2018;19:603–15. 2. Cancer Genome Atlas Network. Genomic classiﬁcation of cutaneous 5. Larkin J, Ascierto PA, Dreno B, Atkinson V, Liszkay G, Maio M, et al. melanoma. Cell 2015;161:1681–96. Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. 3. Chapman PB, Hauschild A, Robert C, Haanen JB, Garbe C, Testori A, et al. N Engl J Med 2014;371:1867–76. Improved survival with vemurafenib in melanoma with BRAF V600E 6. Robert C, Karaszewska B, Schachter J, Rutkowski P, Mackiewicz A, mutation. N Engl J Med 2011;364:2507–16. Stroiakovski D, et al. Improved overall survival in melanoma 4. Dummer R, Ascierto PA, Gogas HJ, Arance A, Mandala M, Liszkay G, with combined dabrafenib and trametinib. N Engl J Med 2015; et al. Encorafenib plus binimetinib versus vemurafenib or encorafenib 372:30–9.

OF10 Mol Cancer Ther; 18(12) December 2019 Molecular Cancer Therapeutics

Transcriptional Programs Predict Response to MAPK Inhibition

7. Shi H, Hugo W, Kong X, Hong A, Koya RC, Moriceau G, et al. Acquired 29. Kakavand H, Rawson RV, Pupo GM, Yang JYH, Menzies AM, Carlino MS, resistance and clonal evolution in melanoma during BRAF inhibitor et al. PD-L1 expression and immune escape in melanoma resistance to therapy. Cancer Discov 2014;4:80–93. MAPK inhibitors. Clin Cancer Res 2017;23:6054–61. 8. Wagle N, Van Allen EM, Treacy DJ, Frederick DT, Cooper ZA, Taylor-Weiner 30. Garman B, Anastopoulos IN, Krepler C, Brafford P, Sproesser K, Jiang Y, A, et al. MAP kinase pathway alterations in BRAF-mutant melanoma et al. Genetic and genomic characterization of 462 melanoma patient- patients with acquired resistance to combined RAF/MEK inhibition. derived xenografts, tumor biopsies, and cell lines. Cell Rep 2017;21: Cancer Discov 2014;4:61–8. 1936–52. 9. Long GV, Fung C, Menzies AM, Pupo GM, Carlino MS, Hyman J, et al. 31. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. Increased MAPK reactivation in early resistance to dabrafenib/trametinib The Molecular Signatures Database (MSigDB) hallmark gene set collection. combination therapy of BRAF-mutant metastatic melanoma. Cell Syst 2015;1:417–25. Nat Commun 2014;5:5694. 32. Yoon S, Seger R. The extracellular signal-regulated kinase: multiple 10. Long GV, Weber JS, Infante JR, Kim KB, Daud A, Gonzalez R, et al. Overall substrates regulate diverse cellular functions. Growth Factors 2006; survival and durable responses in patients with BRAF V600-mutant met- 24:21–44. astatic melanoma receiving dabrafenib combined with trametinib. J Clin 33. Widmer DS, Cheng PF, Eichhoff OM, Belloni BC, Zipser MC, Schlegel Oncol 2016;34:871–8. NC, et al. Systematic classification of melanoma cells by phenotype- 11. Long GV, Flaherty KT, Stroyakovskiy D, Gogas H, Levchenko E, de Braud F, specific gene expression mapping. Pigment Cell Melanoma Res 2012; et al. Dabrafenib plus trametinib versus dabrafenib monotherapy in 25:343–53. patients with metastatic BRAF V600E/K-mutant melanoma: long-term 34. Smith MP, Brunton H, Rowling EJ, Ferguson J, Arozarena I, Miskolczi Z, survival and safety analysis of a phase 3 study. Ann Oncol 2017;28: et al. Inhibiting drivers of non-mutational drug tolerance is a salvage 1631–9. strategy for targeted melanoma therapy. Cancer Cell 2016;29:270–84. 12. Konieczkowski DJ, Johannessen CM, Abudayyeh O, Kim JW, Cooper ZA, 35. Paraiso KH, Xiang Y, Rebecca VW, Abel EV, Chen YA, Munko AC, et al. Piris A, et al. A melanoma cell state distinction influences sensitivity to PTEN loss confers BRAF inhibitor resistance to melanoma cells through the MAPK pathway inhibitors. Cancer Discov 2014;4:816–27. suppression of BIM expression. Cancer Res 2011;71:2750–60. 13. Muller€ J, Krijgsman O, Tsoi J, Robert L, Hugo W, Song C, et al. Low MITF/ 36. Xing F, Persaud Y, Pratilas CA, Taylor BS, Janakiraman M, She QB, et al. AXL ratio predicts early resistance to multiple targeted drugs in melanoma. Concurrent loss of the PTEN and RB1 tumor suppressors attenuates RAF Nat Commun 2014;5:5712. dependence in melanomas harboring (V600E)BRAF. Oncogene 2012;31: 14. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, et al. Molecular 446–57. classification of cutaneous malignant melanoma by gene expression 37. O'Connor L, Strasser A, O'Reilly LA, Hausmann G, Adams JM, Cory S, et al. profiling. Nature 2000;406:536–40. Bim: a novel member of the Bcl-2 family that promotes apoptosis. EMBO J 15. Hoek KS, Schlegel NC, Brafford P, Sucker A, Ugurel S, Kumar R, et al. 1998;17:384–95. Metastatic potential of melanomas defined by specific gene expression 38. Aronov AM, Tang Q, Martinez-Botella G, Bemis GW, Cao J, Chen G, et al. profiles with no BRAF signature. Pigment Cell Res 2006;19:290–302. Structure-guided design of potent and selective pyrimidylpyrrole inhibi- 16. Jeffs AR, Glover AC, Slobbe LJ, Wang L, He S, Hazlett JA, et al. A gene tors of extracellular signal-regulated kinase (ERK) using conformational expression signature of invasive potential in metastatic melanoma cells. control. J Med Chem 2009;52:6362–8. PLoS One 2009;4:e8461. 39. Verfaillie A, Imrichova H, Atak ZK, Dewaele M, Rambow F, Hulselmans G, 17. Wellbrock C, Arozarena I. Microphthalmia-associated transcription factor et al. Decoding the regulatory landscape of melanoma reveals TEADS as in melanoma development and MAP-kinase pathway targeted therapy. regulators of the invasive cell state. Nat Commun 2015;6:6683. Pigment Cell Melanoma Res 2015;28:390–406. 40. Wongchenko MJ, McArthur GA, Dreno B, Larkin J, Ascierto PA, Sosman J, 18. Gao H, Korn JM, Ferretti S, Monahan JE, Wang Y, Singh M, et al. High- et al. Gene expression profiling in BRAF-mutated melanoma reveals throughput screening using patient-derived tumor xenografts to predict patient subgroups with poor outcomes to vemurafenib that may be clinical trial drug response. Nat Med 2015;21:1318–25. overcome by cobimetinib plus vemurafenib. Clin Cancer Res 2017;23: 19. Bertotti A, Migliardi G, Galimi F, Sassi F, Torti D, Isella C, et al. A 5238–45. molecularly annotated platform of patient-derived xenografts ("xenopa- 41. Hoek KS, Eichhoff OM, Schlegel NC, Dobbeling U, Kobert N, Schaerer L, tients") identifies HER2 as an effective therapeutic target in cetuximab- et al. In vivo switching of human melanoma cells between proliferative and resistant colorectal cancer. Cancer Discov 2011;1:508–23. invasive states. Cancer Res 2008;68:650–6. 20. Migliardi G, Sassi F, Torti D, Galimi F, Zanella ER, Buscarino M, et al. 42. Tirosh I, Izar B, Prakadan SM, Wadsworth MH II, Treacy D, Trombetta JJ, Inhibition of MEK and PI3K/mTOR suppresses tumor growth but does not et al. Dissecting the multicellular ecosystem of metastatic melanoma by cause tumor regression in patient-derived xenografts of RAS-mutant colo- single-cell RNA-seq. Science 2016;352:189–96. rectal carcinomas. Clin Cancer Res 2012;18:2515–25. 43. Van Allen EM, Wagle N, Sucker A, Treacy DJ, Johannessen CM, Goetz EM, 21. Tentler JJ, Tan AC, Weekes CD, Jimeno A, Leong S, Pitts TM, et al. Patient- et al. The genetic landscape of clinical resistance to RAF inhibition in derived tumour xenografts as models for oncology drug development. metastatic melanoma. Cancer Discov 2014;4:94–109. Nat Rev Clin Oncol 2012;9:338–50. 44. Hugo W, Shi H, Sun L, Piva M, Song C, Kong X, et al. Non-genomic and 22. Hidalgo M, Amant F, Biankin AV, Budinska E, Byrne AT, Caldas C, et al. immune evolution of melanoma acquiring MAPKi resistance. Cell 2015; Patient-derived xenograft models: an emerging platform for translational 162:1271–85. cancer research. Cancer Discov 2014;4:998–1013. 45. Wu M, Hemesath TJ, Takemoto CM, Horstmann MA, Wells AG, Price ER, 23. Cassidy JW, Caldas C, Bruna A. Maintaining tumor heterogeneity in et al. c-Kit triggers dual phosphorylations, which couple activation and patient-derived tumor xenografts. Cancer Res 2015;75:2963–8. degradation of the essential melanocyte factor Mitf. Genes Dev 2000;14: 24. Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear 301–12. model analysis tools for RNA-seq read counts. Genome Biol 2014;15:R29. 46. Tsukasa S, Weinberg RA. EMT, CSCs, and drug resistance: the mechanistic 25. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers link and clinical implications. Nat Rev Clin Oncol 2017;14:611. differential expression analyses for RNA-sequencing and microarray stud- 47. Liu J, Suresh Kumar KG, Yu D, Molton SA, McMahon M, Herlyn M, et al. ies. Nucleic Acids Res 2015;43:e47. Oncogenic BRAF regulates beta-Trcp expression and NF-kappaB activity in 26. Robinson M, Oshlack A. A scaling normalization method for differential human melanoma cells. Oncogene 2007;26:1954–8. expression analysis of RNA-seq data. Genome Biol 2010;11:R25. 48. Caramel J, Papadogeorgakis E, Hill L, Browne GJ, Richard G, Wierinckx A, 27. Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: an R package for et al. A switch in the expression of embryonic EMT-inducers drives the determining the relevant number of clusters in a data set. J Stat Softw 2014; development of malignant melanoma. Cancer Cell 2013;24:466–80. 61:1–36. 49. Brighton HE, Angus SP, Bo T, Roques J, Tagliatela AC, Darr DB, et al. New 28. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, mechanisms of resistance to MEK inhibitors in melanoma revealed by et al. Gene set enrichment analysis: a knowledge-based approach for intravital imaging. Cancer Res 2018;78:542–57. interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 50. Lemieux E, Bergeron S, Durand V, Asselin C, Saucier C, Rivard N. Consti- 2005;102:15545–50. tutively active MEK1 is sufficient to induce epithelial-to-mesenchymal

www.aacrjournals.org Mol Cancer Ther; 18(12) December 2019 OF11

Feng et al.

transition in intestinal epithelial cells and to promote tumor invasion and 54. Grifﬁn M, Scotto D, Josephs DH, Mele S, Crescioli S, Bax HJ, et al. BRAF metastasis. Int J Cancer 2009;125:1575–86. inhibitors: resistance and the promise of combination treatments for 51. Diesch J, Sanij E, Gilan O, Love C, Tran H, Fleming NI, et al. Widespread melanoma. Oncotarget 2017;8:78174–92. FRA1-dependent control of mesenchymal transdifferentiation programs in 55. Wagle MC, Kirouac D, Klijn C, Liu B, Mahajan S, Junttila M, et al. A colorectal cancer cells. PLoS One 2014;9:e88950. transcriptional MAPK pathway activity score (MPAS) is a clinically relevant 52. Bakiri L, Macho-Maschler S, Custic I, Niemiec J, Guio-Carrion A, biomarker in multiple cancer types. NPJ Precis Oncol 2018;2:7. Hasenfuss SC, et al. Fra-1/AP-1 induces EMT in mammary epithelial 56. Nathanson KL, Martin AM, Wubbenhorst B, Greshock J, Letrero R, cells by modulating Zeb1/2 and TGFbeta expression. Cell Death Differ D'Andrea K, et al. Tumor genetic analyses of patients with metastatic 2015;22:336–50. melanoma treated with the BRAF inhibitor dabrafenib (GSK2118436). 53. Frederick DT, Piris A, Cogan AP, Cooper ZA, Lezcano C, Ferrone CR, et al. Clin Cancer Res 2013;19:4868–78. BRAF inhibition is associated with enhanced melanoma antigen expres- 57. Xue Y, Martelotto L, Baslan T, Vides A, Solomon M, Mai TT, et al. An sion and a more favorable tumor microenvironment in patients with approach to suppress the evolution of resistance in BRAF(V600E)-mutant metastatic melanoma. Clin Cancer Res 2013;19:1225–31. cancer. Nat Med 2017;23:929–37.

OF12 Mol Cancer Ther; 18(12) December 2019 Molecular Cancer Therapeutics

Distinct Transcriptional Programming Drive Response to MAPK Inhibition in BRAFV600-Mutant Melanoma Patient-Derived Xenografts

Tianshu Feng, Javad Golji, Ailing Li, et al.

Mol Cancer Ther Published OnlineFirst September 16, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/1535-7163.MCT-19-0028

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