Published OnlineFirst November 18, 2020; DOI: 10.1158/2159-8290.CD-20-0706 RESEARCH ARTICLE Machine -Learning and Chemicogenomics Approach Defi nes and Predicts Cross-Talk of Hippo and MAPK Pathways Trang H. Pham 1 , Thijs J. Hagenbeek 1 , Ho-June Lee 1 , Jason Li 2 , Christopher M. Rose 3 , Eva Lin 1 , Mamie Yu 1 , Scott E. Martin1 , Robert Piskol 2 , Jennifer A. Lacap 4 , Deepak Sampath 4 , Victoria C. Pham 3 , Zora Modrusan 5 , Jennie R. Lill3 , Christiaan Klijn 2 , Shiva Malek 1 , Matthew T. Chang 2 , and Anwesha Dey 1 ABSTRACT Hippo pathway dysregulation occurs in multiple cancers through genetic and non- genetic alterations, resulting in translocation of YAP to the nucleus and activation of the TEAD family of transcription factors. Unlike other oncogenic pathways such as RAS, defi ning tumors that are Hippo pathway–dependent is far more complex due to the lack of hotspot genetic alterations. Here, we developed a machine-learning framework to identify a robust, cancer type–agnostic gene expression signature to quantitate Hippo pathway activity and cross-talk as well as predict YAP/TEAD dependency across cancers. Further, through chemical genetic interaction screens and multiomics analyses, we discover a direct interaction between MAPK signaling and TEAD stability such that knockdown of YAP combined with MEK inhibition results in robust inhibition of tumor cell growth in Hippo dysregulated tumors. This multifaceted approach underscores how computational models combined with experimental studies can inform precision medicine approaches including predictive diagnostics and combination strategies. SIGNIFICANCE: An integrated chemicogenomics strategy was developed to identify a lineage- independent signature for the Hippo pathway in cancers. Evaluating transcriptional profi les using a machine-learning method led to identifi cation of a relationship between YAP/TAZ dependency and MAPK pathway activity. The results help to nominate potential combination therapies with Hippo pathway inhibition. 1 Department of Discovery Oncology, Genentech, Inc., South San Francisco, Corresponding Authors: Anwesha Dey, Genentech Inc., 1 DNA Way, MS California. 2 Department of Bioinformatics, Genentech, Inc., South San Fran- 41-1a, South San Francisco, CA 94080. Phone: 650-678-8953; Fax: cisco, California. 3 Department of Microchemistry, Proteomics, and Lipidomics, 650-225-6443; E-mail: [email protected] ; and Matthew T. Chang, matthew. Genentech, Inc., South San Francisco, California. 4 Department of Translational [email protected] 5 Oncology, Genentech, Inc., South San Francisco, California. Department of Cancer Discov 2021;11:778–93 Molecular Biology, Genentech, Inc., South San Francisco, California. doi: 10.1158/2159-8290.CD-20-0706 Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/). © 2020 American Association for Cancer Research. T.H. Pham, T.J. Hagenbeek, and H.-J. Lee contributed equally to this work. 778 | CANCER DISCOVERY MARCH 2021 AACRJournals.org Downloaded from cancerdiscovery.aacrjournals.org on September 28, 2021. © 2021 American Association for Cancer Research. Published OnlineFirst November 18, 2020; DOI: 10.1158/2159-8290.CD-20-0706 INTRODUCTION (4–6). These led to the discovery of the conserved Hippo path- way core components consisting of serine/threonine kinases One challenge of cancer precision medicine is the hetero- named Mammalian STE20-like 1/2 (MST1/2) with adaptor geneity of genetic and nongenetic alterations that result in protein SAV1 that directly phosphorylate the large tumor aberrant pathway signaling. Recurrent mutations and genetic suppressors (LATS1/2). Together with the kinase activator alterations have been identified in many oncogenic signaling MOB1, LATS1/2 can phosphorylate the two major down- pathways, including MAPK and PI3K (1, 2), whereas other stream coactivators YAP (YAP1) and TAZ (WWTR1; Fig. 1A, signaling pathways such as Hippo lack canonical hotspot inset). When the pathway is deregulated, unphosphorylated mutations. Yet dysregulation in Hippo pathway signaling is YAP and TAZ are translocated to the nucleus and activate known to drive oncogenesis across numerous cancer types. downstream target gene expression by binding to TEAD The Hippo pathway is emerging as the target of drug dis- family transcription factors (TF; refs. 7–13; Fig. 1A, inset). covery efforts, but it lacks hotspot mutations; identifying Widespread dysregulation of the Hippo pathway components relevant Hippo pathway–dependent patient population(s) has been observed in multiple human cancer types including and biomarker(s) of response is a prerequisite for precision glioma and breast, liver, lung, prostate, colorectal, and gas- medicine in tumors that leverage this pathway. tric cancers (14–17). Furthermore, tumors with dysregulated The Hippo pathway controls multiple cellular functions Hippo components are not only insensitive to the intrinsic that drive oncogenesis, including proliferation, cell fate deter- cellular death barriers (3, 18) but also resistant to chemo and mination, and cell survival. Perturbation of the pathway has molecular targeted therapies (19–21). been shown to trigger tumorigenesis in mice (3). The pathway Extensive studies have established the importance of the is evolutionarily conserved across diverse species and was first Hippo pathway in biology and cancers. As drug develop- identified inDrosophila melanogaster through multiple genetic ment interest in targeting the pathway continues to grow screens for gene mutations that cause overgrowth phenotype (22–25), one key clinical challenge is to identify patient March 2021 CANCER DISCOVERY | 779 Downloaded from cancerdiscovery.aacrjournals.org on September 28, 2021. © 2021 American Association for Cancer Research. Published OnlineFirst November 18, 2020; DOI: 10.1158/2159-8290.CD-20-0706 RESEARCH ARTICLE Pham et al. MST1/2 A SAV1 B P Nucleus Cytoplasm TCGA cervical squamous 14 LATS1/2 12 MOB1 count) 10 P 2 P P 8 (log YAP/TAZ P YAP/TAZ mRNA expression 6 P TEAD 4 14-3-3 YAP1 MST1 4% 12 STK3 1% NF2 2% Cervical squam. LATS1 6% LATS2 3% 10 YAP1 12% Most frequent n = 50 n = 140 YAP1 amplification 8 LATS1 alteration Ovarian LATS2 alteration amplified) TCGA head and neck squamous MST1 alteration NF2 alteration 14 Head and neck squam. 12 YAP1 6 count) 10 2 Urothelial bladder 8 (log Esophageal mRNA expression 4 6 4 Sarcoma YAP1 Melanoma Lung adeno. MST1 2% Glioblastoma STK3 1% % of samples ( 2 Uterine Uterine sarc. NF2 2% Lung squam. Stomach adeno Renal (clear cell) AML Colon LATS1 2% Liver Renal (papillary) DLBC Mesothelioma LATS2 2% 0 Thymoma uveal mel Cholangiocarcinoma YAP1 6% 02468101231 n = 65 n = 429 Genetic alteration % of samples altered (other) Amplification Deep deletion Tr uncating mutation Inframe mutationMissense mutation C siNTC Upregulated siYAP/TAZ Downregulated nregula dow ted SF268 383 D on ge m ne m s o ( 221 C n Detroit 562 7/7 (2%) = 3 , ES-2 6/7 (9%) 7 2 1 5/7 (10%) , 4 RVH-421 6 4/7 (11%) % HCC1954 3/7 (14%) ) SKG-II 2/7 (20%) OVCAR-8 1/7 (34%) 040 80 02,000 4,000 01234 YAP1 mRNA # of differentially CTGF (RPKM) expressed genes (log fold change) 2 F Significance score (−log10 P value) 0 1230 123 E Unclustered Genes not (n = 135) broadly expressed Cluster 1 (n = 4) (n = 220) Genes with inconsistent changes in expression Cluster 2 (n = 2,994) (n = 145) Common Cluster 3 downregulated Genes without consistent WGCNA (n = 80) changes in expression genes Cluster 4 (n = 3,721) upon siYAP1/TAZ KD (n = 621) (n = 41) Detroit 562 (HNSCC) PA-TU-8902 (Panc) Figure 1. Frequent YAP1 amplification associates with upregulation ofYAP1 RNA expression and aberrant pathway signaling. A, Frequency of YAP1 copy-number amplifications y( axis) compared with the most frequent alteration in core Hippo pathway members other than YAP1 (x axis) across TCGA cohort. Each point is colored based on the most frequently mutated core Hippo pathway member. B, Pattern of mutations in core Hippo pathway members is mutually exclusive across cancers, and shown here are cervical squamous and head/neck squamous. Top of each oncoprint is YAP1 mRNA expression where YAP1 overexpression was found predominantly in YAP1-amplified samples. C, RNA-seq data from siYAP1+WWTR1 vs. siNTC of seven different cell lines carrying YAP1 amplifications, resulting in broad gene expression changes including significant downregulation ofCTGF in all cell lines. D, Forty- six percent of significantly downregulated genes were identified in at least three cell lines uponYAP1/WWTR1 knockdown. E, Schematic of weighted gene co-expression network analysis (WGCNA) that defines four gene clusters associated withYAP1 /WWTR1 knockdown. Common downregulated genes defined as significantly downregulated in at least three of seven cell lines. F, ATAC-seq analysis identifies that Cluster 2 genes are mostly associated with loss of chromatin accessibility upon YAP1/WWTR1 knockdown in Detroit 562 and PA-TU-8902 cells. 780 | CANCER DISCOVERY MARCH 2021 AACRJournals.org Downloaded from cancerdiscovery.aacrjournals.org on September 28, 2021. © 2021 American Association for Cancer Research. Published OnlineFirst November 18, 2020; DOI: 10.1158/2159-8290.CD-20-0706 Machine-Learning Approach Predicts Hippo Pathway Dependency RESEARCH ARTICLE populations that would benefit from such a therapy. Previ- broadly expressed across all tissues in addition to being sig- ous studies on the Hippo pathway have either defined broad nificantly and consistently downregulated