Machine learning and chemico-genomics approach defines …...Nov 18, 2020 · Supplementary Table...

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TITLE PAGE

Title: Machine learning and chemico-genomics approach defines and predicts cross-talk of Hippo and MAPK

pathways

Authors and affiliations: Trang H. Pham1♯, Thijs J. Hagenbeek1♯, Ho-June Lee1♯, Jason Li2, Christopher M. Rose3, Eva

Lin1, Mamie Yu1, Scott E. Martin1, Robert Piskol2, Jennifer A. Lacap4, Deepak Sampath4, Victoria C. Pham3, Zora

Modrusan5, Jennie R. Lill3, Christiaan Klijn2, Shiva Malek1, Matthew T. Chang2♯*, Anwesha Dey1♯*

Departments of Discovery Oncology1, Bioinformatics2, Microchemistry, Proteomics & Lipidomics3, Translational

Oncology4, and Molecular Biology5, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, USA

♯These authors contributed equally to this work

*To whom correspondence should be addressed and lead contact

Running title: Machine-learning approach predicts Hippo pathway dependency

Disclosure of Potential Conflicts of Interest

All Genentech employees are shareholders at Roche

Corresponding authors: Matthew T. Chang, Anwesha Dey

Mailing address: Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080

E-Mails: [email protected]; [email protected]

Phone: 650-467-2310

Research. on May 11, 2021. © 2020 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on November 18, 2020; DOI: 10.1158/2159-8290.CD-20-0706

mailto:[email protected]

http://cancerdiscovery.aacrjournals.org/

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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, defining 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 multi-

omics 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 multi-faceted approach underscores how computational models combined with experimental studies

can inform precision medicine approaches including predictive diagnostics and combination strategies.

Significance: An integrated chemico-genomics strategy was developed to identify a lineage-independent signature

for the Hippo pathway in cancers. Evaluating transcriptional profiles using a machine learning method led to

identification of a relationship between YAP/TAZ dependency and MAPK pathway activity. The results help to

nominate potential combination therapies with Hippo pathway inhibition.

INTRODUCTION

One challenge of cancer precision medicine is the heterogeneity of genetic and non-genetic alterations that result

in aberrant pathway signaling. Recurrent mutations and genetic alterations have been identified in many

oncogenic signaling pathways, including MAPK and PI3K (1,2), while other signaling pathways such Hippo lack

canonical hotspot mutations. Yet dysregulation in Hippo pathway signaling is known to drive oncogenesis across

numerous cancer types The Hippo pathway is emerging as the target of drug discovery efforts, but it lacks hotspot

mutations; identifying relevant Hippo pathway dependent patient population(s) and biomarker(s) of response is a

prerequisite for precision medicine in tumors that leverage this pathway.




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The Hippo pathway controls multiple cellular functions that drive oncogenesis, including proliferation, cell fate

determination, and cell survival. Perturbation of the pathway has been shown to trigger tumorigenesis in mice (3).

The pathway is evolutionarily conserved across diverse species and was first identified in Drosophila melanogaster

through multiple genetic screens for gene mutations that cause overgrowth phenotype (4-6). These led to the

discovery of the conserved Hippo pathway core components consisting of serine/ threonine kinases named

Mammalian STE20-like 1/2 (MST1/2) with adaptor protein SAV1 that directly phosphorylate the large tumor

suppressors (LATS1/2). Together with the kinase activators MOB1, LATS1/2 can phosphorylate the two major

downstream coactivators YAP (YAP1) and TAZ (WWTR1) (Fig 1A inset). When the pathway is deregulated,

unphosphorylated YAP and TAZ are translocated to the nucleus and activate downstream target gene expression

by binding to TEAD family transcription factors (7-13) (Fig 1A inset). Widespread dysregulation of the Hippo

pathway components has been observed in multiple human cancer types including glioma, breast, liver, lung,

prostate, colorectal, and gastric cancers (14-17). Furthermore, tumors with dysregulated Hippo components are

not only insensitive to the intrinsic cellular death barriers (3,18) but are also resistant to chemo and molecular

targeted therapies (19-21).

Extensive studies have established the importance of the Hippo pathway in biology and cancers. As drug

development interest in targeting the pathway continues to grow (22-25), one key clinical challenge is to identify

patient population(s) that would benefit from such a therapy. Previous studies on the Hippo pathway have either

defined broad genetic alterations in pathway component(s) or focused on individual cancer types or cancer cell

lines. This experimental strategy has established the role of the Hippo pathway in cancers; however, regulation of

Hippo pathway signaling can be highly complex with many linked signaling inputs from the orthogonal pathways

(26). Here, we employ an integrated experimental-computational strategy to identify a lineage-independent

signature for the Hippo pathway in cancers. By evaluating transcriptional profiles, we observed a relationship

between YAP/TAZ dependency and MAPK pathway activity, leading us to nominate potential combination

therapies with Hippo pathway inhibition.




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RESULTS

Computational pan-cancer approach to quantify Hippo pathway dysregulation

In order to understand the role of the Hippo pathway in human cancers, we first examined the pathway

alternations using the TCGA data(27). While genetic alterations in the Hippo pathway are infrequent (1-15% across

individual cancer types), YAP1 amplifications are among the most frequent alterations pan-cancer in the Hippo

pathway (Fig. 1A) and most frequently observed in cervical and head and neck squamous cell cancer patients. As

expected, genetic YAP1 amplifications but not other Hippo pathway alterations were exclusively associated with

YAP1 RNA overexpression in multiple cancer types (Fig. 1B). Furthermore, genetic YAP1 amplifications, along with

alterations in other Hippo pathway members, were mutually exclusive across cancer patient samples (Fig. 1B)

suggesting these low frequency mutations may function similarly to deregulate the Hippo pathway.

As YAP is a transcriptional co-activator, the most frequently altered regulator of the Hippo pathway, and previously

associated with treatment resistance (19,20), we hypothesized that its oncogenic potential must be mediated by

its downstream transcriptional target genes. We aimed to develop a first-principles approach to map a lineage-

independent transcriptional signature for Hippo deregulation. We first identified 7 cell lines originating from

different tissues but all carrying YAP1 amplification (Copy number: 6.29 +/- 1.50) with markedly YAP1 mRNA

overexpression (Fig. 1C). We performed knockdown of YAP1 and its paralog WWTR1 then performed RNA-Seq on

the parental and YAP1/WWTR1 knockdown lines. YAP1/WWTR1 knockdown resulted in broad transcriptomic

deregulation in all cell lines (Fig. 1C). While CTGF expression (a canonical Hippo pathway target gene) was

significantly decreased in all cell lines (Fig. 1C), there was no clear association between CTGF expression or

magnitude of global gene expression changes to a cell line’s sensitivity to YAP1/WWTR1 knockdown

(Supplementary Fig. 1A,B). Taken together, this suggested YAP/TAZ dependency may be more complex which

necessitates expanding beyond a single marker of pathway activity to capture the pathway dependency over

different cell lineages.

We performed an unbiased weighted correlation network analysis (28,29) among a consensus set of genes that

were broadly expressed across all tissues, in addition, significantly and consistently downregulated (in at least 3




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out of 7 cell lines) upon YAP1/WWTR1 knockdown (Fig. 1D). This identified 4 distinct gene clusters of co-expressed

genes and 1 cluster of non-correlated genes (Fig. 1E). Interestingly, we noted that many of the canonical Hippo

pathway regulated genes (eg., CTGF, CYR61, etc.) were all found within gene Cluster 2 suggesting that Cluster 2

may be most proximal to Hippo pathway signaling. Among the 145 genes in Cluster 2, 86% (n=124) have not been

reported in previous YAP/TAZ gene signatures (Supplementary Fig. 1C) in which we orthogonally validate several

genes using RT-PCR (Supplementary Fig. 1D). To further validate Cluster 2, we leveraged recent systematic CRISPR

and RNAi dependency screens (30). While these data set only utilize single-gene knockout, nevertheless, we

performed gene-wide regression analysis with overlapping cell lines to assess whether the new gene set is

associated with a given gene knockout/knockdown. Among the most significant gene dependencies, this analysis

confirmed many Hippo pathway effectors included WWTR1, YAP1, and TEAD1 (Supplementary Fig. 1E,

Supplementary Table 1). We then performed an unbiased analysis of somatic genetic predictors of Cluster 2 single-

sample GSEA scores in TCGA pan-cancer cohort (Supplementary Fig. 1F). Among the most significant results

included NF2 loss-of-function mutations and homozygous deletions (Supplementary Fig. 1F,G), consistent with

Hippo pathway regulation. Furthermore, we performed RNA-Seq on three independent, NF2-null (Hippo pathway

altered) cell lines (ie., GOS-3 [glioma], MDA-MB-231 [TNBC], and MS751 [cervical]) after YAP1/WWTR1

knockdown. Consistent with the original 7 YAP1-amplified cell lines, we observed similar numbers of overlapping,

significantly downregulated genes in each of the three independent NF2-null cell lines (Supplementary Fig. 1H).

Lastly, we performed ATAC-Seq on Detroit 562 and PA-TU-8902, a pancreatic adenocarcinoma cancer cell line with

TEAD4 amplification, and confirmed that Cluster 2 genes were most strongly associated with loss of chromatin

accessibility upon YAP1/WWTR1 knockdown (Fig. 1F and Supplementary Fig. 1 I,J). Taken together, Cluster 2 genes

included the most well-known canonical Hippo pathway marker genes, most correlated with previous reported

Hippo pathway activity genes, and were associated with loss of chromatin accessibility upon knockdown.

Machine learning approach to predict YAP/WWTR1 dependency and MAPK pathway combination

Aberrant Hippo pathway signaling has been known to drive oncogenesis in several cancer types, many of which

lack a known Hippo pathway genetic alteration. To better identify potential Hippo pathway dependent




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populations, we sought to predict YAP/TAZ dependency using the cluster genes we have identified here. We

identified and performed RNA-Seq on a broader set of 42 cancer cell lines exhibiting a spectrum of Hippo pathway

activity. Next, we assessed each cell line’s sensitivity to YAP1/WWTR1 knockdown to train a machine-learning (ML)

computational model to predict YAP/TAZ dependency given a cell line’s parental cluster gene expression profile.

The ensemble-based algorithm learned a combination of gene expression values to predict the change in viability

after YAP1/WWTR1 knockdown (Fig. 2A). Cluster 2 score was the most correlated to predicted dependency further

supporting that Cluster 2 is most proximal to aberrant Hippo pathway signaling, the primary driver of YAP/TAZ

dependency (Supplementary Fig. 2A, and Supplementary Table 2). Given Cluster 2 identified included many novel

genes not reported in previous gene sets, we next benchmarked our gene set compared to previously published

gene sets (21,31). We observed that our gene set performed better, independent of algorithm or training data

(Supplementary Fig. 2B,C and Methods). While we see that the known genes (eg., CTGF, CYR61, etc.) have high

importance/weight in the ML model (Supplementary Fig. 2D), many of the genes not found in previous gene sets

(21,31) were among the greatest importance/weight in the ML model’s predictive power (Supplementary Fig. 2E

and Supplementary Table 3) include CCDC42EP1, TNFRSF12A (32), and PHLDB2I (33).

Certain tissue lineages and histological cell types were significantly associated with YAP/TAZ dependency including

hematological cell lines which are predicted to be not dependent on YAP1/WWTR1 knockdown (p-value < 10-47)

while mesothelioma histological subtype was among the most predicted to be YAP/TAZ dependent (Fig. 2A,B). We

then validated our ML model by selecting 12 additional cell lines to confirm the predicted YAP/TAZ dependency; 6

cell lines which were predicted to be YAP/TAZ dependent and 6 predicted independent from a variety of different

lineages (Fig. 2C-E, and Supplementary Fig. 2F-J). This provides a landscape of YAP/TAZ dependency across cancer

cell line models and enables nomination of cell line models as well as prioritization of cancer indications that would

potentially benefit from a Hippo pathway inhibitor.

To functionally annotate gene clusters, we performed a systematic gene signature correlation analysis of the gene

clusters with other previously published gene signatures (Supplementary Table 4). As expected, Cluster 2 scores

were highly correlated with the previously published YAP gene signature




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(CORDENONSI_YAP_CONSERVED_SIGNATURE, Pearson : 0.91) (Supplementary Fig. 2K). Cluster 1 and 3 scores

were associated with several proliferation-associated gene signatures (HALLMARK_MYC_TARGETS_V2, Pearson :

0.95) or epithelial-to-mesenchymal transition (HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, Pearson :

0.87) (Supplementary Fig. 2 L,M), respectively; both of which have been previously implicated in aberrant Hippo

pathway signaling (16,34-36). Interestingly, Cluster 4 was strongly associated with a KRAS dependency gene

signature (SINGH_KRAS_DEPENDENCY_SIGNATURE, Pearson : 0.87) (Fig. 2F) and, while previous reports have

suggested YAP1 overexpression as a bypass mechanism to KRAS activation (19), this result suggests that the MAPK

pathway may play a role in the context of Hippo signaling. We hypothesized the other gene clusters may also be

associated with Hippo signaling although not directly downstream. Beyond Cluster 2 scores as the strongest single

predictor of YAP/TAZ dependency, we noted that cell lines with the largest magnitude of decrease in Cluster 4

genes were also those that were most dependent on YAP1/WWTR1 knockdown (Fig. 2G). Taken together, this

suggests that additional suppression of MAPK pathway may serve to further enhance therapeutic efficacy of a

Hippo pathway inhibitor.

MEK inhibitors in combination with YAP1 knockdown enhance response in YAP1-amplified cancer cell lines

As Hippo pathway inhibitors are under active development, identifying clinically actionable combinations become

an important next step in augmenting therapeutic response. In order to determine whether MAPK is a uniquely

actionable pathway that cross-talks with Hippo pathway dysregulation, we undertook a chemical genetic screening

approach. We screened a drug library of 487 small-molecule compounds in Detroit 562 cells stably transfected

with an inducible YAP1 shRNA. Detroit 562 cells are very sensitive to YAP1 knockdown alone so we decided to

knockdown only YAP1 (Supplementary Fig. 3A) in our screen to reduce any small hairpin related RNA toxicity. We

assessed whether addition of each compound to the doxycycline-induced knockdown of YAP1 had a greater effect

on cell viability than the non-induced shYAP1 arm. We observed that MEK and ERK inhibitors were among the

highest scoring hits showing the largest impact on viability in combination with YAP1 knockdown (adjusted p <

0.1), while broad-spectrum cytotoxic chemotherapies did not modulate the effect of YAP1 knockdown, suggesting

abrogating MAPK signaling further sensitizes cells to YAP1 knockdown (Fig. 3A, Supplementary Fig. 3B-D, and




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Supplementary Table 5). We sought to expand this observation to a larger panel of YAP1-amplified cell lines

treated with several MEK inhibitors including cobimetinib, selumetinib, and PD-901 (Fig. 3B-E and Supplementary

Fig. 3E,F). While this sensitization was observed in Hippo pathway deregulated, YAP1-amplified cell lines, this

combination did not show further sensitization in squamous cell cancer lines that lack Hippo pathway alteration(s)

(Fig. 3C). The combination of MEK inhibition and YAP1 knockdown promoted caspase-mediated cell death that as

measured by increase in caspase 3/7 activity, which was reversed upon treatment with a pan-caspase inhibitor

(QVD) (Fig. 3F). Clonogenic assays also confirmed the cooperation of YAP1 knockdown and MEK inhibitors in all

three YAP1-amplified cell lines (Fig. 3G,H). This combination is unlikely due to general toxicity as both SK-N-FI (a

Hippo-independent model) and MCF10-A (a non-malignant breast epithelial model) did not show further

sensitization. (Supplementary Fig. 3G). Importantly, also in vivo where inducible YAP1 depletion and cobimetinib

combination exhibited significant tumor regression in Detroit 562 xenograft assays (Fig. 3I and Supplementary Fig.

3H). Taken together, this small molecule drug library screen provided an orthogonal validation of the role of the

MAPK pathway in YAP dependent cancers.

FOSL1/AP-1 is a common node in MAPK and Hippo pathways

To further assess the impact of cobimetinib and YAP1 knockdown, in Detroit 562, we performed RNA-Seq and

ATAC-Seq comparing YAP1 knockdown, or cobimetinib treatment, and the combination of YAP1 knockdown and

cobimetinib treatment to the control treatment. Together, the combination treatment significantly decreased

expression of proliferation genes (Fig. 4A) compared to each individual treatment, consistent with in vitro and in

vivo observations (Fig. 3G-I and Supplementary Fig. 3C-F). Furthermore, Cluster 2 genes were downregulated in the

YAP1 knockdown and the combination treatment but not in cobimetinib treatment alone (Fig. 4A). Conversely,

MAPK pathway genes were downregulated upon MEK inhibition and combination treatment but not after YAP1

knockdown alone (Fig. 4A). While neither Hippo nor MAPK pathway genes were further suppressed upon the

combination treatment, we hypothesized that YAP1 knockdown and cobimetinib jointly affect a common node

rather than further downregulating their individual pathways. We noted an overlapping 3,479 peaks exhibiting loss

of chromatin accessibility in the combination of YAP1 knockdown and cobimetinib treatment were also found in

the single treatments alone (Fig. 4B). Motif enrichment analysis revealed decreased chromatin accessibility at




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TEAD and AP-1 binding sites upon YAP1 depletion and MEK inhibition (Fig. 4C), respectively. While enrichment was

significant upon MEK inhibition, the combination treatment exhibited greater significance of AP-1 motif in peaks

with loss of chromatin accessibility (Fig. 4C). Previous studies have shown that TEADs and AP-1 can coregulate

genes transcription through changes in enhancer and promoter regions (33,37). Taken together, these suggests

that concomitant YAP1 depletion and MEK inhibition serve to further enhance the loss of AP-1 binding sites

through Hippo and MAPK pathway, respectively.

As cobimetinib inhibitors impact MEK kinase activity, we performed global phosphoproteomics analysis in Detroit

562 to identify changes in phosphorylation sites across proteins upon YAP1 depletion and/or MEK inhibition. This

identified significant changes in 18,800 phosphopeptides across 8,500 proteins. Consistent with the enriched AP-1

motif in peaks with loss of chromatin accessibility, we noted 2-fold decrease in FOSL1 phosphorylation (Fig. 4D-F,

Supplementary Fig. 4A and Supplementary Table 6,7). In addition, we observe a significant decrease in AP1/FOSL1

target gene expression in the combination treatment (Supplementary Fig. 4B) compared to YAP1 knockdown or

cobimetinib treatment alone suggesting the combination may further contribute to loss of FOSL1 activity. As

previous studies have suggested that TEADs (38) may have numerous dimerization partners, we hypothesized that

FOSL1 may interact with TEADs (33,37). Consistent with previous reports, we confirm that TEAD directly interacts

with FOSL1 (Supplementary Fig. 4C,D) through co-immunoprecipitation. Furthermore, YAP1 is required for FOSL1-

TEAD interaction while YAP-TEAD interaction is independent of FOSL1 (Supplementary Fig. 4C). Consistent with

these observations, FOSL1-TEAD interaction is abolished upon cobimetinib treatment (Supplementary Fig. 4D) and

deletion of FOSL1, together with YAP1, mimics the synergistic effect observed with cobimetinib (Supplementary

Fig. 4E,F).

MEK inhibition and YAP1 knockdown decreases TEAD protein half-life

Given that combination of YAP1 depletion and cobimetinib treatment both impact TEAD interaction partners, we

hypothesized that modulating these two interaction partners may affect TEAD protein stability. While neither YAP1

depletion nor cobimetinib treatment alone changed TEAD protein levels (Fig. 5A,B), we observed a significant

decrease in TEAD protein half-life upon the combination of cobimetinib and YAP1 depletion following




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cycloheximide (CHX) chase in YAP1-amplified cell lines (Fig. 5A,B, and Supplementary Fig. 5A) while TEAD

transcript levels were unaltered (Supplementary Fig. 5B). Decrease in TEAD protein half-life was reversed upon

MG132 treatment 24 hours post-CHX treatment (Fig. 5C,D, and Supplementary Fig. 5C) suggesting that the

decrease in TEAD half-life is mediated by proteasomal degradation. These data imply that combination of YAP1

depletion and cobimetinib treatment results in decrease TEAD stability. Furthermore, we noted that several

proliferation genes such as MYC and FOSL2 (Fig. 5E,F) have nearby TEAD and AP-1 binding sites with the potential

to regulate their expression. Together our findings suggest that the convergence of YAP1 depletion and treatment

with cobimetinib is mediated through the cooperative interaction between AP1/FOSL1 and TEAD.

DISCUSSION

In this study, we developed a machine-learning approach to understand Hippo pathway activity (Fig. 1). This has

identified a robust, lineage-independent predictive Hippo pathway signature (Fig. 2) and nominated the MAPK

pathway as potential focus for drug combinations that was orthogonally identified and confirmed through a small-

molecule drug screen (Fig. 3). Further investigation revealed a novel mechanism in which both Hippo and MAPK

pathway regulated TEAD function through decreasing its stability with observed loss of chromatin accessibility at

TEAD-binding motifs (Fig. 4, 5).

The Hippo pathway is emerging as an important area for targeted drug discovery efforts but greater understanding

of this pathway is warranted. As opposed to previous efforts that have derived a curated list of Hippo pathway

target genes, here we utilized a machine-learning approach to systematically define 4 core target gene clusters

that are altered as a direct result of loss of Hippo signaling. This lineage-independent approach identified many

novel genes not reported in previous YAP/TAZ gene sets. This has enabled accurate prediction of YAP/TAZ

dependency in vitro and yielded a signature that can be used to define and prioritize cell line models and patient

populations. We validated our gene set to be robust across different genotypes and cancer types. To our

knowledge, this is the first lineage-independent, unbiased method that is predictive of Hippo pathway dependency

and thus can serve as a valuable a tool to identify biomarker of interest in a tumor-agnostic manner.




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We then uncovered the molecular mechanism underpinning the effects of combined inhibition of MAPK and Hippo

pathways through several orthogonal technologies. We performed ATAC-Seq after YAP1 depletion and/or

cobimetinib treatment; results here suggested that the combination converges on the loss of chromatin

accessibility at AP-1 and TEAD binding sites. The combination of modulating both TEAD interaction partners, YAP

and FOSL1 (via MEK inhibition), resulted in decreased TEAD protein stability (Fig. 5G). Given previous studies have

suggested differential regulation of YAP and TAZ (39,40), future studies will be necessary to elucidate these

mechanistic differences, if any, in the context of MAPK pathway inhibition.

Lastly, this approach serves as a framework for systematic characterization of signaling pathways. Here we focused

on the Hippo pathway in the context of combinations with MAPK inhibitors. Earlier work has shown the

cooperative activity of YAP/TAZ/TEAD and AP-1 at many enhancers and promoters (33,37) as well as a role for

Hippo pathway inhibitors to combat resistance to BRAF V600E inhibition (20). As MEK inhibitors are currently in

the clinic and Hippo pathway inhibitor are in development, our studies suggest that co-targeting these pathways

may achieve a deeper therapeutic response compared to single-agent treatment alone in Hippo pathway

dependent cancers. Our unbiased characterization through a lineage-independent approach to study the Hippo

pathway activity and dependency underscores the importance of understanding pathway cross-talk as a strategy

to nominate potential treatment combinations (Fig. 5G). Our findings here have translational impact not only in

Hippo dependent cancers, but also tumors with MAPK pathway alterations in primary as well as resistance settings

(20,41).

In summary, our study has made several significant contributions to understanding the Hippo pathway, and in

addition, we developed an approach to identify possible pathway targets. This computational-experimental study

defines a framework to establish a new paradigm to apply the machine-learning tool box to accelerate biology and

drug development efforts.

MATERIALS AND METHODS

Cell lines, antibodies, and other reagents




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Cell lines used in this study are Detroit 562, COLO-680N, HEp-2, OVCAR-8 were obtained from American Type

Culture Collection (ATCC). Detroit 562 with shYAP1 was generated by transfecting in the shYAP1-pLKO lentiviral

vector and selecting for Puromycin positive cells. Cell line authentication was conducted for Short Tandem Repeat

(STR) Profiling using the Promega PowerPlex 16 System. This is performed once when receiving new cell line and

compared to external STR profiles of cell lines (when available) to determine cell line ancestry. Cell line

authentication was routinely conducted by SNP-based genotyping using Fluidigm multiplexed assays at the

Genentech cell line core facility.

Antibodies used in this study are Pan-TEAD (13295,CST); YAP(14074,CST); p44/42 MAPK (Erk1/2, 4696, CST);

Phospho-p44/42 MAPK (p-Erk1/2, 4370, CST); TAZ (70148, CST); MAX (sc-765 and sc-8011, Santa Cruz); α-Tubulin

(3873,CST); β-Actin (3700,CST); cleaved PARP (9541,CST); FRA1(5281,CST); Myc-tag (2278-CST);

V5(680602,Biolegend); Anti-rabbit and anti-mouse HRP linked (7074 and 7076, CST), IRDye anti-rabbit and anti-

mouse (68070 and 32211, LI-COR). cobimetinib was synthesized at Genentech. Selumetinib (S1008), PD0325901

(S1036), and gemcitabine (S1714) were purchased from Selleckchem. Cycloheximide solution (C4859) and MG132

solution (M7449) were purchased from Sigma-Aldrich.

Cell viability assay and colony growth

Cells were seeded at 500-1000/well on a 96-well plate for 24 hours. They were then treated with indicated siRNAs

(final concentration of 25nM) or indicated inhibitors (with indicated concentration). Cell growth was assessed

using Cell Titer-Glo Luminescent Cell Viability Assay (Promega). All cell viability data were collected and calculated

for at least 6 replicates per time point, per condition. IC50 for the inhibitors was determined by fitting the nonlinear

regression curve generated by GraphPad Prism.

Cells were seeded at 50000-70000/well on a 6-well plate for 24 hours. They were then treated with indicated

siRNAs (final concentration of 25nM) or indicated inhibitors (with indicated concentration) for 6-10 days. Colony

formation was then accessed with crystal violet stain




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Caspase 3/7 Activation Assay

In a 96 wells plate, cells were treated with indicated siRNAs, cobimetinib, or Q-VD-OPH, a pan Caspase inhibitor

(treatment can be alone or in the indicated various combinations). Caspase-3/7 activation was measured using

Caspase-Glo 3/7 assay reagent containing proluminescent caspase-3/7 substrate, tetrapeptide sequence DEVD

(Promega). The amount of luminescence is displayed as fold changes of treatments to siNTC/DMSO-treated

control.

Chemical genetics screen

A library comprising 485 compounds including targeted agents, chemotherapeutics, and tool compounds was used

to screen for inhibitors that exhibit enhanced efficacy in the context of YAP1 knockdown. Compounds were

obtained from in-house synthesis or purchased from commercial vendors and managed as previously described1.

Detroit 562 cells harboring a doxycycline-inducible shRNA targeting YAP1 were treated with and without

doxycycline for 72 hours prior to seeding into 384 well plates (BD Falcon) at a density of 1000 cells per well. Cells

were maintained at 37 °C, 5% CO2 and +/- doxycycline throughout the course of the experiment. 24 hours after

seeding, cells were treated with a 9-point dose titration of each compound or DMSO control (0.1%) for 96 h. Cell

viability was then assessed using CellTiter-Glo (Promega). IC50 (concentration yielding 50% reduction in viability)

and mean viability (roughly equivalent to the area under the dose response curve)(42) values were determined by

fitting curves using Genedata Screener software (Genedata). Compounds exhibiting more activity in the context of

YAP1 knockdown were determined by calculating both the difference in mean viability and fold-change in IC50

between doxycycline treated and non-treated arms. Screening results can be found in Supplemental Table 3.

Animal work

For tumor xenograft models, Detroit shYAP1 cells (4.25 x 106) were injected subcutaneously into right thoracic

regions of 6-10 weeks old Nu/Nu (nude-CRL) mice. When tumors reach a mean volume between 150-300 mm3,

mice were then grouped into 10 per group for further intervention treatments including Doxycycline (1ug/ul),

cobimetinib (7.5 mg/kg) and combination of doxycycline and cobimetinib. Tumor volume was collected and




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calculated after 3 days and up to 28 days. Small group of mice were euthanized at 6 weeks post treatment for

immunoblotting. All animal experiments were approved by Genentech Animal Care and Use Committee.

Immunoprecipitation and immunoblotting

Cells were lysed in RIPA lysis buffer (89900, Thermo Fisher Scientific) containing protease inhibitor (Roche), and

phosphatase inhibitor (Roche). Lysates were prepared by taking supernatants from centrifugation at 12,000 g for

15 min at 4°C. Equivalent amounts of proteins were loaded and separated by SDS-PAGE followed by transferring to

membranes.

For endogenous co-immunoprecipitation experiments, 1×107 cells were lysed using RIPA buffer (Thermo) and

immunoprecipitation with indicated antibody overnight at 4°C. After washing with RIPA buffer (Thermo), co-

immunoprecipitated endogenous proteins were then detected by immunoblotting.

Global proteome and phosphoproteome sample preparation

Cell pellets were lysed in 8 M urea, 150 mM NaCl, 50 mM HEPES (pH 7.2), complete-mini (EDTA free) protease

inhibitor (Roche), PHOStop phosphatase inhibitor (Roche), 1 mM Na-orthovanadate, 2.5 mM Na-pyrophosphate,

and 1 mM beta-glycerol-phosphate by 15X passages through a 21g needle. Protein concentrations were then

estimated by BCA assay (ThermoFisher Pierce, Rockford, IL) and 2.5 mg of protein material was used for further

sample preparation. Disulfide bonds were reduced by incubation with 5 mM DTT (45 min, 37°C), followed by

alkylation of cysteine residues by 15 mM IAA (30 min, RT Dark), and finally capped by the addition of 5 mM DTT (15

min, RT Dark). Proteins were then precipitated by chloroform/methanol precipitation and resuspended in

digestion buffer (8 M urea, 150 mM NaCl, 50 mM HEPES pH 7.2). Initial protein digestion was performed by the

addition of 1:100 LysC followed by incubation at 37°C for 3 hours. Samples were then diluted to 1.5 M urea with 50

mM HEPES (pH 7.2) before the addition of 1:50 Trypsin and incubation overnight at 37°C. Peptide mixtures were

acidified and desalted via solid phase extraction (SPE; SepPak - Waters, Boston, MA).




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Before phosphopeptide enrichment a 100µg aliquot of peptides was saved for global proteome analysis.

Phosphopeptides were enriched from the remaining material utilizing iron-IMAC based magnetic beads as

previously described (43). To enable multiplexed quantitation both the phosphopeptide enriched and unenriched

peptide pools were resuspended in 200 mM HEPES (pH 8.5) and mixed with tandem mass tags (TMT, ThermoFisher

Pierce, Rockford, IL) at a label to protein ratio of 2:1. After 1 hour of labeling the reaction was quenched by the

addition of 5% hydroxylamine and incubated at room temperature for 15 min. Labeled peptides were then mixed,

acidified, and purified by SPE.

Labeled samples were separated by offline high pH reversed-phase fractionation using an ammonium formate

based buffer system delivered by a 1100 HPLC system (Agilent). Peptides were separated over a 2.1x150 mm, 3.5

µm 300Extend-C18 Zorbax column (Agilent) and separated over a 75-minute gradient from 5% ACN to 85% ACN

into 96 fractions. The fractions were concatenated into either 24 or 12 samples for proteome and

phosphoproteome samples, respectively. Fractions were concatenated by mixing different parts of the gradient to

produce samples that would be orthogonal to downstream low pH reversed phase LC-MS/MS. Samples were dried,

desalted by SPE, and dried again.

Quantitative mass spectrometry and data analysis

nanoLC-MS/MS analysis was performed on an Orbitrap Fusion Lumos mass spectrometer (ThermoFisher, San Jose,

CA) coupled to a Dionex Ultimate 3000 RSLC-nano (ThermoFisher, Santa Clara, CA). Peptides were separated over a

100 µm X 250 mm PicoFrit column (New Objective) packed with 1.7 µm BEH-130 C18 (Waters, Boston, MA) at a

flow rate of 450 nL/min for a total run time of 180 min. The gradient spanned from 2% Buffer B (0.1%

FA/98%ACN/2% water) to 30% B over 155 minutes and then to 50% B at 160 minutes. For mass spectrometry

analysis peptides were surveyed within FTMS1 analyses (120,000 resolution, AGC = 1x106, maximum injection time

[max IT] = 50 ms) and the top 10 peaks were selected for MS/MS ensuring that any given peak was only selected

once in a 45 second window (ppm tolerance = 5 ppm). For dynamic exclusion the “one precursor per charge state”

was ON for proteome analysis and OFF for phosphoproteome analysis. For MS2 analysis precursors were isolated

using the quadrupole (0.5 Th window), fragmented using CAD (NCE = 35, AGC = 2x104, max IT = 100 ms), and




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analyzed in the ion trap (scan speed = Turbo). Following MS2 analysis, the top 8 [proteome] or 6

[phosphoproteome] ions were simultaneously selected (synchronous precursor selection – SPS, AGC = 2.5x105

[proteome] or 3.0x105 [phosphoproteome], max IT = 150 [proteome] or 200 [phosphoproteome] ms) and

fragmented by HCD (NCE=55) before analysis in the Orbitrap (resolution = 50,000). Raw data files are available via

the MASSIVE data repository using the identifier MSV000085111.

All mass spectrometry data was searched using Mascot against a concatenated target-decoy human database

(downloaded June 2016) containing common contaminant sequences. For the database search a precursor mass

tolerance of 50 ppm, fragment ion tolerance of 0.8 Da, and up to 2 missed cleavages. For global proteome and

phosphoproteome analysis carbamidomethyl cysteine (+57.0214) and TMT labeled n-terminus and lysine

(+229.1629) were applied as static modifications, while methionine oxidation (+15.9949) was set as a dynamic

modification. For phosphoproteome analysis TMT labeled tyrosine (+229.1629) and phosphorylation of serine,

threonine, and tyrosine (+79.9663) were also set as dynamic modifications. Peptide spectral matches for each run

were filtered using line discriminant analysis (LDA) to a false discovery rate (FDR) of 2% and subsequently as an

aggregate to a protein level FDR of 2%. Localization of phosphorylation sites was performed using a modified

version of the Ascore algorithm. TMT-MS3 quantification was performed using Mojave, with only those PSMs

possessing isolation specificities greater than or equal to 0.5 considered for the final dataset. Intensities of each

PSM were added to the peptide and then protein (proteome) or phosphoisoform (phosphoproteome) level.

Expression is reported as relative abundance, which is the measured intensity of any given channel divided by the

total intensity for that protein or phosphoisoform.

RNA extraction, cDNA synthesis and quantitative RT-PCR

Tumors and cell lines were dissociated and lysed for RNA isolation using RNAeasy Mini kit (Qiagen) with the on-

column DNA elimination. cDNA was prepared by reverse transcription using the iScript cDNA Synthesis Kit (Bio-

Rad) as the manufacture’s protocol. The quantitative RT-PCR was performed using QuantStudio 7 Flex machine

with TaqMan probes for YAP1 (Hs00902712_g1), WWTR1 (Hs00210007_m1), TEAD1 (Hs00173359_m1), TEAD2

(Hs01055894_m1), TEAD3 (Hs00243231_m1), TEAD4 (Hs01125032_m1), and GAPDH (Hs02786624_g1), CTCF

(Hs00902016_m1), CYR61 (Hs00155479_m1), CPA4 (Hs00275311_m1), THBS1 (Hs00962908_m1), PTRF




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(Hs00396859_m1), EPHA2 (Hs01072272_m1), EDN1(Hs00174961_m1), TSPAN3 (Hs00170681_m1), NDRG1

(Hs00608387_m1), EMP2 (Hs00171315_m1) (Applied Biosystems). Relative expression of each gene to GAPDH of

target genes was assessed for at least 2-3 biological replicates.

RNA library preparation and sequencing

Total RNA was extracted using the Qiagen RNAeasy Mini Kit (Qiagen) with the on-column DNA elimination. The

concentration was identified using NanoDrop 8000 (Thermo Fisher Scientific). Quality control was done by

determine RNA integrity with Bioanalyzer 2100(Agilent Technologies). About 500 ng of RNA was used for library

synthesis using the TrueSeq RNA Sample Preparation kit v2 (Illumina). Size of the libraries was confirmed using

2200 TapeStation and High Sensitivity D1K screen tape (Agilent Technologies), and the concentration was

determined by a qPCR-based method using the Library Quantification Kit (KAPA). The libraries were multiplexed

and then sequenced on Illumina HiSeq2500 (Illumina) to generate 30M of single-end 50–base pair reads.

The RNA-seq data have been deposited in the Gene Expression Omnibus (GEO) with the accession code

GSE161019. Data or other materials are available from the corresponding author upon request.

RNA-Seq alignment and feature counting

For RNA-seq data analysis, RNA-seq reads were first aligned to ribosomal RNA sequences to remove ribosomal

reads. The remaining reads were aligned to the mouse reference genome (GRCm38) using GSNAP (44,45) version

“2013-10-10,” allowing a maximum of two mismatches per 75 base sequence (parameters: ‘-M 2 -n 10 -B 2 -i 1 -N 1

-w 200000 -E 1 --pairmax-rna=200000 --clip-overlap)(46,47). Transcript annotation was based on the Ensembl

genes database (release 77). To quantify gene expression levels, the number of reads mapped to the exons of each

RefSeq gene was calculated.

RNA-Seq differential gene expression

Differential gene expression was performed with DESeq2(48). A prefilter was applied, so that only genes with at

least a median RPKM (reads per kilobase per million mapped reads) value of 10 in one condition were analyzed. P-

values for other genes were simply set to 1 and log fold changes were set to 0 for visualization purposes, but such




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genes were not included in the multiple testing correction. Q-values were obtained by correcting P-values for

multiple hypotheses using the Benjamini-Hochberg procedure. Genes were considered if they had a Q-value of less

than 0.05 and were protein-coding. Counts were transformed to log2 counts per million, quantile-normalized, and

precision-weighted with the “voom” function of the limma package(49).

ATAC-Seq and data analysis

Reads were aligned to the human reference genome (NCBI Build 38) using GSNAP31 version ‘2013-10-10’, allowing

a maximum of two mismatches per read sequence (parameters: ‘-M 2 -n 10 -B 2 -i 1–pairmax-dna = 1000–terminal-

threshold = 1000–gmap-mode = none–clip-overlap’). Reads aligning to locations in the human genome that contain

substantial sequence homology to the MT chromosome or to blacklisted regions identified by the ENCODE

consortium were omitted from downstream analyses. Properly paired reads derived from non-duplicate

sequencing fragments were used to quantify chromatin accessibility according to the ENCODE pipeline standards

with minor modifications as follows. Accessible genomic locations were identified by calling peaks with Macs2 (50)

(using insertion-centred pseudo-fragments (73 base pairs; community standard) generated on the basis of the start

positions of the mapped reads. Accessible peak locations were identified as described: in brief, we called peaks on

a group-level pooled sample containing all pseudo-fragments observed in all samples within each group. Peaks in

the pooled sample that were independently identified in two or more of the constituent biological replicates were

retained for downstream analysis, using the union of all group-level reproducible peaks

(https://www.encodeproject.org/atac-seq/#standards). We quantified the level of chromatin accessibility within

each peak for each replicate as the number of pseudo-fragments that overlapped the peak in question and

normalized these estimates using the TMM method (51). We identified differentially accessible peaks between

groups in the framework of a linear model implemented with the limma R package (52) and incorporating precision

weights calculated with the voom function in the limma R package (53). We identified enriched transcription factor

(TF) motifs using HOMER v4.7 (54). To evaluate the significance of the TF enrichment we defined peaks as

significantly differentially accessible based on a range of FDR adjusted P value thresholds between 1 and 0.01 and

an |log2[fold change]| in accessibility ≥ 1 (estimated from the model coefficients). Given the strong enrichment of




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the top motifs across a wide range of P value cutoffs, we decided to consider peaks as different across groups for a

|log2[fold change]| ≥ 1 and FDR adjusted P value ≤ 0.05 in subsequent analyses.

The ATAC-seq data have been deposited in the Gene Expression Omnibus (GEO) with the accession code

GSE161019. Data or other materials are available from the corresponding author upon request.

Hippo pathway cluster analysis

Statistically significant downregulated genes in 3 or more of the 7 cell lines were considered for common

downregulated genes. Genes were subsequently filtered based on their expression and genes that were

upregulated any cell line upon YAP1/WWTR1 knockdown were remove. Signed co-expression networks were built

using WGCNA package in R (minModuleSize = 10, reassignThreshold = 1e-6, deepSplit = 2, mergeCutHeight = 0.15)

using RNA expression of pan-cancer cell lines (55).

Permutation analysis

For each differential accessibility comparison (e.g., siYAP1/WWTR1 vs. siNTC), ATAC-seq peaks were found within a

window of 1,000 base pairs before the start and 1,000 bp after the end of every gene in human GENCODE genset,

release 27 (genome assembly version GRCh38). The peaks were then defined as opening (log fold change > 1,

adjusted P-value<0.05), closing (log fold change< -1, adjusted P-value<0.05), or background (everything else), and

only peaks that were annotated as “protein-coding” were retained. The total number of closing peaks within the

1000bp window of n genes in each Hippo pathway cluster was compared to the total number of closing peaks

within 1000bp of n randomly sampled genes, repeated for 1 million permutations. The p-value was taken as the

number of times the number of randomly sampled closing peaks was greater than that of the Hippo cluster’s,

divided by 1 million.

Prediction of YAP/TAZ dependency

YAP1/WWTR1 knockdown was performed on 42 cell lines and change in viability was assessed using Cell Titer Glo

after 7 days. Effect of viability was quantified as a ratio of fraction of remaining luminescence compared to non-




20

targeting control (ie., 1 would mean complete loss of luminescence and 0 as no change). To predict this effect of

viability, we constructed a random forest model using the expression of Cluster 1-4 genes of 31 (70%) of cell lines

to predict the effect of viability. Predication accuracy was determined by minimizing root mean square error from

5-fold cross-validation of the training data.

Gene set benchmarking

We identified 2 additional YAP/TAZ gene set: Cordenonsi et al. (21), or Wang et al. (TCGA) (31). In addition, we

included our Cluster 0 gene set (ie., genes that were significantly downregulated by YAP1/WWTR1 knockdown but

not assigned into a gene cluster, likely not proximal to Hippo pathway activity) as a negative control. First, we

trained a ML model using either our gene set (Cluster 2), the Cordenonsi et al. (21), or Wang et al. (TCGA) (31) gene

sets using the same training data and ML algorithm (parallel random forest, caret v6.0-85). The ML model trained

using Cluster 2 gene set outperformed the ML models trained from Cordenonsi et al. (21) and Wang et al. (31) in

both root mean square error (RMSE) and R-squared metrics (Supplementary Fig. 2B). This was not dependent on

the ML algorithm used to train the ML model -- we repeated this using two other common ML algorithms: support

vector machines (SVM) and boosted generalized linear model. For both ML algorithms, our Cluster 2 gene set

outperformed the other gene sets. Lastly, this difference in performance is not due to the initial training data used

in model training. We created 500 random iterations of training data (createDataPartition, caret v6.0-85) and our

gene set significantly outperformed the other two gene sets (Wang et al., p-value < 10-71, Cordenonsi et al. p-

value < 10-65; paired t-test). Importance of individual genes in a given ML model was calculated using varImp (caret

v6.0-85). Genes within our gene were defined as novel if not previously reported in either Cordenonsi et al. (21) or

Wang et al. (31) and known if present in one or both gene sets.

Statistical analysis

Beside the RNA-Seq and ATAC-Seq studies that have their own statistical analysis, all of the statistical analysis for in

vivo and in vitro studies was done using the Student t-test (two-tailed, unpaired) to compare treatment groups

with control group. All of the in vitro experiments were performed at least 3 times. For the in vivo work, the

gender, age and weight of animals were matched and the sample size is 3-10 mice per group. A P-value of less than




21

0.05 was considered as statistically significant (*, p<0.05). Significant values as well as number of replicates are

noted for each experiment in the respective figure legends.

siRNA transfection and ATAC-seq

siRNA transfection was performed in Detriot-562 and PATU-8902 cell lines (obtained from American Type Culture

Collection (ATCC)). Cells were seeded at 15000/well on a 6-well plate or 1000/well on a 96-well plate for 24 hours.

They were then treated with the siRNAs (final concentration of 20nM) using Lipofectamine RNAi Max in serum free

RPMI media for 72 hours before collection. siRNAs were purchased from Dharmacon, including siNTC (D-001810-

10), siYAP1 (L-012200-00), siWWTR1 (L-016083-00), siTEAD1 (J-012603-05), siTEAD2 (J-012611-09), siTEAD3 (L-

012604-00), siTEAD4 (J-019570-09), siFOSL1 (LQ-004341-00). Knockdown was confirmed by western blot with the

following antibodies from Cell Signaling Technology: Pan-TEAD (13295, CST); YAP (14074, CST); TAZ (70148, CST);

FOSL1 (5281, CST); β-Actin (3700, CST).

ATAC-seq was performed as previously described (56,57). A total of 1 × 105 cell pellets were washed with PBS and

cells were pelleted by centrifugation. Cell pellets were resuspended in 100 μL of ATAC-resuspension buffer (10 mM

Tris HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2), and nuclei were pelleted by centrifugation. The nuclei were

resuspended in 50 μL reaction buffer containing Tn5 transposase (2.5 μL Tn5 transposase, 25 μL 2× TD buffer,

16.5 μL PBS, 0.5 μL 1% digitonin, 0.5 μL 10% Tween-20, 5 μL H2O) (Illumina). The reaction was carried out at 37°C

for 30 minutes. Tagmented DNA was isolated by MinElute PCR Purification Kit (Qiagen). Libraries were later

generated and sequenced on NextSeq500 (Illumina).

Crispr RNA transfection

Crisper transfection was performed in Detriot-562 and OVCAR-8 cell lines (obtained from American Type Culture

Collection (ATCC)). Cells were seeded at 10000/well on a 6-well plate for 24 hours. They were then transfected

with the RNP complex that contains designed guide RNAs siRNAs (final concentration of 1M) using Lipofectamine

CRISPR Max Cas9 transfection reagent for 72 hours before collection. Cas9 protein was purchase from Invitrogen

(Truecut Cas9 protein v2, A364498). crRNA and transcrRNA were purchased from IDT. Multiple guide RNAs for




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YAP1 were tested to get to the 2 optimized sequences of gYAP1: ACATCGATCAGACAACAACA and gYAP2:

CCACAGGGAGGCGTCATGGG. Transient knockout/knockdown was confirmed by western blot with the following

antibodies from Cell Signaling Technology: YAP (14074, CST); β-Actin (3700, CST).

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FIGURE LEGENDS:



https://www.nature.com/articles/nature17987#supplementary-information

https://www.nature.com/articles/nbt.3080#supplementary-information


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Figure 1: Frequent YAP1 amplification associates with upregulation of YAP1 RNA expression and aberrant

pathway signaling (A) Frequency of YAP1 copy number amplifications (y-axis) compared to 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 are mutually exclusive across cancers, 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 7 different cell lines carrying YAP1

amplifications resulting in broad gene expression changes including significantly downregulation of CTGF in all cell

lines. (D) 46% of significantly downregulated genes were identified in at least 3 cell lines upon YAP1/WWTR1

knockdown. (E) Schematic of WCGA analysis that define four gene clusters associated with YAP1/WWTR1

knockdown. Common downregulated genes defined as significantly downregulated in at least 3 out of 7 cell lines.

(F) ATAC-Seq analysis identify Cluster 2 genes are most associated with loss of chromatin accessibility upon

YAP1/WWTR1 knockdown in Detroit 562 and PA-TU-8902.

Figure 2: Machine learning approach defines Hippo pathway dependency and proposes parallel pathways for

combination strategy. (A) Landscape of predicted YAP/TAZ dependency across cell lines based on parental RNA

expression. (B) Mesothelioma cell lines predicted to be significantly YAP/TAZ dependent and hematological cell

lines predicted to be not dependent on YAP/TAZ. (C) Validation of ML model prediction of YAP/TAZ dependency in

12 cell line models. (D,E) Effect on cell viability of 2 cell lines from predicted dependent group and predicted not

dependent groupupon YAP1/WWTR1 knockdown. (F) Cluster 4 score is significantly associated with a KRAS

dependency signature. (G) Average change in expression of Cluster 4 genes is an independent predictor of

YAP/TAZ dependency

Figure 3: MEK inhibitors sensitize with YAP1 knockdown in YAP1-amplified cancer cell lines. (A) A small molecule

library of 487 tool compounds were tested in Detroit 562 cells stably transfected with a doxycycline-inducible

construct of YAP1 shRNA. The y-axis represents differences in mean viability over a 9-point dose response curve in

either the presence or absence of doxycycline. Compounds are ranked from largest decrease in mean viability




26

upon YAP1 knockdown to increase in viability upon YAP1 knockdown (antagonism). Several inhibitor classes are

highlighted as indicated by colored circles. (B) YAP1 knockdown and cobimetinib cooperatively inhibit cell growth

in YAP1-amplified cancer cell lines. 2 representative YAP1-amplified cell lines (Detroit 562 or COLO-680N) were

transfected either with siNTC or siYAP1 followed by treatment with cobimetinib at various indicated

concentrations for 48 hours after siRNA transfection. Cell viability was assessed at day 3 post-treatment using Cell

Titer-Glo [mean ± SD (n=3) for each concentration point. (C) Cobimetinib selectively enhances cell growth

inhibition effect of siYAP1 in YAP1-amplified cancer cells. YAP1-amplified cancer cells (red) and cancer cells without

YAP1 alternation (blue) were co-treated with siYAP1 and cobimetinib. The mean differences between siNTC and

siYAP1 at various concentration of cobimetinib were assessed (n=6). Note that the slopes of the mean differences

in YAP1-amplified lines are positive showing synergistic interaction of the combination. (D) YAP1 knockdown and

MEK inhibitors including selumetinib and PD-901 cooperatively inhibit cell growth in YAP1-amplified cancer cells.

Detroit 562 cells were transfected with siNTC or siYAP1 in combination with MEK inhibitors at various

concentrations. siRNA transfection was done 48 hours prior drug treatment. Cell viability was assessed at day 3

post-treatment using Cell Titer-Glo. Error bars represent mean ± SD (n=3). (E) MEK inhibition selectively enhances

cell growth inhibition effect of YAP1 knockdown in YAP1-amplified cancer cells. Detroit 562 cells were transfected

with siYAP1 in the presence of MEK inhibitors or gemcitabine. Then, the mean differences between siNTC and

siYAP1 at various concentrations of drugs were assessed (n=6). (F) siYAP1 and cobimetinib combination induces

caspase activation. Detroit 562 cells were treated either with siYAP1 or cobimetinib (0.5 M) or in combination.

Caspase-3/7 activity was assessed using Caspase-Glo 3/7. Error bars represent mean ± SD (n=3). (G,H) Combination

of YAP1 knockdown and MEK inhibition significantly affect colony formation in YAP1-amplified cancer cell lines.

Detroit 562/shNTC and Detroit 562/shYAP1 were plated and subjected to MEK inhibitor treatment as previously

described. Colony formation was then accessed with crystal violet stain (n=3). The same was done for the other

YAP1-amplified cell line (COLO680N and HEp-2) (n=3). (I) Doxycycline-induced YAP1 knockdown inhibits Detroit

562 tumor growth. Detroit 562/shYAP1 cells were subcutaneously injected into the mice to allow tumor

establishment. The total number of mice was then divided into group of n=10 for sucrose, doxycycline to induce

shYAP1, cobimetinib (7.5mg/kg) and combination of doxycycline and cobimetinib. Tumor volume was measured

and compared and statistical analysis was done using unpaired two-sided Student’s t-test.




27

Figure 4: AP-1/FOSL1 is a common node of MAPK and Hippo pathway activity. (A) Log2 fold change of

cobimetinib, YAP1 knockdown, or the combination in Cluster 2, MAPK, and proliferation gene expression.

Combination does not further downregulate Hippo or MAPK pathway genes but enhances loss of proliferation. (B)

3,479 peaks share loss of chromatin accessibility in the combination, cobimetinib or YAP1 knockdown alone. (C)

HOMER motif analysis identified TEAD motif in YAP1 knockdown alone or in combination with MEK inhibition. AP-1

binding site motif was more significant in the combination treatment as compared to YAP1 knockdown or

cobimetinib alone. (D) Schematic for global phospho-proteomic analyses for each condition are indicated.

Replicates (n=2) were used for each condition and standard proteomic pipeline followed for analyses. (E,F)

Combination of MEK inhibition and YAP1 knockdown decreases phosphorylation of FOSL1 transcription factor.

Cells were treated with cobimetinib for 1 hour before harvest. Relative abundance of total and phosphorylated

FOSL1 in the indicated conditions and as determined by global proteomic analyses is shown.

Figure 5: Combination of MEK inhibition and YAP1 knockdown decreases TEAD protein half-life. (A,B) Decrease

in TEAD half-life by combination of cobimetinib and YAP1 knockdown. Indicated YAP1-amplified cells were treated

with YAP1 guide RNA for 72 hours in the absence or presence of MEK inhibitors, cobimetinib (C, 0.5 M) for 48

hours (n=3). Then, CHX (100 g/ml) was added at indicated times followed by immunoblotting with indicated

antibodies. (C,D) YAP1 knockdown/cobimetinib reduces TEAD half-life, which is rescued by MG132 treatment.

YAP1-amplified cells were treated with siYAP1/YAP1 guide RNA or cobimetinib (0.5 M) or in combination for 48

hours. 100 g/ml of cycloheximide (CHX) or 10 M of MG132 was added 8 hours before cell harvest. Expression of

indicated proteins was assessed by immunoblotting. -Tubulin served as a loading control (n=3). (E,F) Examples of

genomic region at the MYC and FOSL2 loci showing decreased accessibility of sites containing either the AP-1 (blue

arrow) or TEAD (red arrow) motifs with both siYAP1 and treatment with cobimetinib. (G) Proposed model for

integrated ML/experimental workflow.




Published OnlineFirst November 18, 2020.Cancer Discov Trang H Pham, Thijs J Hagenbeek, Ho-June Lee, et al. predicts cross-talk of Hippo and MAPK pathwaysMachine learning and chemico-genomics approach defines and

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