Kinome inhibition states and multiomics data enable prediction of cell viability in diverse cancer types.

Protein kinases play a vital role in a wide range of cellular processes, and compounds that inhibit kinase activity emerging as a primary focus for targeted therapy development, especially in cancer. Consequently, efforts to characterize the behavior of kinases in response to inhibitor treatment, as well as downstream cellular responses, have been performed at increasingly large scales. Previous work with smaller datasets have used baseline profiling of cell lines and limited kinome profiling data to attempt to predict small molecule effects on cell viability, but these efforts did not use multi-dose kinase profiles and achieved low accuracy with very limited external validation. This work focuses on two large-scale primary data types, kinase inhibitor profiles and gene expression, to predict the results of cell viability screening. We describe the process by which we combined these data sets, examined their properties in relation to cell viability and finally developed a set of computational models that achieve a reasonably high prediction accuracy (R2 of 0.78 and RMSE of 0.154). Using these models, we identified a set of kinases, several of which are understudied, that are strongly influential in the cell viability prediction models. In addition, we also tested to see if a wider range of multiomics data sets could improve the model results and found that proteomic kinase inhibitor profiles were the single most informative data type. Finally, we validated a small subset of the model predictions in several triple-negative and HER2 positive breast cancer cell lines demonstrating that the model performs well with compounds and cell lines that were not included in the training data set. Overall, this result demonstrates that generic knowledge of the kinome is predictive of very specific cell phenotypes, and has the potential to be integrated into targeted therapy development pipelines.

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies.

Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Recent strategies targeting the kinome with combination therapies have shown promise, such as trametinib and dabrafenib in advanced melanoma, but empirical design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico filtering prior to experimental testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generated combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with transcriptomics from CCLE to build machine learning models with elastic-net feature selection to predict cell line sensitivity across nine cancer types, with accuracy R2 ∼ 0.75-0.9. We then validated the model by using a PDX-derived TNBC cell line and saw good global accuracy (R2 ∼ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ∼ 0.9). Additionally, the model was able to predict a highly synergistic combination of trametinib and omipalisib for TNBC treatment, which incidentally was recently in phase I clinical trials. Our choice of tree-based models for greater interpretability allowed interrogation of highly predictive kinases in each cancer type, such as the MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

Neratinib, a pan ERBB/HER inhibitor, restores sensitivity of PTEN-null, BRAFV600E melanoma to BRAF/MEK inhibition.

Introduction: Approximately 50% of melanomas harbor an activating BRAFV600E mutation. Standard of care involves a combination of inhibitors targeting mutant BRAF and MEK1/2, the substrate for BRAF in the MAPK pathway. PTEN loss-of-function mutations occur in ~40% of BRAFV600E melanomas, resulting in increased PI3K/AKT activity that enhances resistance to BRAF/MEK combination inhibitor therapy. Methods: To compare the response of PTEN null to PTEN wild-type cells in an isogenic background, CRISPR/Cas9 was used to knock out PTEN in a melanoma cell line that harbors a BRAFV600E mutation. RNA sequencing, functional kinome analysis, and drug synergy screening were employed in the context of BRAF/MEK inhibition. Results: RNA sequencing and functional kinome analysis revealed that the loss of PTEN led to an induction of FOXD3 and an increase in expression of the FOXD3 target gene, ERBB3/HER3. Inhibition of BRAF and MEK1/2 in PTEN null, BRAFV600E cells dramatically induced the expression of ERBB3/HER3 relative to wild-type cells. A synergy screen of epigenetic modifiers and kinase inhibitors in combination with BRAFi/MEKi revealed that the pan ERBB/HER inhibitor, neratinib, could reverse the resistance observed in PTEN null, BRAFV600E cells. Conclusions: The findings indicate that PTEN null BRAFV600E melanoma exhibits increased reliance on ERBB/HER signaling when treated with clinically approved BRAFi/MEKi combinations. Future studies are warranted to test neratinib reversal of BRAFi/MEKi resistance in patient melanomas expressing ERBB3/HER3 in combination with its dimerization partner ERBB2/HER2.

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies.

Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Kinase inhibitors are one of the fastest growing drug classes in oncology, but resistance acquisition to kinase-targeting monotherapies is inevitable due to the dynamic and interconnected nature of the kinome in response to perturbation. Recent strategies targeting the kinome with combination therapies have shown promise, such as the approval of Trametinib and Dabrafenib in advanced melanoma, but similar empirical combination design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico screening prior to in-vitro or in-vivo testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generate combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with baseline transcriptomics from CCLE to build robust machine learning models to predict cell line sensitivity from NCI-ALMANAC across nine cancer types, with model accuracy R2 ~ 0.75-0.9 after feature selection using elastic-net regression. We further validated the model's ability to extend to real-world examples by using the best-performing breast cancer model to generate predictions for kinase inhibitor combination sensitivity and synergy in a PDX-derived TNBC cell line and saw reasonable global accuracy in our experimental validation (R2 ~ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ~ 0.9). Additionally, the model was able to predict a highly synergistic combination of Trametinib (MEK inhibitor) and Omipalisib (PI3K inhibitor) for TNBC treatment, which incidentally was recently in phase I clinical trials for TNBC. Our choice of tree-based models over networks for greater interpretability also allowed us to further interrogate which specific kinases were highly predictive of cell sensitivity in each cancer type, and we saw confirmatory strong predictive power in the inhibition of MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

Irreversible JNK1-JUN inhibition by JNK-IN-8 sensitizes pancreatic cancer to 5-FU/FOLFOX chemotherapy.

Over 55,000 people in the United States are diagnosed with pancreatic ductal adenocarcinoma (PDAC) yearly, and fewer than 20% of these patients survive a year beyond diagnosis. Chemotherapies are considered or used in nearly every PDAC case, but there is limited understanding of the complex signaling responses underlying resistance to these common treatments. Here, we take an unbiased approach to study protein kinase network changes following chemotherapies in patient-derived xenograft (PDX) models of PDAC to facilitate design of rational drug combinations. Proteomics profiling following chemotherapy regimens reveals that activation of JNK-JUN signaling occurs after 5-fluorouracil plus leucovorin (5-FU + LEU) and FOLFOX (5-FU + LEU plus oxaliplatin [OX]), but not after OX alone or gemcitabine. Cell and tumor growth assays with the irreversible inhibitor JNK-IN-8 and genetic manipulations demonstrate that JNK and JUN each contribute to chemoresistance and cancer cell survival after FOLFOX. Active JNK1 and JUN are specifically implicated in these effects, and synergy with JNK-IN-8 is linked to FOLFOX-mediated JUN activation, cell cycle dysregulation, and DNA damage response. This study highlights the potential for JNK-IN-8 as a biological tool and potential combination therapy with FOLFOX in PDAC and reinforces the need to tailor treatment to functional characteristics of individual tumors.

GSK2801, a BAZ2/BRD9 Bromodomain Inhibitor, Synergizes with BET Inhibitors to Induce Apoptosis in Triple-Negative Breast Cancer.

Screening of an inhibitor library targeting kinases and epigenetic regulators identified several molecules having antiproliferative synergy with extraterminal domain (BET) bromodomain (BD) inhibitors (JQ1, OTX015) in triple-negative breast cancer (TNBC). GSK2801, an inhibitor of BAZ2A/B BDs, of the imitation switch chromatin remodeling complexes, and BRD9, of the SWI/SNF complex, demonstrated synergy independent of BRD4 control of P-TEFb-mediated pause-release of RNA polymerase II. GSK2801 or RNAi knockdown of BAZ2A/B with JQ1 selectively displaced BRD2 at promoters/enhancers of ETS-regulated genes. Additional displacement of BRD2 from rDNA in the nucleolus coincided with decreased 45S rRNA, revealing a function of BRD2 in regulating RNA polymerase I transcription. In 2D cultures, enhanced displacement of BRD2 from chromatin by combination drug treatment induced senescence. In spheroid cultures, combination treatment induced cleaved caspase-3 and cleaved PARP characteristic of apoptosis in tumor cells. Thus, GSK2801 blocks BRD2-driven transcription in combination with BET inhibitor and induces apoptosis of TNBC. IMPLICATIONS: Synergistic inhibition of BDs encoded in BAZ2A/B, BRD9, and BET proteins induces apoptosis of TNBC by a combinatorial suppression of ribosomal DNA transcription and ETS-regulated genes.

Application of Integrated Drug Screening/Kinome Analysis to Identify Inhibitors of Gemcitabine-Resistant Pancreatic Cancer Cell Growth.

Continuous exposure of a pancreatic cancer cell line MIA PaCa-2 (MiaS) to gemcitabine resulted in the formation of a gemcitabine-resistant subline (MiaR). In an effort to discover kinase inhibitors that inhibited MiaR growth, MiaR cells were exposed to kinase inhibitors (PKIS-1 library) in a 384-well screening format. Three compounds (UNC10112721A, UNC10112652A, and UNC10112793A) were identified that inhibited the growth of MiaR cells by more than 50% (at 50 nM). Two compounds (UNC10112721A and UNC10112652A) were classified as cyclin-dependent kinase (CDK) inhibitors, whereas UNC10112793A was reported to be a PLK inhibitor. Dose-response experiments supported the efficacy of these compounds to inhibit growth and increase apoptosis in 2D cultures of these cells. However, only UNC10112721A significantly inhibited the growth of 3D spheroids composed of MiaR cells and GFP-tagged cancer-associated fibroblasts. Multiplexed inhibitor bead (MIB)-mass spectrometry (MS) kinome competition experiments identified CDK9, CLK1-4, DYRK1A, and CSNK1 as major kinase targets for UNC10112721A in MiaR cells. Another CDK9 inhibitor (CDK-IN-2) replicated the growth inhibitory effects of UNC10112721A, whereas inhibitors against the CLK, DYRK, or CSNK1 kinases had no effect. In summary, these studies describe a coordinated approach to discover novel kinase inhibitors, evaluate their efficacy in 3D models, and define their specificity against the kinome.

Combined kinase inhibitors of MEK1/2 and either PI3K or PDGFR are efficacious in intracranial triple-negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC), lacking expression of hormone and human epidermal growth factor receptor 2 receptors, is an aggressive subtype that frequently metastasizes to the brain and has no FDA-approved systemic therapies. Previous literature demonstrates mitogen-activated protein kinase kinase (MEK) pathway activation in TNBC brain metastases. Thus, we aimed to discover rational combinatorial therapies with MEK inhibition, hypothesizing that co-inhibition using clinically available brain-penetrant inhibitors would improve survival in preclinical models of TNBC brain metastases. METHODS: Using human-derived TNBC cell lines, synthetic lethal small interfering RNA kinase screens were evaluated with brain-penetrant inhibitors against MEK1/2 (selumetinib, AZD6244) or phosphatidylinositol-3 kinase (PI3K; buparlisib, BKM120). Mice bearing intracranial TNBC tumors (SUM149, MDA-MB-231Br, MDA-MB-468, or MDA-MB-436) were treated with MEK, PI3K, or platelet derived growth factor receptor (PDGFR; pazopanib) inhibitors alone or in combination. Tumors were analyzed by western blot and multiplexed kinase inhibitor beads/mass spectrometry to assess treatment effects. RESULTS: Screens identified MEK+PI3K and MEK+PDGFR inhibitors as tractable, rational combinations. Dual treatment of selumetinib with buparlisib or pazopanib was synergistic in TNBC cells in vitro. Both combinations improved survival in intracranial SUM149 and MDA-MB-231Br, but not MDA-MB-468 or MDA-MB-436. Treatments decreased mitogen-activated protein kinase (MAPK) and PI3K (Akt) signaling in sensitive (SUM149 and 231Br) but not resistant models (MDA-MB-468). Exploratory analysis of kinome reprogramming in SUM149 intracranial tumors after MEK ± PI3K inhibition demonstrates extensive kinome changes with treatment, especially in MAPK pathway members. CONCLUSIONS: Results demonstrate that rational combinations of the clinically available inhibitors selumetinib with buparlisib or pazopanib may prove to be promising therapeutic strategies for the treatment of some TNBC brain metastases. Additionally, effective combination treatments cause widespread alterations in kinase pathways, including targetable potential resistance drivers.

Enhancer Remodeling during Adaptive Bypass to MEK Inhibition Is Attenuated by Pharmacologic Targeting of the P-TEFb Complex.

Targeting the dysregulated BRAF-MEK-ERK pathway in cancer has increasingly emerged in clinical trial design. Despite clinical responses in specific cancers using inhibitors targeting BRAF and MEK, resistance develops often involving nongenomic adaptive bypass mechanisms. Inhibition of MEK1/2 by trametinib in patients with triple-negative breast cancer (TNBC) induced dramatic transcriptional responses, including upregulation of receptor tyrosine kinases (RTK) comparing tumor samples before and after one week of treatment. In preclinical models, MEK inhibition induced genome-wide enhancer formation involving the seeding of BRD4, MED1, H3K27 acetylation, and p300 that drives transcriptional adaptation. Inhibition of the P-TEFb-associated proteins BRD4 and CBP/p300 arrested enhancer seeding and RTK upregulation. BRD4 bromodomain inhibitors overcame trametinib resistance, producing sustained growth inhibition in cells, xenografts, and syngeneic mouse TNBC models. Pharmacologic targeting of P-TEFb members in conjunction with MEK inhibition by trametinib is an effective strategy to durably inhibit epigenomic remodeling required for adaptive resistance.Significance: Widespread transcriptional adaptation to pharmacologic MEK inhibition was observed in TNBC patient tumors. In preclinical models, MEK inhibition induces dramatic genome-wide modulation of chromatin, in the form of de novo enhancer formation and enhancer remodeling. Pharmacologic targeting of P-TEFb complex members at enhancers is an effective strategy to durably inhibit such adaptation. Cancer Discov; 7(3); 302-21. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 235.

MicroRNA 9-3p targets ß1 integrin to sensitize claudin-low breast cancer cells to MEK inhibition.

MEK1/2 inhibitors such as AZD6244 are in clinical trials for the treatment of multiple cancers, including breast cancer. Targeted kinase inhibition can induce compensatory kinome changes, rendering single therapeutic agents ineffective. To identify target proteins to be used in a combinatorial approach to inhibit tumor cell growth, we used a novel strategy that identified microRNAs (miRNAs) that synergized with AZD6244 to inhibit the viability of the claudin-low breast cancer cell line MDA-MB-231. Screening of a miRNA mimic library revealed the ability of miR-9-3p to significantly enhance AZD6244-induced extracellular signal-regulated kinase inhibition and growth arrest, while miR-9-3p had little effect on growth alone. Promoter methylation of mir-9 genes correlated with low expression of miR-9-3p in different breast cancer cell lines. Consistent with miR-9-3p having synthetic enhancer tumor suppressor characteristics, miR-9-3p expression in combination with MEK inhibitor caused a sustained loss of c-MYC expression and growth inhibition. The ß1 integrin gene (ITGB1) was identified as a new miR-9-3p target, and the growth inhibition seen with small interfering RNA knockdown or antibody blocking of ITGB1 in combination with MEK inhibitor phenocopied the growth inhibition seen with miR-9-3p plus AZD6244. The miRNA screen led to identification of a druggable protein, ITGB1, whose functional inhibition synergizes with MEK inhibitor.

SWI/SNF chromatin-remodeling factor Smarcd3/Baf60c controls epithelial-mesenchymal transition by inducing Wnt5a signaling.

We previously identified a gene signature predicted to regulate the epithelial-mesenchymal transition (EMT) in both epithelial tissue stem cells and breast cancer cells. A phenotypic RNA interference (RNAi) screen identified the genes within this 140-gene signature that promoted the conversion of mesenchymal epithelial cell adhesion molecule-negative (EpCAM-) breast cancer cells to an epithelial EpCAM+/high phenotype. The screen identified 10 of the 140 genes whose individual knockdown was sufficient to promote EpCAM and E-cadherin expression. Among these 10 genes, RNAi silencing of the SWI/SNF chromatin-remodeling factor Smarcd3/Baf60c in EpCAM- breast cancer cells gave the most robust transition from the mesenchymal to epithelial phenotype. Conversely, expression of Smarcd3/Baf60c in immortalized human mammary epithelial cells induced an EMT. The mesenchymal-like phenotype promoted by Smarcd3/Baf60c expression resulted in gene expression changes in human mammary epithelial cells similar to that of claudin-low triple-negative breast cancer cells. These mammary epithelial cells expressing Smarcd3/Baf60c had upregulated Wnt5a expression. Inhibition of Wnt5a by either RNAi knockdown or blocking antibody reversed Smarcd3/Baf60c-induced EMT. Thus, Smarcd3/Baf60c epigenetically regulates EMT by activating WNT signaling pathways.