Defining the KRAS- and ERK-dependent transcriptome in KRAS-mutant cancers.

How the KRAS oncogene drives cancer growth remains poorly understood. Therefore, we established a systemwide portrait of KRAS- and extracellular signal-regulated kinase (ERK)-dependent gene transcription in KRAS-mutant cancer to delineate the molecular mechanisms of growth and of inhibitor resistance. Unexpectedly, our KRAS-dependent gene signature diverges substantially from the frequently cited Hallmark KRAS signaling gene signature, is driven predominantly through the ERK mitogen-activated protein kinase (MAPK) cascade, and accurately reflects KRAS- and ERK-regulated gene transcription in KRAS-mutant cancer patients. Integration with our ERK-regulated phospho- and total proteome highlights ERK deregulation of the anaphase promoting complex/cyclosome (APC/C) and other components of the cell cycle machinery as key processes that drive pancreatic ductal adenocarcinoma (PDAC) growth. Our findings elucidate mechanistically the critical role of ERK in driving KRAS-mutant tumor growth and in resistance to KRAS-ERK MAPK targeted therapies.

Concurrent Inhibition of IGF1R and ERK Increases Pancreatic Cancer Sensitivity to Autophagy Inhibitors.

The aggressive nature of pancreatic ductal adenocarcinoma (PDAC) mandates the development of improved therapies. As KRAS mutations are found in 95% of PDAC and are critical for tumor maintenance, one promising strategy involves exploiting KRAS-dependent metabolic perturbations. The macrometabolic process of autophagy is upregulated in KRAS-mutant PDAC, and PDAC growth is reliant on autophagy. However, inhibition of autophagy as monotherapy using the lysosomal inhibitor hydroxychloroquine (HCQ) has shown limited clinical efficacy. To identify strategies that can improve PDAC sensitivity to HCQ, we applied a CRISPR-Cas9 loss-of-function screen and found that a top sensitizer was the receptor tyrosine kinase (RTK) insulin-like growth factor 1 receptor (IGF1R). Additionally, reverse phase protein array pathway activation mapping profiled the signaling pathways altered by chloroquine (CQ) treatment. Activating phosphorylation of RTKs, including IGF1R, was a common compensatory increase in response to CQ. Inhibition of IGF1R increased autophagic flux and sensitivity to CQ-mediated growth suppression both in vitro and in vivo. Cotargeting both IGF1R and pathways that antagonize autophagy, such as ERK-MAPK axis, was strongly synergistic. IGF1R and ERK inhibition converged on suppression of glycolysis, leading to enhanced dependence on autophagy. Accordingly, concurrent inhibition of IGF1R, ERK, and autophagy induced cytotoxicity in PDAC cell lines and decreased viability in human PDAC organoids. In conclusion, targeting IGF1R together with ERK enhances the effectiveness of autophagy inhibitors in PDAC. SIGNIFICANCE: Compensatory upregulation of IGF1R and ERK-MAPK signaling limits the efficacy of autophagy inhibitors chloroquine and hydroxychloroquine, and their concurrent inhibition synergistically increases autophagy dependence and chloroquine sensitivity in pancreatic ductal adenocarcinoma.

CIP2A Interacts with TopBP1 and Drives Basal-Like Breast Cancer Tumorigenesis.

Basal-like breast cancers (BLBC) are characterized by defects in homologous recombination (HR), deficient mitotic checkpoint, and high-proliferation activity. Here, we discover CIP2A as a candidate driver of BLBC. CIP2A was essential for DNA damage-induced initiation of mouse BLBC-like mammary tumors and for survival of HR-defective BLBC cells. CIP2A was dispensable for normal mammary gland development and for unperturbed mitosis, but selectively essential for mitotic progression of DNA damaged cells. A direct interaction between CIP2A and a DNA repair scaffold protein TopBP1 was identified, and CIP2A inhibition resulted in enhanced DNA damage-induced TopBP1 and RAD51 recruitment to chromatin in mammary epithelial cells. In addition to its role in tumor initiation, and survival of BRCA-deficient cells, CIP2A also drove proliferative MYC and E2F1 signaling in basal-like triple-negative breast cancer (BL-TNBC) cells. Clinically, high CIP2A expression was associated with poor patient prognosis in BL-TNBCs but not in other breast cancer subtypes. Small-molecule reactivators of PP2A (SMAP) inhibited CIP2A transcription, phenocopied the CIP2A-deficient DNA damage response (DDR), and inhibited growth of patient-derived BLBC xenograft. In summary, these results demonstrate that CIP2A directly interacts with TopBP1 and coordinates DNA damage-induced mitotic checkpoint and proliferation, thereby driving BLBC initiation and progression. SMAPs could serve as a surrogate therapeutic strategy to inhibit the oncogenic activity of CIP2A in BLBCs. SIGNIFICANCE: These results identify CIP2A as a nongenetic driver and therapeutic target in basal-like breast cancer that regulates DNA damage-induced G2-M checkpoint and proliferative signaling.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor.

Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

Systematic mapping of cancer cell target dependencies using high-throughput drug screening in triple-negative breast cancer.

While high-throughput drug screening offers possibilities to profile phenotypic responses of hundreds of compounds, elucidation of the cell context-specific mechanisms of drug action requires additional analyses. To that end, we developed a computational target deconvolution pipeline that identifies the key target dependencies based on collective drug response patterns in each cell line separately. The pipeline combines quantitative drug-cell line responses with drug-target interaction networks among both intended on- and potent off-targets to identify pharmaceutically actionable and selective therapeutic targets. To demonstrate its performance, the target deconvolution pipeline was applied to 310 small molecules tested on 20 genetically and phenotypically heterogeneous triple-negative breast cancer (TNBC) cell lines to identify cell line-specific target mechanisms in terms of cytotoxic and cytostatic drug target vulnerabilities. The functional essentiality of each protein target was quantified with a target addiction score (TAS), as a measure of dependency of the cell line on the therapeutic target. The target dependency profiling was shown to capture inhibitory information that is complementary to that obtained from the structure or sensitivity of the drugs. Comparison of the TAS profiles and gene essentiality scores from CRISPR-Cas9 knockout screens revealed that certain proteins with low gene essentiality showed high target addictions, suggesting that they might be functioning as protein groups, and therefore be resistant to single gene knock-out. The comparative analysis discovered protein groups of potential multi-target synthetic lethal interactions, for instance, among histone deacetylases (HDACs). Our integrated approach also recovered a number of well-established TNBC cell line-specific drivers and known TNBC therapeutic targets, such as HDACs and cyclin-dependent kinases (CDKs). The present work provides novel insights into druggable vulnerabilities for TNBC, and opportunities to identify multi-target synthetic lethal interactions for further studies.

Multiobjective optimization identifies cancer-selective combination therapies.

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.

We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrate high predictive performance of comboFM in various prediction scenarios using data from cancer cell line pharmacogenomic screens. Subsequent experimental validation of a set of previously untested drug combinations further supports the practical and robust applicability of comboFM. For instance, we confirm a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.

Low-Dose Vertical Inhibition of the RAF-MEK-ERK Cascade Causes Apoptotic Death of KRAS Mutant Cancers.

We address whether combinations with a pan-RAF inhibitor (RAFi) would be effective in KRAS mutant pancreatic ductal adenocarcinoma (PDAC). Chemical library and CRISPR genetic screens identify combinations causing apoptotic anti-tumor activity. The most potent combination, concurrent inhibition of RAF (RAFi) and ERK (ERKi), is highly synergistic at low doses in cell line, organoid, and rat models of PDAC, whereas each inhibitor alone is only cytostatic. Comprehensive mechanistic signaling studies using reverse phase protein array (RPPA) pathway mapping and RNA sequencing (RNA-seq) show that RAFi/ERKi induced insensitivity to loss of negative feedback and system failures including loss of ERK signaling, FOSL1, and MYC; shutdown of the MYC transcriptome; and induction of mesenchymal-to-epithelial transition. We conclude that low-dose vertical inhibition of the RAF-MEK-ERK cascade is an effective therapeutic strategy for KRAS mutant PDAC.

A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening.

Accurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates, and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

Atypical KRASG12R Mutant Is Impaired in PI3K Signaling and Macropinocytosis in Pancreatic Cancer.

Allele-specific signaling by different KRAS alleles remains poorly understood. The KRAS G12R mutation displays uneven prevalence among cancers that harbor the highest occurrence of KRAS mutations: It is rare (∼1%) in lung and colorectal cancers, yet relatively common (∼20%) in pancreatic ductal adenocarcinoma (PDAC), suggesting context-specific properties. We evaluated whether KRASG12R is functionally distinct from the more common KRASG12D- or KRASG12V-mutant proteins (KRASG12D/V). We found that KRASG12D/V but not KRASG12R drives macropinocytosis and that MYC is essential for macropinocytosis in KRASG12D/V- but not KRASG12R-mutant PDAC. Surprisingly, we found that KRASG12R is defective for interaction with a key effector, p110α PI3K (PI3Kα), due to structural perturbations in switch II. Instead, upregulated KRAS-independent PI3Kγ activity was able to support macropinocytosis in KRASG12R-mutant PDAC. Finally, we determined that KRASG12R-mutant PDAC displayed a distinct drug sensitivity profile compared with KRASG12D-mutant PDAC but is still responsive to the combined inhibition of ERK and autophagy. SIGNIFICANCE: We determined that KRASG12R is impaired in activating a key effector, p110α PI3K. As such, KRASG12R is impaired in driving macropinocytosis. However, overexpression of PI3Kγ in PDAC compensates for this deficiency, providing one basis for the prevalence of this otherwise rare KRAS mutant in pancreatic cancer but not other cancers.See related commentary by Falcomatà et al., p. 23.This article is highlighted in the In This Issue feature, p. 1.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets.

The lack of functional understanding of most mutations in cancer, combined with the non-druggability of most proteins, challenge genomics-based identification of oncology drug targets. We implemented a machine-learning-based approach (idTRAX), which relates cell-based screening of small-molecule compounds to their kinase inhibition data, to directly identify effective and readily druggable targets. We applied idTRAX to triple-negative breast cancer cell lines and efficiently identified cancer-selective targets. For example, we found that inhibiting AKT selectively kills MFM-223 and CAL148 cells, while inhibiting FGFR2 only kills MFM-223. Since the effects of catalytically inhibiting a protein can diverge from those of reducing its levels, targets identified by idTRAX frequently differ from those identified through gene knockout/knockdown methods. This is critical if the purpose is to identify targets specifically for small-molecule drug development, whereby idTRAX may produce fewer false-positives. The rapid nature of the approach suggests that it may be applicable in personalizing therapy.

Prediction of drug combination effects with a minimal set of experiments.

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

KRAS Suppression-Induced Degradation of MYC Is Antagonized by a MEK5-ERK5 Compensatory Mechanism.

Our recent ERK1/2 inhibitor analyses in pancreatic ductal adenocarcinoma (PDAC) indicated ERK1/2-independent mechanisms maintaining MYC protein stability. To identify these mechanisms, we determined the signaling networks by which mutant KRAS regulates MYC. Acute KRAS suppression caused rapid proteasome-dependent loss of MYC protein, through both ERK1/2-dependent and -independent mechanisms. Surprisingly, MYC degradation was independent of PI3K-AKT-GSK3ß signaling and the E3 ligase FBWX7. We then established and applied a high-throughput screen for MYC protein degradation and performed a kinome-wide proteomics screen. We identified an ERK1/2-inhibition-induced feedforward mechanism dependent on EGFR and SRC, leading to ERK5 activation and phosphorylation of MYC at S62, preventing degradation. Concurrent inhibition of ERK1/2 and ERK5 disrupted this mechanism, synergistically causing loss of MYC and suppressing PDAC growth.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.

Genomic perturbations reveal distinct regulatory networks in intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma remains a highly heterogeneous malignancy that has eluded effective patient stratification to date. The extent to which such heterogeneity can be influenced by individual driver mutations remains to be evaluated. Here, we analyzed genomic (whole-exome sequencing, targeted exome sequencing) and epigenomic data from 496 patients and used the three most recurrently mutated genes to stratify patients (IDH, KRAS, TP53, "undetermined"). Using this molecular dissection approach, each subgroup was determined to possess unique mutational signature preferences, comutation profiles, and enriched pathways. High-throughput drug repositioning in seven patient-matched cell lines, chosen to reflect the genetic alterations specific for each patient group, confirmed in silico predictions of subgroup-specific vulnerabilities linked to enriched pathways. Intriguingly, patients lacking all three mutations ("undetermined") harbored the most extensive structural alterations, while isocitrate dehydrogenase mutant tumors displayed the most extensive DNA methylome dysregulation, consistent with previous findings. CONCLUSION: Stratification of intrahepatic cholangiocarcinoma patients based on occurrence of mutations in three classifier genes (IDH, KRAS, TP53) revealed unique oncogenic programs (mutational, structural, epimutational) that influence pharmacologic response in drug repositioning protocols; this genome dissection approach highlights the potential of individual mutations to induce extensive molecular heterogeneity and could facilitate advancement of therapeutic response in this dismal disease. (Hepatology 2018).

Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells.

BACKGROUND: Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive type of cancer that lacks effective targeted therapy. Despite detailed molecular profiling, no targeted therapy has been established. Hence, with the aim of gaining deeper understanding of the functional differences of TNBC subtypes and how that may relate to potential novel therapeutic strategies, we studied comprehensive anticancer-agent responses among a panel of TNBC cell lines. METHOD: The responses of 301 approved and investigational oncology compounds were measured in 16 TNBC cell lines applying a functional profiling approach. To go beyond the standard drug viability effect profiling, which has been used in most chemosensitivity studies, we utilized a multiplexed readout for both cell viability and cytotoxicity, allowing us to differentiate between cytostatic and cytotoxic responses. RESULTS: Our approach revealed that most single-agent anti-cancer compounds that showed activity for the viability readout had no or little cytotoxic effects. Major compound classes that exhibited this type of response included anti-mitotics, mTOR, CDK, and metabolic inhibitors, as well as many agents selectively inhibiting oncogene-activated pathways. However, within the broad viability-acting classes of compounds, there were often subsets of cell lines that responded by cell death, suggesting that these cells are particularly vulnerable to the tested substance. In those cases we could identify differential levels of protein markers associated with cytotoxic responses. For example, PAI-1, MAPK phosphatase and Notch-3 levels associated with cytotoxic responses to mitotic and proteasome inhibitors, suggesting that these might serve as markers of response also in clinical settings. Furthermore, the cytotoxicity readout highlighted selective synergistic and synthetic lethal drug combinations that were missed by the cell viability readouts. For instance, the MEK inhibitor trametinib synergized with PARP inhibitors. Similarly, combination of two non-cytotoxic compounds, the rapamycin analog everolimus and an ATP-competitive mTOR inhibitor dactolisib, showed synthetic lethality in several mTOR-addicted cell lines. CONCLUSIONS: Taken together, by studying the combination of cytotoxic and cytostatic drug responses, we identified a deeper spectrum of cellular responses both to single agents and combinations that may be highly relevant for identifying precision medicine approaches in TNBC as well as in other types of cancers.

Long-Term ERK Inhibition in KRAS-Mutant Pancreatic Cancer Is Associated with MYC Degradation and Senescence-like Growth Suppression.

Induction of compensatory mechanisms and ERK reactivation has limited the effectiveness of Raf and MEK inhibitors in RAS-mutant cancers. We determined that direct pharmacologic inhibition of ERK suppressed the growth of a subset of KRAS-mutant pancreatic cancer cell lines and that concurrent phosphatidylinositol 3-kinase (PI3K) inhibition caused synergistic cell death. Additional combinations that enhanced ERK inhibitor action were also identified. Unexpectedly, long-term treatment of sensitive cell lines caused senescence, mediated in part by MYC degradation and p16 reactivation. Enhanced basal PI3K-AKT-mTOR signaling was associated with de novo resistance to ERK inhibitor, as were other protein kinases identified by kinome-wide siRNA screening and a genetic gain-of-function screen. Our findings reveal distinct consequences of inhibiting this kinase cascade at the level of ERK.