A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level.

Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce "efficacious combination benefit" (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.

Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations.

Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. SIGNIFICANCE: We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.

AKT-mTORC1 reactivation is the dominant resistance driver for PI3Kß/AKT inhibitors in PTEN-null breast cancer and can be overcome by combining with Mcl-1 inhibitors.

The PI3K pathway is commonly activated in breast cancer, with PI3K-AKT pathway inhibitors used clinically. However, mechanisms that limit or enhance the therapeutic effects of PI3K-AKT inhibitors are poorly understood at a genome-wide level. Parallel CRISPR screens in 3 PTEN-null breast cancer cell lines identified genes mediating resistance to capivasertib (AKT inhibitor) and AZD8186 (PI3Kß inhibitor). The dominant mechanism causing resistance is reactivated PI3K-AKT-mTOR signalling, but not other canonical signalling pathways. Deletion of TSC1/2 conferred resistance to PI3Kßi and AKTi through mTORC1. However, deletion of PIK3R2 and INPPL1 drove specific PI3Kßi resistance through AKT. Conversely deletion of PIK3CA, ERBB2, ERBB3 increased PI3Kßi sensitivity while modulation of RRAGC, LAMTOR1, LAMTOR4 increased AKTi sensitivity. Significantly, we found that Mcl-1 loss enhanced response through rapid apoptosis induction with AKTi and PI3Kßi in both sensitive and drug resistant TSC1/2 null cells. The combination effect was BAK but not BAX dependent. The Mcl-1i + PI3Kß/AKTi combination was effective across a panel of breast cancer cell lines with PIK3CA and PTEN mutations, and delivered increased anti-tumor benefit in vivo. This study demonstrates that different resistance drivers to PI3Kßi and AKTi converge to reactivate PI3K-AKT or mTOR signalling and combined inhibition of Mcl-1 and PI3K-AKT has potential as a treatment strategy for PI3Kßi/AKTi sensitive and resistant breast tumours.

Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial Intelligence-Enabled Studies of Acute Phosphoproteomic Changes.

We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.

Effective drug combinations in breast, colon and pancreatic cancer cells.

Combinations of anti-cancer drugs can overcome resistance and provide new treatments1,2. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS-TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.

SLFN11 informs on standard of care and novel treatments in a wide range of cancer models.

BACKGROUND: Schlafen 11 (SLFN11) has been linked with response to DNA-damaging agents (DDA) and PARP inhibitors. An in-depth understanding of several aspects of its role as a biomarker in cancer is missing, as is a comprehensive analysis of the clinical significance of SLFN11 as a predictive biomarker to DDA and/or DNA damage-response inhibitor (DDRi) therapies. METHODS: We used a multidisciplinary effort combining specific immunohistochemistry, pharmacology tests, anticancer combination therapies and mechanistic studies to assess SLFN11 as a potential biomarker for stratification of patients treated with several DDA and/or DDRi in the preclinical and clinical setting. RESULTS: SLFN11 protein associated with both preclinical and patient treatment response to DDA, but not to non-DDA or DDRi therapies, such as WEE1 inhibitor or olaparib in breast cancer. SLFN11-low/absent cancers were identified across different tumour types tested. Combinations of DDA with DDRi targeting the replication-stress response (ATR, CHK1 and WEE1) could re-sensitise SLFN11-absent/low cancer models to the DDA treatment and were effective in upper gastrointestinal and genitourinary malignancies. CONCLUSION: SLFN11 informs on the standard of care chemotherapy based on DDA and the effect of selected combinations with ATR, WEE1 or CHK1 inhibitor in a wide range of cancer types and models.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.

AZD0364 Is a Potent and Selective ERK1/2 Inhibitor That Enhances Antitumor Activity in KRAS-Mutant Tumor Models when Combined with the MEK Inhibitor, Selumetinib.

The RAS-regulated RAF-MEK1/2-ERK1/2 (RAS/MAPK) signaling pathway is a major driver in oncogenesis and is frequently dysregulated in human cancers, primarily by mutations in BRAF or RAS genes. The clinical benefit of inhibitors of this pathway as single agents has only been realized in BRAF-mutant melanoma, with limited effect of single-agent pathway inhibitors in KRAS-mutant tumors. Combined inhibition of multiple nodes within this pathway, such as MEK1/2 and ERK1/2, may be necessary to effectively suppress pathway signaling in KRAS-mutant tumors and achieve meaningful clinical benefit. Here, we report the discovery and characterization of AZD0364, a novel, reversible, ATP-competitive ERK1/2 inhibitor with high potency and kinase selectivity. In vitro, AZD0364 treatment resulted in inhibition of proximal and distal biomarkers and reduced proliferation in sensitive BRAF-mutant and KRAS-mutant cell lines. In multiple in vivo xenograft models, AZD0364 showed dose- and time-dependent modulation of ERK1/2-dependent signaling biomarkers resulting in tumor regression in sensitive BRAF- and KRAS-mutant xenografts. We demonstrate that AZD0364 in combination with the MEK1/2 inhibitor, selumetinib (AZD6244 and ARRY142886), enhances efficacy in KRAS-mutant preclinical models that are moderately sensitive or resistant to MEK1/2 inhibition. This combination results in deeper and more durable suppression of the RAS/MAPK signaling pathway that is not achievable with single-agent treatment. The AZD0364 and selumetinib combination also results in significant tumor regressions in multiple KRAS-mutant xenograft models. The combination of ERK1/2 and MEK1/2 inhibition thereby represents a viable clinical approach to target KRAS-mutant tumors.

AZD4320, A Dual Inhibitor of Bcl-2 and Bcl-xL, Induces Tumor Regression in Hematologic Cancer Models without Dose-limiting Thrombocytopenia.

PURPOSE: Targeting Bcl-2 family members upregulated in multiple cancers has emerged as an important area of cancer therapeutics. While venetoclax, a Bcl-2-selective inhibitor, has had success in the clinic, another family member, Bcl-xL, has also emerged as an important target and as a mechanism of resistance. Therefore, we developed a dual Bcl-2/Bcl-xL inhibitor that broadens the therapeutic activity while minimizing Bcl-xL-mediated thrombocytopenia. EXPERIMENTAL DESIGN: We used structure-based chemistry to design a small-molecule inhibitor of Bcl-2 and Bcl-xL and assessed the activity against in vitro cell lines, patient samples, and in vivo models. We applied pharmacokinetic/pharmacodynamic (PK/PD) modeling to integrate our understanding of on-target activity of the dual inhibitor in tumors and platelets across dose levels and over time. RESULTS: We discovered AZD4320, which has nanomolar affinity for Bcl-2 and Bcl-xL, and mechanistically drives cell death through the mitochondrial apoptotic pathway. AZD4320 demonstrates activity in both Bcl-2- and Bcl-xL-dependent hematologic cancer cell lines and enhanced activity in acute myeloid leukemia (AML) patient samples compared with the Bcl-2-selective agent venetoclax. A single intravenous bolus dose of AZD4320 induces tumor regression with transient thrombocytopenia, which recovers in less than a week, suggesting a clinical weekly schedule would enable targeting of Bcl-2/Bcl-xL-dependent tumors without incurring dose-limiting thrombocytopenia. AZD4320 demonstrates monotherapy activity in patient-derived AML and venetoclax-resistant xenograft models. CONCLUSIONS: AZD4320 is a potent molecule with manageable thrombocytopenia risk to explore the utility of a dual Bcl-2/Bcl-xL inhibitor across a broad range of tumor types with dysregulation of Bcl-2 prosurvival proteins.

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

Differences in Signaling Patterns on PI3K Inhibition Reveal Context Specificity in KRAS-Mutant Cancers.

It is increasingly appreciated that drug response to different cancers driven by the same oncogene is different and may relate to differences in rewiring of signal transduction. We aimed to study differences in dynamic signaling changes within mutant KRAS (KRAS MT), non-small cell lung cancer (NSCLC), colorectal cancer, and pancreatic ductal adenocarcinoma (PDAC) cells. We used an antibody-based phosphoproteomic platform to study changes in 50 phosphoproteins caused by seven targeted anticancer drugs in a panel of 30 KRAS MT cell lines and cancer cells isolated from 10 patients with KRAS MT cancers. We report for the first time significant differences in dynamic signaling between colorectal cancer and NSCLC cell lines exposed to clinically relevant equimolar concentrations of the pan-PI3K inhibitor pictilisib including a lack of reduction of p-AKTser473 in colorectal cancer cell lines (P = 0.037) and lack of compensatory increase in p-MEK in NSCLC cell lines (P = 0.036). Differences in rewiring of signal transduction between tumor types driven by KRAS MT cancers exist and influence response to combination therapy using targeted agents.

Simulated ablation for detection of cells impacting paracrine signalling in histology analysis.

Intra-tumour phenotypic heterogeneity limits accuracy of clinical diagnostics and hampers the efficiency of anti-cancer therapies. Dealing with this cellular heterogeneity requires adequate understanding of its sources, which is extremely difficult, as phenotypes of tumour cells integrate hardwired (epi)mutational differences with the dynamic responses to microenvironmental cues. The later comes in form of both direct physical interactions, as well as inputs from gradients of secreted signalling molecules. Furthermore, tumour cells can not only receive microenvironmental cues, but also produce them. Despite high biological and clinical importance of understanding spatial aspects of paracrine signaling, adequate research tools are largely lacking. Here, a partial differential equation (PDE)-based mathematical model is developed that mimics the process of cell ablation. This model suggests how each cell might contribute to the microenvironment by either absorbing or secreting diffusible factors, and quantifies the extent to which observed intensities can be explained via diffusion-mediated signalling. The model allows for the separation of phenotypic responses to signalling gradients within tumour microenvironments from the combined influence of responses mediated by direct physical contact and hardwired (epi)genetic differences. The method is applied to a multi-channel immunofluorescence in situ hybridisation (iFISH)-stained breast cancer histological specimen, and correlations are investigated between: HER2 gene amplification, HER2 protein expression and cell interaction with the diffusible microenvironment. This approach allows partial deconvolution of the complex inputs that shape phenotypic heterogeneity of tumour cells and identifies cells that significantly impact gradients of signalling molecules.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a public, freely available, integrative translational research and drug discovery knowlegebase. canSAR informs researchers to help solve key bottlenecks in cancer translation and drug discovery. It integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and unique, comprehensive and orthogonal 'druggability' assessments. canSAR is widely used internationally by academia and industry. Here we describe major enhancements to canSAR including new and expanded data. We also describe the first components of canSARblack-an advanced, responsive, multi-device compatible redesign of canSAR with a question-led interface.

Leveraging Human Genetics to Guide Cancer Drug Development.

PURPOSE: The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development and repurposing in oncology. MATERIALS AND METHODS: Across 37 cancers, we identified 955 genetic risk variants from the National Human Genome Research Institute-European Bioinformatics Institute genome-wide association study catalog. We linked these variants to target genes using strategies that were based on linkage disequilibrium, DNA three-dimensional structure, and integration of predicted gene function and expression. With the use of the Informa Pharmaprojects database, we identified genes that are targets of unique drugs and assessed the level of enrichment that would be afforded by incorporation of genetic information in preclinical and phase II studies. For targets not under development, we implemented machine learning approaches to assess druggability. RESULTS: For all preclinical targets incorporating genetic information, a 2.00-fold enrichment of a drug being successfully approved could be achieved (95% CI, 1.14- to 3.48-fold; P = .02). For phase II targets, a 2.75-fold enrichment could be achieved (95% CI, 1.42- to 5.35-fold; P < .001). Application of genetic information suggests potential repurposing of 15 approved nononcology drugs. CONCLUSION: The findings illustrate the value of using insights from the genetics of inherited cancer susceptibility discovery projects as part of a data-driven strategy to inform drug discovery. Support for cancer germline genetic information for prospective targets is available online from the Institute of Cancer Research.

SiGNet: A signaling network data simulator to enable signaling network inference.

Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.

canSAR: an updated cancer research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and druggability data. canSAR is widely used to rapidly access information and help interpret experimental data in a translational and drug discovery context. Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities, new disease and cancer cell line summaries and new and enhanced batch analysis tools.

canSAR: updated cancer research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a public integrative cancer-focused knowledgebase for the support of cancer translational research and drug discovery. Through the integration of biological, pharmacological, chemical, structural biology and protein network data, it provides a single information portal to answer complex multidisciplinary questions including--among many others--what is known about a protein, in which cancers is it expressed or mutated, and what chemical tools and cell line models can be used to experimentally probe its activity? What is known about a drug, its cellular sensitivity profile and what proteins is it known to bind that may explain unusual bioactivity? Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities and new target, cancer cell line, protein family and 3D structure summaries and tools.