Prediction Of Drug Combinations Research Articles

Defining and Targeting Adaptations to Oncogenic KRASG12C Inhibition Using Quantitative Temporal Proteomics

Covalent inhibitors of the KRASG12C oncoprotein have recently been developed and are being evaluated in clinical trials. Resistance to targeted therapies is common and may limit long-term efficacy of KRAS inhibitors (KRASi). To identify pathways of adaptation to KRASi and predict drug combinations that circumvent resistance, we use mass-spectrometry-based quantitative temporal proteomics to profile the proteomic response to KRASi in pancreatic and lung cancer 2D and 3D cellular models. We quantify 10,805 proteins, representing the most comprehensive KRASi proteome (https://manciaslab.shinyapps.io/KRASi/). Our data reveal common mechanisms of acute and long-term response between KRASG12C-driven tumors. Based on these proteomic data, we identify potent combinations of KRASi with phosphatidylinositol 3-kinase (PI3K), HSP90, CDK4/6, and SHP2 inhibitors, in some instances converting a cytostatic response to KRASi monotherapy to a cytotoxic response to combination treatment. Overall, using quantitative temporal proteomics, we comprehensively characterize adaptations to KRASi and identify combinatorial regimens with potential therapeutic utility.

Exploiting stochastic Petri nets with fuzzy parameters to predict efficient drug combinations for Spinal Muscular Atrophy

Randomness and uncertainty are two major problems one faces while modeling nonlinear dynamics of molecular systems. Stochastic and fuzzy methods are used to cope with these problems, but there is no consensus among researchers regarding which method should be used when. This is because the areas of applications of these methods are overlapping with differences in opinions. In the present work, we demonstrate how to use stochastic Petri nets with fuzzy parameters to manage random timing of biomolecular events and deal with the uncertainty of reaction rates in biological networks. The approach is demonstrated through a case study of simulation-based prediction of efficient drug combinations for spinal muscular atrophy, for which we obtained very promising results. The feasibility of the approach is assessed through statistical analysis of deterministic, pure stochastic and fuzzy stochastic simulation results. Statistical analysis reveals that all three models produce significantly different results which, when coupled with the fact that fuzzy stochastic model provides the closest approximation of underlying biological network, successfully coping not only with randomness but also uncertainty, suggests that fuzzy stochastic model is the most appropriate choice for the present case study. The proposed approach can be adapted or extended to other biological networks.

Prediction of Synergistic Drug Combinations by Learning from Deep Representations of Multiple Networks.

Drug combination therapy can improve drug efficacy, reduce drug dosage, and overcome drug resistance. Many studies have focused on predicting synergistic drug combinations. However, existing methods fail to consider the heterogeneous characteristics of drugs fully, and it is difficult to identify effective drug combinations. Therefore, we propose a new integrated prediction model based on deep representations by integrating information from multiple domains to accurately and effectively predict drug combinations.

Abstract 5097: Clinical trial outcomes for drug combination therapies can be predicted by modeling independent action using cancer cell line screens

Abstract Background: In many cancer settings, it is evident that combination drug therapies show increased efficacy compared to monotherapies. However, the development of new combination therapies is slowed by the fact that it is infeasible to experimentally evaluate the vast number of possible drug combinations. In an effort to overcome this problem, we developed a computational method which uses monotherapy screening data in cell lines to estimate the efficacy of combinations of two or more chemotherapy drugs. When validated against published phase 3 clinical cancer trial results, our method is capable of predicting clinical trial success or failure with a high degree of sensitivity and specificity. Methods and Results: We processed existing high-throughput in vitro drug screening datasets (i.e. GDSC and CTRPv2) to obtain fitted dose response curves for more than 600,000 drug-cell line pairs, including data for 695 unique compounds and 1347 unique cell lines. We then used this dataset to develop a method of estimating the efficacy of combinations of two or more drugs. While previous efforts to predict drug combination efficacy have largely focused on dose-agnostic estimates of drug synergy, we modeled dose-dependent estimates of independent drug action, where each patient treated with a drug combination only derives benefit from the single drug in the combination to which they are most sensitive. For method validation, we systematically searched clinicaltrials.gov to identify completed, phase 3 cancer clinical trials that evaluated drug combinations for which we could estimate efficacy and which had at least 50 patients per trial arm. Of the >9,000 clinical trials considered, 37 trials (11 successful and 26 unsuccessful) met our inclusion criteria. Using clinically determined plasma concentrations for each drug, we then predicted relative efficacy for each of the experimental drug combinations versus the control group therapies in these trials. These predictions were clustered into two groups (predicted high benefit vs low benefit) using k-means clustering, and these clusters were compared to whether or not the clinical trials were considered a success by the original study investigators. Our method correctly classified 84% of the trials, with a sensitivity of 73% and a specificity of 88%. Furthermore, efficacy predictions for combinations containing erlotinib were dependent on EGFR mutation status, suggesting that this method may be useful for identifying molecular subtypes which are sensitive to a given combination therapy. Conclusion: Clinically meaningful predictions of drug combination efficacy can be generated from single-agent, in vitro drug screens under the assumption of independent drug action. This enables efficacy predictions to be made for >150,000 2-drug combinations and millions of 3- and 4-drug combinations using the existing GDSC and CTRPv2 datasets. Citation Format: Alexander Ling, R. Stephanie Huang. Clinical trial outcomes for drug combination therapies can be predicted by modeling independent action using cancer cell line screens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5097.

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity. Combination chemotherapy, in which two or more drugs are applied simultaneously, offers one promising approach to address these concerns, since two single-target drugs may synergize with one another through interconnected biological processes. However, the identification of effective dual therapies has been particularly challenging; because the search space is large, combination success rates are low. Here, we present our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. Our approach involves using biologically relevant drug and cell line features with machine learning. Our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. Our approach also achieves a mean primary metric of 0.39 with ten repetitions of 10-fold cross-validation. Further, we analyzed our model’s predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. Through further analysis of our predictions, we were also ble to gain insight into mechanisms and potential biomarkers of synergistic drug pairs.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Network-based prediction of drug combinations

Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.

Combined MEK and BCL-2/XL Inhibition Is Effective in High-Grade Serous Ovarian Cancer Patient-Derived Xenograft Models and BIM Levels Are Predictive of Responsiveness.

Most patients with late-stage high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy but inevitably relapse and develop resistance, highlighting the need for novel therapies to improve patient outcomes. The MEK/ERK pathway is activated in a large subset of HGSOC, making it an attractive therapeutic target. Here, we systematically evaluated the extent of MEK/ERK pathway activation and efficacy of pathway inhibition in a large panel of well-annotated HGSOC patient-derived xenograft models. The vast majority of models were nonresponsive to the MEK inhibitor cobimetinib (GDC-0973) despite effective pathway inhibition. Proteomic analyses of adaptive responses to GDC-0973 revealed that GDC-0973 upregulated the proapoptotic protein BIM, thus priming the cells for apoptosis regulated by BCL2-family proteins. Indeed, combination of both MEK inhibitor and dual BCL-2/XL inhibitor (ABT-263) significantly reduced cell number, increased cell death, and displayed synergy in vitro in most models. In vivo, GDC-0973 and ABT-263 combination was well tolerated and resulted in greater tumor growth inhibition than single agents. Detailed proteomic and correlation analyses identified two subsets of responsive models-those with high BIM at baseline that was increased with MEK inhibition and those with low basal BIM and high pERK levels. Models with low BIM and low pERK were nonresponsive. Our findings demonstrate that combined MEK and BCL-2/XL inhibition has therapeutic activity in HGSOC models and provide a mechanistic rationale for the clinical evaluation of this drug combination as well as the assessment of the extent to which BIM and/or pERK levels predict drug combination effectiveness in chemoresistant HGSOC.

Prediction of Drug Combinations with a Network Embedding Method

There are several diseases having a complicated mechanism. For such complicated diseases, a single drug cannot treat them very well because these diseases always involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug combination is an effective way to treat such diseases. However, determination of effective drug combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and cheap methods in this regard. Designing effective computational methods incorporating advanced computational techniques to predict drug combinations is an alternative and feasible way. In this study, we proposed a novel network embedding method, which can extract topological features of each drug combination from a drug network that was constructed using chemical-chemical interaction information retrieved from STITCH. These topological features were combined with individual features of drug combination reported in one previous study. Several advanced computational methods were employed to construct an effective prediction model, such as synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset, minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS) methods that were adopted to analyze features and extract optimal features for building an optimal support machine vector (SVM) classifier. The constructed optimal SVM classifier yielded an MCC of 0.806, which is superior to the classifier only using individual features with or without SMOTE. The performance of the classifier can be improved by combining the topological features and essential features of a drug combination.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction.

MotivationDrug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data.ResultsThis tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans.Availability and implementationDrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction.Supplementary information Supplementary data are available at Bioinformatics online.

TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction.

Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs. Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance. TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python. Supplementary data are available at Bioinformatics online.

An integrated framework for identification of effective and synergistic anti-cancer drug combinations.

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.

Network Propagation Predicts Drug Synergy in Cancers.

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.Significance: This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. Cancer Res; 78(18); 5446-57. ©2018 AACR.

Abstract PR12: A combined protein-protein interaction and genetic interaction map defines new and critical Kras effectors in non-small cell lung cancer

Abstract Despite intensive study, no drugs in clinical use specifically target KRAS-mutant tumors. Uncharacterized feedback mechanisms and parallel pathways have stymied the treatment of KRAS-mutant tumors with Raf and PI3K inhibitors, and the KRas protein itself does not easily accommodate binding of small-molecule inhibitors. These challenges demand more systematic and quantitative characterization of the physical and genetic relationships between Ras regulators and effectors. To that end, we used tandem affinity purification of Kras, Hras and Nras, their activated alleles and key proteins with known regulatory (GEFs, GAPs) or effector (Raf, RalGDS, RIN1/2) roles in both 293 cells and A549 NSCLC cells to generate a high-confidence protein-protein interaction (PPI) network. This map of 220 proteins and 1,400 physical interactions was used to design an sgRNA library with 10 guides/gene. This library was screened in Cas9-expressing A549 cells and grown for 14 days before analysis for dropout or enhanced representation of sgRNAs. Approximately 120 genes showed positive or negative growth effects. PPIs and genetic interactions (GIs) were cross-referenced with public PPI data and TCGA patient data to assemble a combined physical PPI and genetic map informed by cancer mutations. This map suggests many hypotheses for PPIs critical for growth control. This set was used to construct a sgRNA library covering 120 genes of probable relevance to the Ras pathway with ~60 “safe harbor” control sgRNAs. This library was screened in a two-cassette sgRNA system testing 14K pairwise genetic effects to identify quantitative changes in growth in A549 and H23 NSCLC lines. This screen showed &amp;gt;100 genetic interactions, which in conjunction with PPIs, identify coupling between the Raf/MEK/ERK kinase, Ral and Rap GTPase, RNA processing, and cell adhesion pathways. The screen identified new candidate effector pathways for cell adhesion, RNA processing, Rap GTPase regulation, and protein processing, including the RADIL, RGL, and RIN Kras effectors. Validation focused using the synthetic lethal interactions observed in the sgRNA screen to predict drug combinations showing drug synergy in A549 and H23 cells. Using 11-point dose titrations and isobologram analysis of drug combinations, we see strong synergy among PI3 kinase, Raf, and Erk inhibitors in these cells. Using the recently described Kras G12C inhibitor, expressed in H23 cells, we have validated that sgRNA deletion of the the key Kras effector for specific pathways including cell adhesion (RADIL), growth signaling (RAF), and endocytosis/ macropinocytosis (RIN) are affected and that use of the Kras inhibitor ARS-853 shows much reduced effects on specific Kras effector pathways in cells deleted for these effectors. These systematic data underscore the limitations of our current understanding of Kras-driven cancers, revealing new genetic vulnerabilities and target candidates. This abstract is also being presented as Poster A28. Citation Format: Marcus R. Kelly, Kyuho Han, Nancie Mooney, Edwin Jeng, Kaja Kostyrko, Alejandro Sweet-Cordero, Michael Bassik, Peter K. Jackson. A combined protein-protein interaction and genetic interaction map defines new and critical Kras effectors in non-small cell lung cancer [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr PR12.

Ensemble Prediction of Synergistic Drug Combinations Incorporating Biological, Chemical, Pharmacological, and Network Knowledge

Combinatorial therapy may reduce drug side effects and improve drug efficacy, making combination therapy a promising strategy to treat complex diseases. However, in the existing computational methods, the natural properties and network knowledge of drugs have not been adequately and simultaneously considered, making it difficult to identify effective drug combinations. Computational methods that incorporate multiple sources of information (biological, chemical, pharmacological, and network knowledge) offer more opportunities to screen synergistic drug combinations. Therefore, we developed a novel Ensemble Prediction framework of Synergistic Drug Combinations (EPSDC) to accurately and efficiently predict drug combinations by integrating information from multiple-sources. EPSDC constructs feature vector of drug pair by concatenating different types of drug similarities, and then uses these groups in a feature-based base predictor. Next, transductive learning is applied on heterogeneous drug-target networks to achieve a network-based score for the drug pair. Finally, two types of ensemble rules are introduced to combine the feature-based score and the network-based score, and then potential drug combinations are prioritized. To demonstrate the effect of the ensemble rule, comprehensive experiments were conducted to compare single models and ensemble models. The experimental results indicated that our method outperformed the state-of-the-art method in five-fold cross validation and de novo prediction tests on the two benchmark datasets. We further analyzed the effect of maximum length of the meta-path and the impacts of different types of features. Moreover, the practical usefulness of our method was confirmed in the predicted novel drug combinations. The source code of EPSDC is available at https://github.com/KDDing/EPSDC.

Abstract 1313: A combined protein-protein interaction and genetic interaction map defines new and critical Kras effectors in NSCLC

Abstract Despite intensive study, no drugs in clinical use specifically target KRAS-mutant tumors. Uncharacterized feedback mechanisms and parallel pathways have stymied the treatment of KRAS-mutant tumors with Raf and PI3K inhibitors, and the KRas protein itself does not easily accommodate binding of small-molecule inhibitors. These challenges demand more systematic and quantitative characterization of the physical and genetic relationships between Ras regulators and effectors. Thus we used tandem affinity purification of Kras, Hras and Nras, their activated alleles and key proteins with known regulatory (GEFs, GAPs) or effector (Raf, RalGDS) roles in both 293 cells and A549 NSCLC cells to generate a high-confidence protein-protein interaction (PPI) network. This map of 220 proteins and 1,400 physical interactions was used to design an sgRNA library with 10 guides/gene. This library was screened in Cas9-expressing A549 cells and grown for 14 days before analysis for dropout or enhanced representation of sgRNAs. Approximately 120 genes showed positive or negative growth effects. PPIs and genetic interactions (GIs) were cross-referenced with public PPI data and TCGA patient data to assemble a combined physical PPI and genetic map informed by cancer mutations. This map suggests many hypotheses for PPIs critical for growth control. This set was used to construct a sgRNA library covering 120 genes of probable relevance to the Ras pathway with ~60 “safe harbor” control sgRNAs. This library was screened in a two-cassette sgRNA system testing 14K pairwise genetic effects to identify quantitative changes in growth in A549 and H23 NSCLC lines. This screen showed &amp;gt;100 genetic interactions, which in conjunction with PPIs identify coupling between the Raf/MEK/ERK kinase, Ral and Rap GTPase, RNA processing and cell adhesion pathways. The screen identified new candidate effector pathways for cell adhesion, RNA processing, Rap GTPase regulation and protein processing, including the RADIL, RGL and RIN Kras effectors. Validation focused on using the synthetic lethal interactions observed in the sgRNA screen to predict drug combinations showing drug synergy in A549 and H23 cells. Using 11-point dose titrations and isobologram analysis of drug combinations, we see strong synergy among PI3 kinase, Raf and Erk inhibitors in these cells. Using the recently described Kras G12C inhibitor, expressed in H23 cells, we have validated that sgRNA deletion of the the key Kras effector for specific pathway,s including cell adhesion (RADIL), growth signaling (RAF) and endocytosis/ macropinocytosis (RIN), is affected and that use of the Kras inhibitor ARS-853 shows much-reduced effects on specific Kras effector pathways in cells deleted for these effectors. These systematic data underscore the limitations of our current understanding of Kras-driven cancers, revealing new genetic vulnerabilities and target candidates. Citation Format: Peter K. Jackson. A combined protein-protein interaction and genetic interaction map defines new and critical Kras effectors in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1313.

SAHA and cisplatin sensitize gastric cancer cells to doxorubicin by induction of DNA damage, apoptosis and perturbation of AMPK-mTOR signalling

Chemotherapy remains the most prescribed anti-cancer therapy, despite patients suffering severe side effects and frequently developing chemoresistance. These complications can be partially overcome by combining different chemotherapeutic agents that target multiple biological pathways. However, selecting efficacious drug combinations remains challenging. We previously used fission yeast Schizosaccharomycespombe as a surrogate model to predict drug combinations, and showed that suberoylanilide hydroxamic acid (SAHA) and cisplatin can sensitise gastric adenocarcinoma cells toward the cytotoxic effects of doxorubicin. Yet, how this combination undermines cell viability is unknown. Here, we show that SAHA and doxorubicin markedly enhance the cleavage of two apoptosis markers, caspase 3 and poly-ADP ribose polymerase (PARP-1), and increase the phosphorylation of γH2AX, a marker of DNA damage. Further, we found a prominent reduction in Ser485 phosphorylation of AMP-dependent protein kinase (AMPK), and reductions in its target mTOR and downstream ribosomal protein S6 phosphorylation. We show that SAHA contributes most of the effect, as confirmed using another histone deacetylase inhibitor, trichostatin A. Overall, our results show that the combination of SAHA and doxorubicin can induce apoptosis in gastric adenocarcinoma in a synthetically lethal manner, and that fission yeast offers an efficient tool for identifying potent drug combinations against human cancer cells.

Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer.

Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types.

Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients

The molecular pathways that drive cancer progression and treatment resistance are highly redundant and variable between individual patients with the same cancer type. To tackle this complex rewiring of pathway cross-talk, personalized combination treatments targeting multiple cancer growth and survival pathways are required. Here we implemented a computational-experimental drug combination prediction and testing (DCPT) platform for efficient in silico prioritization and ex vivo testing in patient-derived samples to identify customized synergistic combinations for individual cancer patients. DCPT used drug-target interaction networks to traverse the massive combinatorial search spaces among 218 compounds (a total of 23,653 pairwise combinations) and identified cancer-selective synergies by using differential single-compound sensitivity profiles between patient cells and healthy controls, hence reducing the likelihood of toxic combination effects. A polypharmacology-based machine learning modeling and network visualization made use of baseline genomic and molecular profiles to guide patient-specific combination testing and clinical translation phases. Using T-cell prolymphocytic leukemia (T-PLL) as a first case study, we show how the DCPT platform successfully predicted distinct synergistic combinations for each of the three T-PLL patients, each presenting with different resistance patterns and synergy mechanisms. In total, 10 of 24 (42%) of selective combination predictions were experimentally confirmed to show synergy in patient-derived samples ex vivo The identified selective synergies among approved drugs, including tacrolimus and temsirolimus combined with BCL-2 inhibitor venetoclax, may offer novel drug repurposing opportunities for treating T-PLL.Significance: An integrated use of functional drug screening combined with genomic and molecular profiling enables patient-customized prediction and testing of drug combination synergies for T-PLL patients. Cancer Res; 78(9); 2407-18. ©2018 AACR.