Synergy Prediction Research Articles

A simple gene set-based method accurately predicts the synergy of drug pairs.

BackgroundThe advance in targeted therapy has greatly increased the effectiveness of clinical cancer therapy and reduced the cytotoxicity of treatments to normal cells. However, patients still suffer from cancer relapse due to the occurrence of drug resistance. It is of great need to explore potential combinatorial drug therapy since individual drug alone may not be sufficient to inhibit continuous activation of cancer-addicted genes or pathways. The DREAM challenge has confirmed the potentiality of computational methods for predicting synergistic drug combinations, while the prediction accuracy can be further improved.MethodsBased on previous reports, we hypothesized the similarity in biological functions or genes perturbed by two drugs can determine their synergistic effects. To test the feasibility of the hypothesis, we proposed three scoring systems: co-gene score, co-GS score, and co-gene/GS score, measuring the similarities in genes with significant expressional changes, enriched gene sets, and significantly changed genes within an enriched gene sets between a pair of drugs, respectively. Performances of these scoring systems were evaluated by the probabilistic c-index (PC-index) devised by the DREAM consortium. We also applied the proposed method to the Connectivity Map dataset to explore more potential synergistic drug combinations.ResultsUsing a gold standard derived by the DREAM consortium, we confirmed the prediction power of the three scoring systems (all P-values < 0.05). The co-gene/GS score achieved the best prediction of drug synergy (PC-index = 0.663, P-value < 0.0001), outperforming all methods proposed during DREAM challenge. Furthermore, a binary classification test showed that co-gene/GS scoring was highly accurate and specific. Since our method is constructed on a gene set-based analysis, in addition to synergy prediction, it provides insights into the functional relevance of drug combinations and the underlying mechanisms by which drugs achieve synergy.ConclusionsHere we proposed a novel and simple method to predict and investigate drug synergy, and validated its efficacy to accurately predict synergistic drug combinations and to comprehensively explore their underlying mechanisms. The method is widely applicable to expression profiles of other drug treatments and is expected to accelerate the realization of precision cancer treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0310-3) contains supplementary material, which is available to authorized users.

Abstract 790: Coupled dynamics of drug synergy, gene expression, and alternative splicing in combination therapies in breast cancer

Abstract Drug combination therapies in the cancer setting often succeed where mono-therapies fail, facilitating durable and robust responses that may curtail metastases and even be accompanied by milder side-effects. Predicting synergistic and antagonistic combinations based on the gene expression data of mono-therapy drug-tumor response is an important open problem wherein the role of transcriptional splicing dynamics is often ignored or too poorly correlated with phenotypes to be useful. In this work we leverage the inherent transcript/exon level resolution of RNA-seq data to infer combination-specific splicing signatures associated with additive and synergistic (subadditive) drug combinations as defined by canonical viability measurements in a time-course experiment. We integrate splicing information into a gene regulatory network (GRN) to compute leading order effects of splicing on the GRN by adaptive network refinement. Briefly, we use RNA-seq to study the transcriptional response over time (0, 3, 6, 9, 12, and 24 h) for three drugs (A, B and C) and their combinations (AB, AC and BC) in MCF-7 (ER+) breast cancer cells lines. Cell viability measurements show that one of the combinations (AB) is strongly synergistic, whereas the other two (AC and BC) are merely additive. We show via rigorous linear modeling of RNA-seq count data at the exon level that in addition to a novel transcriptional signature driven by differential expression, the combination AB transcriptional landscape is characterized by persistent alternative splicing signatures mostly comprised of genes which are not differentially expressed with respect to A or B but whose functional role has been dramatically changed by the addition (deletion) of a key regulatory protein domain encoded by the extra(missing) exon. We construct an isoform-level co-expression network (IRN) to probe the regulatory changes this dynamical splicing induces and show that it crucially contributes to the emergence of extensive transcriptional cascades by creating and removing key gene-gene correlations and altering the modular structure of the network. Using this approach we show that a key isoform of Protein beta-arrestin-1 (ARBB1), which participates in desensitization of G-protein coupled receptors through intracellular response to Estrogen, is enriched in the calcium ion-dependent exocytosis module of IRN, while ARRB1 remains functionally uninformative in the regular GRN. Our results suggest that any gene-signature based drug synergy prediction algorithm must take into account alternative splicing in order to effectively characterize the novel pathways being activated in the synergistic drug-tumor interaction. Citation Format: Xintong Chen, Gustavo Stolovitzky, Bojan Losic. Coupled dynamics of drug synergy, gene expression, and alternative splicing in combination therapies in breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 790.

Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer.

The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown.

Abstract PR04: A network-based approach for drug synergy prediction from gene expression data

Abstract The goal of combination therapy is to reduce the onset of drug resistance, while inducing a synergistic therapeutic effect, akin to synthetic lethality. Specifically, the measured reduction in tumor viability induced by a synergistic compound combination should be higher than expected based on additive behavior. Yet, assessing the synthetic lethality potential of a large collections of compounds is impractical, and the ability to predict drug synergy and synthetic lethality using quantitative approaches has not been experimentally validated. In addition, synthetic lethality has been shown to be cell-context dependent. To address these issues, we propose and experimentally validate a novel computational framework to efficiently explore the space of synthetic lethality in context specific fashion, to predict synergistic compound combinations. The reconstruction of accurate, context-specific molecular regulatory networks has shown significant potential in the rational elucidation of synergistic regulatory mechanisms in the cell. Our method leverages cell context-specific regulatory networks dissected by the ARACNe algorithm to infer changes in the activity of regulatory proteins (transcription factors and signaling proteins) based on the expression of their target genes. Specifically, we infer changes in protein activity, rather than in their corresponding gene expression, because most small molecule compounds modulate the activity but not the expression of their target proteins. We define a compound's functional Mode of Action (fMoA) as the specific protein activity signature induced by the compound in a specific cell type. To predict synergistic compounds, we propose two alternative approaches. (1) Target Molecular Phenotype (TMP): we first define a desired TMP to be induced by compound combination (e.g. cell death). We then leverage large collections of expression profiles following compound perturbations, such as the Connectivity Map (CMAP), to identify compounds whose combined fMoA most closely matches the TMP. We further prioritize compound pairs based on (a) the orthogonality of their individual fMoA, on the assumption that compounds with the same fMoA will affect the same pathways and thus will have a more additive behavior and (b) the complementary coverage of the TMP by their fMoA (i.e. compounds that implement distinct and complementary components of the TMP). (2) Sensitivity Induction (SI): we first obtain a protein activity signature of sensitivity to a given compound CA, by comparing the gene expression profiles of CA-sensitive vs CA-resistant cells. We then search for other compounds, whose fMoA most closely matches the CA's sensitivity signature, which are thus likely to act as sensitizers. We test the methods by using CMAP, as well as a dataset generated in our lab by profiling a set of three diffuse large B cell lymphoma (DLBCL) cell lines at 6h, 12h and 24h following perturbation with 92 drugs. We found that fMoA accurately captures the molecular targets of the assayed compounds. We then tested the synergistic predictions by measuring their overlap with all the pairs of 14 distinct compounds assessed in DLBCL cells (DREAM8 challenge data). For this analysis, we used both a general B-cell toxicity signature and an ABC-DLBCL-specific tumor progression signature as TMP. Finally, we studied the use of the SI approach to identify compounds that are candidate sensitizer of estrogen therapy (tamoxifen) in luminal ER+ breast tumors. This abstract is also presented as Poster A38. Citation Format: Mariano J. Alvarez, Yao Shen, Charles Karan, Mukesh Bansal, Michela Mattioli, Sergey Pampou, Andrea Califano. A network-based approach for drug synergy prediction from gene expression data. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR04.