Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance

Abstract

BackgroundIdentification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance.ResultsWe developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein–protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group.ConclusionsOur results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1282-3) contains supplementary material, which is available to authorized users.

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

“…Various studies have suggested that including information on the interaction of the genes (and their protein products) or their involvement in different pathways can improve the accuracy of different bioinformatics tasks [26] such as gene prioritization [27], gene function prediction [28], gene set characterization [29], and tumor subtyping [30]. Since the genes (and their protein products) involved in a drugs mechanism of action biochemically and functionally interact with each other, we sought to determine whether including these interactions could improve CDR prediction.…”

Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform

“…Knowledge-guided gene prioritization: KnowEnG also offers a knowledge-guided mode of this pipeline, where the ProGENI algorithm of Emad et al 27 is used to incorporate a network encoding prior knowledge into the identification of phenotype-related genes (Figure 3A), using random walk-based techniques similar to those used in the NBS clustering approach 18 . We had previously tested ProGENI on the task of prioritizing drug response-related genes.…”

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform

“…Knowledge-guided gene prioritization. KnowEnG also offers a knowledge-guided mode of this pipeline, where the ProGENI algorithm of Emad and colleagues [47] is used to incorporate a network encoding prior knowledge into the identification of phenotype-related genes (Fig 4A), using random walk-based techniques similar to those used in the NBS clustering approach [11]. We had previously tested ProGENI on the task of prioritizing drug responserelated genes.…”

“…is available on a collection of samples, along with a phenotypic score for each sample. For instance, Emad and colleagues [47] used this pipeline to identify genes whose basal expression in a cancer cell line is predictive of the cell line's response to a cytotoxic treatment. Similar analyses have been performed in other published studies [48], although without incorporating a knowledge network.…”