Abstract 1302: OCTAD: An open cancer therapeutic discovery workspace in the era of precision medicine
Authors: Chen Bin 
Published: August 2018

Rapidly decreasing costs of RNA sequencing have enabled large-scale profiling of cancer tumor samples with precisely defined clinical and molecular features (e.g., low-grade IDH1 mutant glioma). Identifying drugs targeting a specific subset of cancer patients, particularly those who do not respond to conventional treatments, is critically important for translational research. Many studies have demonstrated the utility of a systems-based approach that connects cancers to efficacious drugs through gene expression signatures to prioritize drugs from a large drug library. From our previous work on liver cancer, Ewing's sarcoma, and basal cell carcinoma, we have shown that the success of this approach is made possible by critical procedures, such as quality control of tumor samples, selection of appropriate reference tissues, evaluation of disease signatures, and weighting cancer cell lines. There is a plethora of relevant datasets and analysis modules that are publicly available, yet are isolated in distinct silos, making it tedious to implement this approach in translational research. As such, we present the current protocol, which we envision as a best practice to prioritize drugs for further experimental evaluation, primarily based on open transcriptomic datasets and the free open-source R language and Bioconductor packages. In this project, we retrieved patient tumor samples based on specified clinical and/or molecular features from the Genomic Data Commons Data Portal using an API. We then created a gene expression signature for these samples through employing normalized RNA-seq counts processed in the UCSC Xena project, where all RNA-seq samples from TCGA, TARGET, and GTEx were aligned and normalized using the same pipeline. We evaluated the quality of samples based on their purity and correlation with cancer cell lines. The reference tissue samples were selected based on their profile similarity with GTEx samples. We evaluated each disease signature via a cross-validation approach. We then created drug signatures using a similar procedure from large-scale, open-access platforms, namely the LINCS L1000 library, which consists of over 20,000 compounds. Our pipeline can then compute and assess the reversal potency between the disease signature and each drug signature. The drugs that present high reversal potency are prioritized as drug hits. Finally, we performed enrichment analysis of drug hits to identify compelling enriched targets and pathways. For our pilot study, we use IDH1 mutant oligodendroglioma as a case study, where the efficacy of over 300 LINCS compounds was measured in three relevant cell lines. We have shown that our prediction corroborates with the experimental data significantly. We envision that this open workspace will help efficiently identify new therapeutic candidates for other cancers.

Citation Format: Bin Chen. OCTAD: An open cancer therapeutic discovery workspace in the era of precision medicine [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 1302.