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Platinum-based chemotherapies are first-line treatments for ovarian cancer (OC) patients. Although chemotherapy has a high initial response rate, some patients exhibit inherent chemo-resistance. With advancements of molecular and genomic profiling, it is of high interest to identify molecular and genomic signatures predictive of chemo- sensitivity priori to treatment initiation in order to better personalize care decisions. Previous efforts have made use of mRNA expression levels of selected genes responsible for repairing DNA damage, under the hypothesis that chemo efficacy is associated with their proficiency. However, the resulting scores have been difficult to interpret. In this study, we designed a single-sample based approach known as eCARD to investigate chemo-sensitivity in ovarian cancer patients from The Cancer Genome Atlas. We demonstrated that the proposed single-sample based approach can lead to a molecular-based chemo-sensitivity score predictive of prognosis, which validates in 5 independent cohorts, and associates with increasing mutation burden and likelihood of BRCA1/2 mutation.

Heterogeneous drug data representation among different druggable genome knowledge resources and datasets delays effective cancer therapeutic target discovery within the broad scientific community. The objective of the present paper is to describe the challenges and lessons learned from our efforts in developing and evaluating a standards-based drug normalization framework targeting cancer druggable genome datasets. Our findings suggested that mechanisms need to be established to deal with spelling errors and irregularities in normalizing clinical drug data in The Cancer Genome Atlas (TCGA), whereas the annotations from NCI Thesaurus (NCIt) and PubChem are two layers of normalization that potentially bridge between the clinical phenotypes and the druggable genome knowledge for effective cancer therapeutic target discovery.

Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.

The underlying molecular mechanisms of adverse drug events (ADEs) associated with cancer therapy drugs may overlap with their antineoplastic mechanisms. In a previous study, we developed an ADE-based tumor stratification framework (known as ADEStrata) with a case study of breast cancer patients receiving aromatase inhibitors, and demonstrated that the prediction of per-patient ADE propensity simultaneously identifies high-risk patients experiencing poor outcomes. In this study, we aim to evaluate the ADEStrata framework with a different tumor type and chemotherapy class - ovarian cancer treated with platinum chemotherapeutic drugs. We identified a cohort of ovarian cancer patients receiving cisplatin (a standard platinum therapy) from The Cancer Genome Atlas (TCGA) (n=156). We demonstrated that somatic variant prioritization guided by known ADEs associated with cisplatin could be used to stratify patients treated with cisplatin and uncover tumor subtypes with different clinical outcomes.