Abstract
Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.
Highlights
The possibility to integrate clinical data with high-throughput data at the single patient level has gained increasing interest in different fields of medicine [1]
We provide an automatic analysis methodology to compute the overall survival analysis (OS) and the progression-free survival (PFS) from a whole DMET dataset produced by using the Affymetrix DMET PLUS platform and successively extended by adding temporal data
This section describes the main features of the OSAnalyzer tool and presents an experimental case study of section a genomics dataset with data by using
Summary
The possibility to integrate clinical data with high-throughput data at the single patient level has gained increasing interest in different fields of medicine [1]. Clinicians can use this new integrated data to evaluate, in a more comprehensive way, the efficacy of the therapy in a cohort of patients, to draw more detailed conclusion on the benefits of the treatment, or to modify the drug dosage to reduce the side effects by following the genomic features of each patient and improving the efficacy of the treatment [2]. This scenario, introduces new challenges from a computational point of view, since the high-throughput technologies such as Generation Sequencing (NGS). Conclusions: we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients
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