Abstract
BackgroundThe advent of high-throughput technologies to profile human tumors has generated unprecedented insight into our molecular understanding of cancer. However, analysis of such high dimensional data is challenging and requires significant expertise which is not routinely available to many cancer researchers.ResultsTo overcome this limitation, we developed a freely accessible and user friendly Program to Identify Molecular Signatures (PIMS). Importantly, such signatures allow important insight into cancer biology, as well as provide clinical tools to identify potential biomarkers that might provide means to accurately stratify patients into different risk or treatment groups. We evaluated the performance of PIMS by identifying and testing predictive and prognostic gene signatures for breast cancer, using multiple breast tumor microarray cohorts representing hundreds of patients. Importantly, PIMS identified signatures classified patients into high and low risk groups with at least similar performance to other commonly used gene signature selection techniques.ConclusionsOur program is contained entirely within a Microsoft Excel file and therefore requires no installation of any additional programs or training. Hence, PIMS provides an accessible tool for cancer researchers to identify predictive and prognostic gene signatures to advance their research.Electronic supplementary materialThe online version of this article (doi:10.1186/1756-0500-7-546) contains supplementary material, which is available to authorized users.
Highlights
The advent of high-throughput technologies to profile human tumors has generated unprecedented insight into our molecular understanding of cancer
We conclude that Program to Identify Molecular Signatures (PIMS) provides an accessible tool for cancer researchers to identify predictive and prognostic gene signatures to advance their research aims
Because it is difficult to know, a priori, the optimal number of n-features to include in a gene signature, we introduced leave-one-out cross-validation (LOOCV) as a means to identify an optimal number of features to include
Summary
The advent of high-throughput technologies to profile human tumors has generated unprecedented insight into our molecular understanding of cancer Analysis of such high dimensional data is challenging and requires significant expertise which is not routinely available to many cancer researchers. We demonstrate its use to identify prognostic gene signatures, which stratify breast cancer patients into high and low risk groups, as well as predictive gene signatures, which stratify breast cancer patients into chemotherapy responsive and nonresponsive groups. These findings suggest that our program is robust and can be used to develop predictive and prognostic gene signatures for user defined contexts. We conclude that PIMS provides an accessible tool for cancer researchers to identify predictive and prognostic gene signatures to advance their research aims
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