Abstract
Heterogeneity is a fundamental feature of complex phenotypes. So far, genomic screenings have profiled thousands of samples providing insights into the transcriptome of the cell. However, disentangling the heterogeneity of these transcriptomic Big Data to identify defective biological processes remains challenging. Here we present GSECA, a method exploiting the bimodal behavior of RNA-sequencing gene expression profiles to identify altered gene sets in heterogeneous patient cohorts. Using simulated and experimental RNA-sequencing data sets, we show that GSECA provides higher performances than other available algorithms in detecting truly altered biological processes in large cohorts. Applied to 5941 samples from 14 different cancer types, GSECA correctly identified the alteration of the PI3K/AKT signaling pathway driven by the somatic loss of PTEN and verified the emerging role of PTEN in modulating immune-related processes. In particular, we showed that, in prostate cancer, PTEN loss appears to establish an immunosuppressive tumor microenvironment through the activation of STAT3, and low PTEN expression levels have a detrimental impact on patient disease-free survival. GSECA is available at https://github.com/matteocereda/GSECA.