Extending differential gene expression testing to handle genome aneuploidy in cancer

Genome aneuploidy, characterized by Copy Number Variations (CNVs), profoundly alters gene expression in cancer. CNVs can directly influence transcription through gene dosage effects or indirectly through compensatory regulatory mechanisms. However, existing differential gene expression (DGE) testing methods do not differentiate between these mechanisms, conflating all expression changes and limiting biological interpretability. This misclassification can obscure key genes involved in tumor adaptation and progression, hindering biomarker discovery and leading to incomplete insights into cancer biology.

To address this, we developed DeConveil, a computational framework that extends traditional DGE analysis by integrating CNV data. Using a Generalized Linear Model (GLM) with a Negative Binomial (NB) distribution, DeConveil models RNA-seq expression counts while accounting for copy number (CN) gene dosage effects. We proposed a more fine-grained gene decomposition into dosage-sensitive (DSGs), dosage-insensitive (DIGs), and dosage-compensated (DCGs), which explicitly de-couples changes due to CNAs and bona fide changes in transcriptional regulation.

Analysis of TCGA datasets from aneuploid solid cancers resulted in notable reclassification of genes, refining and expanding upon the results from conventional methods. Functional enrichment analysis identified distinct biological roles for DSGs, DIGs, and DCGs in tumor progression, immune regulation, and cell adhesion. In a breast cancer case study, DeConveil’s CN-aware analysis facilitated the identification of both known and novel prognostic biomarkers, including lncRNAs, linking gene expression signatures to survival outcomes. Utilizing these biomarkers for each gene group significantly improved patient risk stratification, yielding more accurate predictions compared to conventional methods.

These results highlight DeConveil’s ability to disentangle CNV-driven from regulatory transcriptional changes, enhancing gene classification and biomarker discovery. By improving transcriptomic analysis, DeConveil provides a powerful tool for cancer research, precision oncology, with potential applications in therapeutic target identification.