Sparse expression bases in cancer reveal tumor drivers

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Cancer genome evolution involves complex transcriptional dysregulation.
• Identifying driver genes from sequence data alone is challenging due to passenger mutations.
• Novel methods are needed to connect expression variation with driver events.

Purpose of the Study:

• To define a new category of cancer driver genes: selected expression regulators (SERs).
• To introduce a novel computational method, SPARROW, for identifying SERs.
• To validate the utility of SPARROW in discovering cancer-associated genes and biomarkers.

Main Methods:

• Developed SPARROW (SPAR: selected exp R: essi O: n regulators identified W: ith penalized regression), a sparse regression technique.
• Applied SPARROW to genome-wide tumor expression data.
• Validated identified SERs and their associations with known cancer drivers, processes, and survival outcomes.

Main Results:

• SPARROW successfully identified selected expression regulators (SERs), linking expression variation to driver events.
• SERs identified by SPARROW outperformed popular methods in detecting known driver mutations and cancer-associated genes.
• SPARROW identified PYCARD as an apoptotic biomarker for obatoclax in acute myeloid leukemia, which was validated in patient samples.

Conclusions:

• SPARROW is a powerful tool for discovering driver genes missed by sequence-based methods.
• Selected expression regulators (SERs) represent a significant category of cancer drivers.
• The PYCARD-obatoclax association highlights SPARROW's potential for identifying targeted therapies.