Transcriptomic buffering detection: a computational approach to identifying essential gene dependencies in pancreatic cancer.

MOTIVATION: Loss-of-function (LOF) mutations in essential genes (Goners) should be lethal, yet some tumours survive through compensatory buffering mechanisms. Existing computational methods for identifying synthetic lethal (SL) interactions focus on known tumour suppressor losses, missing essential gene dependencies. We developed a computational pipeline to identify SL buffering mechanisms by correlating Goner LOF burden with transcriptomic compensation.

RESULTS

Applying our pipeline to pancreatic adenocarcinoma (PAAD), we integrated CRISPR essentiality screens (DepMap), mutation data, and RNA expression profiles from 165 TCGA-PAAD cases. This PAAD analysis supports the feasibility of the pipeline but does not nominate a transcriptomic buffer; applicability to other cancer types remains to be tested.

AVAILABILITY AND IMPLEMENTATION: Analysis code and documentation are available at https://github.com/d3dd/goblet. Implemented in Python 3.9+ using pandas, scipy, and gseapy. All data are publicly available from TCGA and DepMap.