Machine Learning Uncovers Previously Undetectable Genetic Deletions in Cancer Panel Sequencing

We developed a novel machine-learning pipeline for detection of genetic deletions and zygosity evaluation within MASTERsg, a precision oncology program using the TSO500 DNA gene panel to identify targetable alterations in advanced cancers. The TSO500 pipeline detects copy number variations (CNVs) through bin count averaging but may miss focal CNVs not meeting thresholds. As a proof-of-concept we used deletions in PTEN, which confer sensitivity to mTOR and Akt inhibition. An Extreme Gradient Boosting model trained on tumor samples with PTEN deletions and PTEN wild-type status enabled the correct identification of missed focal deletions, which were subsequently confirmed by immunohistochemistry. Additional cases of PTEN loss could be identified in 34 of 204 previously profiled, seemingly uneventful samples, which were validated in 75% of stainable cases. The findings underscore the substantial clinical potential of this method to uncover therapeutically relevant alterations beyond established diagnostic pipelines.

• Type: Other
• Archive: European Genome-phenome Archive (EGA)