University of Trieste develops INCOMMON tool for cancer gene dosage analysis

Researchers at the University of Trieste, led by Professor Giulio Caravagna, have introduced a new computational approach called INCOMMON (Inference of Copy number and Mutation Multiplicity in Oncology). The open‑source method estimates how many mutated copies of a gene are present in a tumor and assesses the impact of this “mutant gene dosage” on prognosis and metastasis risk.

Published in Nature Genetics, the study applied INCOMMON to more than 60,000 clinical samples covering 39 cancer types and over 500,000 mutations, using standard targeted sequencing panels. By combining advanced statistical inference with machine‑learning models, the tool extracts additional information from existing diagnostic data without requiring new assays. The researchers suggest that the added insight could improve patient stratification and treatment decisions. The work was supported by the Fondazione AIRC.