The intricate interplay between somatic mutations and copy number alterations critically influences tumour evolution and patient prognosis. Traditional genomic studies often overlook this interplay by analysing these two biomarker types in isolation. We developed INCOMMON, a computational method to detect allele-specific copy number alterations from clinical targeted panels without matched normal, discover recurrent tumour-specific patterns of co-existing mutations and copy-number alterations, and stratify patients based on these composite genotypes for downstream analyses of survival, metastatic propensity and organotropism. The tool can be used as an open-source R package available at https://github.com/caravagnalab/INCOMMON, and a shiny application available at https://ncalonaci.shinyapps.io/incommon/. This repository contains all the scripts that we used to analyse PCAWG, TCGA, MSK-MetTropism and AACR GENIE-Dfci data, and all the relevant results in the form of data tables.