Abstract Advanced cancer genomics relies on multi-region sequencing to detect intra-tumor heterogeneity. These data allow for backtracking tumor’s evolutionary history by inferring its clonal population structure (Roth et al. 2014) and reconstructing a phylogenetic tree (Malikic et al. 2015). However, methods for mapping subclonal copy number changes onto tumor evolutionary trees are still lacking. McGranahan et al. 2017 presented LOHHLA, a tool for estimation of allele-specific HLA loss from sequencing data, which demonstrated the effects of loss of heterozygosity of HLA on early-stage non-small-cell lung cancer. Here, we present MAPping SubClonal Events (MAPSCE), a tool for mapping subclonal copy number events on the tumor evolutionary trees. Similar to LOHHLA, MAPSCE uses Quadratic Programming for the branch test, where copy-number states before and after a branch are estimated, one branch at a time. Additionally, it relies on the Bayesian Information Criterion from bootstrapped samples to compare the results for each branch and the null hypothesis (H0 = no subclonal event). While mapping loss of heterozygosity events of HLA allele in TRACERx 100 cohort (Jamal-Hanjani et al. 2017) MAPSCE pre-empts the need for manual review on LOHHLA’s initial results, can take into account multiple possible solutions and maintains high accuracy of the results. Using simulated data sets we tested the robustness of MAPSCE to the addition of noise to the cluster CCFs that define each tumor clone. MAPSCE outperforms LOHHLA’s mapping of subclonal LOH events when adding noise up to ±25% CCF. Sub-clonal LOH affecting larger clones are easier to detect and both LOHHLA and MAPSCE produce similar results. However, only MAPSCE maintains high accuracy in detecting events affection smaller clones. In data sets with simulated amplification and duplication events, MAPSCE can also identify duplication and amplification events with at least 70% accuracy up to ±20% CCF noise range. In the future, we are planning to extend this approach to other data types such as gene expression and methylation data, a combination of which would allow us to identify the biological events underlying the formation of new tumor clones, to ultimately integrate these in a comprehensive model of cancer evolution. Citation Format: Mark Tran, Nicholas McGranahan, Chris Barnes, Javier Herrero. Mapping of subclonal events in multi-region multi-omics oncogenomics studies [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A016.