Somatic copy number aberrations (SCNAs) are key genomic events in cancer and represent potential targets for individualized therapies [1]. ASCAT (allele-specific copy number analysis of tumours) [2] is a widely used tool for inferring SCNAs from allele-specific genomic signals. It involves a bi-variate signal denoising step based on a zero-norm penalized least-squares criterion called ASPCF (allele-specific piecewise constant function), where a regularization parameter must be selected to strike a balance between the smoothness of the solution and the fitting of all genomic data. This may limit its sensitivity to focal alterations—especially in noisy data such as those derived from formalin-fixed paraffin-embedded (FFPE) samples. We introduce a multi-resolution enhancement to ASCAT that automatically adapts the smoothing process across chromosomes via data-driven parameter selection. Specifically, we refine the Allele-Specific Piecewise Constant Function (ASPCF) algorithm [2] by incorporating a Bayesian Information Criterion-based strategy [3,4] to optimize regularization strength on a per-chromosome basis. This adaptive framework addresses the heterogeneous signal quality often seen in clinical samples, including FFPE-derived data, where elevated noise levels can obscure clinically relevant SCNAs. Tested on whole-genome and whole-exome sequencing data, our method demonstrates improved detection of patient-specific genomic alterations with reduced overfitting. Validation by clinical experts confirms its potential to enhance the resolution and reliability of SCNA calls, thus facilitating more robust and automated genomic profiling for personalized medicine applications. As part of the DHEAL-COM project, we will further develop this approach by integrating patient-specific mutation data into the model, aiming to inform the computational analysis with biologically meaningful individual profiles. In particular, we will investigate how to combine SCNA information with chemical reaction networks to model and predict the functional impact of genomic alterations, with the ultimate goal of advancing personalized therapeutic strategies.