Dominant COSMIC Mutational Signatures and Unsupervised Machine Learning Define Mechanism-Based Therapeutic Stratification in Blast-Crisis CML: Implications in Precision Medicine of Relapsed and Refractory Cancers
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Background/Objectives : Blast-crisis chronic myeloid leukemia (BC-CML) represents a genomically unstable, therapeutically refractory phase driven by clonal evolution beyond BCR::ABL1 dependence. Clinical outcomes remain poor, with median survival under one year. Mutation-level analyses inadequately resolve biological heterogeneity or inform rational patient-specific therapy. We therefore implemented a mutational process–anchored framework integrating genomics, machine learning (ML), and artificial intelligence (AI)–guided drug prioritization at single-patient resolution. Methods : Whole-exome sequencing, COSMIC mutational signature deconvolution, unsupervised ML clustering, and AI-guided pharmacogenomic prioritization were applied to 224 patients with CML (190 chronic phase, 15 accelerated phase, 19 blast crisis) and 19 healthy controls. Results : Mutation burden increased significantly with disease progression, and blast crisis exhibited complex co-mutation architectures involving TP53, BRCA2, IDH1/2, DNMT3A, JAK2, and CSF3R. While gene-level patterns were heterogeneous, dominant mutational processes converged across cases. COSMIC signature extraction stratified BC-CML into three biologically coherent clusters: homologous recombination deficiency/genomic instability (Signatures 3/5), epigenetic–metabolic dysregulation (Signatures 1/2), and oxidative stress–associated cytokine signaling mutagenesis (Signatures 13/18). These process-defined states mechanistically link DNA repair failure, chromatin remodeling, and inflammatory signaling to blast transformation. Integration with an AI-guided pharmacogenomic framework translated signature biology into rational, cluster-specific therapeutic prioritization, including PARP inhibition, IDH targeting, CDK4/6 blockade, JAK pathway inhibition, and redox-modulating strategies. Dominant signature resolution reduced multidrug ambiguity and supported single-patient therapeutic parsimony. External concordance assessment across independent pharmacogenomic datasets supported predicted vulnerabilities. Conclusions : This study establishes a mechanistically interpretable, process-anchored precision oncology framework for BC-CML linking genomic architecture to actionable therapeutic decision-making warranting clinical validation. The approach can further be implemented for AL-AI-prioritized precision treatment of other relapsed, refractory and metastatic cancers.
Source: Preprints.org
Authors: Zafar Iqbal, Abdulkareem AlGarni, Yaqob Taleb, Nasser Mohammed AlQahtani, Muhammad Farooq Sabar, Muhammad Alshuaibi, Essa Al Mansour, Mashael AlShuker, Tahani Alqurashi, Azfar Athar Ishaqui, Nawaf Alanazi, Adnan Iqbal, Amer Mehmood, Abdullah AlRuwaily, Lubna Alnuaim, Mohammed Parvez, Nouf Fallatah, ahmed Aamir Butt, Muhammad Umar Assad, Kaleem Ahmed, Sulman Basit, Sahrish Khan, Rizwan Naeem, Masood A. Shammas, Giuseppe Saglio, Sohail Rao
Institutions: National Guard Health Affairs, King Abdullah International Medical Research Center, University of the Punjab, King Khalid University, University of Karachi, King Saud University, Trinity College Dublin, International Islamic University, Islamabad, Taibah University, Albert Einstein College of Medicine, Harvard University, University of Turin
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7155696863