Harnessing Quantum Gradient Machine Learning to Decode Subtelomeric Methylation in Telomere Maintenance Pathways
Don Roosan, Rubayat Khan, Saif Nirzhor, Avik Mahata, Hasiba Khan
Abstract
The overarching objective of this study is to determine whether subtelomeric DNA methylation states directly govern the choice of telomere length maintenance mechanisms in cancer cells that depend on either alternative lengthening of telomeres (ALT) or telomerase (TERT). Prior evidence suggests that ALT+ cells frequently exhibit hypomethylated subtelomeric regions, facilitating homologous recombination, whereas TERT+ cells maintain higher levels of subtelomeric methylation that support telomerase-based elongation. By integrating BioNano DLS optical mapping data, quantum-derived probabilities for coverage and position, and transcriptomic profiling of ATRX, DAXX, and TERT, this study aims to clarify how methylation-mediated chromatin accessibility affects telomere biology in cancer.