Abstract
Abstract Cancer progression and resistance to therapy are driven by clonal evolution. Recent studies have shown that adult diffuse gliomas have divergent clonal dynamics. However, the genomic and epigenomic factors that influence molecular changes after treatment are not well understood. To address this, we analyzed sequencing and clinical data from 544 adult diffuse glioma patients to identify predictors of cancer evolution. Using machine learning methods CELLO2, we predict grade progression and treatment-induced hypermutation based on tumor characteristics and clinical features collected at diagnosis. We found that MYC copy number gain is more prevalent as a founding alteration in the East Asian gliomas and it is the most significant predictor of hypermutation under temozolomide treatment. Further experiments using patient-derived gliomaspheres and established glioma cell lines confirmed the critical role of MYC amplification and pathway activation in hypermutagenesis. We also showed that c-Myc binding to open chromatin and transcriptionally active regions increases the vulnerability of genomic regions to TMZ-induced mutagenesis. This study highlights MYC as an early predictor of cancer evolution and provides a machine learning platform to improve patient management by predicting cancer dynamics.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.