Machine learning-based extrachromosomal DNA identification in large-scale cohorts reveals its clinical implications in cancer

基于机器学习的大规模群体染色体外 DNA 识别揭示了其在癌症中的临床意义

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作者:Shixiang Wang #, Chen-Yi Wu #, Ming-Ming He #, Jia-Xin Yong #, Yan-Xing Chen, Li-Mei Qian, Jin-Ling Zhang, Zhao-Lei Zeng, Rui-Hua Xu, Feng Wang, Qi Zhao

Abstract

The clinical implications of extrachromosomal DNA (ecDNA) in cancer therapy remain largely elusive. Here, we present a comprehensive analysis of ecDNA amplification spectra and their association with clinical and molecular features in multiple cohorts comprising over 13,000 pan-cancer patients. Using our developed computational framework, GCAP, and validating it with multifaceted approaches, we reveal a consistent pan-cancer pattern of mutual exclusivity between ecDNA amplification and microsatellite instability (MSI). In addition, we establish the role of ecDNA amplification as a risk factor and refine genomic subtypes in a cohort from 1015 colorectal cancer patients. Importantly, our investigation incorporates data from four clinical trials focused on anti-PD-1 immunotherapy, demonstrating the pivotal role of ecDNA amplification as a biomarker for guiding checkpoint blockade immunotherapy in gastrointestinal cancer. This finding represents clinical evidence linking ecDNA amplification to the effectiveness of immunotherapeutic interventions. Overall, our study provides a proof-of-concept of identifying ecDNA amplification from cancer whole-exome sequencing (WES) data, highlighting the potential of ecDNA amplification as a valuable biomarker for facilitating personalized cancer treatment.

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