计算生物学
癌症
基因组
人工智能
生物信息学
生物
计算机科学
机器学习
基因
遗传学
作者
Madison Darmofal,Shalabh Suman,Gurnit Atwal,Michael Toomey,Jie‐Fu Chen,Jason C. Chang,Efsevia Vakiani,Anna M. Varghese,Anoop Balakrishnan Rema,Aijazuddin Syed,Nikolaus Schultz,Michael F. Berger,Quaid Morris
出处
期刊:Cancer Discovery
[American Association for Cancer Research]
日期:2024-02-27
标识
DOI:10.1158/2159-8290.cd-23-0996
摘要
Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically-relevant tumor type predictions to guide treatment decisions in real time.
科研通智能强力驱动
Strongly Powered by AbleSci AI