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.

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使用有针对性的临床基因组测序数据预测肿瘤类型的深度学习模型

肿瘤类型指导癌症的临床治疗决策，但基于组织学的诊断仍然具有挑战性。基因组改变可以高度诊断肿瘤类型，并且已经探索了根据基因组特征训练的肿瘤类型分类器，但最准确的方法在临床上并不可行，依赖于全基因组测序（WGS）衍生的特征，或预测有限的癌症类型。我们使用临床靶向癌症基因组测序的 39,787 个实体瘤数据集中的基因组特征来开发基因组衍生诊断集成 (GDD-ENS)：一种使用深度神经网络对肿瘤类型进行分类的超参数集成。GDD-ENS 对 38 种癌症类型的高置信度预测准确率达到 93%，可与基于 WGS 的方法相媲美。GDD-ENS 还可以指导罕见类型和原发灶未知的癌症的诊断，并结合患者特定的临床信息以改进预测。总体而言，将 GDD-ENS 整合到前瞻性临床测序工作流程中可以提供临床相关的肿瘤类型预测，以实时指导治疗决策。