One way to look at genome panels in cancer is as a collection of hundreds of individual genetic diagnostic tests, such as, EGFR mutation, EML4-ALK translocation, that can each be used to extract useful clinical information to guide therapy. However, the behavior of the collection of mutations can also act as a clinical parameter of value. For example, the tumor mutational burden (TMB), which scores the total mutational load within a tumor, is used to measure the proclivity of a tumor to respond to immuno-oncologic agents.¹ In this issue of JAMA Oncology, Penson et al² from Memorial Sloan Kettering Cancer Center advanced this concept further in describing an approach that uses artificial intelligence to assess higher meaning of the mutational profile from a 468-gene cancer panel, the Memorial Sloan Kettering–Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). From a training cohort of tumors from 7791 patients with a variety of cancers, they used single-nucleotide variations, indels, copy number changes, and structural rearrangements to build classifiers that could distinguish the tissue of origin of each tumor. They then validated the classifier in an independent test cohort of 11 644 patient tumors. Their results showed an accuracy of between 73.8% and 74.1% in predicting the correct tissue of origin with greater successes in some tumor types than others. The best predictor was for uveal melanomas, gliomas, and colorectal cancers, whereas, the poorest was for esophagogastric, ovarian, and head and neck cancer, cancers with greatest genomic mutational heterogeneity. A unique aspect of their predictor is that a probability score was assigned to each result that allowed the clinician to have an estimate of the certainty of the tissue assignment. Thus, even in those problematic tumors, misdiagnosis could be avoided by censoring the ambiguous cases.

查看癌症基因组面板的一种方法是收集数百个单独的基因诊断测试，例如EGFR突变、EML4-ALK易位，每一个都可用于提取有用的临床信息以指导治疗。然而，突变集合的行为也可以作为价值的临床参数。例如，肿瘤突变负荷 (TMB) 对肿瘤内的总突变负荷进行评分，用于衡量肿瘤对免疫肿瘤药物反应的倾向。¹在本期JAMA Oncology中，Penson 等人²来自纪念斯隆凯特琳癌症中心的纪念斯隆凯特琳癌症中心进一步推进了这一概念，描述了一种使用人工智能评估来自 468 基因癌症小组的突变谱的更高意义的方法，即纪念斯隆凯特琳可操作癌症目标的综合突变分析 (MSK-IMPACT) ）。从来自 7791 名患有各种癌症的患者的肿瘤训练队列中，他们使用单核苷酸变异、插入缺失、拷贝数变化和结构重排来构建可以区分每个肿瘤起源组织的分类器。然后，他们在一个包含 11 644 名患者肿瘤的独立测试队列中验证了分类器。他们的结果显示，在预测正确的起源组织方面的准确性在 73.8% 到 74.1% 之间，并且在某些肿瘤类型中的成功率高于其他类型。最好的预测因子是葡萄膜黑色素瘤、神经胶质瘤和结直肠癌，而最差的是食管癌、卵巢癌和头颈癌，这些癌症具有最大的基因组突变异质性。他们的预测器的一个独特方面是，为每个结果分配了一个概率分数，使临床医生能够估计组织分配的确定性。因此，即使在那些有问题的肿瘤中，也可以通过审查模棱两可的病例来避免误诊。