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.
Identify all potential conflicts of interest that might be relevant to your comment.
Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.
Err on the side of full disclosure.
If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.
Not all submitted comments are published. Please see our commenting policy for details.
Liu ET, Mockus SM. Tumor Origins Through Genomic Profiles. JAMA Oncol. Published online November 14, 2019. doi:https://doi.org/10.1001/jamaoncol.2019.3981
Browse and subscribe to JAMA Network podcasts!
Customize your JAMA Network experience by selecting one or more topics from the list below.
Create a personal account or sign in to: