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Machine learning algorithm analysis using a commercial 592-gene NGS panel to accurately predict tumor lineage for carcinoma of unknown primary (CUP).
Abstract
3083
Background: The diagnosis of a malignancy is typically informed by clinical presentation and tumor tissue features including cell morphology, immunohistochemistry, cytogenetics, and molecular markers. However, in approximately 5-10% of cancers, ambiguity is high enough that no tissue of origin can be determined and the specimen is labeled as a Cancer of Occult\Unknown Primary (CUP). Lack of reliable classification of a tumor poses a significant treatment dilemma for the oncologist leading to inappropriate and/or delayed treatment. Methods: 40,000 tumor patients with NGS data were used to construct a multiple parameter lineage-specific classification system using an advanced machine learning approach. The dataset for each classifier was split 50% for training and the other 50% for testing. The training task for each classifier was to identify the cases that were similar to the cases it was trained on against a backdrop of randomly selected cases of other histological origins. Results: Tumor lineage classifiers predicted the correct classifications where the primary site was known with accuracies ranging between 85% and 95%. When applied to CUP cases (n = 500), an unequivocal result could be obtained 100% of the time. Conclusions: Lineage predictors can render a histologic diagnosis to CUP cases that can inform treatment and potentially improve outcomes.
... Caris has developed an analysis based on a machine learning algorithm trained and validated on more than 40 000 tumor samples with NGS data to identify primary tumor site, with tumor lineage classifier accuracy ranging from 82% to 96%. 61 This assay, dubbed the MI Genomic Profiling Similarity (GPS) score, is now commercially available. Overall, these early results seem to be comparable to the accuracy of previous tissue of origin tests, although whether these result in clinically meaningful improvements in outcome remains unknown. ...
Cancers of unknown primary (CUPs) are histologically confirmed, metastatic malignancies with a primary tumor site that is unidentifiable on the basis of standard evaluation and imaging studies. CUP comprises 2-5% of all diagnosed cancers worldwide and is characterized by early and aggressive metastasis. Current standard evaluation of CUP requires histopathologic evaluation and identification of favorable risk subtypes that can be more definitively treated or have superior outcomes. Current standard treatment of the unfavorable risk subtype requires assessment of prognosis and consideration of empiric chemotherapy. The use of molecular tissue of origin tests to identify the likely primary tumor site has been extensively studied, and here we review the rationale and the evidence for and against the use of such tests in the assessment of CUPs. The expanding use of next generation sequencing in advanced cancers offers the potential to identify a subgroup of patients who have actionable genomic aberrations and may allow for further personalization of therapy.
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