Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin

Jackson Michuda, Alessandra Breschi, Joshuah Kapilivsky, Kabir Manghnani, Calvin McCarter, Adam J. Hockenberry, Brittany Mineo, Catherine Igartua, Joel T. Dudley, Martin C. Stumpe, Nike Beaubier, Maryam Shirazi, Ryan Jones, Elizabeth Morency, Kim Blackwell, Justin Guinney, Kyle A. Beauchamp, Timothy Taxter

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Introduction: Cancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings—in addition to ambiguous clinical presentations such as recurrence versus new primary—a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8–11 months. Methods: Here, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype. Results: We show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. Discussion: Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.

Original languageEnglish
Pages (from-to)499-511
Number of pages13
JournalMolecular Diagnosis and Therapy
Volume27
Issue number4
DOIs
StatePublished - Jul 2023
Externally publishedYes

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