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

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. Supplementary Information The online version contains supplementary material available at 10.1007/s40291-023-00650-5.

Assessing the utility of molecular diagnostic classification for cancers of unknown primary

“…The Tempus TO test is a CAP/CLIA validated molecular diagnostic classifier that uses whole‐exome capture RNA‐seq data as input to provide diagnostic predictions from 68 possible subtypes. The model was trained on 43,726 tumor samples of known origin and has an overall accuracy of 91% based on an independent validation cohort consisting of 9210 tumor samples 11 . For individual patient records, results are reported with one or more predicted diagnoses including the probability that the sample belongs to the given label.…”

“…The model was trained on 43,726 tumor samples of known origin and has an overall accuracy of 91% based on an independent validation cohort consisting of 9210 tumor samples. 11 For individual patient records, results are reported with one or more predicted diagnoses including the probability that the sample belongs to the given label. Samples where the highest-probability label is lower than the 30% (TO v1.0) or 35% (TO v1.1) threshold are reported with an "indeterminate" result.…”

“…[8][9][10] The Tempus Tumor Origin (TO) test uses RNA expression data to select from 68 possible histological subtypes and showed a 91% classification accuracy when applied to an independent validation set of samples with known primary. 11 A limited number of studies have evaluated the clinical utility of tissue-of-origin diagnosis via molecular diagnostic classifier in the setting of CUP owing to methodological challenges with studying this population. 12 Several randomized studies have found no significant difference in CUP patient outcomes according to whether they received tissue-specific or empiric therapies, 13,14 while another showed that patients receiving tissue-specific therapies have improved outcomes.…”

“…Numerous assays that rely on either genomic profiling, microRNA expression, DNA methylation, transcriptome expression, or some combination of these data sources have been developed to aid in diagnosing the tissue of origin for CUP patients 8–10 . The Tempus Tumor Origin (TO) test uses RNA expression data to select from 68 possible histological subtypes and showed a 91% classification accuracy when applied to an independent validation set of samples with known primary 11 …”

A review on trends in development and translation of omics signatures in cancer

“… [152] , [153] . These new classifiers can in general achieve an overall accuracy above 90% and support classification of more than 30 types of tumors from different origins [154] , [155] .

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