Cancer lineage/tissue-of-origin assignment in cancers of unknown primary remains a challenge even when aided by massively parallel sequencing. The stakes are high for patients as many contemporary therapeutic strategies are disease-specific, and the biological differences can influence the patients’ responses. Herein, we provide an example of how Bayesian analysis can be used to merge data from clinical history, histology, immunohistochemistry (IHC) and cancer DNA sequencing to assist in tissue-of-origin assignment. Iterative Bayesian analysis is performed through a set of simple calculations to calculate the OR between the differential diagnoses. We illustrate a clinical case, where the distinction between a primary lung versus metastatic bladder cancer was aided meaningfully by iterative Bayesian analyses, incorporating IHC and sequencing data.
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Handling editor Runjan Chetty.
Contributors J-YY and RBW performed the Bayesian analysis. JNR analysed the sequencing data. NV analysed the cytology and immunohistochemistry data. RBC clinically managed the patient. All authors contributed to manuscript writing.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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