Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin.
Mol Diagn Ther
; 27(4): 499-511, 2023 07.
Article
en En
| MEDLINE
| ID: mdl-37099070
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Primarias Desconocidas
/
Transcriptoma
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Observational_studies
/
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Mol Diagn Ther
Asunto de la revista:
BIOLOGIA MOLECULAR
/
FARMACOLOGIA
/
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos