ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2016
Pan-cancer transcriptome analysis reveals a gene expression signature for the identification of tumor tissue origin.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
- Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets.The Journal of clinical investigation · 2019Trial
- Clinical application of 90-gene expression test in a patient with occult breast cancer: a case report and literature review.Breast cancer (Tokyo, Japan) · 2025Review
- Cell Settling, Migration, and Stochastic Cancer Gene Expression Suggest Potassium Membrane Flux May Initiate pH Reversal.Biomolecules · 2025Article
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- Master regulators governing protein abundance across ten human cancer types.bioRxiv : the preprint server for biology · 2024Article
- A deep profile of gene expression across 18 human cancers.bioRxiv : the preprint server for biology · 2024Article
- MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images.Scientific reports · 2024Article
- Article
- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC bioinformatics · 2024Article
- ssMutPA: single-sample mutation-based pathway analysis approach for cancer precision medicine.GigaScience · 2024Article
- Comparison of the classifiers based on mRNA, microRNA and lncRNA expression and DNA methylation profiles for the tumor origin detection.Frontiers in genetics · 2024Article
- Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer.BMC genomics · 2023Article
- A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing.Scientific reports · 2023Article
- Cancer of unknown primary (CUP) through the lens of precision oncology: a single institution perspective.Journal of cancer research and clinical oncology · 2023Observational
- Identification of Driver Epistatic Gene Pairs Combining Germline and Somatic Mutations in Cancer.International journal of molecular sciences · 2023Article
- Intra-tumor heterogeneity, turnover rate and karyotype space shape susceptibility to missegregation-induced extinction.PLoS computational biology · 2023Article
- Primary tumor type prediction based on US nationwide genomic profiling data in 13,522 patients.Computational and structural biotechnology journal · 2023Article
- Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning.BioData mining · 2022Article
- Transcriptomics and solid tumors: The next frontier in precision cancer medicine.Seminars in cancer biology · 2022Review
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9 authors.
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Abstract
Carcinoma of unknown primary, wherein metastatic disease is present without an identifiable primary site, accounts for ~3-5% of all cancer diagnoses. Despite the development of multiple diagnostic workups, the success rate of primary site identification remains low. Determining the origin of tumor tissue is, thus, an important clinical application of molecular diagnostics. Previous studies have paved the way for gene expression-based tumor type classification. In this study, we have established a comprehensive database integrating microarray- and sequencing-based gene expression profiles of 16 674 tumor samples covering 22 common human tumor types. From this pan-cancer transcriptome database, we identified a 154-gene expression signature that discriminated the origin of tumor tissue with an overall leave-one-out cross-validation accuracy of 96.5%. The 154-gene expression signature was first validated on an independent test set consisting of 9626 primary tumors, of which 97.1% of cases were correctly classified. Furthermore, we tested the signature on a spectrum of diagnostically challenging tumors. An overall accuracy of 92% was achieved on the 1248 tumor specimens that were poorly differentiated, undifferentiated or from metastatic tumors. Thus, we have identified a 154-gene expression signature that can accurately classify a broad spectrum of tumor types. This gene panel may hold a promise to be a useful additional tool for the determination of the tumor origin.
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