title: RNA-Seq accurately identifies cancer biomarker signatures to distinguish tissue of origin.
abstract: Metastatic cancer of unknown primary (CUP) accounts for up to 5% of all new cancer cases, with a 5-year survival rate of only 10%. Accurate identification of tissue of origin would allow for directed, personalized therapies to improve clinical outcomes. Our objective was to use transcriptome sequencing (RNA-Seq) to identify lineage-specific biomarker signatures for the cancer types that most commonly metastasize as CUP (colorectum, kidney, liver, lung, ovary, pancreas, prostate, and stomach). RNA-Seq data of 17,471 transcripts from a total of 3,244 cancer samples across 26 different tissue types were compiled from in-house sequencing data and publically available International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Robust cancer biomarker signatures were extracted using a 10-fold cross-validation method of log transformation, quantile normalization, transcript ranking by area under the receiver operating characteristic curve, and stepwise logistic regression. The entire algorithm was then repeated with a new set of randomly generated training and test sets, yielding highly concordant biomarker signatures. External validation of the cancer-specific signatures yielded high sensitivity (92.0% ± 3.15%; mean ± standard deviation) and specificity (97.7% ± 2.99%) for each cancer biomarker signature. The overall performance of this RNA-Seq biomarker-generating algorithm yielded an accuracy of 90.5%. In conclusion, we demonstrate a computational model for producing highly sensitive and specific cancer biomarker signatures from RNA-Seq data, generating signatures for the top eight cancer types responsible for CUP to accurately identify tumor origin.
authors.affiliation: University of Michigan Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA 48109.
authors.forename: Iris H
authors.initials: IH
authors.lastname: Wei
authors.affiliation: University of Michigan Department of Biostatistics, University of Michigan Medical School, Ann Arbor, MI, USA 48109.
authors.forename: Yang
authors.initials: Y
authors.lastname: Shi
authors.affiliation: University of Michigan Department of Biostatistics, University of Michigan Medical School, Ann Arbor, MI, USA 48109.
authors.forename: Hui
authors.initials: H
authors.lastname: Jiang
authors.affiliation: Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA 48109 ; University of Michigan Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA 48109.
authors.forename: Chandan
authors.initials: C
authors.lastname: Kumar-Sinha
authors.affiliation: Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA 48109 ; University of Michigan Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA 48109 ; Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA 48109 ; University of Michigan Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA 48109 ; Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, MI, USA 48109.
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D045744:Cell Line, Tumor; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016015:Logistic Models; D008297:Male; D008957:Models, Genetic; D009382:Neoplasms, Unknown Primary / Q000235:genetics* / Q000473:pathology; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor