Classification of unknown primary tumors with a data-driven method based on a large microarray reference database

Ojala, K.; Kilpinen, Sami; Kallioniemi, Olli

doi:10.1186/gm279

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…Interestingly, we found that the mutation spectrum of CTCs of colorectal cancer patients resembles the mutation signature of colorectal cancer, suggesting that the identification of mutation signatures of CTCs could help to identify the origin of the primary tumor site. Carcinoma of unknown primary site (CUP) constitutes approximately 3-5% of all newly diagnosed malignancies [ 30 , 31 ], hence it is being recognized as one of common cancer diagnoses. The capability to identify the origin site of these neoplasms is an unmet need for effective therapy because a substantial fraction of the current treatment regimens require prior knowledge of the type and origin of the tumor.…”

Section: Discussionmentioning

confidence: 99%

Molecular characterization of circulating colorectal tumor cells defines genetic signatures for individualized cancer care

et al. 2017

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Studies on circulating tumor cells (CTCs) have largely focused on platform development and CTC enumeration rather than on the genomic characterization of CTCs. To address this, we performed targeted sequencing of CTCs of colorectal cancer patients and compared the mutations with the matched primary tumors. We collected preoperative blood and matched primary tumor samples from 48 colorectal cancer patients. CTCs were isolated using a label-free microfiltration device on a silicon microsieve. Upon whole genome amplification, we performed amplicon-based targeted sequencing on a panel of 39 druggable and frequently mutated genes on both CTCs and fresh-frozen tumor samples. We developed an analysis pipeline to minimize false-positive detection of somatic mutations in amplified DNA. In 60% of the CTC-enriched blood samples, we detected primary tumor matching mutations. We found a significant positive correlation between the allele frequencies of somatic mutations detected in CTCs and abnormal CEA serum level. Strikingly, we found driver mutations and amplifications in cancer and druggable genes such as APC, KRAS, TP53, ERBB3, FBXW7 and ERBB2. In addition, we found that CTCs carried mutation signatures that resembled the signatures of their primary tumors. Cumulatively, our study defined genetic signatures and somatic mutation frequency of colorectal CTCs. The identification of druggable mutations in CTCs of preoperative colorectal cancer patients could lead to more timely and focused therapeutic interventions.

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Section: Discussionmentioning

confidence: 99%

Molecular characterization of circulating colorectal tumor cells defines genetic signatures for individualized cancer care

et al. 2017

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“…Thus far, there have been a number of studies focused on identifying unique signatures that distinguish among different cancer types, using immunohistochemistry [8] , [9] , [10] , [11] , cytogenetic studies [12] , [13] , [14] , comparative microarray analysis [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , combined microarray and quantitative polymerase chain reaction techniques [23] , [24] , bead-based miRNA profiling [25] , and more recently, limited, high-throughput sequencing data combined with microarray [26] . Currently, only qRT-PCR [23] , [24] and microarray-based [19] , [22] assays are commercially available for use, with diagnostic accuracies ranging from 74 – 85%.…”

Section: Introductionmentioning

confidence: 99%

RNA-Seq Accurately Identifies Cancer Biomarker Signatures to Distinguish Tissue of Origin

et al. 2014

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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.

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“…46 Indeed, pancreatic cancer is the most difficult type of carcinoma of unknown primary to identify using our method as well as all published methods. 8,24,[47][48][49][50] Additional research is needed to successfully translate the 154-gene signature from gene expression microarray to real-time reverse transcription polymerase chain reaction assays, thus allowing broader access and utilization in the clinical setting. In routine practice, most diagnostic materials are formalin-fixed and paraffin-embedded; thus, it will be highly interesting to assess the usefulness of the 154-gene signature in formalin-fixed and paraffin-embedded samples.…”

Section: Discussionmentioning

confidence: 99%

Pan-cancer transcriptome analysis reveals a gene expression signature for the identification of tumor tissue origin

Xu¹,

Chen²,

et al. 2016

Modern Pathology

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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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Classification of unknown primary tumors with a data-driven method based on a large microarray reference database

Cited by 13 publications

References 30 publications

Molecular characterization of circulating colorectal tumor cells defines genetic signatures for individualized cancer care

Molecular characterization of circulating colorectal tumor cells defines genetic signatures for individualized cancer care

RNA-Seq Accurately Identifies Cancer Biomarker Signatures to Distinguish Tissue of Origin

Pan-cancer transcriptome analysis reveals a gene expression signature for the identification of tumor tissue origin

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