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

Xu, Qinghua; Chen, Jinying; Ni, Shujuan; Tan, Cong; Xu, Ming; Dong, Lei; Lin, Yuan; Wang, Qifeng; Du, Xiang

doi:10.1038/modpathol.2016.60

Cited by 58 publications

(55 citation statements)

References 49 publications

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“…Com-prehensive multi-cancer databases vastly extend our knowledge of tumorigenesis by providing avenues for deciphering diagnostic pan-cancer signatures distinguishing tumor types and having prognostic, predictive, and therapeutic potential (6,7). Integrative pan-cancer analyses elucidate tumor lineage unique signatures (8) and trace metastatic lesions to tissues of origin (9). However, the use of whole tumor data sets as precise scoring determinants of immune inference requires complex deconvolution (10), complicated by the influence of tumor expression programs on TILs (11).…”

Section: Introductionmentioning

confidence: 99%

Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets

Monette

Morou

Al-Banna

et al. 2019

Journal of Clinical Investigation

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Figure 1. Distinct comprehensive transcriptomics from paired CD8 + and CD19 + profiles from ccRCC blood, tumors and tissues, and control donor blood isolates. (A and B) PCA demonstrating distinct DEG profiles from comprehensive HTA 2.0 microarray analyses of (A) CD8 + (n = 15) and (B) CD19 + (n = 15) immune cell subsets from TILs and TIL-Bs, TIICs, and circulating ptPBLs, and cdPBLs (n = 10). (C) Four-way Venn diagram demonstrating percentage overlaps of DEGs identified by microarrays across different source biospecimens analyzed. (D) Venn diagram showing that ptPBLs have greater numbers of differentially represented exon-exon PSR junctions compared with TILs, relative to TIICs from paired CD8 + samples (P < 0.05; ANOVA, Transcriptome Analysis Console v.3, Affymetrix). Thirteen percent of shared PSR junctions exist between ptPBLs and TILs, representing 33% of total genes common to ptPBLs and TILs having shared isoform identity. (E) GO PM proteins identified by Partek and unsupervised hierarchical clustering algorithm-generated heatmaps demonstrating that the 4 different CD8 + isolates are stratified according to PM, using log 2 expression values applying the Euclidean distance metric and complete linkage clustering method (R programming language; R-studio). Heatmaps demonstrate the unsupervised clustering of PBL isolates as most closely related, with TILs and TIICs at their boundaries, suggesting that their profiles may be influenced by the cancer microenvironment. (F) Feasibility of using PM-associated proteins toward identifying pan-cancer DEGs that can stratify patients is demonstrated by PCA biplots of PM DEGs from CD8 + cdPBL and ptPBL isolates created on log 2 values using the biplot function (R; R-studio). diff., differential; id., identity; PSR, probe-selected region.

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Section: Introductionmentioning

confidence: 99%

Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets

Monette

Morou

Al-Banna

et al. 2019

Journal of Clinical Investigation

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“…Up to now, several bioinformatics methods (e.g., decision trees, support vector machines, and others) have been used to analyze tissue origin of tumors using RNA‐seq data. These studies are all based on microarray‐based gene expression signatures to identify a “molecular fingerprint” including tens to hundreds of genes to discriminate cancers of different tissue origin.…”

Section: Introductionmentioning

confidence: 99%

“…21 However, recent research from Hoadley et al confirmed that subtyping of tumors based on mRNA expression data (RNA-seq) had the most significant correlation with tissue origin. 22 Up to now, several bioinformatics methods (e.g., decision trees, 23,24 support vector machines, [25][26][27][28] and others 29 ) have been used to analyze tissue origin of tumors using RNA-seq data. These studies are all based on microarray-based gene expression signatures to identify a "molecular fingerprint" including tens to hundreds of genes to discriminate cancers of different tissue origin.…”

Section: Introductionmentioning

confidence: 99%

A naive Bayes algorithm for tissue origin diagnosis (TOD‐Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system

Jiang

Shen

Ding

et al. 2017

Intl Journal of Cancer

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Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive Bayes algorithm (TOD-Bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and ∼1,000 feature genes were used to train and validate the TOD-Bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-Bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-Bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment.

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“…The gene expression for each sample was analyzed by the 90-gene expression assay. Then, similarity scores for each of 21 tumor types were calculated, which showed similarities in the gene expression pattern between the sample and the indicated tumor type [15] The similarity score values ranged from 0 (very low similarity) to 100 (very high similarity), which summed up to 100 across all 21 tumor types. The tumor type with the highest similarity score was considered to indicate the tissue of origin.…”

Section: Discussionmentioning

confidence: 99%

“…The 90-gene expression assay correctly classi ed the lung cancer specimens but misclassi ed the gallbladder cancer specimen as a gastroesophageal tumor. These errors are likely due to the underrepresentation bias of gallbladder cancer and bronchus mucoepidermoid carcinoma among the 90-gene speci c pan-cancer transcriptome database [15], given that gallbladder cancer was not included in the database and mucoepidermoid carcinoma only accounts for 0.1%-0.2% of all pulmonary tumors [19]. The third case was diagnosed as synchronous triple primary tumors in the rectum, gastric antrum and gastric cardia.…”

Section: Discussionmentioning

confidence: 99%

Gene Expression Profiling For Diagnosis of Multiple Primary Malignant Tumors

Zheng

Sun

Kuai

et al. 2020

Preprint

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Background: The incidence of multiple primary malignant tumors (MPMTs) is rising due to the development of screening technologies, significant treatment advances and the increased aging of the population. For patients with a prior cancer history, distinguishing tumor recurrence or metastasis from a second malignant tumor has important prognostic and therapeutic implications and still represents a difficult problem in clinical practice. Methods: In this study, we evaluated the performance of a 90-gene expression assay and explored its potential diagnostic utility for MPMTs across a broad spectrum of tumor types. Twenty-four MPMT patients from Sir Run Run Shaw Hospital, college of medicine, Zhejiang University were enrolled in this study. A total of 51 MPMT specimens met all quality control criteria and were analyzed by the 90-gene expression assay. Results: For each clinical specimen, the tumor type predicted by the 90-gene expression assay was compared with its reference diagnosis with an overall accuracy of 94.1% (48 of 51, 95% confidence interval: 0.83-0.98). Additionally, the hierarchical clustering of 90-gene expression profiling in 51 specimens revealed MPMT samples were grouped together depending on tumor types or system types rather than individual MPMT patients. Conclusions: Therefore, the 90-gene expression assay provides flexibility and accuracy in identifying the tissue origin of MPMTs, especially in squamous cell carcinoma. Future incorporation of the 90-gene expression assay in the pathological diagnosis will assist oncologists in applying precise treatments, leading to improved care and outcomes for MPMT patients.

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

Cited by 58 publications

References 49 publications

Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets

Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets

A naive Bayes algorithm for tissue origin diagnosis (TOD‐Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system

Gene Expression Profiling For Diagnosis of Multiple Primary Malignant Tumors

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