A monotonic and prognostic genomic signature from fibroblasts for colorectal cancer initiation, progression, and metastasis
The differential gene expression patterns between normal colonic fibroblasts (NCF), carcinoma-associated fibroblasts from primary tumors (CAF-PT), and CAFs from hepatic metastasis (CAF-LM) are hypothesized to be useful for predicting relapse in primary tumors. A transcriptomic profile of NCF (n = 9)...
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Veröffentlicht in: | Molecular cancer research 2014-09, Vol.12 (9), p.1254-1266 |
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creator | Berdiel-Acer, Mireia Cuadras, Daniel Díaz-Maroto, Natalia Guillén Sanjuan, Xavier Serrano, Teresa Berenguer, Antoni Moreno, Victor Gonçalves-Ribeiro, Samuel Salazar, Ramon Villanueva, Alberto Molleví, David G |
description | The differential gene expression patterns between normal colonic fibroblasts (NCF), carcinoma-associated fibroblasts from primary tumors (CAF-PT), and CAFs from hepatic metastasis (CAF-LM) are hypothesized to be useful for predicting relapse in primary tumors. A transcriptomic profile of NCF (n = 9), CAF-PT (n = 14), and CAF-LM (n = 11) was derived. Prediction Analysis of Microarrays (PAM) was used to obtain molecular details for each fibroblast class, and differentially expressed transcripts were used to classify patients according to recurrence status. A number of transcripts (n = 277) were common to all three types of fibroblasts and whose expression level was sequentially deregulated according to the transition: NCF→CAF-PT→CAF-LM. Importantly, the gene signature was able to accurately classify patients with primary tumors according to their prognosis. This capacity was exploited to obtain a refined 19-gene classifier that predicted recurrence with high accuracy in two independent datasets of patients with colorectal cancer and correlates with fibroblast migratory potential. The prognostic power of this genomic signature is strong evidence of the link between the tumor-stroma microenvironment and cancer progression. Furthermore, the 19-gene classifier was able to identify low-risk patients very accurately, which is of particular importance for stage II patients, who would benefit from the omission of chemotherapy, especially T4N0 patients, who are clinically classified as being at high risk.
A defined stromal gene expression signature predicts relapse in patients with colorectal cancer. |
doi_str_mv | 10.1158/1541-7786.MCR-14-0121 |
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A defined stromal gene expression signature predicts relapse in patients with colorectal cancer.</description><identifier>ISSN: 1541-7786</identifier><identifier>EISSN: 1557-3125</identifier><identifier>DOI: 10.1158/1541-7786.MCR-14-0121</identifier><identifier>PMID: 24829396</identifier><language>eng</language><publisher>United States</publisher><subject>Carcinogenesis - genetics ; Colorectal Neoplasms - diagnosis ; Colorectal Neoplasms - genetics ; Colorectal Neoplasms - pathology ; Fibroblasts - metabolism ; Fibroblasts - pathology ; Gene Expression Regulation, Neoplastic ; Genome, Human ; Humans ; Neoplasm Metastasis ; Neoplasm Recurrence, Local - genetics ; Neoplasm Recurrence, Local - pathology ; Prognosis ; Transcriptome</subject><ispartof>Molecular cancer research, 2014-09, Vol.12 (9), p.1254-1266</ispartof><rights>2014 American Association for Cancer Research.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c422t-630b2e803e5c4ca3a3a1e9bacecbf1e671e2422b2f0fbfd2abba9e8692e2548a3</citedby><cites>FETCH-LOGICAL-c422t-630b2e803e5c4ca3a3a1e9bacecbf1e671e2422b2f0fbfd2abba9e8692e2548a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,3343,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24829396$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Berdiel-Acer, Mireia</creatorcontrib><creatorcontrib>Cuadras, Daniel</creatorcontrib><creatorcontrib>Díaz-Maroto, Natalia Guillén</creatorcontrib><creatorcontrib>Sanjuan, Xavier</creatorcontrib><creatorcontrib>Serrano, Teresa</creatorcontrib><creatorcontrib>Berenguer, Antoni</creatorcontrib><creatorcontrib>Moreno, Victor</creatorcontrib><creatorcontrib>Gonçalves-Ribeiro, Samuel</creatorcontrib><creatorcontrib>Salazar, Ramon</creatorcontrib><creatorcontrib>Villanueva, Alberto</creatorcontrib><creatorcontrib>Molleví, David G</creatorcontrib><title>A monotonic and prognostic genomic signature from fibroblasts for colorectal cancer initiation, progression, and metastasis</title><title>Molecular cancer research</title><addtitle>Mol Cancer Res</addtitle><description>The differential gene expression patterns between normal colonic fibroblasts (NCF), carcinoma-associated fibroblasts from primary tumors (CAF-PT), and CAFs from hepatic metastasis (CAF-LM) are hypothesized to be useful for predicting relapse in primary tumors. A transcriptomic profile of NCF (n = 9), CAF-PT (n = 14), and CAF-LM (n = 11) was derived. Prediction Analysis of Microarrays (PAM) was used to obtain molecular details for each fibroblast class, and differentially expressed transcripts were used to classify patients according to recurrence status. A number of transcripts (n = 277) were common to all three types of fibroblasts and whose expression level was sequentially deregulated according to the transition: NCF→CAF-PT→CAF-LM. Importantly, the gene signature was able to accurately classify patients with primary tumors according to their prognosis. This capacity was exploited to obtain a refined 19-gene classifier that predicted recurrence with high accuracy in two independent datasets of patients with colorectal cancer and correlates with fibroblast migratory potential. The prognostic power of this genomic signature is strong evidence of the link between the tumor-stroma microenvironment and cancer progression. Furthermore, the 19-gene classifier was able to identify low-risk patients very accurately, which is of particular importance for stage II patients, who would benefit from the omission of chemotherapy, especially T4N0 patients, who are clinically classified as being at high risk.
A defined stromal gene expression signature predicts relapse in patients with colorectal cancer.</description><subject>Carcinogenesis - genetics</subject><subject>Colorectal Neoplasms - diagnosis</subject><subject>Colorectal Neoplasms - genetics</subject><subject>Colorectal Neoplasms - pathology</subject><subject>Fibroblasts - metabolism</subject><subject>Fibroblasts - pathology</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Genome, Human</subject><subject>Humans</subject><subject>Neoplasm Metastasis</subject><subject>Neoplasm Recurrence, Local - genetics</subject><subject>Neoplasm Recurrence, Local - pathology</subject><subject>Prognosis</subject><subject>Transcriptome</subject><issn>1541-7786</issn><issn>1557-3125</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo9kMtKBDEQRYMovj9BydKFral0px9LGXyBIoiuQ5KpDJHuRJPMQvx50zpKLaoK7q1bHEJOgF0AiP4SRANV1_XtxePiuYKmYsBhi-yDEF1VAxfb87zR7JGDlN4Y4wy6dpfs8abnQz20--Trik7Bhxy8M1T5JX2PYeVDymVdoQ9T6cmtvMrriNTGMFHrdAx6VCknakOkJowhoslqpEZ5g5E677JT2QV__nMvYko_yxwwYS5WlVw6IjtWjQmPN_2QvN5cvyzuqoen2_vF1UNlGs5z1dZMc-xZjcI0RtWlAAetDBptAdsOkBeh5pZZbZdcaa0G7NuBIxdNr-pDcvZ7t_zyscaU5eSSwXFUHsM6SRAtDN3QQ1ek4ldqYkgpopXv0U0qfkpgcuYuZ6ZyZioLdwmNnLkX3-kmYq0nXP67_kDX3_QHgoU</recordid><startdate>201409</startdate><enddate>201409</enddate><creator>Berdiel-Acer, Mireia</creator><creator>Cuadras, Daniel</creator><creator>Díaz-Maroto, Natalia Guillén</creator><creator>Sanjuan, Xavier</creator><creator>Serrano, Teresa</creator><creator>Berenguer, Antoni</creator><creator>Moreno, Victor</creator><creator>Gonçalves-Ribeiro, Samuel</creator><creator>Salazar, Ramon</creator><creator>Villanueva, Alberto</creator><creator>Molleví, David G</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>201409</creationdate><title>A monotonic and prognostic genomic signature from fibroblasts for colorectal cancer initiation, progression, and metastasis</title><author>Berdiel-Acer, Mireia ; 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A transcriptomic profile of NCF (n = 9), CAF-PT (n = 14), and CAF-LM (n = 11) was derived. Prediction Analysis of Microarrays (PAM) was used to obtain molecular details for each fibroblast class, and differentially expressed transcripts were used to classify patients according to recurrence status. A number of transcripts (n = 277) were common to all three types of fibroblasts and whose expression level was sequentially deregulated according to the transition: NCF→CAF-PT→CAF-LM. Importantly, the gene signature was able to accurately classify patients with primary tumors according to their prognosis. This capacity was exploited to obtain a refined 19-gene classifier that predicted recurrence with high accuracy in two independent datasets of patients with colorectal cancer and correlates with fibroblast migratory potential. The prognostic power of this genomic signature is strong evidence of the link between the tumor-stroma microenvironment and cancer progression. Furthermore, the 19-gene classifier was able to identify low-risk patients very accurately, which is of particular importance for stage II patients, who would benefit from the omission of chemotherapy, especially T4N0 patients, who are clinically classified as being at high risk.
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subjects | Carcinogenesis - genetics Colorectal Neoplasms - diagnosis Colorectal Neoplasms - genetics Colorectal Neoplasms - pathology Fibroblasts - metabolism Fibroblasts - pathology Gene Expression Regulation, Neoplastic Genome, Human Humans Neoplasm Metastasis Neoplasm Recurrence, Local - genetics Neoplasm Recurrence, Local - pathology Prognosis Transcriptome |
title | A monotonic and prognostic genomic signature from fibroblasts for colorectal cancer initiation, progression, and metastasis |
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