Experimentally Derived Metastasis Gene Expression Profile Predicts Recurrence and Death in Patients With Colon Cancer

By J. Smith, N. Deane, Fei Wu, N. Merchant, Bing Zhang, Aixiang Jiang, Pengcheng Lu, J. C. Johnson, C. Schmidt, C. Bailey and 9 more. Gastroenterology, 2009

704 citations recorded by the source corpus.

BACKGROUND & AIMS Staging inadequately predicts metastatic risk in patients with colon cancer. We used a gene expression profile derived from invasive, murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify patients with colon cancer at risk of recurrence. METHODS This phase 1, exploratory biomarker study used 55 patients with colorectal cancer from Vanderbilt Medical Center (VMC) as the training dataset and 177 patients from the Moffitt Cancer Center as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined with comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A metastasis score derived from the biologically based classifier was tested in the Moffitt dataset. RESULTS A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathologic stages and specifically in stage II and stage III patients. The metastasis score was shown to independently predict risk of cancer recurrence and death in univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk of cancer recurrence (hazard ratio, 4.7; 95% confidence interval, 1.566–14.05). Furthermore, the metastasis score identified patients with stage III disease whose 5-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not increase survival time. CONCLUSION A gene expression profile identified from an experimental model of colon cancer metastasis predicted cancer recurrence and death, independently of conventional measures, in patients with colon cancer.

Subfields: Oncology

View Paper: https://www.semanticscholar.org/paper/3d61ce2cf9513c66668bf4468e9a595d60f219cc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Oncology

Citations in the corpus

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes (2012, methodology): “In fact, for the ten other cancer outcome studies we investigated [5,13,33,52–58], a TRANSFAC-based network gave the best results in seven, and a HPRD-based network in three of the studies.”
Single-cell dissection of transcriptional heterogeneity in human colon tumors (2011, background): “Lee Moffit Cancer Center, the Vanderbilt Medical Center and the Royal Melbourne Hospita”
Dependency of colorectal cancer on a TGF-beta-driven programme in stromal cells for metastasis initiation (2012, methodology): “To correlate TGFB and TBRS expression with clinical disease progression, we pooled two publicly available sets of Affymetrix transcriptomic profiles (GSE17537 and GSE14333) corresponding to primary CRCs for which clinical follow-up was available.”
Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells (2016, background): “We therefore interrogated for expression of Treg signature genes transcriptomic datasets obtained from resected tumor tissues of a cohort of 177 CRC patients (GSE17536; Smith et al., 2010) and of a cohort of 263 NSCLC patients (GSE41271; Sato et al.”
The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. (2011, methodology): “At the moment of performing this study, there were six publicly available CRC gene expression datasets annotated with clinical information in the Gene Expression Omnibus at NCBI (GSE17538, GSE10402, GSE12945, GSE5206, GSE12032 and GSE14333).”