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A population-based gene signature is predictive of breast cancer survival and chemoresponse.

ABSTRACT: It remains a critical issue to improve the survival rate in patients with recurrent or metastatic breast cancer. This study sought to develop a prognostic scheme based on a 28-gene signature in a broad patient population, including those with advanced disease. Clinically annotated transcriptional profiles of 1,734 breast cancer patients were obtained to validate the 28-gene signature in prognostic categorization. The 28-gene signature generated significant patient stratification with regard to breast cancer disease-free survival (log-rank P<0.0001; n=1,337) and overall survival (log-rank P<0.0001; n=806) in Kaplan-Meier analyses. The gene expression signature provides refined prognosis of disease-free survival (log-rank P<0.006; Kaplan-Meier analysis) within each classic clinicopathologic factor-defined subgroup, including LN-, LN+, ER-, ER+ and tumor grade II. Furthermore, it was investigated whether this gene signature predicts chemoresponse to drugs commonly used to treat breast cancer. The mRNA expression levels of this gene signature in NCI-60 cell lines were used to predict chemoresponse to CMF, tamoxifen, paclitaxel, docetaxel, and doxorubicin (adriamycin). The 28-gene prognostic signature accurately (P<0.02) predicted chemotherapeutic response to the studied drugs. This study confirmed the prognostic applicability of the breast cancer gene signature in a broad clinical setting. This prognostic signature is also predictive of drug response in cancer cell lines.

SUBMITTER: Rathnagiriswaran S

PROVIDER: S-EPMC3103999 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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A population-based gene signature is predictive of breast cancer survival and chemoresponse.

Rathnagiriswaran Shruti S Wan Ying-Wooi YW Abraham Jame J Castranova Vincent V Qian Yong Y Guo Nancy L NL

International journal of oncology 20100301 3

It remains a critical issue to improve the survival rate in patients with recurrent or metastatic breast cancer. This study sought to develop a prognostic scheme based on a 28-gene signature in a broad patient population, including those with advanced disease. Clinically annotated transcriptional profiles of 1,734 breast cancer patients were obtained to validate the 28-gene signature in prognostic categorization. The 28-gene signature generated significant patient stratification with regard to b ...[more]

PMID: 20126981

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Project description:OBJECTIVE:Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers. METHODS AND MATERIALS:An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: (1) identifying genes significantly associated with lung cancer survival; (2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; (3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; (4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and (5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks. RESULTS:A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P<0.04, Kaplan-Meier analyses) in three independent cohorts (n=427). The expression-defined prognostic prediction is strongly related to smoking association and smoking cessation (P<0.02; Pearson's Chi-squared tests). The 6-gene signature is an accurate prognostic factor (hazard ratio=1.89, 95% CI: [1.04, 3.43]) compared to common clinical covariates in multivariate Cox analysis. The 6-gene signature also provides an accurate diagnosis of lung cancer with an overall accuracy of 73% in a cohort of smokers (n=164). The coexpression patterns derived from the implication networks were validated with interactions reported in the literature retrieved with STRING8, Ingenuity Pathway Analysis, and Pathway Studio. CONCLUSIONS:The pathway-based approach identified a smoking-associated 6-gene signature that predicts lung cancer risk and survival. This gene signature has potential clinical implications in the diagnosis and prognosis of lung cancer in smokers.

Project description:BackgroundLung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.Methodology/principal findingsFrom genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.Conclusions/significanceThe results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.

Project description:Introduction: Targeting of the tumor vasculature has evolved into an integral part of existing standard anti-cancer therapies. However, recent pre-clinical and clinical studies have shown that the efficacy of these treatments is temporary at most and frequently followed by the renewed tumor growth, indicating that these targets may not be robust. Methods: We transcriptionally profiled laser capture microdissection isolated microvessels from tumor and matching adjacent normal tissue samples obtained from breast cancer patients diagnosed with infiltrating ductal carcinoma. Gene expression profiles were generated and analyzed using Genespring and the web-based WebGestalt bioinformatics Toolkit. Several differentially expressed genes were validated by immunohistochemistry. To further mine our data set we used a novel integrative systems biology network approach to identify a gene signature that could robustly stratify breast cancer patient populations. Results: Vascular enrichment of the LCM samples was confirmed by Q-PCR of CD31. Hierarchical two-dimensional clustering of the transcriptome data identified 219 significantly upregulated transcripts in at least 4/8 patient samples (cut-off >1.3-fold, p<0.05). Several genes AGRN, FLNA and ILR4 were selected and their protein overexpression was confirmed in the tumor tissue. Additional mining of the data set using the clinical information associated with these tissues by applying an integrative bioinformatics network method generated a 15-gene ‘Vascular-Derived Prognostic Predictor’ that is able to stratify patient populations in to high- and low- risk groups. Conclusions: The overall heterogeneity observed in the vascular gene expression patterns in the infiltrating ductal carcinoma samples poses a significant problem for these individual genes to be used as a robust vascular breast cancer specific marker or therapeutic target. Using an integrative bioinformatics network approach allowed us to identify a 15-gene ‘Vascular-Derived Prognostic Predictor’ that can robustly identify patients with an increased risk of local recurrence. Stratifying patient populations using this gene signature may lead to better patient-tailored treatment regimes. Two-color microarrays comparing laser-captured microvessels from the tumors and matching adjacent normal tissues of 8 breast cancer patients

Project description:Background Currently, no gene-expression signature (GES) established from node-positive cohorts, able to predict breast cancer evolution after systemic adjuvant chemotherapy, exists. Methods Gene-expression profiles of 252 node-positive patients (median follow-up: 7.7 years), mostly included in a randomized clinical trial (PACS01), receiving systemic adjuvant regimen, were determined by means of cDNA custom array representing 5,776 distinct genes. Findings In the training cohort, we established a 38-GES for the purpose of predicting time to distant metastasis. The 38-GES yielded unadjusted hazard ratio of 4.86 (95% CI=2.76-8.56). Even when adjusted with the two best clinicopathological prognostic scoring: NPI and Adjuvant!, 38-GES HR were 3.30 (1.81-5.99) and 3.40 (1.85-6.24), respectively. Furthermore, 38-GES improved mostly NPI and Adjuvant! intermediate-risk classified patients. NPI intermediate-risk patients (7-year MFS=87.2%) were divided into 2/3 (7-year MFS=95.5%) close to NPI low-risk group and 1/3 (7-year MFS=69.3%) close to NPI high-risk group (HR=6.97 [2.51-19.36]). Adjuvant! intermediate-risk patients (7-year MFS=88.0%) were divided into 2/3 (7-year MFS=94.8%) close to Adjuvant! low-risk group and 1/3 (7-year MFS=71.7%) close to Adjuvant! high-risk group (HR=5.31 [5.38-11.87]). The 38-GES was validated on gene-expression datasets from three external node-positive breast cancer subcohorts (n=224) generated from different microarray platforms. The 38-GES yielded unadjusted HR=2.95 (1.74-5.01). Furthermore, 38-GES showed performance in supplementary cohorts with different lymph-node status and endpoint (1,031 new patients). Interpretation The 38-GES represents a robust tool able to type systemic adjuvant treated node-positive patients at high risk of metastatic relapse, and especially powerful to separate NPI or Adjuvant! intermediate-risk node-positive patients. Keywords: disease-state analysis 252 breast cancer patients at diagnosis examined with spotted cDNA nylon membrane. Patient details: PACS01x are 2 parts of a clinical trial : PACS01A : patients had received 6 cycles of FEC100 PACS01B : patients had received 3 cycles of FEC100 then 3 cycles of Docetaxel CCRG are patients from our local cancer center (Cancer Center René Gauducheau) included with the same criteria as PACS01A (6 cycles of FEC100).

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A population-based gene signature is predictive of breast cancer survival and chemoresponse.

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A population-based gene signature is predictive of breast cancer survival and chemoresponse.

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