Transcription profiling of human node-negative breast cancer patients to validate a 70-gene prognostic signature on a high-throughput microarray
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Glas AM
PROVIDER: S-ECPF-TABM-115 | biostudies-other | 2006
REPOSITORIES: biostudies-other
Similar Datasets
Project description:A 70-gene signature has been shown in a single institution to have prognostic value in patients with node-negative breast cancer. The purpose of the present study was to validate the signature in independent patient samples.
| S-ECPF-TABM-77 | biostudies-other
Project description:A 70-gene signature has been shown to be a powerful predictor of disease outcome in young breast cancer patients. The microarrays were not designed for high throughput processing. To facilitate the use in a diagnostic setting, the profile was translated into a customized microarray containing a reduced set of 1,900 probes.
2007-02-23 | E-TABM-115 | biostudies-arrayexpress
Project description:A 70-gene signature has been shown in a single institution to have prognostic value in patients with node-negative breast cancer. The purpose of the present study was to validate the signature in independent patient samples.
2007-11-27 | E-TABM-77 | biostudies-arrayexpress
Project description:Background: Recently a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N-) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Materials and Methods: Gene expression profiling of frozen samples from 198 N- systemically untreated patients was performed at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were performed with the genomic risk and adjusted for the clinical risk, defined by Adjuvant!Online. Results: The actual 5- and 10-year time to distant metastasis (TDM) were 98% (88%-100%) and 94% (83%-98%) respectively for the good profile group and 76% (68%- 82%) and 73% (65%-79%) for the poor profile group. The actual 5- and 10-year overall survival (OS) were 98% (88%-100%) and 87% (73%-94%) respectively for the good profile group and 84% (77%-89%) and 72% (63%-78%) for the poor profile group. We observed a strong time-dependency of this signature, leading to an adjusted HR of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years, and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for TDM and OS respectively. Conclusion: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases. Experiment Overall Design: dataset of microarray experiments from primary breast tumors used to validate the 76-gene signature (VERIDEX). No replicate, no reference sample.
| S-ECPF-GEOD-7390 | biostudies-other
Project description:Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs).We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates.Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients.RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.
| S-EPMC3961800 | biostudies-other
Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Experiment Overall Design: We used microarrays to identify lung metastasis-related genes in a series of 23 patients with breast cancer metastases. No replicate, no reference sample.
| S-ECPF-GEOD-11078 | biostudies-other
Project description:Estrogen receptor (ER) expression and proliferative activity are established prognostic factors in breast cancer. In a search for additional prognostic motives we analyzed the gene expression patterns of 200 tumors of patients who were not treated by systemic therapy after surgery using a discovery approach. After performing hierarchical cluster analysis, we identified co-regulated genes related to the biological process of proliferation, steroid hormone receptor expression, as well as B cell and T cell infiltration. We calculated metagenes as surrogate for all genes contained within a particular cluster and visualized the relative expression in relation to time to metastasis with principal component analysis. Distinct patterns led to the hypothesis of a prognostic role of the immune system in tumors with high expression of proliferation associated genes. In multivariate Cox regression analysis the proliferation metagene showed a significant association with metastasis-free survival of the whole discovery cohort (Hazard Ratio (HR) 2.20, 95% confidence interval (CI) 1.40-3.46). The B cell metagene showed additional independent prognostic information in carcinomas with high proliferative activity (HR 0.66, 95% CI 0.46 - 0.97). A prognostic influence of the B-cell metagene was independently confirmed by multivariate analysis in a first validation cohort enriched for high grade tumors (n=286, HR 0.78, 95% CI 0.62-0.98), and a second validation cohort enriched for younger patients (n=302, HR 0.83, 95% CI 0.7-0.97). Thus, we could demonstrate in three cohorts of untreated node-negative breast cancer patients, that the humoral immune system plays a pivotal role for metastasis-free survival of carcinomas of the breast. Experiment Overall Design: Population based N0 untreated breast cancer cohort study including 200 samples
| S-ECPF-GEOD-11121 | biostudies-other
Project description:BackgroundThe prognosis of lymph node-negative triple-negative breast cancer (TNBC) is still worse than that of other subtypes despite adjuvant chemotherapy. Reliable prognostic biomarkers are required to identify lymph node-negative TNBC patients at a high risk of distant metastasis and optimize individual treatment.MethodsWe analyzed the RNA sequencing data of primary tumor tissue and the clinicopathological data of 202 lymph node-negative TNBC patients. The cohort was randomly divided into training and validation sets. Least absolute shrinkage and selection operator Cox regression and multivariate Cox regression were used to construct the prognostic model.ResultsA clinical prognostic model, seven-gene signature, and combined model were constructed using the training set and validated using the validation set. The seven-gene signature was established based on the genomic variables associated with distant metastasis after shrinkage correction. The difference in the risk of distant metastasis between the low- and high-risk groups was statistically significant using the seven-gene signature (training set: P < 0.001; validation set: P = 0.039). The combined model showed significance in the training set (P < 0.001) and trended toward significance in the validation set (P = 0.071). The seven-gene signature showed improved prognostic accuracy relative to the clinical signature in the training data (AUC value of 4-year ROC, 0.879 vs. 0.699, P = 0.046). Moreover, the composite clinical and gene signature also showed improved prognostic accuracy relative to the clinical signature (AUC value of 4-year ROC: 0.888 vs. 0.699, P = 0.029; AUC value of 5-year ROC: 0.882 vs. 0.693, P = 0.038). A nomogram model was constructed with the seven-gene signature, patient age, and tumor size.ConclusionsThe proposed signature may improve the risk stratification of lymph node-negative TNBC patients. High-risk lymph node-negative TNBC patients may benefit from treatment escalation.
| S-EPMC8481824 | biostudies-literature
Project description:We recently established a 67 genes-expression prognostic signature related to genome complexity in sarcomas. We validated this signature in 60 GISTs, determining patient prognosis.
2011-12-16 | E-MTAB-373 | biostudies-arrayexpress
Project description:Background: Recently a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N-) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Materials and Methods: Gene expression profiling of frozen samples from 198 N- systemically untreated patients was performed at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were performed with the genomic risk and adjusted for the clinical risk, defined by Adjuvant!Online. Results: The actual 5- and 10-year time to distant metastasis (TDM) were 98% (88%-100%) and 94% (83%-98%) respectively for the good profile group and 76% (68%- 82%) and 73% (65%-79%) for the poor profile group. The actual 5- and 10-year overall survival (OS) were 98% (88%-100%) and 87% (73%-94%) respectively for the good profile group and 84% (77%-89%) and 72% (63%-78%) for the poor profile group. We observed a strong time-dependency of this signature, leading to an adjusted HR of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years, and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for TDM and OS respectively. Conclusion: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases. Experiment Overall Design: dataset of microarray experiments from primary breast tumors used to validate the 76-gene signature (VERIDEX). No replicate, no reference sample.
2008-06-16 | E-GEOD-7390 | biostudies-arrayexpress