Homo sapiens
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PROVIDER: PRJNA106779 | ENA |
REPOSITORIES: ENA
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Project description:When triple-negative breast cancer (TNBC) patients have residual disease after neoadjuvant chemotherapy (NACT), they have a high risk of metastatic relapse. With immune infiltrate in TNBC being prognostic and predictive of response to treatment, our aim was to develop an immunologic transcriptomic signature using post NACT samples to predict relapse.
2022-09-20 | GSE185507 | GEO
Project description:Purpose: Our study aimed to disclose the specific gene expression profile representing peritoneal relapses inherent in primary gastric cancers and to identify patients at high risk of peritoneal relapse in a prospective study on the basis of the molecular prediction. Experimental Design: RNA samples from 141 primary gastric cancer tissues after curative surgery were profiled using oligonucleotide microarrays covering 30,000 human probes. Firstly we constructed molecular prediction system and validated the robustness and prognostic validity of the analysis by 500 times multiple random sampling in 56 retrospective set consisting of 38 relapse free and 18 peritoneal relapse patients. Secondly we applied this prediction to 85 prospective set to assess the predictive accuracy and prognostic validity. Results: In retrospective phase, 500 times multiple random sampling analysis yielded 68% predictive accuracy in average and 22 gene expression profile associated with peritoneal relapse was identified. This prediction could identify significantly poor prognostic patients. In prospective phase, the molecular prediction yielded 76.9% overall accuracy. KaplanâMeier analysis with peritoneal relapse free survival showed a significant difference between âgood signature groupâ and âpoor signature groupâ (Log-rank p=0.0017). Multivariate analysis by Cox regression hazards model revealed that the molecular prediction was the only independent peritoneal relapse prognostic factor. Conclusions: Gene expression profile inherent in primary gastric cancer tissues can be useful to predict peritoneal relapse prospectively after curative surgery and individualize postoperative management to improve the prognosis of advanced gastric cancers. Of 141 samples, 56 represented the retrospective phase and 85 represented the prospective phase.
2010-05-23 | E-GEOD-15081 | biostudies-arrayexpress
Project description:Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.
2022-02-14 | PXD021491 | Pride
Project description:<p><strong>PURPOSE:</strong> Detecting signals of micrometastatic disease in early breast cancer (EBC) patients could improve risk stratification and allow better tailoring of adjuvant therapies. We have previously shown that postoperative serum metabolomic profiles are predictive of relapse in a single-centre cohort of ERnegative EBC patients. Here, we investigated this further using pre-operative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial. </p><p><strong>METHODS:</strong> Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or =6 years clinical follow up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated based on the likelihood of the sample being misclassified as metastatic. </p><p><strong>RESULTS:</strong> In the training set, the RF model separated EBC from MBC with discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an area under the curve of 0.747 in receiver operator characteristics analysis. Accuracy was maximised at 71.3% (sensitivity 70.8%, specificity 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of AdjuvantOnline risk of relapse score<strong>.</strong></p><p><strong>CONCLUSIONS:</strong> In a multicentre group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathological risk factors.</p>
2017-03-02 | MTBLS424 | MetaboLights
Project description:Purpose: Our study aimed to disclose the specific gene expression profile representing peritoneal relapses inherent in primary gastric cancers and to identify patients at high risk of peritoneal relapse in a prospective study on the basis of the molecular prediction. Experimental Design: RNA samples from 141 primary gastric cancer tissues after curative surgery were profiled using oligonucleotide microarrays covering 30,000 human probes. Firstly we constructed molecular prediction system and validated the robustness and prognostic validity of the analysis by 500 times multiple random sampling in 56 retrospective set consisting of 38 relapse free and 18 peritoneal relapse patients. Secondly we applied this prediction to 85 prospective set to assess the predictive accuracy and prognostic validity. Results: In retrospective phase, 500 times multiple random sampling analysis yielded 68% predictive accuracy in average and 22 gene expression profile associated with peritoneal relapse was identified. This prediction could identify significantly poor prognostic patients. In prospective phase, the molecular prediction yielded 76.9% overall accuracy. Kaplan–Meier analysis with peritoneal relapse free survival showed a significant difference between ‘good signature group’ and ‘poor signature group’ (Log-rank p=0.0017). Multivariate analysis by Cox regression hazards model revealed that the molecular prediction was the only independent peritoneal relapse prognostic factor. Conclusions: Gene expression profile inherent in primary gastric cancer tissues can be useful to predict peritoneal relapse prospectively after curative surgery and individualize postoperative management to improve the prognosis of advanced gastric cancers.
2009-03-04 | GSE15081 | GEO
Project description:Breast cancer is a hugely heterogeneous disease, and markers for disease subtypes and therapy response remain poorly defined. For that reason, we employed a retrospective study in node-positive breast cancer to identify molecular signatures of gene expression correlating with metastatic free survival. Patients were primarily included in FEC100 (fluorouracil, epirubicin and cyclophosphamide) arms of two multicentric phase III clinical trials (PACS01 and PEGASE01 - FNCLCC). Data from nylon microarrays containing 8.032 cDNA unique sequences, representing 5.776 distinct genes, have been used to develop a predictive model for treatment outcome. We obtained the gene expression profiles of 150 population-based patients, and used stringent univariate selection technique based on Cox regression combined with principal component analysis to identify signature associated with prognosis and impact of FEC100 chemotherapy. Our work identified a gene-signature of metastatic relapse. Most of the 14 selected genes have a clear role in breast cancer, neoplasia or chemotherapy resistance. Furthermore, we showed the interest of combining transcriptomic data with clinical data into a clinicogenomic model for patients subtyping. The described model adds predictive accuracy to that provided by the well established Nottingham prognostic index or by the genomic predictor alone. Keywords: Gene-expression profiling
2007-10-18 | GSE7017 | GEO
Project description:Up to 40% of patients with Estrogen Receptor positive (ER+) breast cancer experience relapse. Breast cancer stem cells (BCSCs) are known to be involved in therapy resistance, relapse, and development of more aggressive and metastatic tumors. Therefore, there is an urgent need to identify genes/pathways that drive stem-like cell properties in ER+ breast tumors. Using single-cell RNA sequencing and bioinformatic approaches, with additional follow-up studies, we identified a unique quiescent stem-like cell population that is driven by ER and NFkB in multiple ER+ breast cancer models. Moreover, a gene signature derived from this stem-like population is expressed in endocrine therapy-resistant and metastatic cell populations and predictive of poor patient outcome. These findings indicate a novel role for ER and NFkB crosstalk in BCSCs biology and understanding the mechanism by which these pathways promote stem properties may be exploited to improve outcomes for ER+ breast cancer patients at risk of relapse.
2022-12-16 | GSE205415 | GEO
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.
2009-03-07 | E-GEOD-11078 | biostudies-arrayexpress
Project description:Background: Metastases result in 90% of all cancer deaths. Prostate cancer primary tumors evolve to become metastatic through selective alterations, such as amplification and deletion of genomic DNA. Methods: Genomic DNA copy number alterations were used to develop a gene signature that measured the metastatic potential of a prostate cancer primary tumor. We studied the genomic landscape of these alterations in 294 primary tumors and 49 metastases from 5 independent cohorts. Receiver-operating characteristic cross-validation and Kaplan-Meier survival analysis were performed to assess the accuracy of our predictive model. The signature was measured in a panel of 337 cancer cell lines from 29 different tissue origins. Results: We identified 399 copy number alterations around genes that were over-represented in metastases and predictive of whether a primary tumor will metastasize. Cross-validation analysis resulted in a predictive accuracy of 80.5% and log rank analysis of the metastatic potential score was significantly related to the endpoint of metastasis-free survival (p=0.014). The metastatic signature was observed in cell lines originating from lung, breast, colon, thyroid, rectum, pancreas and melanoma. The signature was comprised in part of genes of known or putative metastatic role — 8 solute carrier genes, 6 Cadherin family genes and 5 potassium channel genes — that function in metabolism, cell-to-cell adhesion and escape from anoikis/apoptosis. Conclusions: Somatic Copy number alterations are an independent predictor of metastatic potential. The data indicate a prognostic utility for using primary tumor genomics to assist in clinical decision making and developing therapeutics for prostate and likely other cancers. genomic DNA from 29 prostate cancer tumors with matched normals run on Affymetrix 6.0 SNP arrays.
2013-12-01 | E-GEOD-27105 | biostudies-arrayexpress
Project description:Prognostic signature of epithelial-mesenchymal transition in breast cancer
| PRJNA110335 | ENA