Building Prognostic Models for Breast Cancer Patients Using Clinical Variables and Gene Expression Signatures
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Prat Aparicio Aleix
PROVIDER: S-ECPF-GEOD-15393 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Our findings indicate that the integration of expression signatures and clinicopathological factors can better determine the individual risk of recurrence for newly diagnosed patients with lymph-node negative ER-positive breast cancer. Models incorporating other variables yet to be discovered will be needed to obtain robust prognostic models for ER-negative and HER2-positive breast cancer patients.
2010-10-25 | GSE15393 | GEO
Project description:Our findings indicate that the integration of expression signatures and clinicopathological factors can better determine the individual risk of recurrence for newly diagnosed patients with lymph-node negative ER-positive breast cancer. Models incorporating other variables yet to be discovered will be needed to obtain robust prognostic models for ER-negative and HER2-positive breast cancer patients. A large data set was created by combining five different publicly available microarray datasets of node-negative breast cancer patients treated with local therapy only. The microarray gene expression data was combined using the batch effect adjustment by the Distance Weighted Discrimination method.
2010-10-25 | E-GEOD-15393 | biostudies-arrayexpress
Project description:Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.
| S-EPMC3025826 | biostudies-literature
Project description:BACKGROUND: During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms. RESULTS: We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters. CONCLUSION: Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.
| S-EPMC2533026 | biostudies-literature
Project description:BackgroundBreast tissue composition (epithelium, non-fatty stroma, and adipose) changes qualitatively and quantitatively throughout the lifespan, and may mediate relationships between risk factors and breast cancer initiation. We sought to identify relationships between tissue composition, risk factors, tumor characteristics, and gene expression.MethodsParticipants were 146 patients from the Polish Breast Cancer Study, with data on risk factor and clinicopathological characteristics. Benign breast tissue composition was evaluated using digital image analysis of histologic sections. Whole-genome microarrays were performed on the same tissue blocks.ResultsMean epithelial, non-fatty stromal, and adipose proportions were 8.4% (SD = 4.9%), 27.7% (SD = 24.0%), and 64.0% (SD = 24.0%), respectively. Among women <50 years old, stroma proportion decreased and adipose proportion increased with age, with approximately 2% difference per year (P < 0.01). The variation in epithelial proportion with age was modest (0.1% per year). Higher epithelial proportion was associated with obesity (7.6% in nonobese vs. 10.1% in obese; P = 0.02) and with poorly differentiated tumors (7.8% in well/moderate vs. 9.9% in poor; P = 0.05). Gene expression signatures associated with epithelial and stromal proportion were identified and validated. Stroma-associated genes were in metabolism and stem cell maintenance pathways, whereas epithelial genes were enriched for cytokine and immune response pathways.ConclusionsBreast tissue composition was associated with age, body mass index, and tumor grade, with consequences for breast gene expression.ImpactBreast tissue morphologic factors may influence breast cancer etiology. Composition and gene expression may act as biomarkers of breast cancer risk and progression.
| S-EPMC4278568 | biostudies-literature
Project description:A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.
| S-EPMC2902824 | biostudies-other
Project description:PurposeLymphocytic infiltration of tumors predicts improved survival in patients with breast cancer. Previous studies have suggested that this survival benefit is confined predominantly to the basal-like subtype. Immune infiltration in ovarian tumors is also associated with improved prognosis. Currently, it is unclear what aspects of the immune response mediate this improved outcome.Experimental designUsing The Cancer Genome Atlas mRNA-seq data and a large microarray dataset, we evaluated adaptive immune gene expression by genomic subtype in breast and ovarian cancer. To investigate B-cells observed to be prognostic within specific subtypes, we developed methods to analyze B-cell population diversity and degree of somatic hypermutation (SHM) from B-cell receptor (BCR) sequences in mRNA-seq data.ResultsImproved metastasis-free/progression-free survival was correlated with B-cell gene expression signatures, which were restricted mainly to the basal-like and HER2-enriched breast cancer subtypes and the immunoreactive ovarian cancer subtype. Consistent with a restricted epitope-driven response, a subset of basal-like and HER2-enriched breast tumors and immunoreactive ovarian tumors showed high expression of a low-diversity population of BCR gene segments. More BCR segments showed improved prognosis with increased expression in basal-like breast tumors and immunoreactive ovarian tumors compared with other subtypes. Basal-like and HER2-enriched tumors exhibited more BCR sequence variants in regions consistent with SHM.ConclusionTaken together, these data suggest the presence of a productive and potentially restricted antitumor B-cell response in basal-like breast and immunoreactive ovarian cancers. Immunomodulatory therapies that support B-cell responses may be a promising therapeutic approach to targeting these B-cell infiltrated tumors.
| S-EPMC4102637 | biostudies-literature
Project description:PurposeBreast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients.MethodsThe RNA-seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value.ResultsBased on the immune score, we found that the high-score group showed better clinical outcomes than the low-score group. Twenty-five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low-risk group had longer overall survival than the high-risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25-gene prognostic signatures and several clinicopathological prognostic factors was constructed.ConclusionsThis study revealed the significance of immune infiltration and constructed a 25-gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.
| S-EPMC7957182 | biostudies-literature
Project description:Breast cancer is a heterogeneous disease. Although gene expression profiling has led to the definition of several subtypes of breast cancer, the precise discovery of the subtypes remains a challenge. Clinical data is another promising source. In this study, clinical variables are utilized and integrated to gene expressions for the stratification of breast cancer. We adopt two phases: gene selection and clustering, where the integration is in the gene selection phase; only genes whose expressions are most relevant to each clinical variable and least redundant among themselves are selected for further clustering. In practice, we simply utilize maximum relevance minimum redundancy (mRMR) for gene selection and k-means for clustering. We compare the results of our method with those of two commonly used only expression-based breast cancer stratification methods: prediction analysis of microarray 50 (PAM50) and highest variability (HV). The result is that our method outperforms them in identifying subtypes significantly associated with five-year survival and recurrence time. Specifically, our method identified recurrence-associated breast cancer subtypes that were not identified by PAM50 and HV. Additionally, our analysis discovered three survival-associated luminal-A subgroups and two survival-associated luminal-B subgroups. The study indicates that screening clinically relevant gene expressions yields improved breast cancer stratification.
| S-EPMC6385100 | biostudies-literature
Project description:The development of oncolytic viruses has recently made great progress towards being available to cancer patients. With the breakthrough into clinics, it is crucial to analyze the existing clinical experience and use it as a basis for treatment improvements. Here, we report clinical data from 290 patients treated with oncolytic adenovirus. Using clinical variables and treatment characteristics, we constructed statistical models with regard to treatment response and overall survival (OS). Additionally, we investigated effects of neutralizing antibodies, tumor burden, and peripheral blood leucocyte counts on these outcomes. We found the absence of liver metastases to correlate with an improved rate of disease control (P = 0.021). In multivariate evaluation, patients treated with viruses coding for immunostimulatory granulocyte macrophage colony-stimulating factor were linked to better prognosis (hazard ratio (HR) 0.378, P < 0.001), as well as women with any cancer type (HR 0.694, P = 0.017). In multivariate analysis for imaging response, patients treated via intraperitoneal injection were more likely to achieve disease control (odds ratio (OR) 3.246, P = 0.027). Patients with low neutrophil-to-lymphocyte ratio before treatment had significantly longer OS (P < 0.001). These findings could explain some of the variation seen in treatment outcomes after virotherapy. Furthermore, the results offer hypotheses for treatment optimization and patient selection in oncolytic adenovirus immunotherapy.
| S-EPMC5088758 | biostudies-literature