A new genome-driven integrated classification of breast cancer and its implications.
Ontology highlight
ABSTRACT:
SUBMITTER: Dawson SJ
PROVIDER: S-EPMC3590990 | biostudies-other | 2013 Mar
REPOSITORIES: biostudies-other
Similar Datasets
Project description:BACKGROUND: IntClust is a classification of breast cancer comprising 10 subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from 1,000 breast tumors and validated in a further 1,000. We present a reliable method for subtyping breast tumors into the IntClust subtypes based on gene expression and demonstrate the clinical and biological validity of the IntClust classification. RESULTS: We developed a gene expression-based approach for classifying breast tumors into the ten IntClust subtypes by using the ensemble profile of the index discovery dataset. We evaluate this approach in 983 independent samples for which the combined copy-number and gene expression IntClust classification was available. Only 24 samples are discordantly classified. Next, we compile a consolidated external dataset composed of a further 7,544 breast tumors. We use our approach to classify all samples into the IntClust subtypes. All ten subtypes are observable in most studies at comparable frequencies. The IntClust subtypes are significantly associated with relapse-free survival and recapitulate patterns of survival observed previously. In studies of neo-adjuvant chemotherapy, IntClust reveals distinct patterns of chemosensitivity. Finally, patterns of expression of genomic drivers reported by TCGA (The Cancer Genome Atlas) are better explained by IntClust as compared to the PAM50 classifier. CONCLUSIONS: IntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers. IntClust is a driver-based breast cancer classification and is likely to become increasingly relevant as more targeted biological therapies become available.
| S-EPMC4166472 | biostudies-literature
Project description:Background The New York Heart Association (NYHA) classification has served as a fundamental tool for risk stratification of heart failure (HF) and determines clinical trial eligibility and candidacy for drugs and devices. However, its ability to adequately stratify risk is unclear. Methods and Results To compare NYHA class with objective assessments and survival in patients with HF, we performed secondary analyses of 4 multicenter National Institutes of Health-funded HF clinical trials that included patients classified as NYHA class II or III: TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist), DIG (The Effect of Digoxin on Mortality and Morbidity in Patients With Heart Failure), HF-ACTION (Efficacy and Safety of Exercise Training in Patients With Chronic Heart Failure), and GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure). Twenty-month cumulative survival was compared between classes using Kaplan-Meier curves and the log rank test. NT-proBNP (N-terminal pro-B-type natriuretic peptide), Kansas City Cardiomyopathy Questionnaire (KCCQ) scores, 6-minute walk distances, left ventricular ejection fraction, and cardiopulmonary test parameters were compared using Wilcoxon rank sum tests and percentage overlap using kernel density estimations. Cumulative mortality varied significantly across NYHA classes and HF clinical trials (likelihood ratio, P<0.001). Mortality at 20 months for NYHA class II ranged from 7% for patients in HF-ACTION to 15% in GUIDE-IT, whereas mortality for NYHA class III ranged from 12% in TOPCAT to 26% in GUIDE-IT. There was substantial percentage overlap in values for NT-proBNP levels (79% and 69%), KCCQ scores (63% and 54%), 6-minute walk distances (63% and 54%), and left ventricular ejection fraction (88% and 83%). Similarly, there was substantial overall in values for minute ventilation-carbon dioxide production relationship (71%), maximal oxygen uptake (54%), and exercise duration (53%). Conclusions The NYHA system poorly discriminates HF patients across the spectrum of functional impairment. These findings raise important questions about the need for improved phenotyping of these patients to facilitate risk stratification and response to interventions.
| S-EPMC6912957 | biostudies-literature
Project description:The molecular complexity of triple-negative breast cancers (TNBCs) provides a challenge for patient management. We set out to characterize this heterogeneous disease by combining transcriptomics and genomics data, with the aim of revealing convergent pathway dependencies with the potential for treatment intervention. A Bayesian algorithm was used to integrate molecular profiles in two TNBC cohorts, followed by validation using five independent cohorts (n = 1,168), including three clinical trials. A four-gene decision tree signature was identified, which robustly classified TNBCs into six subtypes. All four genes in the signature (EXO1, TP53BP2, FOXM1, and RSU1) are associated with either genomic instability, malignant growth, or treatment response. One of the six subtypes, MC6, encompassed the largest proportion of tumors (∼50%) in early diagnosed TNBCs. In TNBC patients with metastatic disease, the MC6 proportion was reduced to 25%, and was independently associated with a higher response rate to platinum-based chemotherapy. In TNBC cell line data, platinum sensitivity was recapitulated, and a sensitivity to the inhibition of the phosphatase PPM1D was revealed. Molecularly, MC6-TNBCs displayed high levels of telomeric allelic imbalances, enrichment of CD4+ and CD8+ immune signatures, and reduced expression of genes negatively regulating the MAPK signaling pathway. These observations suggest that our integrative classification approach may identify TNBC patients with discernible and theoretically pharmacologically tractable features that merit further studies in prospective trials.
| S-EPMC6330084 | biostudies-literature
Project description:Metastasis remains the primary cause of patient morbidity and mortality in solid tumors and is due to the action of a large number of tumor-autonomous and non-autonomous factors. Here we report the results of a genome-wide integrated strategy to identify novel metastasis susceptibility candidate genes and molecular pathways in breast cancer metastasis. This analysis implicates a number of transcriptional regulators and suggests cell-mediated immunity is an important determinant. Moreover, the analysis identified novel or FDA-approved drugs as potentially useful for anti-metastatic therapy. Further explorations implementing this strategy may therefore provide a variety of information for clinical applications in the control and treatment of advanced neoplastic disease.
| S-EPMC4830524 | biostudies-literature
Project description:The immune system has a substantial effect on colorectal cancer (CRC) progression. Additionally, the response to immunotherapeutics and conventional treatment options (e.g., chemotherapy, radiotherapy and targeted therapies) is influenced by the immune system. The molecular characterization of colorectal cancer (CRC) has led to the identification of favorable and unfavorable immunological attributes linked to clinical outcome. With the definition of consensus molecular subtypes (CMSs) based on transcriptomic profiles, multiple characteristics have been proposed to be responsible for the development of the tumor immune microenvironment and corresponding mechanisms of immune escape. In this review, a detailed description of proposed immune phenotypes as well as their interaction with different therapeutic modalities will be provided. Finally, possible strategies to shift the CRC immune phenotype towards a reactive, anti-tumor orientation are proposed per CMS.
| S-EPMC5666908 | biostudies-literature
Project description:The mechanisms underlying drug addiction remain nebulous. Furthermore, new psychoactive substances (NPS) are being developed to circumvent legal control; hence, rapid NPS identification is urgently needed. Here, we present the construction of the comprehensive database of controlled substances, AddictedChem. This database integrates the following information on controlled substances from the US Drug Enforcement Administration: physical and chemical characteristics; classified literature by Medical Subject Headings terms and target binding data; absorption, distribution, metabolism, excretion, and toxicity; and related genes, pathways, and bioassays. We created 29 predictive models for NPS identification using five machine learning algorithms and seven molecular descriptors. The best performing models achieved a balanced accuracy (BA) of 0.940 with an area under the curve (AUC) of 0.986 for the test set and a BA of 0.919 and an AUC of 0.968 for the external validation set, which were subsequently used to identify potential NPS with a consensus strategy. Concurrently, a chemical space that included the properties of vectorised addictive compounds was constructed and integrated with AddictedChem, illustrating the principle of diversely existing NPS from a macro perspective. Based on these potential applications, AddictedChem could be considered a highly promising tool for NPS identification and evaluation.
| S-EPMC9227411 | biostudies-literature
Project description:BackgroundSialophorin is a transmembrane sialoglycoprotein. Normally, the molecule is only produced by white blood cells where it regulates functions such as intercellular adhesion, intracellular signalling, apoptosis, migration and proliferation.MethodsNormal breast tissue and primary breast tumours were analysed by immunohistochemistry for sialophorin expression. The sialophorin-positive breast cancer cell line MCF7 was engineered to stably express either non-targeted or sialophorin-targeted small interfering RNA (siRNA). Assays were then performed in vitro to assess apoptosis, intracellular adhesion, transendothelial migration and cytotoxicity. An orthotopic mouse model assayed ability to produce tumours in vivo.ResultsNormal breast epithelial cells exhibit expression of the N-terminal domain of sialophorin in the cytoplasm but not the nucleus. The majority of these normal cells are also negative for expression of the C-terminal domain. In contrast, malignant breast epithelial cells exhibit N-terminal expression both in the cytoplasm and nucleus and the majority express the C-terminus in the nucleus. Using differential patterns of intracellular expression of the N and C termini of sialophorin, we define six subtypes of breast cancer that are independent of histological and receptor status classification. Targeting sialophorin with siRNA resulted in the MCF7 breast cancer cell line exhibiting increased homotypic adhesion, decreased transendothelial migration, increased susceptibility to apoptosis, increased vulnerability to lysis by natural killer cells and decreased ability to produce tumours in mice.ConclusionOur results indicate that intracellular patterns of sialophorin expression define a new molecular classification of breast cancer and that sialophorin represents a novel therapeutic target.
| S-EPMC3887278 | biostudies-literature
Project description:Metastatic prostate cancers are recognized to exhibit subtypes categorized by underlying genomic alterations and phenotypes largely partitioned by androgen receptor signaling and neuroendocrine activity. In the present study we evaluated a phenotypic classification approach originally developed for subtyping breast carcinomas using the PAM50 gene signature. PAM50 subtypes associated with specific genotypes such as RB1 loss and phenotypes such as small cell/neuroendocrine carcinoma as well as tumor histology including cribriform morphology. In the context of clinical translation, PAM50 classification segregated tumors into groups with distinct druggable targets such as cell surface proteins amenable to antibody-drug-conjugates (ADCs). Classification into Luminal A, Luminal B and Basal tumors associated with time on androgen receptor signaling inhibitors, and responses to taxane chemotherapy. These findings support further clinical investigation of PAM50-based classification for prostate cancer patient stratification in therapeutic studies.
2022-05-03 | GSE199596 | GEO
Project description:Breast cancer classification has been in the focus of numerous large worldwide efforts, analyzing the molecular basis of breast cancer subtypes, aiming to associate them with clinical outcome and improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles is yet to be elucidated. In this work, we examined inter-tumor heterogeneity by performing a mass-spectrometry (MS)-based proteomic analysis of more than 130 clinical breast samples originating from three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them one that represents a novel luminal subtype characterized by increased cancer signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. Altogether, our results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making.
2019-07-29 | PXD009766 | Pride
Project description:Breast cancer is the most common cancer in women in developed countries. To identify common breast cancer susceptibility alleles, we conducted a genome-wide association study in which 582,886 SNPs were genotyped in 3,659 cases with a family history of the disease and 4,897 controls. Promising associations were evaluated in a second stage, comprising 12,576 cases and 12,223 controls. We identified five new susceptibility loci, on chromosomes 9, 10 and 11 (P = 4.6 x 10(-7) to P = 3.2 x 10(-15)). We also identified SNPs in the 6q25.1 (rs3757318, P = 2.9 x 10(-6)), 8q24 (rs1562430, P = 5.8 x 10(-7)) and LSP1 (rs909116, P = 7.3 x 10(-7)) regions that showed more significant association with risk than those reported previously. Previously identified breast cancer susceptibility loci were also found to show larger effect sizes in this study of familial breast cancer cases than in previous population-based studies, consistent with polygenic susceptibility to the disease.
| S-EPMC3632836 | biostudies-literature