How many etiological subtypes of breast cancer: two, three, four, or more?
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ABSTRACT:
SUBMITTER: Anderson WF
PROVIDER: S-EPMC4148600 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Genome-wide expression data from tumors and cell lines in breast cancer, together with drug response of cell lines, open prospects for integrative analyses that can lead to better personalized therapy. Drug responses and expression data collected from cell lines and tumors were used to generate tripartite networks connecting clusters of patients to cell lines and cell lines to drugs, to connect drugs to patients. Various approaches were applied to connect cell lines to tumor clusters: a standard method that uses a biomarker gene set, and new methods that compute metasignatures for transcription factors and histone modifications given upregulated genes in cell lines or tumors. The results from the metasignature analysis identify two major clusters of patients: one enriched for active histone marks and one for repressive marks. The tumors enriched for activation marks are correlated with poor prognosis. Overall, the analyses suggest new patient clustering, discover dysregulated pathways, and recommend individualized use of drugs to treat subsets of patients.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e35; doi:10.1038/psp.2013.11; advance online publication 27 March 2013.
| S-EPMC3615534 | biostudies-literature
Project description:The yeast [PSI+] prion, formed by the Sup35 (eRF3) protein, has multiple structural variants differing in the strength of nonsense suppressor phenotype. Structure of [PSI+] and its variation are characterized poorly. Here, we mapped Sup35 amyloid cores of 26 [PSI+] ex vivo prions of different origin using proteinase K digestion and mass spectrometric identification of resistant peptides. In all [PSI+] variants the Sup35 amino acid residues 2-32 were fully resistant and the region up to residue 72 was partially resistant. Proteinase K-resistant structures were also found within regions 73-124, 125-153, and 154-221, but their presence differed between [PSI+] isolates. Two distinct digestion patterns were observed for region 2-72, which always correlated with the "strong" and "weak" [PSI+] nonsense suppressor phenotypes. Also, all [PSI+] with a weak pattern were eliminated by multicopy HSP104 gene and were not toxic when combined with multicopy SUP35. [PSI+] with a strong pattern showed opposite properties, being resistant to multicopy HSP104 and lethal with multicopy SUP35. Thus, Sup35 prion cores can be composed of up to four elements. [PSI+] variants can be divided into two classes reliably distinguishable basing on structure of the first element and the described assays.
| S-EPMC6600473 | biostudies-literature
Project description:BackgroundSingle sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets.MethodsThirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided.ResultsSCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value.ConclusionOur results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.
| S-EPMC3283537 | biostudies-literature
Project description:The aim of this study was to describe breast tumor subtypes by common breast cancer risk factors and to determine correlates of subtypes using baseline data from two pooled prospective breast cancer studies within a large health maintenance organization.Tumor data on 2544 invasive breast cancer cases subtyped by estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (Her2) status were obtained (1868 luminal A tumors, 294 luminal B tumors, 288 triple-negative tumors and 94 Her2-overexpressing tumors). Demographic, reproductive and lifestyle information was collected either in person or by mailed questionnaires. Case-only odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression, adjusting for age at diagnosis, race/ethnicity, and study origin.Compared with luminal A cases, luminal B cases were more likely to be younger at diagnosis (P = 0.0001) and were less likely to consume alcohol (OR = 0.74, 95% CI = 0.56 to 0.98), use hormone replacement therapy (HRT) (OR = 0.66, 95% CI = 0.46 to 0.94), and oral contraceptives (OR = 0.73, 95% CI = 0.55 to 0.96). Compared with luminal A cases, triple-negative cases tended to be younger at diagnosis (P < or = 0.0001) and African American (OR = 3.14, 95% CI = 2.12 to 4.16), were more likely to have not breastfed if they had parity greater than or equal to three (OR = 1.68, 95% CI = 1.00 to 2.81), and were more likely to be overweight (OR = 1.82, 95% CI = 1.03 to 3.24) or obese (OR = 1.97, 95% CI = 1.03 to 3.77) if premenopausal. Her2-overexpressing cases were more likely to be younger at diagnosis (P = 0.03) and Hispanic (OR = 2.19, 95% CI = 1.16 to 4.13) or Asian (OR = 2.02, 95% CI = 1.05 to 3.88), and less likely to use HRT (OR = 0.45, 95% CI = 0.26 to 0.79).These observations suggest that investigators should consider tumor heterogeneity in associations with traditional breast cancer risk factors. Important modifiable lifestyle factors that may be related to the development of a specific tumor subtype, but not all subtypes, include obesity, breastfeeding, and alcohol consumption. Future work that will further categorize triple-negative cases into basal and non-basal tumors may help to elucidate these associations further.
| S-EPMC2716499 | biostudies-other
Project description:IntroductionBreast cancer comprises clinically distinct subtypes, but most risk statistics consider breast cancer only as a single entity. To estimate subtype-specific lifetime breast cancer risks, we took advantage of population-based data for which information regarding tumor expression of estrogen receptor (ER), progesterone receptor (PR) and HER2/neu (HER2) was newly available.MethodsWe included women whose breast cancer was diagnosed in the state of California from 2006 to 2007 and was reported to the National Cancer Institute's Surveillance, Epidemiology and End Results Program (N = 40,936). We calculated absolute lifetime and age-specific probabilities (percent, 95% confidence interval) of developing breast cancer subtypes defined by ER, PR, and HER2 status - luminal (ER and/or PR-positive, HER2-negative), HER2-positive (ER and PR-positive or negative, HER2-positive), and triple-negative (ER-negative, PR-negative, and HER2-negative) - separately for white, black, Hispanic, and Asian women.ResultsThe luminal breast cancer subtype predominates across racial/ethnic groups, with lifetime risk lowest in Hispanic women (4.60%, 4.41-4.80%) and highest in white women (8.10%, 7.94-8.20%). HER2-positive breast cancer varies less by race (1.56-1.91%). Lifetime risk of triple-negative breast cancer is highest in black women (1.98%, 1.80-2.17%), compared to 0.77% (0.67-0.88%) for Asians, 1.04% (0.96-1.13%) for Hispanics and 1.25% (1.20-1.30%) for whites. Across racial/ethnic groups, nearly half of all luminal breast cancers occur after age 70.ConclusionsThese absolute risk estimates may inform health policy and resource planning across diverse populations, and can help patients and physicians weigh the probabilities of developing specific breast cancer subtypes against competing health risks.
| S-EPMC3046442 | biostudies-literature
Project description:Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.
| S-EPMC5144978 | biostudies-literature
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform. A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm). Our agglomerative approach was cross-validated by using two other clustering algorithms (k-means and consensus clustering). TNBC miRNAs subgroups were also correlated with the Lehmann intrinsic subtypes, finding a significant enrichment of immmnomodulartory and basal 1 subtypes in our high risk miRNA subgroups. 3 cell line models (MDA MB 468, MDA MB 231 and HS578T) were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs that together with the altered miRNAs within the subgroups allowed us to define interesting miRNAs for further functional analysis.
2018-08-16 | GSE86277 | GEO
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform. A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm). Our agglomerative approach was cross-validated by using two other clustering algorithms (k-means and consensus clustering). TNBC miRNAs subgroups were also correlated with the Lehmann intrinsic subtypes, finding a significant enrichment of immmnomodulartory and basal 1 subtypes in our high risk miRNA subgroups. 3 cell line models (MDA MB 468, MDA MB 231 and HS578T) were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs that together with the altered miRNAs within the subgroups allowed us to define interesting miRNAs for further functional analysis.
2018-08-16 | GSE86281 | GEO
Project description:BackgroundHeterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC), and therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study was to define robust TNBC subtypes with clinical relevance.MethodsGene expression profiling by means of DNA chips was conducted in an internal TNBC cohort composed of 238 patients. In addition, external data (n = 257), obtained by using the same DNA chip, were used for validation. Fuzzy clustering was followed by functional annotation of the clusters. Immunohistochemistry was used to confirm transcriptomics results: CD138 and CD20 were used to test for plasma cell and B lymphocyte infiltrations, respectively; MECA79 and CD31 for tertiary lymphoid structures; and UCHL1/PGP9.5 and S100 for neurogenesis.ResultsWe identified three molecular clusters within TNBC: one molecular apocrine (C1) and two basal-like-enriched (C2 and C3). C2 presented pro-tumorigenic immune response (immune suppressive), high neurogenesis (nerve infiltration), and high biological aggressiveness. In contrast, C3 exhibited adaptive immune response associated with complete B cell differentiation that occurs in tertiary lymphoid structures, and immune checkpoint upregulation. External cohort subtyping by means of the same approach proved the robustness of these results. Furthermore, plasma cell and B lymphocyte infiltrates, tertiary lymphoid structures, and neurogenesis were validated at the protein levels by means of histological evaluation and immunohistochemistry.ConclusionOur work showed that TNBC can be subcategorized in three different subtypes characterized by marked biological features, some of which could be targeted by specific therapies.
| S-EPMC6525459 | biostudies-literature
Project description:Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (<2 cm) and distant from (5-10 cm) the primary tumor, using tissues from cosmetic reduction mammoplasties as baseline. Four distinct transcriptomic subtypes are identified within matched normal tissues: metabolic; immune; matrisome/epithelial-mesenchymal transition, and non-coding enriched. Key components of the subtypes are supported by proteomic and tissue composition analyses. We find that the metabolic subtype is associated with poor prognosis (p < 0.001, HR6.1). Examination of genes representing the metabolic signature identifies several genes able to prognosticate outcome from histologically normal tissues. A subset of these have been reported for their predictive ability in cancer but, to the best of our knowledge, these have not been reported altered in matched normal tissues. This study takes an important first step toward characterizing matched normal tissues resected at pre-defined margins from the primary tumor. Unlocking the predictive potential of unexcised tissue could prove key to driving the realization of personalized medicine for breast cancer patients, allowing for more biologically-driven analyses of tissue margins than morphology alone.
| S-EPMC7442642 | biostudies-literature