Expression data for Vanderbilt triple-negative breast cancer subtype classification
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE226289 | GEO | 2023/03/04
REPOSITORIES: GEO
Similar Datasets
Project description:Expression data for Vanderbilt triple-negative breast cancer subtype classification
| PRJNA939561 | ENA
Project description:Pancreatic ductal adenocarcinoma (PDAC) is characterized by high heterogeneity, and the postoperative prognosis of different patients often varies greatly. Therefore, the classification of pancreatic cancer patients and precise treatment becomes particularly important. In this study, 1H NMR spectroscopy was used to analyze the 76 PDAC serum samples and identify the potential metabolic subtypes. The metabolic characteristics of each metabolic subtype were screened out and the relationship between metabolic subtype and the long-term prognosis was further identified. The clinical stages of PDAC did not show the metabolic differences at the serum metabolomic level. And three metabolic subtypes, basic, choline-like and amino acid-enriched types, were defined by the HCA of the serum metabolites and the disturbed metabolic pathways. The characteristic metabolites of each PDAC subtype were identified, and the metabolite model was established to distinguish the PDAC patients in the different subtypes. Among the three metabolic subtypes, choline-like type displayed better long-term prognosis compared with the other two types of patients. Metabolic subtypes are of clinical importance and can fully express the heterogeneity in the actual life activities of pancreatic cancer. The excavation of metabolic subtypes based on this will be more accurate and in line with clinical reality, so as to guide clinical precision individualization treatment.
2022-03-04 | ST002113 | MetabolomicsWorkbench
Project description:Multiple Myeloma (MM) is the second most prevalent blood cancer (10%) after non-Hodgkin's lymphoma and represents approximately 1% of all cancers and 2% of all cancer deaths. MM is a complex disease characterized by numerous genetic alterations and recent mRNA profiling studies have attempted to subclassify the disease to build pathogenetic and prognostic models Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Here we use high accuracy, quantitative proteomics to segregate cancer subtypes directly at the level of expressed proteins. Multiple myeloma is a heterogeneous disease in its initial clinical features as well as its outcome. We investigated two subtypes of Multiple Myeloma: multiple myeloma associated with t(4;14) chromosomal translocation as well as t(4;14)-negative MM subtype. The t(4;14) translocation, found in 15% of multiple myeloma cases, indicates a poor prognosis. Super-SILAC mix was combined of cell lysates from 8 diverse cell lines labelled with heavy amino acids (Lys8 and Arg10). The Super-SILAC library is mixed with samples (lysates), and quantitative mass spectrometric analysis is performed using a setup consisting of LC and on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels. This way we have analysed 20 patient samples from present MM. Shotgun proteomic analysis yielded a proteome of more than 5300 quantified proteins overall (3000 on an average per individual sample). High accuracy of quantification allowed robust separation of subtypes by hierarchical clustering on the protein level.
2024-07-15 | PXD003138 | Pride
Project description:There are many subtypes of dementia, and identification of diagnostic biomarkers that are minimally-invasive, low-cost, and efficient is desired. Circulating microRNAs (miRNAs) have recently gained attention as easily accessible and non-invasive biomarkers. We conducted a comprehensive miRNA expression analysis of serum samples from 1348 Japanese dementia patients, composed of four subtypes—Alzheimer’s disease (AD), vascular dementia, dementia with Lewy bodies (DLB), and normal pressure hydrocephalus—and 246 control subjects. We used this data to construct dementia subtype prediction models based on penalized regression models with the multiclass classification. We constructed a final prediction model using 46 miRNAs, which classified dementia patients from an independent validation set into four subtypes of dementia. Network analysis of miRNA target genes revealed important hub genes, SRC and CHD3, associated with the AD pathogenesis. Moreover, MCU and CASP3, which are known to be associated with DLB pathogenesis, were identified from our DLB-specific target genes. Our study demonstrates the potential of blood-based biomarkers for use in dementia-subtype prediction models. We believe that further investigation using larger sample sizes will contribute to the accurate classification of subtypes of dementia.
2021-10-22 | GSE167559 | GEO
Project description:Breast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2-neu) of the tumor cells. Gene expression profiling of whole breast tumors further stratifies breast cancer into several molecular subtypes which also co-segregate with the receptor status of the tumor cells. We postulated that cancer associated fibroblasts (CAFs) within the tumor stroma may exhibit subtype specific gene expression profiles and thus contribute to the biology of the disease in a subtype specific manner. Several studies have reported gene expression profile differences between CAFs and normal breast fibroblasts but in none of these studies were the results stratified based on tumor subtypes. To address whether gene expression in breast cancer associated fibroblasts varies between breast cancer subtypes, we compared the gene expression profiles of early passage primary CAFs isolated from twenty human breast cancer samples representing three main subtypes; seven ER+, seven triple negative (TNBC) and six Her2+. We observed significant expression differences between CAFs derived from Her2+ breast cancer and CAFs from TNBC and ER+ cancers, particularly in pathways associated with cytoskeleton and integrin signaling. In the case of Her2+ breast cancer, the signaling pathways found to be selectively up regulated in CAFs may contribute to the more invasive properties and unfavorable prognosis of Her2+ breast cancer. These data demonstrate that in addition to the distinct molecular profiles that characterize the neoplastic cells, CAF gene expression is also differentially regulated in distinct subtypes of breast cancer. We isolated CAFs from twenty primary breast cancer samples representing three main subtypes (ER+ (n=7), TNBC (n=7), Her2+ (n=6)) and performed gene expression profile analyses on RNA isolated from these early passage CAFs. Those samples were done in two batches with 4 samples repeated in both batches. One TNBC sample was found to be an outlier and not used in the analysis.
2013-12-20 | E-GEOD-37614 | biostudies-arrayexpress
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:Molecular subtype classification of urothelial carcinoma in Lynch syndrome
| PRJNA414169 | ENA
Project description:Sadanandam et al. (2013) recently published a study based on the use of microarray data to classify colorectal cancer (CRC) samples. The classification claimed to have strong clinical implications, as reflected in the paper title: “A colorectal cancer classification system that associates cellular phenotype and responses to therapy”. They defined five subtypes: (i) inflammatory; (ii) goblet-like; (iii) enterocyte; (iv) transit-amplifying; and (v) stem-like. Based on drug sensitivity data from 21 patients, they also reported that the so-called stem-like subtype show differential sensitivity to FOLFIRI. This is the key result in their publication, since it implies a direct relation between the subtype and the choice of CRC therapy (i.e. FOLFIRI response). However, our analyses using the same drug sensitivity data and results from additional patients showed that the CRC classification reported by Sadanandam et al. is not predictive of FOLFIRI response. We used the classification algorithm to obtain CRC subtypes from our samples. Then we tested the subtype-drug response association performing a retrospective study.
2014-09-10 | E-GEOD-54483 | biostudies-arrayexpress
Project description:Comparison the mRNA expression profiles of 101 CRC tissues to those from matched 35 non-neoplastic colon mucosal tissues from patients with stage III CRCs treated with FOLFOX adjuvant chemotherapy in each molecular subtype. Gene expression–based subtyping is widely accepted as a relevant source of disease stratification. Results provide important information of molecular marker genes for molecular classification.
2017-05-31 | GSE83889 | GEO
Project description:Triple-Negative Breast Cancer (TNBC) is a heterogeneous collection of cancers where personalized treatment is difficult and chemotherapy and immunotherapy combinations are the main treatment options. Many attempts to tackle patient heterogeneity have focused on defining cancer-intrinsic subtypes based on differential tumor mRNA-expression across patient cohorts. While these multi-gene diagnostics have shown success in hormone receptor- positive cancers (e.g. OncotypeDX), no TNBC classifiers have shown clinical utility in predicting patient survival or treatment response. We hypothesize that TNBC-infiltrating immune-cells both affect mRNA-based classification and contribute to treatment response variability. To evaluate this hypothesis, we benchmarked the performance of common TNBC-classification (TNBC-type) and infiltrating immune (CIBERSORT) algorithms on the same underlying datasets. Encouragingly, we found that – as with OncotypeDx– highly proliferative TNBC-subtypes (BL1) show the strongest evidence of response to cytotoxic chemotherapies. Interestingly, this cancer-proliferative signature (BL1) is strongly correlated with enrichment in tumor-infiltrating lymphocyte signatures (TIL) which show superior prognostic and predictive power. In addition, Tumor Associated Macrophage (TAM) signatures show independent predictive and prognostic power for both patient survival and response to anthracycline- and taxane-based chemotherapies. These gene signature-based correlations were validated in a new independent cohort of 67 TNBC-patients treated with neoadjuvant chemotherapy. Overall, these results argue for the independent contributions of both cancer-intrinsic and -extrinsic factors in predicting treatment response in the neoadjuvant setting.
2024-03-20 | GSE260693 | GEO