Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA326262 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Systems-wide quantitative proteomic study of breast cancer tumours
2021-05-25 | E-PROT-27 | ExpressionAtlas
Project description:Purpose: Valproic acid(VPA) has anti-cancer activity attributed to histone deacetylase inhibition(HDACi). We published the Genomically–Derived Sensitivity Signature for VPA(GDSS-VPA), a gene expression biomarker predicting breast cancer sensitivity to VPA in vitro and in vivo. We conducted a window-of-opportunity study examining the tolerability of VPA and the ability of the GDSS-VPA to predict biologic changes in breast tumors from VPA. Methods: Eligible women had untreated breast cancer with breast tumors over 1.5 cm. After a biopsy, women took VPA for 7-12 days, increasing from 30mg/kg/day PO divided BID to a maximum of 50mg/kg/day. After VPA treatment, serum VPA level was measured followed by breast surgery or a biopsy. Tumor proliferation was assessed by Ki-67 immunohistochemistry. Peripheral blood mononuclear cells(PBMCs) histone acetylation was assessed by Western blot. Results: Thirty women were evaluable. The median age was 54(range 31-73). 52% of women tolerated 50mg/kg/day, but 10% missed more than two doses due to adverse events(AEs). Grade 3 AEs included one patient with vomiting and diarrhea and one with fatigue. The end serum VPA level correlated with change in PBMC histone acetylation(rho=0.451, p=0.024). 50% of women with triple-negative breast cancer(TNBC) had a Ki-67 reduction of at least 10%, compared with 17% of other women. GDSS-VPA correlated with a Ki-67 decrease of at least 10%(AUC 0.66). Conclusions: Most women tolerate VPA. VPA treatment caused a decrease in proliferation of breast tumors. The genomic biomarker correlated with decreased proliferation. HDACi is a valid strategy for drug development in TNBC using gene expression biomarkers.
2016-12-17 | GSE83530 | GEO
Project description:Treating unselected cancer patients with new drugs dilutes proof of efficacy when only a fraction of patients respond to therapy. We conducted a meta-analysis on eight primary breast cancer microarray datasets representing diverse breast cancer phenotypes. We present a high-throughput protocol which incorporates drug sensitivity signatures to guide preclinical testing for effective therapeutic agents. Specifically, we focus on drug classes currently undergoing early phase clinical testing. Our genomic and experimental results suggest that the majority of basal-like breast cancers should respond to inhibitors of the phosphatidylinositol-3-kinase pathway, and that a relatively low toxicity histone deacetylase inhibitor, valproic acid, may target aggressive breast cancers. For a subset of drugs, prediction of sensitivity associates with tumor recurrence, suggesting clinical relevance. Preclinical studies using both cell lines and patient tumors grown in 3-dimensional in vitro and orthotopic in vivo preclinical models provide an efficient and highly relevant assessment of drug sensitivity in tumor phenotypes, and validate our genomic analyses. Together, our results show that high-throughput transcriptional profiling can significantly impact drug selection for breast cancer patients. Pre-identification of patient response may not only improve therapeutic response rates, it can also assist in quickly identifying the optimal inclusion criteria for clinical trials. Our model facilitates personalized drug therapy for cancer patients and may be generalized for study of drug efficacy in other diseases. Breast cancer pleural effusion samples from triple negative patients. Compared samples that are computationally predicted to be sensitive to valproic acid and those that are not predicted to be sensitive.
2011-06-01 | E-GEOD-18331 | biostudies-arrayexpress
Project description:Global gene expression profiling was performed on paired tumor biopsies collected before and after 2 weeks of statin treatment with the aim of detecting statin induced changes on tumoral gene expression. In this phase II clinical study using the “window-of-opportunity” design, in which the treatment-free window between a cancer diagnosis and surgical tumor resection is used to study the biological effects of a certain drug, atorvastatin, a lipophilic statin, was prescribed to patients with primary breast cancer for two weeks pre-operatively. Tumor samples subjected to whole genome transcriptional profiling were collected before patients started treatment and after completing treatment.
2015-06-23 | E-GEOD-63427 | biostudies-arrayexpress
Project description:Treating unselected cancer patients with new drugs dilutes proof of efficacy when only a fraction of patients respond to therapy. We conducted a meta-analysis on eight primary breast cancer microarray datasets representing diverse breast cancer phenotypes. We present a high-throughput protocol which incorporates drug sensitivity signatures to guide preclinical testing for effective therapeutic agents. Specifically, we focus on drug classes currently undergoing early phase clinical testing. Our genomic and experimental results suggest that the majority of basal-like breast cancers should respond to inhibitors of the phosphatidylinositol-3-kinase pathway, and that a relatively low toxicity histone deacetylase inhibitor, valproic acid, may target aggressive breast cancers. For a subset of drugs, prediction of sensitivity associates with tumor recurrence, suggesting clinical relevance. Preclinical studies using both cell lines and patient tumors grown in 3-dimensional in vitro and orthotopic in vivo preclinical models provide an efficient and highly relevant assessment of drug sensitivity in tumor phenotypes, and validate our genomic analyses. Together, our results show that high-throughput transcriptional profiling can significantly impact drug selection for breast cancer patients. Pre-identification of patient response may not only improve therapeutic response rates, it can also assist in quickly identifying the optimal inclusion criteria for clinical trials. Our model facilitates personalized drug therapy for cancer patients and may be generalized for study of drug efficacy in other diseases.
2011-06-01 | GSE18331 | GEO
Project description:This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.
Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins
2023-10-09 | BIOMD0000001076 | BioModels
Project description:Development of Transcriptomic Biomarker Signature in Human Saliva to Detect Lung Cancer
2025-01-24 | E-GEOD-32175 | ExpressionAtlas
Project description:All-trans Retinoic acid promotes viral mimicry and antigen presentation in immunologically silent breast-cancer cells
2020-01-13 | E-MTAB-8408 | ExpressionAtlas
Project description:Transcription profiling by array of human breast cancer mammospheres and breast tumors
2025-01-24 | E-GEOD-7515 | ExpressionAtlas
Project description:Gene expression was assessed with Nanostring in the surgical specimens obtained from a Window of Opportunity trial with MK-2206 in early stage breast cancer. Tumor biopsies and surgical specimens were compared for patients who received MK-2206 along with a prospective untreated control group of patients. Greater expression of interferon related genes was seen in surgical specimens following MK-2206 and compared to untreated controls.
2020-05-25 | GSE150509 | GEO