Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA326262 | ENA |
REPOSITORIES: ENA
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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: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
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 | GSE150458 | GEO
Project description:The goal of this study is to define genes that are differentially expressed in Down syndrome leukemic blasts after treatment with valproic acid (VPA) Here we report the identification gene sets that are downregulated in Down syndrome leukemic cell lines after exposure to valproic acid (VPA)
2011-07-13 | GSE30517 | GEO
Project description:Background: Neoadjuvant chemotherapy is increasingly being used to preoperatively shrink breast tumours prior to surgery. This approach also provides the opportunity to study the molecular changes associated with treatment and evaluate whether on-treatment sequential samples can improve response and outcome predictions over diagnostic or excision samples alone. Methods: A total of 95 samples from a cohort of 50 neoadjuvant chemotherapy-treated primary breast cancer patients (aged 29-76, 48% ER+, 20% HER2+) enrolled in the NEO trial taken before, at 2 weeks on-treatment, mid-therapy and at resection were sequenced with Ion Ampliseq transcriptome yielding expression values for 12,635 genes. Differential expression analysis was performed across 16 responders and 34 non-responders defined by pathological complete response and over treatment time to identify significantly differentially expressed genes, pathways and markers indicative of response status. Prediction accuracy was compared with estimations of established gene signatures, for this dataset and validated using data from the I-SPY1 trial. Results: AAGAB was identified as a novel on-treatment biomarker for pathological complete response, with an accuracy of 100% in the NEO training dataset and 78% accuracy in the I-SPY1 Trial. AAGAB levels on treatment were also significantly predictive of term survival (p = 0.048, p = 0.031) in the two cohorts. This single gene on-treatment biomarker, had greater predictive accuracy than established prognostic tests, Mammaprint and Pam50 risk of recurrence score, although interesting both of these tests performed better in the on-treatment rather than the accepted pre-treatment setting (accuracy improving consistently by 2-8%). Conclusion: Changes in gene expression measured in sequential samples from breast cancer patients receiving neoadjuvant chemotherapy resulted in the identification of a novel on-treatment biomarker and suggest that established prognostic tests may have greater prediction accuracy on- than before treatment. These results support the potential use and further evaluation of on- treatment testing in breast cancer to improve the accuracy of tumour response prediction.
2019-07-02 | GSE122630 | GEO