Integrated analysis of ovarian cancer samples to facilitate precision medicine
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE143897 | GEO | 2020/01/27
REPOSITORIES: GEO
Similar Datasets
Project description:Integrated analysis of ovarian cancer samples to facilitate precision medicine
| PRJNA601999 | ENA
Project description:* To evaluate genome-wide drug variants in the Somali population * To be able to detect pharmacogenetic variants of importance in the Somali populaion * To infer HLA alleles and blood group variants in Somalia
2020-03-19 | E-MTAB-8331 | biostudies-arrayexpress
Project description:Biobanking in health care has evolved over the last few decades from simple biological sample repositories to complex and dynamic units with multi-organizational infrastructure networks and has become an essential tool for modern medical research. Cardiovascular tissue biobanking provides a unique opportunity to utilize cardiac and vascular samples for translational research into heart failure and other related pathologies. Current techniques for diagnosis, classification, and treatment monitoring of cardiac disease relies primarily on interpretation of clinical signs, imaging, and blood biomarkers. Further research at the disease source (i.e. myocardium and blood vessels) has been limited by a relative lack of access to quality human cardiac tissue and the inherent shortcomings of most animal models of heart disease. In this review, we describe a model for cardiovascular tissue biobanking and databasing, and its potential to facilitate basic and translational research. We share techniques to procure endocardial samples from patients with hypertrophic cardiomyopathy, heart failure with reduced ejection fraction, and heart failure with preserved ejection fraction, in addition to aortic disease samples. We discuss some of the issues with respect to data collection, privacy, biobank consent, and the governance of tissue biobanking. The development of tissue biobanks as described here has significant scope to improve and facilitate translational research in multi-omic fields such as genomics, transcriptomics, proteomics, and metabolomics. This research heralds an era of precision medicine, in which patients with cardiovascular pathology can be provided with optimized and personalized medical care for the treatment of their individual phenotype.
| S-EPMC8787984 | biostudies-literature
Project description:We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
2017-11-30 | GSE107465 | GEO
Project description:Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer related mortality worldwide. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulated progressive driver mutations, with hepatocytes the most likely cell of origin. However, the landscape of driver mutations in HCC is independent of the underlying aetiologies. Despite an increasing range of systemic treatment options for advanced HCC outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human-mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with histopathology. In a proof-of-principle analysis, we verified response to standard of care treatment and used a linked in vitro-in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy.
2024-12-02 | GSE273806 | GEO
Project description:We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
2017-12-13 | GSE108003 | GEO
Project description:Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer related mortality worldwide. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulated progressive driver mutations, with hepatocytes the most likely cell of origin. However, the landscape of driver mutations in HCC is independent of the underlying aetiologies. Despite an increasing range of systemic treatment options for advanced HCC outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human-mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with histopathology. In a proof-of-principle analysis, we verified response to standard of care treatment and used a linked in vitro-in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy.
2024-12-02 | GSE275443 | GEO
Project description:Medulloblastoma (MB) is among the most common malignant brain tumors in children. Recent studies have identified at least four subgroups of the disease that differ in terms of molecular characteristics and patient outcomes. Despite this heterogeneity, most MB patients receive similar therapies, including surgery, radiation and intensive chemotherapy. Although these treatments prolong survival, many patients still die from the disease, and survivors suffer severe long-term side effects from therapy. We hypothesize that each MB patient is sensitive to different therapies, and that tailoring therapy based on the molecular and cellular characteristics of patients’ tumors will improve outcomes. To test this, we have assembled a panel of orthotopic patient-derived xenografts (PDXs) and subjected them to DNA sequencing, gene expression profiling and high-throughput drug screening. Analysis of DNA sequencing suggests that most MBs do not have actionable mutations that point to effective therapies. In contrast, gene expression and drug response data provide valuable information about potential therapies for every tumor. For example, drug screening demonstrates that actinomycin D – which is used for treatment of sarcoma but rarely for MB – is active against PDXs representing Group 3 MB, the most aggressive form of the disease. Finally, we show that functional analysis of tumor cells can be used in a clinical setting to identify more treatment options than sequencing alone. These studies suggest that it should be possible to move away from a one-size-fits-all approach and begin to treat each patient with therapies that are effective against their tumor.
2020-10-01 | GSE151344 | GEO
Project description:Medulloblastoma (MB) is among the most common malignant brain tumors in children. Recent studies have identified at least four subgroups of the disease that differ in terms of molecular characteristics and patient outcomes. Despite this heterogeneity, most MB patients receive similar therapies, including surgery, radiation and intensive chemotherapy. Although these treatments prolong survival, many patients still die from the disease, and survivors suffer severe long-term side effects from therapy. We hypothesize that each MB patient is sensitive to different therapies, and that tailoring therapy based on the molecular and cellular characteristics of patients’ tumors will improve outcomes. To test this, we have assembled a panel of orthotopic patient-derived xenografts (PDXs) and subjected them to DNA sequencing, gene expression profiling and high-throughput drug screening. Analysis of DNA sequencing suggests that most MBs do not have actionable mutations that point to effective therapies. In contrast, gene expression and drug response data provide valuable information about potential therapies for every tumor. For example, drug screening demonstrates that actinomycin D – which is used for treatment of sarcoma but rarely for MB – is active against PDXs representing Group 3 MB, the most aggressive form of the disease. Finally, we show that functional analysis of tumor cells can be used in a clinical setting to identify more treatment options than sequencing alone. These studies suggest that it should be possible to move away from a one-size-fits-all approach and begin to treat each patient with therapies that are effective against their tumor.
2020-10-01 | GSE151343 | GEO
Project description:BACKGROUND:Increasing affordability of next-generation sequencing (NGS) has created an opportunity for realizing genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information presents a huge challenge for clinicians in interpreting the patient's genomic alterations and selecting the optimum approved or investigational therapy. An elaborate and practical information system is urgently needed to support clinical decision as well as to test clinical hypotheses quickly. RESULTS:Here, we present an integrated clinical and genomic information system (CGIS) based on NGS data analyses. Major components include modules for handling clinical data, NGS data processing, variant annotation and prioritization, drug-target-pathway analysis, and population cohort explorer. We built a comprehensive knowledgebase of genes, variants, drugs by collecting annotated information from public and in-house resources. Structured reports for molecular pathology are generated using standardized terminology in order to help clinicians interpret genomic variants and utilize them for targeted cancer therapy. We also implemented many features useful for testing hypotheses to develop prognostic markers from mutation and gene expression data. CONCLUSIONS:Our CGIS software is an attempt to provide useful information for both clinicians and scientists who want to explore genomic information for precision oncology.
| S-EPMC5918454 | biostudies-literature