Tcga-brca
Tcga-brca - Related Videos
08:53
Identifying the Effects of BRCA1 Mutations on Homologous Recombination using Cells that Express Endogenous Wild-type BRCA1
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Cited by 15 •
2011
We provide a method for testing BRCA1 variants in a tissue culture based assay for homologous recombination repair of DNA damage by depleting endogenous BRCA1 protein from a cell using RNAi and replacing it with a BRCA1 point mutant that contains a coding change.
09:19
Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
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Cited by 4 •
2015
Here, real-time monitoring of tumor cell metabolism, combined with an in vivo chicken embryo chorio-allantoic membrane (CAM) model of metastasis, are used to evaluate novel anti-cancer targets/agents for their ability to sensitize tumor cells to DNA damaging chemotherapeutics.
11:47
Time-lapse Imaging of Primary Preneoplastic Mammary Epithelial Cells Derived from Genetically Engineered Mouse Models of Breast Cancer
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Cited by 4 •
2013
Time-lapse imaging is used to assess behavior of primary preneoplastic mammary epithelial cells derived from genetically engineered mouse models of breast cancer risk to determine if there are correlations between specific behavioral parameters and distinct genetic lesions.
12:53
Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
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Cited by 5 •
2017
This manuscript describes the experimental procedure and software analysis for a bidirectional integration site assay that can simultaneously analyze upstream and downstream vector-host junction DNA. Bidirectional PCR products can be used for any downstream sequencing platform. The resulting data are useful for a high-throughput, quantitative comparison of integrated DNA targets.
FAQs
Its value comes from examining complementary molecular layers together rather than treating any single measurement as sufficient. DNA sequencing can identify genomic alterations, gene-expression analysis can show molecular activity, while copy-number and DNA-methylation data add further biological context. Comparing these layers helps researchers recognize coordinated patterns and connect tumor features with broader disease biology.
Researchers can compare patterns across gene expression, genomic alterations, copy-number changes, and DNA methylation to distinguish groups of breast tumors with different molecular characteristics. This approach supports classification based on tumor biology rather than on one isolated feature. The resulting groups can then be examined for relationships with progression or patient outcomes.
Each measurement captures a different aspect of tumor biology, so combined analysis can reveal relationships that may be missed in a single data type. DNA sequencing identifies alterations, copy-number measurements describe genomic gains or losses, and DNA methylation provides an additional molecular layer. Their comparison helps investigators study pathways and patterns associated with breast tumor progression.
Clinical annotations provide the context needed to relate molecular features to patient outcomes and disease characteristics. After identifying genomic or molecular patterns, researchers can compare those patterns with the available clinical information to assess whether they correspond to meaningful differences among tumors or patients. This connection helps prioritize findings for further cancer research.
A study can begin by selecting the molecular profile most relevant to its question, then comparing tumors or molecular patterns within the dataset. Researchers can integrate additional data types to investigate pathways, classify tumor groups, or evaluate associations with clinical annotations. Findings can subsequently guide biomarker evaluation, hypothesis generation, or experimental follow-up.
Researchers examine whether particular genomic alterations or molecular patterns consistently distinguish tumor groups or relate to patient outcomes. Because the resource combines several molecular measurements with clinical annotations, candidate biomarkers can be evaluated in a broader biological and clinical context. The dataset therefore supports biomarker validation and helps identify findings that merit additional study.
Computational analyses of TCGA-BRCA can identify candidate pathways, molecular patterns, and alterations associated with breast tumors or outcomes. These results generate hypotheses that can be examined through experimental studies, creating a link between large-scale data analysis and laboratory investigation. The same process can also inform research into potential targeted therapies.