Breast cancer research databases are most useful when each is matched to a specific question. PubMed organizes published literature, ClinicalTrials.gov records registered studies, genomic portals expose cohort data, and cell-model resources describe experimental systems. Each contributes a different kind of evidence.
This guide compares the main tools and gives a practical search sequence for moving from a research question to a usable source.
Research databases and model resources compared
| Tool | Question it helps answer | Interpretation limit |
|---|---|---|
| PubMed | What literature has been published? | Trace key claims from reviews to primary studies. |
| ClinicalTrials.gov | What studies are registered and what results are posted? | Registration alone does not establish effectiveness. |
| NCI trial search | Which breast cancer trials are in NCI’s collection? | Use broader registries for complementary searches. |
| GDC and TCGA-BRCA | What tumor-cohort genomic and clinical data are available? | Check cohort selection, assay, access tier, and preprocessing. |
| cBioPortal | How do alterations and clinical features relate in a selected study? | Studies differ in populations, platforms, and annotations. |
| NCBI GEO | Which expression or functional-genomics datasets fit the question? | Read sample metadata and the original experimental design. |
| DepMap | What dependencies and molecular features appear in cancer cell models? | Model dependencies are not clinical response rates. |
| ATCC | What are a cell line’s provenance and recommended culture conditions? | Confirm requirements for the exact model and authenticate local cultures. |
A practical sequence for searching the evidence
- Start with PubMed to define what is already known and identify landmark breast cancer papers or recent primary studies.
- Use NCI and ClinicalTrials.gov to see whether a biological idea has entered human studies and in which population.
- Use GDC/TCGA or cBioPortal to test whether the genomic feature appears in patient cohorts and how common it is within the selected dataset.
- Use GEO to find expression datasets that reproduce or challenge a proposed transcriptional pattern.
- Use DepMap to examine gene dependency, molecular context, or compound sensitivity across cancer cell models.
- Use ATCC or another authoritative model provider to check cell identity, provenance, and recommended culture conditions. Supplier information does not authenticate the cells currently in your laboratory; use appropriate identity and contamination checks for the actual culture.
Preserve the level of evidence and the analysis context
A mutation frequency in TCGA is not a treatment recommendation. A DepMap dependency is not a clinical response rate. A registered trial is not an approved indication. A cell-line result is not a patient outcome. Each resource answers a different level of the evidence chain.
Record the dataset or study identifier, release or access date, cohort filters, sample counts, assay platform, and preprocessing choices. These details make a result interpretable and easier to reproduce. Use clinical and regulatory sources to assess whether a research finding has translated into human evidence or an approved use.
For research and education only. Research findings do not replace individualized medical advice, diagnosis, or treatment recommendations.
