Cancer cell-line selection and profiling for experimental model choice. Cross-references DepMap, Cellosaurus, COSMIC, PharmacoDB to deliver identity verification, mutation/CNV profile, gene dependencies, drug sensitivities, and druggable targets. Use to answer 'which cell line should I use for studying gene X?' or 'is this cell line a good model for cancer Y?'. Outputs ranked recommendations with rationale, growth characteristics, and known pitfalls.
Cancer cell-line selection and profiling for experimental model choice. Cross-references DepMap, Cellosaurus, COSMIC, PharmacoDB to deliver identity verification, mutation/CNV profile, gene dependencies, drug sensitivities, and druggable targets. Use to answer 'which cell line should I use for studying gene X?' or 'is this cell line a good model for cancer Y?'. Outputs ranked recommendations with rationale, growth characteristics, and known pitfalls.
Cancer Cell Line Profiling and Selection
Comprehensive profiling of cancer cell lines for experimental model selection. Transforms a query (cancer type, gene, or cell line name) into an actionable report covering identity verification, molecular features, gene dependencies, drug sensitivities, and druggable targets.
KEY PRINCIPLES:
Decision-first - Answer "which cell line should I use?" not "here is all the data"
Actionable output - Ranked cell line recommendations with rationale
Practical focus - Include availability, growth characteristics, common pitfalls
Gene-aware - When a gene of interest is given, prioritize lines with relevant mutations/dependencies
Source-referenced - Cite database sources for every claim
English-first queries - Always use English terms in tool calls, even if the user writes in another language
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
When to Use
Apply for: cell line selection by cancer type/gene, cell line profiling, gene dependencies, drug sensitivity queries, cell line comparisons, mutation checks.
Phase 0: Tool Parameter Reference (CRITICAL)
BEFORE calling ANY tool, verify parameters against this table.
Input: Cancer type AND/OR Gene of interest AND/OR Cell line name(s)
Phase 1: Cell Line Identification
- Search and verify cell line identity (Cellosaurus)
- Get metadata: species, disease, STR profile, cross-references
- If cancer type given without cell line: find candidate lines (DepMap)
Phase 2: Molecular Profiling
- Mutation landscape (COSMIC, cBioPortal CCLE)
- Gene expression (HPA, DepMap)
- Cancer markers (CellMarker)
Phase 3: Gene Dependencies (CRISPR Screens)
- Gene essentiality scores from DepMap
- Identify selectively essential genes
- Compare across cell lines if multiple candidates
Phase 4: Drug Sensitivity
- IC50/AAC from PharmacoDB (GDSC, CCLE, CTRPv2, PRISM)
- Biomarker associations for drug response
- Drug combination synergy (SYNERGxDB)
Phase 5: Target Druggability & Recommendations
- Druggable targets (DGIdb, OpenTargets)
- Final ranked recommendation with rationale
Phase 1: Cell Line Identification
Goal: Verify cell line identity and find candidates.
If specific cell line given: (1) cellosaurus_search_cell_lines(q="id:<NAME>") → get CVCL accession, species, disease, contamination flags. (2) cellosaurus_get_cell_line_info(accession="CVCL_XXXX") for STR profile. (3) DepMap_get_cell_line(model_name="...") for tissue, cancer_type, MSI, ploidy. (4) PharmacoDB_get_cell_line(operation="get_cell_line", cell_name="...") for datasets.
If cancer type only: (1) DepMap_get_cell_lines(tissue="Lung", page_size=20). (2) Narrow by gene mutations/dependencies in Phases 2-3. (3) CellMarker_search_cancer_markers(operation="search_cancer_markers", cancer_type="Lung").
OUTPUT: Table of candidate cell lines with: name, tissue, cancer type, key identifiers.
Phase 2: Molecular Profiling
Goal: Characterize mutational and expression landscape.
2A Mutations: COSMIC_get_mutations_by_gene(gene="EGFR") + cBioPortal_get_mutations(study_id="ccle_broad_2019", gene_list="EGFR,KRAS,TP53"). Note: gene_list is a comma-separated STRING. CCLE study ID: ccle_broad_2019.
2C Cancer markers: CellMarker_search_by_gene(operation="search_by_gene", gene_symbol="EGFR", species="Human")
OUTPUT: Mutation table (gene, AA change, type) + expression summary per cell line.
Phase 3: Gene Dependencies (CRISPR Screens)
Goal: Determine which genes are essential in candidate cell lines.
LIMITATION: DepMap_get_gene_dependencies returns gene metadata (HGNC ID, Ensembl ID) but NOT per-cell-line CRISPR scores. Full Chronos scores require depmap.org download.
Available tools: (1) DepMap_search_genes(query="EGFR") — validate gene exists. (2) DepMap_get_gene_dependencies(gene_symbol="EGFR") — metadata only. (3) Alternatives: cBioPortal CCLE for mutation data, PubMed for published screens, or direct user to depmap.org/portal.
Interpreting Chronos scores (from DepMap portal): <-0.5 = essential; ~0 = not essential; ~-1.0 = strongly essential. Selective dependency (essential in some lineages only) indicates therapeutic window.
OUTPUT: Gene validation + mutation status per cell line.
Per-cell-line Chronos scores (what the API can't give you): use the bundled script scripts/depmap_gene_dependency.py. It pulls the current DepMap Public release (CRISPRGeneEffect.csv + Model.csv) once via the public download index, caches it, and answers the dependency question directly:
# Cell lines most dependent on a gene (optionally within a lineage)
python scripts/depmap_gene_dependency.py gene KRAS --lineage Pancreas --top 20
# Genes a given cell line is most dependent on
python scripts/depmap_gene_dependency.py cell-line A375 --top 25
Output: cell line, lineage, primary disease, Chronos score (most negative = most dependent; < -0.5 ≈ dependency). For selective-dependency reasoning, compare a gene's scores across lineages.
If DepMap data is unavailable: Use cBioPortal_get_mutations(study_id="ccle_broad_2019", gene_list="KRAS") for mutation data, and the Quick Reference table below for common recommendations.
Phase 4: Drug Sensitivity
Goal: Profile drug response data.
4A PharmacoDB: PharmacoDB_get_experiments(operation="get_experiments", compound_name="Erlotinib", cell_line_name="A549", per_page=20) for dose-response (IC50, AAC, EC50). Omit compound_name to get all drugs for a cell line. Use PharmacoDB_get_biomarker_assoc(compound_name="...", tissue_name="...", mdata_type="mutation") for sensitivity biomarkers.
4B SYNERGxDB: SYNERGxDB_search_combos(drug_name_1="gemcitabine", drug_name_2="erlotinib", sample="lung"). Positive ZIP = synergy. Covers cytotoxic agents only (not targeted therapies/biologics).
Example: PANC-1-Cas9-554 through PANC-1-Cas9-559 (CVCL_WL48-WL53) are pre-validated Cas9 clones
DepMap API Fallbacks
If DepMap_get_gene_dependencies fails (common for some genes):
The Sanger Cell Model Passports API may not index all genes. Note this limitation.
Recommend the user check DepMap portal (depmap.org) directly for CRISPR dependency data.
Use cBioPortal_get_mutations(study_id="ccle_broad_2019", gene_list="<GENE>") as an alternative source for cell line mutation data.
OUTPUT: Ranked cell line table with total scores, per-criterion breakdown, and a text recommendation explaining the top pick and runner-up with biological reasoning.
Common Use Patterns
Pattern
Question Type
Key Tools (in order)
1
"Which cell line for [cancer] + [gene]?"
DepMap_get_cell_lines → DepMap_get_gene_dependencies → COSMIC_get_mutations_by_gene → cBioPortal_get_mutations (ccle_broad_2019) → PharmacoDB_get_experiments → rank by mutation + dependency + drug sensitivity
Use cell line NAME as common key across databases. IDs: DepMap=SIDM, Cellosaurus=CVCL, cBioPortal=sample (e.g. A549_LUNG), PharmacoDB/SYNERGxDB=name string. When names differ ("HCT 116" vs "HCT116"), check Cellosaurus synonyms first.
Mutation-based filtering: cBioPortal_get_mutations(study_id="ccle_broad_2019", gene_list="KRAS") → filter by amino_acid_change → extract cell line names → query other databases.
Error Handling
Issue
Resolution
DepMap returns no results for cell line name
Try alternative names: check Cellosaurus synonyms first
cBioPortal CCLE study ID unknown
Use ccle_broad_2019 as default CCLE study
PharmacoDB cell line name mismatch
Use PharmacoDB_search(operation="search", query="<name>") to find the canonical name
HPA cell line not supported
Only 10 lines supported (hela, mcf7, a549, hepg2, jurkat, pc3, rh30, siha, u251, ishikawa). Skip HPA for other lines
CLUE requires API key
Skip CLUE tools if CLUE_API_KEY not set; note in report
Gene symbol not found in DepMap
Use DepMap_search_genes(query="<symbol>") to check aliases
Cellosaurus accession pattern
Must be CVCL_XXXX format; search first if you only have a name
SYNERGxDB no results for drug combo
Drug may not be in database; SYNERGxDB covers cytotoxic agents, not most targeted therapies
Completeness Checklist
Before finalizing the report, verify:
Cell line identity verified (Cellosaurus or DepMap)
Species confirmed as human (unless otherwise specified)
Key mutations documented (COSMIC or cBioPortal)
Gene dependency assessed (DepMap CRISPR, if gene of interest provided)
Drug sensitivity data included (PharmacoDB, at least one dataset)
Druggability of key targets checked (DGIdb or OpenTargets)
Practical recommendation provided (not just raw data)
All claims cite their source database
Known limitations noted (missing data, unsupported lines)
