Cancer Cell Line Profiling and Selection

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.

Workflow Overview

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.

2B Expression: HPA_get_comparative_expression_by_gene_and_cellline(gene_name="EGFR", cell_line="a549"). Only 10 lines supported: hela, mcf7, a549, hepg2, jurkat, pc3, rh30, siha, u251, ishikawa.

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.

Offline DepMap analysis (when API lacks CRISPR scores): Download CRISPRGeneEffect.csv + Model.csv from https://depmap.org/portal/download/all/. Load with pandas, find gene column (format: "KRAS (3845)"), merge with metadata, filter by lineage, sort by score. Most negative Chronos score = most dependent.

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).

4C CLUE: CLUE_get_cell_lines(operation="get_cell_lines", cell_id="MCF7") — requires CLUE_API_KEY.

OUTPUT: Drug sensitivity table (drug, IC50, AAC, dataset) + synergy data if available.

Phase 5: Target Druggability and Recommendations

5A Druggability: DGIdb_get_drug_gene_interactions(genes=["EGFR", "KRAS"]) + MyGene_query_genes(query="EGFR") → OpenTargets_get_associated_drugs_by_target_ensemblID(ensemblId="...", size=10) + STRING_get_network(protein_ids=["EGFR"], species=9606).

5B Final Recommendation: Synthesize all phases. Explain WHY one line is better for this specific use case.

Decision Criteria with Concrete Thresholds

Total score = sum of (criterion score × weight). Max = 27. Rank cell lines by total score.

Use-Case-Specific Guidance

The best cell line depends on what you're doing with it:

Cellosaurus Derivative Lines

Check for pre-made derivatives — this can save months of lab work:

• cellosaurus_search_cell_lines(q="ca:<PARENT_LINE>", size=20) — finds all derivatives
• Look for: Cas9-expressing clones, drug-resistant derivatives, knockout lines, fluorescent reporter lines
• 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

Quick Reference: Common Cancer Cell Lines by Type

Cross-Referencing Cell Line IDs

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

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)