tooluniverse-cell-line-profiling

# 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**: 1. **Decision-first** - Answer "which cell line should I use?" not "here is all the data" 2. **Multi-source validation** - Cross-reference DepMap, Cellosaurus, COSMIC, PharmacoDB 3. **Actionable output** - Ranked cell line recommendations with rationale 4. **Practical focus** - Include availability, growth characteristics, common pitfalls 5. **Gene-aware** - When a gene of interest is given, prioritize lines with relevant mutations/dependencies 6. **Source-referenced** - Cite database sources for every claim 7. **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. | Tool | Key Parameters | Notes | |------|---------------|-------| | `DepMap_search_cell_lines` | `query` (required) | Search by name, e.g., "A549", "MCF" | | `DepMap_get_cell_line` | `model_name` OR `model_id` | Name: "A549"; ID: "SIDM00001" | | `DepMap_get_cell_lines` | `tissue`, `cancer_type`, `page_size` | Filter by tissue (e.g., "Lung") | | `DepMap_get_gene_dependencies` | `gene_symbol` (required), `model_id` | Gene effect scores; negative = essential | | `DepMap_search_genes` | `query` (required) | Validate gene symbol in DepMap first | | `cellosaurus_search_cell_lines` | `q` (required), `size` | Solr syntax: `id:HeLa`, `ox:9606 AND char:cancer` | | `cellosaurus_get_cell_line_info` | `accession` (required, CVCL_ format) | Full cell line record | | `cellosaurus_query_converter` | `query` (required) | Natural language to Solr syntax | | `COSMIC_search_mutations` | `terms` OR `query`, `max_results` | Search "BRAF V600E" or gene name | | `COSMIC_get_mutations_by_gene` | `gene` OR `gene_name`, `max_results` | All mutations for a gene | | `PharmacoDB_get_cell_line` | `operation="get_cell_line"`, `cell_name` | Cell line metadata + datasets | | `PharmacoDB_get_experiments` | `operation="get_experiments"`, `compound_name`, `cell_line_name`, `dataset_name`, `per_page` | Drug response data (IC50, AAC, EC50) | | `PharmacoDB_get_biomarker_assoc` | `operation="get_biomarker_associations"`, `compound_name`, `tissue_name`, `mdata_type`, `per_page` | Gene-drug sensitivity correlations | | `PharmacoDB_search` | `operation="search"`, `query` | Find PharmacoDB IDs | | `CellMarker_search_cancer_markers` | `operation="search_cancer_markers"`, `cancer_type`, `gene_symbol`, `cell_type` | Cancer cell markers | | `CellMarker_search_by_gene` | `operation="search_by_gene"`, `gene_symbol` (required), `species` | Cell types expressing a gene | | `HPA_get_comparative_expression_by_gene_and_cellline` | `gene_name` (required), `cell_line` (required) | Supported lines: ishikawa, hela, mcf7, a549, hepg2, jurkat, pc3, rh30, siha, u251 | | `SYNERGxDB_search_combos` | `drug_name_1`, `drug_name_2`, `sample` (tissue or cell ID) | Drug combination synergy (ZIP, Bliss, Loewe) | | `SYNERGxDB_list_cell_lines` | - | All cell lines in SYNERGxDB | | `DGIdb_get_drug_gene_interactions` | `genes: list[str]` | Druggable gene interactions | | `OpenTargets_get_associated_drugs_by_target_ensemblID` | `ensemblId`, `size` | Drugs targeting a gene | | `STRING_get_network` | `protein_ids: list[str]`, `species: int` (9606) | PPI network for gene context | | `MyGene_query_genes` | `query` (NOT `q`) | Resolve gene symbol to Ensembl ID | | `cBioPortal_get_mutations` | `study_id`, `gene_list` (STRING, not array) | Cell line mutations from CCLE | --- ## 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. **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: ```bash # 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). **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 | Criterion | Weight | Score 3 (Best) | Score 2 (Acceptable) | Score 1 (Poor) | |-----------|--------|----------------|---------------------|----------------| | **Mutation match** | x3 | Exact mutation (e.g., KRAS G12D) | Same gene, different mutation | No mutation in gene of interest | | **Co-mutation simplicity** | x2 | Few co-mutations (cleaner background) | Moderate co-mutations | Complex background (3+ driver mutations) | | **Gene dependency** | x2 | DepMap score < -0.5 (essential) | Score -0.5 to -0.2 (moderately essential) | Score > -0.2 (not essential) | | **Drug sensitivity data** | x1 | In GDSC + CCLE + PRISM (3+ datasets) | In 1-2 datasets | No drug response data | | **Practical factors** | x1 | Adherent, well-characterized, widely used | Suspension or less common | Hard to culture, contamination-prone | **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: | Use Case | Key Requirements | Extra Considerations | |----------|-----------------|---------------------| | **CRISPR knockout screen** | Adherent growth, good lentiviral transduction, pre-existing Cas9 clones (check Cellosaurus for "-Cas9" derivatives) | Doubling time matters for library coverage; <72h ideal | | **Drug sensitivity testing** | In PharmacoDB/GDSC, known IC50 for reference compounds | Check SYNERGxDB for combo data | | **Xenograft model** | Known tumorigenicity in mice, available PDX data | Check if line forms tumors in nude/NSG mice (Cellosaurus often notes this) | | **Mechanism of action** | Clean genetic background, gene dependency confirmed | Fewer co-mutations = easier to attribute phenotypes | | **Biomarker discovery** | Isogenic pairs available, well-characterized omics | Check if isogenic knockouts exist (Cellosaurus) | | **Drug combination** | In SYNERGxDB with combo data, known single-agent responses | ZIP score available for synergy assessment | #### 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