tooluniverse-cell-line-profiling

What this skill does

# 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.

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## 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.

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## 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 |
| `CLUE_get_cell_lines` | `operation="get_cell_lines"`, `cell_id` | L1000 CMap cell line info (requires CLUE_API_KEY) |
| `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 |

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## 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
```

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## 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.

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## 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.

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## 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 valid