Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.

DepMap — Cancer Dependency Map When to use Use this skill to rank dependencies in cancer models, compare prespecified biomarker cohorts, inspect co essentiality, or relate genetic dependencies to PRISM compound response. These analyses generate target and synthetic lethality hypotheses; they do not establish clinical efficacy, a therapeutic window, or a causal interaction. DepMap integrates Broad and Sanger CRISPR screens and hosts independent RNAi and drug screen datasets. Match each question to its assay and pinned release. RNAi DEMETER2 is a different perturbation modality, not an older CRISPR scoring method. 1. Discover a release, then obtain its files Use [DepMap Downloads](https://depmap.org/portal/data page/) and the selected release's README and release notes. On review, the live catalogue's latest DepMap Public release was 26Q1 , dated 2026 04 01. Rediscover before new work; a release name or expected quarter is not evidence a release is available. Verified public endpoint GET https://depmap.org/portal/api/no captcha/download/files returns a complete CSV catalogue, with no request body, API key, or pagination parameters. Required metadata columns are release , release date , filename , and md5 hash . The live response also has url : older files can have links, while recent release rows have blank links . All 85 entries for 26Q1 had blank url values at review. Treat availability per row, not per endpoint. The [staff announcement](https://forum.depmap.org/t/provide an open endpoint for latest version retrieval/4652) introduced this metadata route in July 2026. The older https://depmap.org/portal/api/download/files catalogue can supply download links but returned an HTML verification page during review, including with HTTP 200. Validate content before parsing. Obtain current files through the portal when verification is required. Refresh expiring signed links immediately before use; do not fabricate storage URLs or assume every new release is on Figshare. No supported contract was verified for the former /api/gene?gene id=... or /api/data/gene dependency examples. Use local release matrices for gene slices. Do not assume a Python package named depmap is an official portal client. From the skill root, use the bundled [helpers](scripts/depmap data.py): Save the selected metadata, retrieval date, release citation, local checksum, and README alongside analysis outputs. Record source dataset terms separately from this skill's license; newer release terms differ from older Figshare releases. For a missing published checksum, retain a local SHA 256 and explicitly state that it was not compared with a publisher checksum. 2. Inspect file contracts before joining The following names were verified in the 26Q1 catalogue. Check that release's README for units and identifier level before treating a file as a numeric matrix. File Use and important contract CRISPRGeneEffect.csv Integrated, model level Chronos effects; rows are ModelID, columns preserve Symbol (EntrezID) CRISPRGeneEffectUncorrected.csv Uncorrected effects; not interchangeable with corrected effects CRISPRGeneDependency.csv Release specific dependency statistics; confirm probability/FDR definition and direction in the README Model.csv Model metadata keyed by ModelID ModelCondition.csv Growth/treatment conditions keyed by ModelConditionID , mapping to ModelID ScreenGeneEffect.csv , ScreenSequenceMap.csv , CRISPRScreenMap.csv Screen level effects and mappings; multiple screens can belong to one model OmicsExpressionTPMLogp1HumanProteinCodingGenes.csv Expression output with sequencing/model metadata and default entry flags; exclude metadata columns from expression calculations OmicsSomaticMutationsMatrixDamaging.csv , OmicsSomaticMutationsMatrixHotspot.csv Distinct mutation annotations; damaging and hotspot calls answer different biological questions OmicsCNGeneWGS.csv , OmicsCNGeneMC WES.csv Separate WGS/WES copy number products; do not concatenate as one uniformly processed cohort PortalOmicsCNGeneLog2.csv Transformed portal copy number product; establish the exact transform before inversion OmicsProfiles.csv , Gene.csv Omics provenance/mappings and gene annotations AchillesScreenQCReport.csv , AchillesSequenceQCReport.csv Screen/sequence QC; apply documented eligibility rules rather than invented universal cutoffs Current model annotations include CellLineName , OncotreeLineage , OncotreePrimaryDisease , and OncotreeSubtype . Legacy sample info.csv fields ( DepMap ID , lineage , primary disease ) require an explicit conversion. Model.csv includes models without CRISPR measurements: absence from the effect matrix is not a nondependency score. Omics may be indexed by SequencingID , ModelConditionID , or ModelID . For a basal model analysis, use the release's IsDefaultEntryForModel == Yes flag, then require one row per ModelID . The condition level flag IsDefaultEntryForMC answers a different question. Subset the assay/datatype before selecting defaults from a mapping table. Never average repeated conditions or count them as independent models without an explicit scientific design. See the [mapping guide source](https://github.com/broadinstitute/depmap portal/blob/master/frontend/packages/portal frontend/src/dataPage/components/MapSection.tsx) and [metadata definitions](https://forum.depmap.org/t/depmap metadata/3694). 3. Interpret Chronos and inspect a target Chronos gene effect is continuous and unbounded. More negative values indicate stronger loss of fitness. In the normalized release matrix, approximately 0 is the nonessential control anchor and −1 is the common essential control anchor; −1 is not a dependency boundary. A filter such as ≤ −0.5 is exploratory, not a p value, FDR, or probability cutoff. Positive effects may reflect growth advantage or technical noise and need validation. For binary calls, inspect the selected file's statistic and direction. A high dependency probability and a low FDR are different rules. Do not silently convert between them. Use release matched positive/negative control lists and QC outputs. See [score and QC caveats](references/dependency analysis.md). The following local data examples are illustrative until run against your chosen files. Helper behavior is tested using synthetic fixtures; current bulk matrices were not downloaded during this review. This is a descriptive comparison against other cancer models, not against normal tissue. Keep the denominator and missingness counts. Preserve complete gene labels; a symbol can be ambiguous, and the helper refuses ambiguous symbol resolution. 4. Test biomarker associations 1. Define the alteration before inspecting target scores. A damaging call matrix does not establish biallelic loss; activating KRAS hotspots require a different definition from generic damaging mutations. 2. Map profiles to the correct model/condition. Use 0 only for an assayed negative, 1 for the prespecified biomarker, and missing for unknown status. 3. Restrict to scientifically comparable models (lineage, culture conditions, screen source and related patient structure). Inspect confounding before testing. 4. Test every eligible gene in the planned family, then adjust all its p values before selecting hits. Report group sizes, effect sizes, p values, and q values. 5. Validate candidate synthetic lethality with matched/isogenic perturbations and rescue or orthogonal evidence; association alone is insufficient. This one sided Mann–Whitney screen is exploratory and does not adjust for covariates. min n=5 is an explicit example floor, not a power guarantee. For inference across lineages, fit an appropriate adjusted model and inspect effect stability within lineages; do not report the helper's q value as confounder adjusted. 5. Co essentiality and drug sensitivity min n is configurable and should be prespecified for the analysis. Sparse genes can dominate rankings with spurious correlations; always retain the pairwise sample count. The helper uses pairwise complete observations and skips constant genes. Pearson/BH output still needs lineage, library, and screen quality checks; co essentiality is not proof of a physical interaction or shared pathway. The [DepMap team's discussion](https://forum.depmap.org/t/crispr co depency top hits obscured by newly added screens/4503) documents this specific sparse coverage failure mode. For PRISM, first select the exact screen/release and endpoint. Treatment info CSVs are annotations, not sensitivity values. The original primary screen's primary screen replicate collapsed logfold change.csv has response rows keyed by cell line row name and columns keyed by treatment column name . Join the corresponding cell line and treatment info files; retain compound, dose, and screen identity. Lower log2 fold change indicates greater loss of viability. Secondary screen AUC comes from dose response curve parameters and is not the same quantity as single dose log fold change. See the [PRISM workflow](references/dependency analysis.md) before loading these files. Validation and sources Before reporting: verify release/file identity, unique joins, identifier level, complete gene labels, cohort membership, missingness, screen eligibility, score units/direction, multiple testing family, and whether evidence is observational. Expression, copy number, and CRISPR can share technical confounders; low expression does not guarantee a measured score of zero. Broad essentiality motivates normal cell/selectivity studies; it does not by itself prove a target is undruggable. [26Q1 release notes](https://forum.depmap.org/t/announcing the 26q1 release/4606): library correction and updated annotations. [25Q2 release notes](https://forum.depmap.org/t/announcing the 25q2 release/4257): omics/default mapping and WGS/WES changes. [Chronos upstream](https://github.com/broadinstitute/chronos): normalization, copy number correction, and hit calling definitions. [Dempster et al., Genome Biology 22:343 (2021)](https://doi.org/10.1186/s13059 021 02540 7): Chronos method, PMID 34930405. [Original PRISM Repurposing resource](https://depmap.org/repurposing/): release specific data and READMEs. The live no captcha catalogue and original PRISM READMEs were fetched successfully. The helper uses Python urllib, which succeeded at review; the same public endpoint returned HTTP 403 with a default requests client. Surface access failures instead of treating an error page as data. Protected portal endpoints returned verification pages; authenticated downloads and current matrix end to end analysis remain unverified. This review makes no claim of a working bearer token API or undocumented gene query endpoints.