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 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

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):

from scripts.depmap_data import fetch_catalogue, select_file, verify_md5

catalogue = fetch_catalogue()
public = catalogue[catalogue["release"].str.startswith("DepMap Public ")]
print(public[["release", "release_date"]].drop_duplicates()
      .sort_values("release_date", ascending=False).head(12))

# Explicit example pin; inspect discovery output before selecting your release.
release = "DepMap Public 26Q1"
record = select_file(catalogue, release, "CRISPRGeneEffect.csv")
print(record[["release", "filename", "md5_hash"]])
# After obtaining this file from the portal:
# verify_md5("CRISPRGeneEffect.csv", record["md5_hash"])

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.

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

and metadata definitions.

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.

from scripts.depmap_data import load_gene_effect, load_models, gene_profile

effects = load_gene_effect("CRISPRGeneEffect.csv")
models = load_models("Model.csv")
profile = gene_profile(effects, models, "KRAS (3845)")
print(profile[["CellLineName", "OncotreeLineage", "gene_effect"]].head(20))

# Inspect actual annotation values, then choose an exact cohort.
print(profile["OncotreeLineage"].value_counts())
known = profile.dropna(subset=["OncotreeLineage"])
lung = known.loc[known["OncotreeLineage"].eq("Lung"), "gene_effect"]
other = known.loc[~known["OncotreeLineage"].eq("Lung"), "gene_effect"]
if lung.empty or other.empty:
    raise ValueError("Both cohorts require observed effects and known lineage")
print({"n_lung": len(lung), "n_other": len(other),
       "other_minus_lung_mean": other.mean() - lung.mean(),
       "lung_fraction_below_exploratory_cutoff": (lung <= -0.5).mean()})

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.

1. Map profiles to the correct model/condition. Use 0 only for an assayed negative,

1 for the prespecified biomarker, and missing for unknown status.

1. Restrict to scientifically comparable models (lineage, culture conditions,

screen source and related-patient structure). Inspect confounding before testing.

1. 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.

1. Validate candidate synthetic lethality with matched/isogenic perturbations and

rescue or orthogonal evidence; association alone is insufficient.

import pandas as pd
from scripts.depmap_data import biomarker_scan

# User-prepared, release-matched annotation after the mapping and curation above.
status = pd.read_csv("curated_biomarker_status.csv", index_col="ModelID")["status"]
results = biomarker_scan(effects, status, min_n=5)
# Positive effect_size means stronger dependency in biomarker-positive models.
candidates = results.loc[(results["qval"] < 0.1) & (results["effect_size"] > 0)]

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

from scripts.depmap_data import coessentiality

correlates = coessentiality(effects, "KRAS (3845)", min_n=500)
print(correlates[["gene", "n", "r", "pval", "qval"]].head(20))

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

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: library correction and updated annotations.
• 25Q2 release notes: omics/default mapping and WGS/WES changes.
• Chronos upstream: normalization, copy-number correction, and hit-calling definitions.
• Dempster et al., Genome Biology 22:343 (2021): Chronos method, PMID 34930405.
• Original PRISM Repurposing resource: 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.