Disparities in metastatic colorectal cancer between Africans and Americans (MSK, 2020)

Written and checked by a reviewer, not generated from metadata.

No reviewed research questions yet. The measurements and field completeness above are machine-extracted and still useful for judging fit.

Written by a reviewer against the source, not inferred from what is missing.

No reviewed limitations yet. Check the field completeness chart above before committing to an analysis. Low coverage on an endpoint you need is the most common blocker.

Articles that analyzed these data, counted separately from articles that only cited the paper. Only the first counts as reuse.

Analyses someone ran on these data end to end, each with a receipt recording when it ran, against which packages and for how long.

Nobody has shared an analysis of this dataset yet. The examples on other datasets cover the common patterns and can be adapted - and if you adapt one, it belongs here.

Starter code generated from this record’s own identifiers, then the access steps.

import requests, pandas as pd

STUDY = "crc_nigerian_2020"
base = "https://www.cbioportal.org/api"
clin = requests.get(f"{base}/studies/{STUDY}/clinical-data",
                    params={"clinicalDataType": "PATIENT", "projection": "SUMMARY"}, timeout=60).json()
patients = pd.DataFrame(clin).pivot_table(index="patientId", columns="clinicalAttributeId",
                                          values="value", aggfunc="first")
print(patients.shape)
profiles = requests.get(f"{base}/studies/{STUDY}/molecular-profiles", timeout=60).json()
print([p["molecularProfileId"] for p in profiles])
# Whole study as flat files: https://cbioportal-datahub.s3.amazonaws.com/crc_nigerian_2020.tar.gz

curl -s https://cd2s.vercel.app/data/datasets/cbio-crc-nigerian-2020.json | python3 -c '
import json, sys
r = json.load(sys.stdin)
for l in r["limitations"]:
    print(l["severity"].upper(), "-", l["statement"][:120])
print("survival endpoint:", r["longitudinal"]["has_survival_endpoint"], "| treatment response:", r["longitudinal"]["has_treatment_response"])'

Access steps

1. 1

   Open the study in cBioPortalFor people5 minutes

   Harmonized molecular and clinical files are open. Cite the original publication, and cite cBioPortal if the portal's harmonized files were used.

   https://www.cbioportal.org/study/summary?id=crc_nigerian_2020

   Generated from repository policy. Check the linked policy before downloading.

2. 2

   Download the whole study as a zipFor peopleminutes

   The datasets page carries the staging files used by the portal.

   https://www.cbioportal.org/datasets

   Generated from repository policy. Check the linked policy before downloading.

3. 3

   Or pull the staging files with git-lfsFor people

   Study folders are named by study id under public/.

   git clone https://github.com/cBioPortal/datahub.git && cd datahub
   git -c lfs.fetchexclude="" lfs pull -I public/crc_nigerian_2020

   https://github.com/cBioPortal/datahub

   Generated from repository policy. Check the linked policy before downloading.

4. 4

   Read the study straight from the REST APIFor agents

   No key is required; pace requests when sweeping many studies.

   curl -s 'https://www.cbioportal.org/api/studies/crc_nigerian_2020/clinical-data?clinicalDataType=PATIENT&projection=SUMMARY'

   https://docs.cbioportal.org/web-api-and-clients/

   Generated from repository policy. Check the linked policy before downloading.

All of it, and more, is in this record’s JSON.

Same disease area, favoring cohorts that add a measurement this one lacks.