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Please see the CGCI-HTMCP-CC Zenodo page to learn more about the images and to obtain any supporting metadata for this collection.","locator":"accession extracted from description prose","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Cross-repository link recovered from prose; verify before relying on it."}]}],"repository":{"name":"NCI Genomic Data Commons","short_name":"GDC","url":"https://portal.gdc.cancer.gov","is_nci_crdc_node":true,"is_nih_repository":true},"additional_repositories":[{"name":"NCI Imaging Data 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The short follow-up is itself scientifically interesting because it reflects real-world attrition in the setting where the cohort was recruited.","required_fields":["demographic.vital_status","diagnoses.days_to_last_follow_up"],"required_modalities":["clinical","outcome"],"feasibility":"direct","approx_n":212,"statistical_note":"Median follow-up is 13.2 months and the maximum is 32.7, so late events are not observed. Report restricted mean survival time or landmark analyses rather than median overall survival, and do not compare medians directly against cohorts with five-year follow-up.","exemplar_pmid":null,"topics":["survival","global-oncology"],"evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.906740Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"question":"Can radiology and histopathology images from this cohort be used to build or validate imaging models on a population that is absent from almost every public imaging training set?","rationale":"Histopathology and radiology are both available for 211 of 212 cases, alongside the molecular data. Imaging models trained largely on North American and European cohorts have documented performance gaps on under-represented populations, and this is one of the few resources that permits an external check.","required_fields":[],"required_modalities":["histopathology","radiology","rna_seq"],"feasibility":"needs_linkage","approx_n":211,"statistical_note":"Suitable for external validation rather than primary training; 211 cases is small for training a deep model from scratch.","exemplar_pmid":null,"topics":["imaging","algorithmic-fairness"],"evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.906740Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. 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Findings describe Ugandan cervical cancer patients and should not be generalised to Black populations elsewhere, whose exposure and treatment contexts differ substantially.","severity":"major","affected_analyses":["Generalization to African-ancestry populations outside Uganda"],"mitigation":"Frame results as specific to this cohort and setting, and report the recruitment context alongside any ancestry claim.","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.906740Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"kind":"access_constraint","statement":"Of 7,160 files, 4,703 are controlled access. 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Derived expression, copy number and methylation products are enough to scope the analysis and check that the cohort fits before you invest in an access request.","url":"https://portal.gdc.cancer.gov/projects/CGCI-HTMCP-CC","requires":[],"est_time":"1-2 hours","cli_snippet":"# Open-access file manifest for this project, no credentials required\ncurl -s 'https://api.gdc.cancer.gov/files' \\\n  --get \\\n  --data-urlencode 'filters={\"op\":\"and\",\"content\":[\n    {\"op\":\"in\",\"content\":{\"field\":\"cases.project.project_id\",\"value\":[\"CGCI-HTMCP-CC\"]}},\n    {\"op\":\"in\",\"content\":{\"field\":\"access\",\"value\":[\"open\"]}}]}' \\\n  --data-urlencode 'fields=file_id,file_name,data_category,data_type' \\\n  --data-urlencode 'size=2000&format=TSV' > htmcp_cc_open_files.tsv\n","audience":"human","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.906740Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"order":3,"action":"Submit a dbGaP data access request for the controlled tier","detail":"Needed for whole genome sequencing and any sequence-level analysis. You will need an eRA Commons account and your institutional signing official. Request the full phenotype file at the same time - that is where stage and HIV-related variables are most likely to live.","url":"https://gdc.cancer.gov/access-data/obtaining-access-controlled-data","requires":["eRA Commons account","institutional signing official approval"],"est_time":"days to a few weeks","cli_snippet":null,"audience":"human","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.906740Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"order":4,"action":"Download with the GDC client","detail":"Once approved, use the GDC Data Transfer Tool with your token. Do not put the token on a command line or into a script that is committed anywhere.","url":"https://gdc.cancer.gov/access-data/gdc-data-transfer-tool","requires":[],"est_time":"hours, depending on volume","cli_snippet":null,"audience":"human","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.906740Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"order":5,"action":"Query the project from code, with no credentials","detail":"The file index is public even where files are not, so an agent can size the cohort before anyone requests access.","url":"https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/","requires":[],"est_time":null,"cli_snippet":"curl -s 'https://api.gdc.cancer.gov/files' --get \\\n  --data-urlencode 'filters={\"op\":\"in\",\"content\":{\"field\":\"cases.project.project_id\",\"value\":[\"CGCI-HTMCP-CC\"]}}' \\\n  --data-urlencode 'fields=file_id,file_name,data_type' \\\n  --data-urlencode 'size=10000' --data-urlencode 'format=TSV'","audience":"agent","evidence":[{"method":"derived","source_url":"https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/","source_label":"GDC access documentation","retrieved_at":"2026-09-21T12:30:59.431642Z","snippet":null,"locator":null,"confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Generated by applying the repository's documented access policy to this dataset's identifiers. It is not a reviewer's account of this dataset."}]},{"order":6,"action":"Pass the token when the agent needs controlled files","detail":"Without a token, an API query may return only the open subset. Compare returned counts with the record before treating them as the whole cohort.","url":"https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/","requires":["GDC authentication token"],"est_time":null,"cli_snippet":"curl -H \"X-Auth-Token: $GDC_TOKEN\" https://api.gdc.cancer.gov/data/<file_id>","audience":"agent","evidence":[{"method":"derived","source_url":"https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/","source_label":"GDC access documentation","retrieved_at":"2026-09-21T12:30:59.431645Z","snippet":null,"locator":null,"confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Generated by applying the repository's documented access policy to this dataset's identifiers. It is not a reviewer's account of this dataset."}]}],"analysis_examples":[{"level":"beginner","title":"Audit whether this dataset can answer your question","question":"Which classes of analysis do this cohort's clinical records actually support, and which are ruled out before I request access?","problem":"A portal can list a field even when every value is an unusable non-answer.","lesson":"Decide whether the analysis is possible before requesting data or writing a protocol.","figure":"Horizontal bars for each probed clinical field, showing the share of cases the field is populated for against the smaller share that carries an informative value, with one-to-many fields marked as not derivable per case.","featured":true,"inputs":["A GDC project identifier","Network access to api.gdc.cancer.gov"],"outputs":["Per-field coverage table separating populated from informative values","Assay coverage as a percentage of the cohort","A yes/no verdict for six common analysis classes","A chart contrasting populated with informative coverage, field by field"],"steps":["Fetch project summary and per-assay case counts","Request facet counts for the clinical fields that gate common analyses","Separate absent fields from fields populated with non-answers","Flag one-to-many fields where per-case percentages cannot be inferred","Retrieve case-level fields and test whether a survival endpoint is derivable","Print a verdict for each analysis class"],"findings":["Four of the six analysis classes pass for CGCI-HTMCP-CC; stage-adjusted modelling and progression-free endpoints are blocked.","Vital status is informative for all 212 cases with 116 deaths, so an overall-survival endpoint is derivable.","Both stage fields and the progression field are informative for 0% of cases, which no case count can fix.","Four fields hold several records per case, so their per-case share cannot be inferred from a facet count at all."],"contributor":null,"contributor_url":null,"language":"python","est_runtime":"about a minute","est_compute":null,"workbook_path":"workbooks/python/01_can_i_answer_this.py","workbook_url":null,"notebook_download_url":"/data/notebooks/01_can_i_answer_this.ipynb","notebook_preview_url":"/data/notebooks/01_can_i_answer_this.png","notebook_preview_width":1021,"notebook_preview_height":813,"colab_url":null,"binder_url":null,"receipt":{"executed":true,"executed_at":"2026-09-21T00:40:59.561676Z","runtime_seconds":6.57,"executor":"nbclient / python 3.12.13 / Darwin 25.5.0 arm64","package_versions":{"python":"3.12.13","pandas":"3.0.6","numpy":"2.5.3","matplotlib":"3.11.2","lifelines":"0.30.0","scipy":"1.18.1","requests":"2.34.2","httpx":"0.28.1"},"n_cells":7,"n_cells_executed":7,"output_hash":"dc14b4c93e7e9d6aff322ff2d0de47b8","data_retrieved_at":null,"error":null},"template_source":"01_can_i_answer_this","evidence":[{"method":"curated","source_url":null,"source_label":"CD2S workbook","retrieved_at":"2026-09-21T12:30:59.450231Z","snippet":null,"locator":"workbooks/python/01_can_i_answer_this.py","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Executed end to end against the live public APIs; the receipt records when, with which package versions and how long it took."}]},{"level":"intermediate","title":"Treatment response, after checking the response field is real","question":"Is performance status associated with response to chemoradiation, and which agents were actually given?","problem":"Disease-status codes and true response categories are easy to mix into one misleading outcome.","lesson":"Validate the response field, collapse repeated records, and state what the observational result cannot prove.","figure":"Two panels: a bar chart of how many cases carry each response label against how many carry none, and box plots of ECOG performance status for responders against progressors.","featured":false,"inputs":["Open GDC clinical records for CGCI-HTMCP-CC","scipy, pandas"],"outputs":["Separation of genuine response categories from disease-status codes","Per-case responder/progressor labels under a stated rule","Mann-Whitney comparison of ECOG between response groups","Agent frequency table","A two-panel chart of response composition and ECOG by response group"],"steps":["Facet the two response-bearing fields and classify their values","Collapse one-to-many treatment records to one label per case","Compare performance status between response groups","Tabulate the therapeutic agents actually recorded"],"findings":["Only 72 of 212 cases carry a usable response label: the follow-up field holds disease status, not response.","ECOG does not separate responders from progressors - Mann-Whitney p = 0.30, median 1 in both groups.","Cisplatin is recorded for 60 cases and paclitaxel for 35, but some agent is named for only 63 of 212."],"contributor":null,"contributor_url":null,"language":"python","est_runtime":"about a minute","est_compute":null,"workbook_path":"workbooks/python/03_treatment_response_cervical.py","workbook_url":null,"notebook_download_url":"/data/notebooks/03_treatment_response_cervical.ipynb","notebook_preview_url":"/data/notebooks/03_treatment_response_cervical.png","notebook_preview_width":1165,"notebook_preview_height":493,"colab_url":null,"binder_url":null,"receipt":{"executed":true,"executed_at":"2026-09-21T00:41:08.473684Z","runtime_seconds":2.87,"executor":"nbclient / python 3.12.13 / Darwin 25.5.0 arm64","package_versions":{"python":"3.12.13","pandas":"3.0.6","numpy":"2.5.3","matplotlib":"3.11.2","lifelines":"0.30.0","scipy":"1.18.1","requests":"2.34.2","httpx":"0.28.1"},"n_cells":7,"n_cells_executed":7,"output_hash":"690629c93931e72c489bbd0b75bf5076","data_retrieved_at":null,"error":null},"template_source":"03_treatment_response_cervical","evidence":[{"method":"curated","source_url":null,"source_label":"CD2S workbook","retrieved_at":"2026-09-21T12:30:59.450306Z","snippet":null,"locator":"workbooks/python/03_treatment_response_cervical.py","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Executed end to end against the live public APIs; the receipt records when, with which package versions and how long it took."}]},{"level":"advanced","title":"Select a dataset the way an agent should","question":"Given a research goal, which dataset should an agent choose, and what must it refuse to do with it?","problem":"Ranking by size or fame can select a large dataset that cannot answer the question.","lesson":"Filter on measured capability, rank on fit, and carry blocking limitations into the analysis brief.","figure":"Horizontal bars of the fit score for each shortlisted dataset, with the selected cohort highlighted, showing whether the ranking is decided by overall fit or by one dominant term.","featured":false,"inputs":["The CD2S structured export","pandas"],"outputs":["Contrast between a size-ranked and a capability-ranked selection","A transparent fit score favoring under-used resources","Blocking limitations read before committing","A machine-readable task brief for the next step","A chart of the ranked fit scores with the selected cohort highlighted"],"steps":["Load the structured index","Show what a naive size-ranked selection returns","Filter on measured capability rather than description","Rank on fit, rewarding under-used datasets","Read blocking limitations from the full record","Emit a task brief including what the agent must not do"],"findings":["A size-ranked agent picks msk_ch_2023 and its 42,714 cases, which record neither treatment response nor a survival endpoint.","Only 51 of 602 datasets record both, measured from field coverage rather than description.","Ranking on fit selects MP2PRT-ALL - 1,510 cases, 100 months of follow-up - and carries its 4 major limitations into the brief."],"contributor":null,"contributor_url":null,"language":"python","est_runtime":"under a minute","est_compute":null,"workbook_path":"workbooks/python/06_agent_dataset_selection.py","workbook_url":null,"notebook_download_url":"/data/notebooks/06_agent_dataset_selection.ipynb","notebook_preview_url":"/data/notebooks/06_agent_dataset_selection.png","notebook_preview_width":1021,"notebook_preview_height":685,"colab_url":null,"binder_url":null,"receipt":{"executed":true,"executed_at":"2026-09-21T00:43:02.488936Z","runtime_seconds":1.19,"executor":"nbclient / python 3.12.13 / Darwin 25.5.0 arm64","package_versions":{"python":"3.12.13","pandas":"3.0.6","numpy":"2.5.3","matplotlib":"3.11.2","lifelines":"0.30.0","scipy":"1.18.1","requests":"2.34.2","httpx":"0.28.1"},"n_cells":8,"n_cells_executed":8,"output_hash":"dc49bfd688a5ab7ef77292e914fdd645","data_retrieved_at":null,"error":null},"template_source":"06_agent_dataset_selection","evidence":[{"method":"curated","source_url":null,"source_label":"CD2S workbook","retrieved_at":"2026-09-21T12:30:59.450379Z","snippet":null,"locator":"workbooks/python/06_agent_dataset_selection.py","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Executed end to end against the live public APIs; the receipt records when, with which package versions and how long it took."}]}],"agent_package":{"json_url":"/data/datasets/gdc-cgci-htmcp-cc.json","jsonld_url":"/data/jsonld/gdc-cgci-htmcp-cc.jsonld","croissant_url":"/data/croissant/gdc-cgci-htmcp-cc.json","instructions_url":"/data/agent/gdc-cgci-htmcp-cc.md","bundle_url":null,"mcp_resource_uri":null},"related_dataset_ids":[],"related_underexplored_ids":[],"review":{"status":"project_curated","reviewer":"CD2S project curation","reviewer_affiliation":null,"reviewed_at":"2026-09-18","sections_reviewed":["useful_for","limitations","inappropriate_uses","access_steps","primary_publications"],"open_questions":["HIV status, CD4 count and antiretroviral therapy history are central to this cohort's scientific value but are not exposed as structured fields in the GDC harmonized clinical records. Confirm with the CGCI data access committee whether they are available in the dbGaP phenotype files before planning an HIV-stratified analysis.","Stage is unpopulated in GDC for all 212 cases. The marker paper reports stage distribution, so it exists somewhere; confirm whether it can be obtained from the supplementary tables or only through controlled access."],"notes":null},"verification":{"last_verified_at":null,"link_checks":[],"n_links_ok":0,"n_links_broken":0,"upstream_version_seen":null,"upstream_version_changed":false,"upstream_counts_seen":{},"upstream_drift":{},"notes":[]},"is_showcase":true,"completeness_score":100.0,"tags":["cgci","distinctive-population","gdc","global-oncology","hiv-associated","idc","imaging","released","treatment-response"],"search_text":null}