Q (sonnet-5.5): Do ovarian cancer patients with BRCA1 or BRCA2 alterations have significantly different overall survival compared to wild-type patients? ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ◀ result Statistical Test Selection Guide ================================ Purpose ------- This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults. HARD RULES — NEVER FABRICATE A STATISTIC ---------------------------------------- ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically: 1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."* 2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead. 3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median". 4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do. 5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison. If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number. Ambiguous-Term Clarification (before any analysis) -------------------------------------------------- If the user's question uses an ambiguous term, ask before computing anything. Examples: - **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars) ▶ list_studies { "search": "ovarian", "limit": 50 } ◀ result {"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","type_of_cancer_id":"mixed","sample_count":25775,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_met_2021"},{"cancer_study_identifier":"ov_tcga","name":"Ovarian Serous Cystadenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"hgsoc","sample_count":617,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ov_tcga"},{"cancer_study_identifier":"hgsoc_tcga_gdc","name":"High-Grade Serous Ovarian Cancer (TCGA GDC, 2025)","type_of_cancer_id":"hgsoc","sample_count":604,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=hgsoc_tcga_gdc"},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"hgsoc","sample_count":585,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=ov_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ovary_geomx_gray_foundation_2024","name":"Ovarian Cancer (Gray Foundation, Cancer Discov 2024)","type_of_cancer_id":"ovary","sample_count":567,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ovary_geomx_gray_foundation_2024"},{"cancer_study_identifier":"ov_tcga_pub","name":"Ovarian Serous Cystadenocarcinoma (TCGA, Nature 2011)","type_of_cancer_id":"hgsoc","sample_count":489,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ov_tcga_pub"},{"cancer_study_identifier":"lgsoc_mapk_msk_2022","name":"Low-Grade Serous Ovarian Cancer (MSK, Clin Cancer Res 2022)","type_of_cancer_id":"lgsoc","sample_count":119,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgsoc_mapk_msk_2022"},{"cancer_study_identifier":"ovary_cptac_gdc","name":"Ovarian Cancer (CPTAC GDC, 2025)","type_of_cancer_id":"ovary","sample_count":112,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ovary_cptac_gdc"},{"cancer_study_identifier":"msk_spectrum_tme_2022","name":"Ovarian Cancer - MSK SPECTRUM (MSK, Nature 2022)","type_of_cancer_id":"ovary","sample_count":82,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_spectrum_tme_2022"},{"cancer_study_identifier":"hgsoc_msk_2021","name":"High-Grade Serous Ovarian Cancer (MSK, NPJ Genome Med 2021)","type_of_cancer_id":"hgsoc","sample_count":45,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=hgsoc_msk_2021"},{"cancer_study_identifier":"ucec_msk_2024","name":"Endometrial and Ovarian Cancer (MSK, Nature Medicine 2024)","type_of_cancer_id":"mixed","sample_count":33,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_msk_2024"},{"cancer_study_identifier":"ovarian_msk_2025","name":"Serous Ovarian Cancer (MSK, 2025)","type_of_cancer_id":"ovary","sample_count":31,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ovarian_msk_2025"},{"cancer_study_identifier":"mixed_msk_tcga_2021","name":"RAD51B Associated Mixed Cancers (MSK, N … (162 more chars) ▶ get_study_guide { "study_id": "ov_tcga_pan_can_atlas_2018" } ▶ read_guide { "uri": "cbioportal://clinical-data-guide" } ◀ result # Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas) **Study ID:** `ov_tcga_pan_can_atlas_2018` See `_tcga_pancan_template.md` for common TCGA clinical attributes. ## Study-Specific Attributes ### Histology | Attribute | Description | Values (samples) | |-----------|-------------|--------| | `GRADE` | Histologic grade | G3 400, G2 65, GX 7, G1 5, GB 2, G4 1, blank 105 | | `CANCER_TYPE_DETAILED` | Histology | Serous Ovarian Cancer (all 585) | ### Not available in this study - **Platinum sensitivity and residual disease**: no treatment-response or surgical-outcome attributes. Survival (`OS_*`, `PFS_*`, `DFS_*`, `DSS_*`) is the only outcome data. - **BRCA / HRD status**: no clinical status attributes. Derive BRCA1/BRCA2 status from mutation (somatic calls only) and CNA data; there is no HRD score. - `AJCC_PATHOLOGIC_TUMOR_STAGE` is blank for all samples. `SUBTYPE` is `OV` (177) or blank (408) and carries no molecular subtype. ## Key Genes | Gene | Frequency | Notes | |------|-----------|-------| | TP53 | ~96% | Near-universal in high-grade serous | | BRCA1 | ~10% germline | PARP inhibitor sensitivity | | BRCA2 | ~6% germline | PARP inhibitor sensitivity | | NF1 | ~4% | RAS pathway | | RB1 | ~2% | Cell cycle | | CDK12 | ~3% | DNA repair | ## Notes - High-grade serous ovarian cancer (HGSOC) is genomically distinct from other ovarian subtypes - TP53 is mutated in virtually all HGSOC - BRCA1/2 mutations (germline or somatic) predict PARP inhibitor response - HRD score predicts platinum and PARP sensitivity even without BRCA mutation - Copy number alterations more prominent than mutations (except TP53) ◀ result # Clinical Data Query Guide ## Overview Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis. ## Data Organization ### Patient-Level vs Sample-Level Data - **Patient-level**: Demographics, overall survival, disease stage (stored once per patient) - **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient) ### Key Tables - `clinical_patient`: Patient-level clinical attributes - `clinical_sample`: Sample-level clinical attributes - `clinical_data_derived`: Pre-joined view combining both levels - `clinical_attribute_meta`: Metadata about available clinical attributes ## Recommended Approach: Use clinical_data_derived The `clinical_data_derived` table is pre-joined and optimized for most queries: ```sql -- Get clinical data for specific attributes SELECT sample_unique_id, patient_unique_id, attribute_name, attribute_value FROM clinical_data_derived WHERE cancer_study_identifier = 'your_study_id' AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS'); ``` ## Clinical Attribute Discovery ### Use clinical_attribute_meta for Discovering Available Attributes **Always start here** to see what clinical attributes are available for a specific study: ```sql -- Discover available clinical attributes for a study SELECT attr_id, description, patient_attribute, cancer_study_id FROM clinical_attribute_meta WHERE cancer_study_id = ( SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'your_study_id' ) ORDER BY patient_attribute, attr_id; ``` **Key Fields:** - **attr_id**: matches attr_id in clinical_sample/clinical_patient tables - **description**: human-readable description of the attribute - **patient_attribute**: true = patient attribute, false = sample attribute - **cancer_study_id**: links to cancer_study table (filter by study) ## Attribute Semantics and Matching ### Case-Insensitive Matching for Attribute Values Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant. When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study: ```sql -- Correct: case-insensitive clinical value filter SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'your_study_id' AND attribute_name = 'MUTATION_STATUS' AND upper(attribute_value) = 'GERMLINE'; ``` Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study. ### Query the Requested Attribute, Not a Proxy Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars) ▶ clickhouse_list_table_columns { "table": "genomic_event_derived" } ◀ result {"columns":[{"name":"sample_unique_id","type":"String"},{"name":"hugo_gene_symbol","type":"String","comment":"HUGO gene symbol (e.g., TP53, KRAS, BRAF). Use for gene-specific queries."},{"name":"entrez_gene_id","type":"Int32"},{"name":"gene_panel_stable_id","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"genetic_profile_stable_id","type":"LowCardinality(String)"},{"name":"variant_type","type":"LowCardinality(String)","comment":"Type of genomic event: \"mutation\" for SNVs/indels, \"cna\" for copy number alterations, \"structural_variant\" for SVs. Always filter by this."},{"name":"mutation_variant","type":"String","comment":"Protein change notation (e.g., p.V600E, p.R175H). Use for specific variant queries. \"NA\" for non-mutation events."},{"name":"mutation_type","type":"LowCardinality(String)"},{"name":"mutation_status","type":"LowCardinality(String)","comment":"For mutations: Somatic, Germline, UNKNOWN, or UNCALLED. Filter mutation_status != \"UNCALLED\" to exclude uncertain calls. Include all other statuses."},{"name":"driver_filter","type":"LowCardinality(String)"},{"name":"driver_filter_annotation","type":"String"},{"name":"driver_tiers_filter","type":"LowCardinality(String)"},{"name":"driver_tiers_filter_annotation","type":"String"},{"name":"cna_alteration","type":"Nullable(Int8)","comment":"Copy number alteration: only 2 = amplification (AMP) and -2 = deep deletion (HOMDEL) are stored. Shallow deletion (-1), diploid (0) and gain (1) are not in this table; query genetic_alteration_derived WHERE profile_type = 'gistic' (alteration_value is a String, e.g. '-1'). NULL for non-CNA events."},{"name":"cna_cytoband","type":"String"},{"name":"sv_event_info","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"off_panel","type":"Bool","comment":"Boolean: 1 = mutation outside gene panel coverage (off-panel), 0 = within panel (on-panel). Filter off_panel = 0 for reliable frequency calculations."}]} ▶ clickhouse_run_select_query { "query": "WITH alt AS ( SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier='ov_tcga_pan_can_atlas_2018' AND hugo_gene_symbol IN ('BRCA1','BRCA2') AND ((variant_type='mutation' AND mutation_status!='UNCALLED') OR (variant_type='cna' AND cna_alteration=-2) OR variant_type='structural_variant') ), os AS ( SELECT patient_unique_id, maxIf(toFloat64OrNull(attribute_value), attribute_name='OS_MONTHS') AS m, maxIf(attribute_value, attribute_name='OS_STATUS') AS s FROM clinical_data_derived WHERE cancer_study_identifier='ov_tcga_pan_can_atlas_2018' AND attribute_name IN ('OS_MONTHS','OS_STATUS') GROUP BY patient_unique_id ) SELECT if(patient_unique_id IN (SELECT patient_unique_id FROM alt),'BRCA1/2 altered','Wild-type') AS grp, count() n, countIf(startsWith(s,'1')) events, countIf(startsWith(s,'0')) censored, min(m) mn, max(m) mx FROM os WHERE m IS NOT NULL AND s!='' GROUP BY grp" } ▶ resolve_and_route { "studyIds": [ "ov_tcga_pan_can_atlas_2018" ] } ◀ result {"rows":[{"grp":"Wild-type","n":528,"events":325,"censored":203,"mn":0.295887168,"mx":180.1952855},{"grp":"BRCA1/2 altered","n":43,"events":16,"censored":27,"mn":0.263010816,"mx":145.4449814}]} ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":585,"studyViewUrl":"https://www.cbioportal.org/study?id=ov_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","RAGNUM_HYPOXIA_SCORE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT","WINTER_HYPOXIA_SCORE"],"molecularProfileIds":["ov_tcga_pan_can_atlas_2018_gistic","ov_tcga_pan_can_atlas_2018_log2CNA","ov_tcga_pan_can_atlas_2018_mutations","ov_tcga_pan_can_atlas_2018_protein_quantification","ov_tcga_pan_can_atlas_2018_protein_quantification_zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ov_tcga_pan_can_atlas_2018_rppa","ov_tcga_pan_can_atlas_2018_rppa_Zscores","ov_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["ov_tcga_pan_can_atlas_2018_armlevel_cna","ov_tcga_pan_can_atlas_2018_genetic_ancestry","ov_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","ov_tcga_pan_can_atlas_2018_methylation_hm450","ov_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["ov_tcga_pan_can_atlas_2018_protein_quantification_zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ov_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation","generic_assay_phosphosite_quantification"]}}]}} ▶ navigate_to_results_view { "studyIds": [ "ov_tcga_pan_can_atlas_2018" ], "genes": [ "BRCA1", "BRCA2" ], "tab": "comparison/survival" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/comparison/survival?cancer_study_list=ov_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2&case_set_id=ov_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/comparison/survival?cancer_study_list=ov_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2&case_set_id=ov_tcga_pan_can_atlas_2018_all","data":{"studyIds":["ov_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":585}],"genes":["BRCA1","BRCA2"],"caseSetId":"ov_tcga_pan_can_atlas_2018_all","pageDescription":"Kaplan-Meier survival analysis comparing groups defined by query genes."}} ═ answer (success) I can't say whether the survival difference is significant. Testing it needs a Kaplan-Meier curve and a log-rank test, and I can't run either here. What I can give you is the per-group counts and a link to the portal's built-in comparison, which runs both. You didn't name a study, so I used **Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)** (`ov_tcga_pan_can_atlas_2018`). The database also has other ovarian cohorts, such as TCGA Firehose Legacy, TCGA GDC and the MSK studies. I can rerun this on any of them. **Definitions used:** "Altered" means a BRCA1 or BRCA2 mutation (uncalled variants excluded), a deep deletion, or a structural variant. "Wild-type" means everyone else. The unit is patients with overall survival data (571 of the 585 samples). | Group | Patients | Deaths | Censored (alive at last follow-up) | Follow-up range (months) | |---|---|---|---|---| | BRCA1/2 altered | 43 (7.5%) | 16 (37%) | 27 | 0.3–145.4 | | Wild-type | 528 (92.5%) | 325 (62%) | 203 | 0.3–180.2 | - **What the counts show:** the altered group is small, and a lower share of its patients had died by last follow-up. That is a raw proportion, not a survival comparison. Follow-up times differ between patients and many are still alive, so the proportions can't tell you whether survival differs. - **Median survival:** I haven't reported medians because they have to come from the Kaplan-Meier curve. Fewer than half of the altered patients had died, so that group's median is probably not reached. - **Caveats:** the TCGA data has no BRCA or HRD status field, so status is inferred from somatic calls. The study guide lists germline BRCA carriers at about 10% for BRCA1 and 6% for BRCA2, so some carriers may be labeled wild-type here. The altered group has only 43 patients, so any test will have limited power. **To get the test:** - [Open the Survival comparison (BRCA1/BRCA2 altered vs. unaltered)](https://www.cbioportal.org/results/comparison/survival?cancer_study_list=ov_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2&case_set_id=ov_tcga_pan_can_atlas_2018_all). It shows the Kaplan-Meier curves, median survival and the log-rank p-value. It uses the portal's default alteration definition, which may differ slightly from mine. - Or run `survival::survfit` and `survdiff` in R, or `lifelines` in Python. Cox regression will give a hazard ratio. [Study overview](https://www.cbioportal.org/study?id=ov_tcga_pan_can_atlas_2018)