Q (sonnet): 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"
}

▶ list_studies
{
 "search": "ovarian"
}

◀ 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)

◀ 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"
}

◀ 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)


▶ read_guide
{
 "uri": "cbioportal://clinical-data-guide"
}

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-guide"
}

◀ 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)

◀ result
# Mutation Frequency Analysis Guide

## IMPORTANT: Reporting Mutation Frequencies
- **ALWAYS report frequencies as percentages**, not raw counts: `frequency = (altered_samples / total_profiled_samples) × 100`
- For quick frequency lookups, **prefer the TCGA Pan-Cancer Atlas study first**, then offer to expand to other studies
- When reporting across multiple studies, show **ranges** (e.g., "TP53 is mutated in 30–60% of samples") rather than a single average
- **NEVER** sum mutation events across studies to compute an aggregate frequency — this can exceed 100% due to double-counting
- Warn users that samples may overlap across cohorts (e.g., MSK studies may share patients)
- **Choose and state the counting unit**: use patient-level frequencies for prevalence/rate questions unless the user explicitly asks for samples; use sample-level frequencies when the user asks about samples.
- **For "across cancer types" questions**, jump to the [Cross-Cancer-Type Mutation Frequency](#cross-cancer-type-mutation-frequency) section below — there is one correct recipe and several common wrong ones.

## Counting Unit: Samples vs Patients

Before answering any mutation count or frequency question, decide whether the unit is samples or patients and state that choice in the answer.

| User wording | Counting unit |
|--------------|---------------|
| "prevalence", "rate", "fraction of patients", "patients with", "how common is" | Patient-level: `COUNT(DISTINCT patient_unique_id)` |
| "samples", "specimens", "biopsies", sample-level cohort composition | Sample-level: `COUNT(DISTINCT sample_unique_id)` |
| Ambiguous | Ask, or default to patient-level for prevalence/rate language and say so |

### Cross-study sample-count caveat

When an answer touches more than one study and reports a sample count, prepend a one-line caveat:

> Sample IDs are unique within cBioPortal study prefixes, not guaranteed biological-sample identifiers across studies; overlapping cohorts can count the same patient/sample more than once.

Prefer one of these safer approaches:

- Use a shipped `cancer_study_query_preferences` cohort such as `pan_cancer_tcga` or `all_studies_non_redundant`.
- Restrict to one named study.
- Aggregate by `patient_unique_id` when the biological question is patient prevalence.

## STOP rule: a frequency above 100% means your query is wrong

If your query returns a frequency over 100%, **do not try to debug or explain the data inconsistency to the user**. The cause is always one of these query bugs:

- Summing mutation events instead of `COUNT(DISTINCT sample_unique_id)` for the numerator
- Using a study-wide sample count as the denominator instead of the gene-specific profiled count
- Cross-study aggregation where the same biological sample appears under multiple `sample_unique_id` values (e.g., MSK-IMPACT and MSK-CHORD share patients)
- **Joining the profiled CTE through `gene_panel` / `gene_panel_list` without a WES branch.** `gene_panel_id = 'WES'` is *not* a row in … (36467 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH altered 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 IN (2, -2))
      )
),
os AS (
    SELECT patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'OS_MONTHS') AS os_months,
        maxIf(attribute_value, attribute_name = 'OS_STATUS') AS os_status
    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 altered), 'BRCA1/2-altered', 'Wild-type') AS group_name,
    count() AS n_patients,
    countIf(startsWith(os_status, '1')) AS n_events,
    countIf(startsWith(os_status, '0')) AS n_censored,
    round(min(os_months),1) AS min_followup_months,
    round(max(os_months),1) AS max_followup_months
FROM os
WHERE os_months IS NOT NULL AND os_status != ''
GROUP BY group_name;
"
}

◀ result
{"rows":[{"group_name":"BRCA1/2-altered","n_patients":49,"n_events":20,"n_censored":29,"min_followup_months":0.3,"max_followup_months":145.4},{"group_name":"Wild-type","n_patients":522,"n_events":321,"n_censored":201,"min_followup_months":0.3,"max_followup_months":180.2}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH altered AS (
    SELECT DISTINCT patient_unique_id, hugo_gene_symbol
    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 IN (2, -2))
      )
)
SELECT hugo_gene_symbol, count(DISTINCT patient_unique_id) AS n_patients
FROM altered
GROUP BY hugo_gene_symbol;
"
}

◀ result
{"rows":[{"hugo_gene_symbol":"BRCA2","n_patients":26},{"hugo_gene_symbol":"BRCA1","n_patients":25}]}

▶ resolve_and_route
{
 "studyIds": [
  "ov_tcga_pan_can_atlas_2018"
 ]
}

◀ 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: MUT AMP HOMDEL",
  "BRCA2: MUT AMP HOMDEL"
 ],
 "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%3A+MUT+AMP+HOMDEL%0ABRCA2%3A+MUT+AMP+HOMDEL&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%3A+MUT+AMP+HOMDEL%0ABRCA2%3A+MUT+AMP+HOMDEL&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)
## Overview Survival Comparison

I can't run Kaplan-Meier estimation or a log-rank test directly, so I can't state whether the survival difference is statistically significant — but here is the descriptive summary from the **Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)** cohort (patient-level; "BRCA1/2-altered" = mutation, amplification, or deep deletion in either gene):

| Group | N patients | Events (deceased) | Censored (living) | Follow-up range (months) |
|---|---|---|---|---|
| BRCA1/2-altered | 49 (BRCA1: 25, BRCA2: 26) | 20 | 29 | 0.3 – 145.4 |
| Wild-type | 522 | 321 | 201 | 0.3 – 180.2 |

A smaller fraction of BRCA1/2-altered patients experienced the survival event (20/49 = 41%) compared to wild-type patients (321/522 = 61%), which is consistent with the general clinical literature that BRCA-associated HGSOC often has better platinum sensitivity — but this is a raw event-rate observation, not a survival estimate, and I have not run the statistical test needed to confirm significance.

To get the actual answer to "significantly different":
- **Median OS** requires Kaplan-Meier estimation (which correctly handles the 230 censored/living patients above) — a raw average or median of `OS_MONTHS` would be biased.
- **Significance** requires a log-rank test comparing the two KM curves.

Both are computed automatically on cBioPortal's Group Comparison Survival tab, linked below (grouped by BRCA1/BRCA2 alteration status: mutation, amplification, or homozygous deletion in either gene):

[View BRCA1/2-altered vs Wild-type Survival Comparison](https://www.cbioportal.org/results/comparison/survival?cancer_study_list=ov_tcga_pan_can_atlas_2018&gene_list=BRCA1%3A+MUT+AMP+HOMDEL%0ABRCA2%3A+MUT+AMP+HOMDEL&case_set_id=ov_tcga_pan_can_atlas_2018_all)

That page will show the KM curves, number-at-risk table, median OS per group (or "not reached"), and the log-rank p-value.

**Notes:**
- This study reports only somatic calls — no germline BRCA status or platinum-sensitivity/HRD score is available in this cohort (per the study's data dictionary), so this comparison reflects somatic + CNA alteration status only, not germline BRCA carriers specifically.
- If you'd prefer to restrict to mutations only (excluding CNA-driven alterations), or to a different ovarian cancer cohort (e.g., the newer TCGA GDC 2025 remap, or an MSK cohort), I can rerun this.