Q (sonnet): In the TCGA PanCancer Atlas endometrial cancer study, what percentage of patients have co-occurring oncogenic mutations in both KRAS and NRAS?

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-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)

◀ 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": "
SELECT driver_filter, count() AS mutations
FROM genomic_event_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND variant_type = 'mutation'
  AND hugo_gene_symbol IN ('KRAS','NRAS')
GROUP BY driver_filter
"
}

◀ result
{"rows":[{"mutations":123}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH profiled AS (
    SELECT DISTINCT c.patient_unique_id
    FROM sample_to_gene_panel_derived s
    JOIN clinical_data_derived c USING (sample_unique_id)
    WHERE s.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND s.alteration_type = 'MUTATION_EXTENDED'
      AND c.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
mut AS (
    SELECT DISTINCT c.patient_unique_id, g.hugo_gene_symbol
    FROM genomic_event_derived g
    JOIN clinical_data_derived c USING (sample_unique_id)
    WHERE g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND g.variant_type = 'mutation'
      AND g.mutation_status != 'UNCALLED'
      AND g.off_panel = 0
      AND g.hugo_gene_symbol IN ('KRAS','NRAS')
)
SELECT
  (SELECT count() FROM profiled) AS profiled_patients,
  uniqExactIf(patient_unique_id, hugo_gene_symbol='KRAS') AS kras_mut,
  uniqExactIf(patient_unique_id, hugo_gene_symbol='NRAS') AS nras_mut,
  (SELECT count() FROM (
      SELECT patient_unique_id FROM mut GROUP BY patient_unique_id HAVING count(DISTINCT hugo_gene_symbol) = 2
  )) AS both_mut
FROM mut
"
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 47\n Code: 47. DB::Exception: Unknown expression or function identifier `patient_unique_id` in scope WITH profiled AS (SELECT DISTINCT c.patient_unique_id FROM sample_to_gene_panel_derived AS s INNER JOIN clinical_data_derived AS c USING (sample_unique_id) WHERE (s.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018') AND (s.alteration_type = 'MUTATION_EXTENDED') AND (c.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018')), mut AS (SELECT DISTINCT c.patient_unique_id, g.hugo_gene_symbol FROM genomic_event_derived AS g INNER JOIN clinical_data_derived AS c USING (sample_unique_id) WHERE (g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018') AND (g.variant_type = 'mutation') AND (g.mutation_status != 'UNCALLED') AND (g.off_panel = 0) AND (g.hugo_gene_symbol IN ('KRAS', 'NRAS'))) SELECT (SELECT count() FROM profiled) AS profiled_patients, uniqExactIf(patient_unique_id, hugo_gene_symbol = 'KRAS') AS kras_mut, uniqExactIf(patient_unique_id, hugo_gene_symbol = 'NRAS') AS nras_mut, (SELECT count() FROM (SELECT patient_unique_id FROM mut GROUP BY patient_unique_id HAVING countDistinct(hugo_gene_symbol) = 2)) AS both_mut FROM mut. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
SELECT count(DISTINCT c.patient_unique_id) AS profiled_patients
FROM sample_to_gene_panel_derived s
JOIN clinical_data_derived c USING (sample_unique_id)
WHERE s.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
  AND s.alteration_type = 'MUTATION_EXTENDED'
  AND c.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
"
}

◀ result
{"rows":[{"profiled_patients":517}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH mut AS (
    SELECT DISTINCT c.patient_unique_id AS pid, g.hugo_gene_symbol AS gene
    FROM genomic_event_derived g
    JOIN clinical_data_derived c USING (sample_unique_id)
    WHERE g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND g.variant_type = 'mutation'
      AND g.mutation_status != 'UNCALLED'
      AND g.off_panel = 0
      AND g.hugo_gene_symbol IN ('KRAS','NRAS')
)
SELECT
  uniqExactIf(pid, gene='KRAS') AS kras_mut_patients,
  uniqExactIf(pid, gene='NRAS') AS nras_mut_patients,
  (SELECT count() FROM (SELECT pid FROM mut GROUP BY pid HAVING count(DISTINCT gene) = 2)) AS both_mut_patients
FROM mut
"
}

◀ result
{"rows":[{"kras_mut_patients":99,"nras_mut_patients":21,"both_mut_patients":4}]}

▶ resolve_and_route
{
 "studyIds": [
  "ucec_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":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529,"studyViewUrl":"https://www.cbioportal.org/study?id=ucec_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":["ucec_tcga_pan_can_atlas_2018_gistic","ucec_tcga_pan_can_atlas_2018_log2CNA","ucec_tcga_pan_can_atlas_2018_mutations","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_tcga_pan_can_atlas_2018_rppa","ucec_tcga_pan_can_atlas_2018_rppa_Zscores","ucec_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["ucec_tcga_pan_can_atlas_2018_armlevel_cna","ucec_tcga_pan_can_atlas_2018_genetic_ancestry","ucec_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","ucec_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_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"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "KRAS: MUT_DRIVER",
  "NRAS: MUT_DRIVER"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT_DRIVER%0ANRAS%3A+MUT_DRIVER&case_set_id=ucec_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT_DRIVER%0ANRAS%3A+MUT_DRIVER&case_set_id=ucec_tcga_pan_can_atlas_2018_all","data":{"studyIds":["ucec_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529}],"genes":["KRAS","NRAS"],"caseSetId":"ucec_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
## Important caveat on "oncogenic"

This study's underlying database has **no driver/pathogenicity annotation stored** for KRAS or NRAS mutations (the `driver_filter` field, which flags a study's own curated driver calls, is empty for all 123 KRAS/NRAS mutation records in this cohort). I therefore cannot report a true "oncogenic-only" percentage directly from the database — the figures below reflect **all non-synonymous mutations**, not an OncoKB-filtered oncogenic subset.

To get the OncoKB-oncogenic-restricted number, use the OncoPrint link below, which applies the `MUT_DRIVER` filter (OncoKB + hotspot annotation computed live by the cBioPortal web app).

## Co-mutation counts (all KRAS/NRAS mutations, patient-level)

**Study:** [Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018) — 517 patients profiled for mutations (whole-exome)

| | Patients | % of profiled |
|---|---|---|
| KRAS mutated | 99 | 19.1% |
| NRAS mutated | 21 | 4.1% |
| **Both KRAS and NRAS mutated** | **4** | **0.8%** |

So **0.8% (4 of 517 profiled patients)** carry mutations in both KRAS and NRAS in this cohort — but again, that count includes all mutation types, not confirmed oncogenic/driver variants specifically.

I cannot state whether this co-occurrence is more or less frequent than expected by chance — that requires Fisher's exact test / mutual exclusivity analysis, which I have not run here.

[View KRAS/NRAS driver-mutation OncoPrint](https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT_DRIVER%0ANRAS%3A+MUT_DRIVER&case_set_id=ucec_tcga_pan_can_atlas_2018_all) — this applies cBioPortal's live OncoKB/hotspot driver filter (`MUT_DRIVER`), which is the correct way to isolate oncogenic-only calls; use the Mutual Exclusivity tab there for a formal co-occurrence test.