Q (haiku): 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://mutation-frequency-guide"
}

▶ list_studies
{
 "search": "TCGA endometrial"
}

◀ 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)

◀ result
{"result":[{"cancer_study_identifier":"ucec_tcga_gdc","name":"Endometrial Carcinoma (TCGA GDC, 2025)","type_of_cancer_id":"ucec","sample_count":547,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_gdc"},{"cancer_study_identifier":"ucec_tcga","name":"Uterine Corpus Endometrial Carcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"ucec","sample_count":549,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga"},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"ucec","sample_count":529,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucec_tcga_pub","name":"Uterine Corpus Endometrial Carcinoma (TCGA, Nature 2013)","type_of_cancer_id":"ucec","sample_count":373,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_pub"}]}

▶ get_study_guide
{
 "study_id": "ucec_tcga_pan_can_atlas_2018"
}

◀ result
# Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)

**Study ID:** `ucec_tcga_pan_can_atlas_2018`

See `_tcga_pancan_template.md` for common TCGA clinical attributes.

## Study-Specific Attributes

### Molecular Classification (TCGA)
| Attribute | Description | Values (patients) |
|-----------|-------------|--------|
| `SUBTYPE` | TCGA molecular subtype | `UCEC_CN_HIGH` 163, `UCEC_MSI` 148, `UCEC_CN_LOW` 147, `UCEC_POLE` 49, blank 22 |

### Histology
There is no `HISTOLOGICAL_TYPE` attribute; use `CANCER_TYPE_DETAILED`, plus `GRADE` for grade.

| Attribute | Values (samples) |
|-----------|--------|
| `CANCER_TYPE_DETAILED` | Uterine Endometrioid Carcinoma 399, Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma 109, Uterine Mixed Endometrial Carcinoma 21 |
| `GRADE` | G3 302, G2 119, G1 97, High Grade 11 |

`AJCC_PATHOLOGIC_TUMOR_STAGE` is blank for all samples.

## Molecular Subtypes
| Subtype | Characteristics | Prognosis |
|---------|-----------------|-----------|
| **POLE** | Ultra-hypermutated (>100 mut/Mb), POLE exonuclease mutations | Excellent |
| **MSI** | Hypermutated, microsatellite instability, MLH1 silencing | Intermediate |
| **CN-low** | Microsatellite stable, few copy number alterations | Intermediate |
| **CN-high** | Serous-like, TP53 mutations, extensive CNA | Poor |

## Key Genes
| Gene | Frequency | Subtype Association |
|------|-----------|---------------------|
| PTEN | ~65% | CN-low, MSI |
| PIK3CA | ~50% | All subtypes |
| PIK3R1 | ~30% | CN-low |
| ARID1A | ~35% | MSI |
| TP53 | ~25% | CN-high (>90% in this subtype) |
| KRAS | ~20% | Various |
| CTNNB1 | ~20% | CN-low |
| POLE | ~7% | Defines POLE subtype |

## Notes
- TCGA molecular classification has prognostic value and guides treatment
- POLE and MSI subtypes are hypermutated but have different mechanisms
- TP53 mutations with CN-high pattern indicate serous-like behavior regardless of histology
- POLE mutations should be in exonuclease domain (proofreading) to be driver
- MSI-H tumors respond well to immunotherapy


▶ clickhouse_run_select_query
{
 "query": "
-- Co-occurring KRAS and NRAS mutations in UCEC TCGA PanCancer Atlas
-- Count patients with mutations in both genes
WITH profiled_patients AS (
    SELECT DISTINCT c.patient_unique_id
    FROM sample_to_gene_panel_derived s
    JOIN (SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived 
          WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018') c 
      ON s.sample_unique_id = c.sample_unique_id
    WHERE s.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' 
      AND s.alteration_type = 'MUTATION_EXTENDED'
),
kras_mutant_patients AS (
    SELECT DISTINCT c.patient_unique_id
    FROM genomic_event_derived ged
    JOIN (SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived 
          WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018') c 
      ON ged.sample_unique_id = c.sample_unique_id
    WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND ged.hugo_gene_symbol = 'KRAS'
      AND ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
),
nras_mutant_patients AS (
    SELECT DISTINCT c.patient_unique_id
    FROM genomic_event_derived ged
    JOIN (SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived 
          WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018') c 
      ON ged.sample_unique_id = c.sample_unique_id
    WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND ged.hugo_gene_symbol = 'NRAS'
      AND ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
),
both_mutant AS (
    SELECT k.patient_unique_id
    FROM kras_mutant_patients k
    INTERSECT
    SELECT n.patient_unique_id
    FROM nras_mutant_patients n
)
SELECT
    (SELECT COUNT(DISTINCT patient_unique_id) FROM profiled_patients) AS total_profiled_patients,
    (SELECT COUNT(DISTINCT patient_unique_id) FROM kras_mutant_patients) AS kras_mutant,
    (SELECT COUNT(DISTINCT patient_unique_id) FROM nras_mutant_patients) AS nras_mutant,
    (SELECT COUNT(DISTINCT patient_unique_id) FROM both_mutant) AS both_mutant,
    ROUND(100.0 * (SELECT COUNT(DISTINCT patient_unique_id) FROM both_mutant) / 
           (SELECT COUNT(DISTINCT patient_unique_id) FROM profiled_patients), 2) AS pct_both_mutant_of_profiled
"
}

◀ 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 (SELECT COUNTDistinct(patient_unique_id) FROM kras_mutant_patients) AS kras_mutant. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_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."}]}

◀ result
{"columns":[{"name":"internal_id","type":"Int32"},{"name":"sample_unique_id","type":"String","comment":"Globally unique sample ID: cancer_study_identifier + \"_\" + sample.stable_id. Empty for patient-level attributes. Use this for sample filtering and joins."},{"name":"patient_unique_id","type":"String","comment":"Globally unique patient ID: cancer_study_identifier + \"_\" + patient.stable_id. Present for both sample and patient-level attributes."},{"name":"attribute_name","type":"LowCardinality(String)","comment":"Clinical attribute name (e.g., SAMPLE_TYPE, CANCER_TYPE, AGE, OS_MONTHS). Use with attribute_value for filtering. AGE may be floored or capped for de-identification (e.g. all children recorded as 18, or everyone 89+ recorded as 89 or 90): before age statistics check for a pile-up at the min/max, and if present compute age from DAYS_TO_BIRTH (-days / 365.25)."},{"name":"attribute_value","type":"String","comment":"Value of the clinical attribute (String). For SAMPLE_TYPE: Primary, Metastasis, Local Recurrence, Unknown. Missing values are empty strings, so use toFloat64OrNull(attribute_value) for numeric comparisons — CAST fails on them."},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"type","type":"LowCardinality(String)","comment":"Data level: \"sample\" for sample-level attributes (e.g., SAMPLE_TYPE), \"patient\" for patient-level attributes (e.g., AGE, OS_MONTHS)."}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Co-occurring KRAS and NRAS mutations in UCEC TCGA PanCancer Atlas
-- Count patients with mutations in both genes

WITH all_mutated_samples AS (
    -- All samples with a mutation in either KRAS or NRAS
    SELECT DISTINCT sample_unique_id, hugo_gene_symbol
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol IN ('KRAS', 'NRAS')
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
),
kras_samples AS (
    SELECT DISTINCT sample_unique_id FROM all_mutated_samples WHERE hugo_gene_symbol = 'KRAS'
),
nras_samples AS (
    SELECT DISTINCT sample_unique_id FROM all_mutated_samples WHERE hugo_gene_symbol = 'NRAS'
),
both_mutated_samples AS (
    SELECT k.sample_unique_id
    FROM kras_samples k
    INTERSECT
    SELECT n.sample_unique_id
    FROM nras_samples n
),
profiled_patients AS (
    SELECT DISTINCT patient_unique_id
    FROM sample_to_gene_panel_derived s
    JOIN genomic_event_derived g ON s.sample_unique_id = g.sample_unique_id
    WHERE s.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' 
      AND s.alteration_type = 'MUTATION_EXTENDED'
),
kras_mutant_patients AS (
    SELECT DISTINCT g.patient_unique_id
    FROM kras_samples k
    JOIN genomic_event_derived g ON k.sample_unique_id = g.sample_unique_id
    WHERE g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
nras_mutant_patients AS (
    SELECT DISTINCT g.patient_unique_id
    FROM nras_samples n
    JOIN genomic_event_derived g ON n.sample_unique_id = g.sample_unique_id
    WHERE g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
both_mutant_patients AS (
    SELECT k.patient_unique_id
    FROM kras_mutant_patients k
    INTERSECT
    SELECT n.patient_unique_id
    FROM nras_mutant_patients n
)
SELECT
    (SELECT COUNT(*) FROM profiled_patients) AS total_profiled_patients,
    (SELECT COUNT(*) FROM kras_mutant_patients) AS kras_mutant_patients,
    (SELECT COUNT(*) FROM nras_mutant_patients) AS nras_mutant_patients,
    (SELECT COUNT(*) FROM both_mutant_patients) AS both_mutant_patients,
    ROUND(100.0 * (SELECT COUNT(*) FROM both_mutant_patients) / 
           (SELECT COUNT(*) FROM profiled_patients), 2) AS pct_both_of_profiled
"
}

◀ result
{"rows":[{"total_profiled_patients":517,"kras_mutant_patients":99,"nras_mutant_patients":21,"both_mutant_patients":4,"pct_both_of_profiled":0.77}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Details of patients with co-occurring KRAS and NRAS mutations
WITH kras_mutations AS (
    SELECT DISTINCT g.patient_unique_id, g.sample_unique_id, g.hugo_gene_symbol, g.mutation_variant
    FROM genomic_event_derived g
    WHERE g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND g.hugo_gene_symbol = 'KRAS'
      AND g.variant_type = 'mutation'
      AND g.mutation_status != 'UNCALLED'
),
nras_mutations AS (
    SELECT DISTINCT g.patient_unique_id, g.sample_unique_id, g.hugo_gene_symbol, g.mutation_variant
    FROM genomic_event_derived g
    WHERE g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND g.hugo_gene_symbol = 'NRAS'
      AND g.variant_type = 'mutation'
      AND g.mutation_status != 'UNCALLED'
),
both_patients AS (
    SELECT DISTINCT k.patient_unique_id
    FROM kras_mutations k
    WHERE EXISTS (
        SELECT 1 FROM nras_mutations n WHERE n.patient_unique_id = k.patient_unique_id
    )
)
SELECT
    k.patient_unique_id,
    k.sample_unique_id AS kras_sample,
    k.mutation_variant AS kras_variant,
    n.sample_unique_id AS nras_sample,
    n.mutation_variant AS nras_variant
FROM kras_mutations k
JOIN nras_mutations n ON k.patient_unique_id = n.patient_unique_id
WHERE k.patient_unique_id IN (SELECT patient_unique_id FROM both_patients)
ORDER BY k.patient_unique_id
"
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 1\n Code: 1. DB::Exception: Resolved identifier 'k.patient_unique_id' in parent scope to expression 'k.patient_unique_id' with correlated columns 'patient_unique_id' (Enable 'allow_experimental_correlated_subqueries' setting to allow correlated subqueries execution). In scope (SELECT _CAST(1, 'UInt64') FROM (SELECT 1 FROM nras_mutations AS n WHERE n.patient_unique_id = k.patient_unique_id) LIMIT _CAST(1, 'UInt64')). (UNSUPPORTED_METHOD) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
-- Details of patients with co-occurring KRAS and NRAS mutations
WITH kras_patients AS (
    SELECT DISTINCT patient_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'KRAS'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
),
nras_patients AS (
    SELECT DISTINCT patient_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'NRAS'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
),
both_patients AS (
    SELECT patient_unique_id FROM kras_patients
    INTERSECT
    SELECT patient_unique_id FROM nras_patients
)
SELECT
    g.patient_unique_id,
    g.hugo_gene_symbol,
    g.mutation_variant,
    g.mutation_type
FROM genomic_event_derived g
WHERE g.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
  AND g.hugo_gene_symbol IN ('KRAS', 'NRAS')
  AND g.variant_type = 'mutation'
  AND g.mutation_status != 'UNCALLED'
  AND g.patient_unique_id IN (SELECT patient_unique_id FROM both_patients)
ORDER BY g.patient_unique_id, g.hugo_gene_symbol
"
}

◀ result
{"rows":[{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-A5-A0G2","hugo_gene_symbol":"KRAS","mutation_variant":"Q61H","mutation_type":"Missense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-A5-A0G2","hugo_gene_symbol":"NRAS","mutation_variant":"D47N","mutation_type":"Missense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-A5-A0G2","hugo_gene_symbol":"NRAS","mutation_variant":"F78S","mutation_type":"Missense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-AX-A0J0","hugo_gene_symbol":"KRAS","mutation_variant":"K176Q","mutation_type":"Missense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-AX-A0J0","hugo_gene_symbol":"NRAS","mutation_variant":"E162*","mutation_type":"Nonsense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-B5-A0JV","hugo_gene_symbol":"KRAS","mutation_variant":"G12D","mutation_type":"Missense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-B5-A0JV","hugo_gene_symbol":"NRAS","mutation_variant":"Q61K","mutation_type":"Missense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-DF-A2KZ","hugo_gene_symbol":"KRAS","mutation_variant":"A146V","mutation_type":"Missense_Mutation"},{"patient_unique_id":"ucec_tcga_pan_can_atlas_2018_TCGA-DF-A2KZ","hugo_gene_symbol":"NRAS","mutation_variant":"Q61R","mutation_type":"Missense_Mutation"}]}

▶ 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",
  "NRAS"
 ],
 "tab": "oncoprint"
}

◀ result
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═ answer (success)
## Summary

In the **TCGA PanCancer Atlas Uterine Corpus Endometrial Carcinoma study (ucec_tcga_pan_can_atlas_2018)**, **0.77% of profiled patients have co-occurring mutations in both KRAS and NRAS** (4 out of 517 mutation-profiled patients).

### Key Findings:

| Metric | Count |
|--------|-------|
| **Total profiled patients** | 517 |
| **KRAS-mutant patients** | 99 (19.1%) |
| **NRAS-mutant patients** | 21 (4.1%) |
| **Co-mutant patients (both KRAS and NRAS)** | 4 |
| **Percentage with both mutations** | **0.77%** |

### The 4 Co-Mutant Patients:

1. **TCGA-A5-A0G2**: KRAS Q61H + NRAS D47N, F78S (multiple NRAS mutations)
2. **TCGA-AX-A0J0**: KRAS K176Q + NRAS E162* (nonsense)
3. **TCGA-B5-A0JV**: KRAS G12D + NRAS Q61K
4. **TCGA-DF-A2KZ**: KRAS A146V + NRAS Q61R

### Interpretation:

Co-occurrence of KRAS and NRAS mutations is extremely rare in endometrial cancer (less than 1%), suggesting these genes are largely mutually exclusive in this disease. This is consistent with their overlapping role in activating RAS/MAPK signaling — tumors typically acquire a single activating mutation in one of the RAS family members rather than both. The few instances observed may represent distinct subclonal populations or be artifacts of sequencing depth.

[View KRAS/NRAS alterations in OncoPrint](https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=KRAS%0ANRAS&case_set_id=ucec_tcga_pan_can_atlas_2018_all)