Q (sonnet): What percentage of endometrial cancer samples have hypermutation (>5000 mutations) and how does this correlate with histological subtype?

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

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

▶ list_studies
{
 "search": "endometrial"
}

◀ 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
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":"ucec_ancestry_cds_msk_2023","name":"Endometrial Cancer (MSK, Cancer Discovery 2023)","type_of_cancer_id":"ucec","sample_count":1882,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_ancestry_cds_msk_2023"},{"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_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_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"},{"cancer_study_identifier":"ucec_msk_2018","name":"Endometrial Cancer (MSK, 2018)","type_of_cancer_id":"ucec","sample_count":197,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_msk_2018"},{"cancer_study_identifier":"ucec_ccr_msk_2022","name":"Endometrial Carcinoma MSI (MSK, Clin Cancer Res 2022)","type_of_cancer_id":"ucec","sample_count":181,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_ccr_msk_2022"},{"cancer_study_identifier":"ucec_cptac_2020","name":"Endometrial Carcinoma (CPTAC, Cell 2020)","type_of_cancer_id":"ucec","sample_count":81,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_cptac_2020"},{"cancer_study_identifier":"ucs_msk_2024","name":"Endometrial Carcinoma (MSK, Molecular Oncology 2024)","type_of_cancer_id":"ucec","sample_count":69,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucs_msk_2024"},{"cancer_study_identifier":"ucec_ccr_cfdna_msk_2022","name":"Endometrial Carcinoma cfDNA (MSK, Clin Cancer Res 2022)","type_of_cancer_id":"ucec","sample_count":44,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_ccr_cfdna_msk_2022"},{"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":"uec_msk_2024","name":"Endometrial cancer HR-positive (MSK, 2024)","type_of_cancer_id":"ucec","sample_count":24,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=uec_msk_2024"},{"cancer_study_identifier":"uccc_nih_2017","name":"Uterine Clear Cell Carcinoma (NIH, Cancer 2017)","type_of_cancer_id … (447 more chars)

▶ get_study_guide
{
 "study_id": "ucec_tcga_pan_can_atlas_2018"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ 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


◀ 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": "
WITH sample_data AS (
    SELECT
        sample_unique_id,
        patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'MUTATION_COUNT') AS mutation_count
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'MUTATION_COUNT'
      AND sample_unique_id != ''
    GROUP BY sample_unique_id, patient_unique_id
),
histology AS (
    SELECT sample_unique_id, attribute_value AS histology
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'CANCER_TYPE_DETAILED'
      AND sample_unique_id != ''
)
SELECT
    h.histology AS histology,
    count() AS n_samples,
    countIf(s.mutation_count > 5000) AS n_hypermutated,
    round(100.0 * countIf(s.mutation_count > 5000) / count(), 1) AS pct_hypermutated,
    round(avg(s.mutation_count), 1) AS mean_mutation_count,
    round(quantile(0.5)(s.mutation_count), 1) AS median_mutation_count
FROM sample_data s
JOIN histology h ON s.sample_unique_id = h.sample_unique_id
GROUP BY h.histology
ORDER BY n_samples DESC
"
}

◀ result
{"rows":[{"histology":"Uterine Endometrioid Carcinoma","n_samples":399,"n_hypermutated":34,"pct_hypermutated":8.5,"mean_mutation_count":1204.3,"median_mutation_count":142.5},{"histology":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","n_samples":109,"n_hypermutated":2,"pct_hypermutated":1.8,"mean_mutation_count":441.6,"median_mutation_count":54},{"histology":"Uterine Mixed Endometrial Carcinoma","n_samples":21,"n_hypermutated":2,"pct_hypermutated":9.5,"mean_mutation_count":1142.9,"median_mutation_count":61}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH sample_data AS (
    SELECT
        sample_unique_id,
        patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'MUTATION_COUNT') AS mutation_count
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'MUTATION_COUNT'
      AND sample_unique_id != ''
    GROUP BY sample_unique_id, patient_unique_id
),
subtype AS (
    SELECT patient_unique_id, attribute_value AS subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
)
SELECT
    if(t.subtype = '', 'Unknown/Blank', t.subtype) AS molecular_subtype,
    count() AS n_samples,
    countIf(s.mutation_count > 5000) AS n_hypermutated,
    round(100.0 * countIf(s.mutation_count > 5000) / count(), 1) AS pct_hypermutated,
    round(avg(s.mutation_count), 1) AS mean_mutation_count,
    round(quantile(0.5)(s.mutation_count), 1) AS median_mutation_count
FROM sample_data s
LEFT JOIN subtype t ON s.patient_unique_id = t.patient_unique_id
GROUP BY molecular_subtype
ORDER BY n_samples DESC
"
}

◀ result
{"rows":[{"molecular_subtype":"UCEC_CN_HIGH","n_samples":163,"n_hypermutated":0,"pct_hypermutated":0,"mean_mutation_count":70.6,"median_mutation_count":54},{"molecular_subtype":"UCEC_MSI","n_samples":148,"n_hypermutated":6,"pct_hypermutated":4.1,"mean_mutation_count":1020.2,"median_mutation_count":578},{"molecular_subtype":"UCEC_CN_LOW","n_samples":147,"n_hypermutated":0,"pct_hypermutated":0,"mean_mutation_count":76.6,"median_mutation_count":50},{"molecular_subtype":"UCEC_POLE","n_samples":49,"n_hypermutated":32,"pct_hypermutated":65.3,"mean_mutation_count":7427.1,"median_mutation_count":7385},{"molecular_subtype":"Unknown/Blank","n_samples":22,"n_hypermutated":0,"pct_hypermutated":0,"mean_mutation_count":126.1,"median_mutation_count":49}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    countIf(mutation_count > 5000) AS n_hypermutated,
    count() AS n_total,
    round(100.0 * countIf(mutation_count > 5000) / count(), 2) AS pct_hypermutated
FROM (
    SELECT sample_unique_id, maxIf(toFloat64OrNull(attribute_value), attribute_name = 'MUTATION_COUNT') AS mutation_count
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'MUTATION_COUNT'
      AND sample_unique_id != ''
    GROUP BY sample_unique_id
)
"
}

◀ result
{"rows":[{"n_hypermutated":38,"n_total":529,"pct_hypermutated":7.18}]}

▶ 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_study_view
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "attributeId": "MUTATION_COUNT",
    "values": [
     {
      "start": 5001
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22MUTATION_COUNT%22%2C%22values%22%3A%5B%7B%22start%22%3A5001%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22MUTATION_COUNT%22%2C%22values%22%3A%5B%7B%22start%22%3A5001%7D%5D%7D%5D%7D","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}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018"
 ],
 "tab": "clinical",
 "groups": [
  {
   "name": "POLE",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_POLE"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "MSI",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_MSI"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "CN-low",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_CN_LOW"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "CN-high",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_CN_HIGH"
       }
      ]
     }
    ]
   }
  }
 ]
}

◀ result
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═ answer (success)
## Hypermutation in Endometrial Cancer — TCGA PanCancer Atlas Cohort

Using the [Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018) cohort (529 samples with mutation data), **38 samples (7.2%)** have >5,000 total mutations.

### By histological subtype (`CANCER_TYPE_DETAILED`)

| Histology | Samples | Hypermutated (>5000) | % Hypermutated | Mean mutation count | Median mutation count |
|---|---|---|---|---|---|
| Uterine Endometrioid Carcinoma | 399 | 34 | **8.5%** | 1,204.3 | 142.5 |
| Uterine Serous Carcinoma / Papillary Serous | 109 | 2 | 1.8% | 441.6 | 54.0 |
| Uterine Mixed Endometrial Carcinoma | 21 | 2 | 9.5% | 1,142.9 | 61.0 |

Hypermutation is heavily concentrated in **endometrioid** histology — nearly all hypermutated samples fall here, consistent with this subtype harboring the POLE-mutant and MSI-high tumors. Serous carcinomas are almost never hypermutated by this definition, despite generally being considered more clinically aggressive.

### By TCGA molecular subtype (`SUBTYPE`) — the mechanistic driver

| Molecular subtype | Samples | Hypermutated (>5000) | % Hypermutated | Mean mutation count | Median mutation count |
|---|---|---|---|---|---|
| **POLE** (ultramutated) | 49 | 32 | **65.3%** | 7,427.1 | 7,385.0 |
| MSI (microsatellite instability) | 148 | 6 | 4.1% | 1,020.2 | 578.0 |
| CN-low | 147 | 0 | 0% | 76.6 | 50.0 |
| CN-high (serous-like) | 163 | 0 | 0% | 70.6 | 54.0 |
| Unknown/blank | 22 | 0 | 0% | 126.1 | 49.0 |

**Interpretation:** Molecular subtype explains the histological pattern far more precisely than histology alone. Nearly all hypermutation (>5000 mutations) traces to the **POLE-ultramutated** subtype (65% of POLE tumors exceed this threshold, with an exonuclease-domain proofreading defect driving extremely high mutation rates), with a smaller contribution from **MSI-high** tumors (mismatch-repair deficiency, typically in the hundreds-to-low-thousands range). The CN-low and CN-high (serous-like) subtypes are essentially never hypermutated. Since POLE and MSI tumors are both classified predominantly as endometrioid histologically, this explains why hypermutation appears "histology-associated" when in fact it is *molecular-subtype*-driven — histology is a confounded proxy.

This is a descriptive breakdown, not a statistical test; a formal comparison of hypermutation rates across subtypes (chi-squared, given 4+ categorical groups) can be run directly in cBioPortal's Group Comparison tool.

### Explore further
- [Study cohort filtered to hypermutated samples (>5000 mutations)](https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22MUTATION_COUNT%22%2C%22values%22%3A%5B%7B%22start%22%3A5001%7D%5D%7D%5D%7D) — browse individual hypermutated cases
- [Group Comparison: POLE vs MSI vs CN-low vs CN-high](https://www.cbioportal.org/comparison/clinical?comparisonId=6ab7f532e497aad53593c5a8) — compare clinical/mutation distributions and run statistical tests across the four molecular subtypes directly in the portal