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

▶ list_guides
{}

◀ result
{"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

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

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-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)

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

◀ result
# Sample and Study Filtering Guide

## Overview
Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria.

## Study-Level Filtering

### 1. Always Filter by Study
Every query should specify a study to ensure consistent results:

```sql
-- Always include study filtering
SELECT *
FROM your_table
WHERE
    cancer_study_identifier = 'your_study_id'
    -- Additional filters...
```

### 2. Find Available Studies

```sql
-- Discover available studies
SELECT
    cancer_study_identifier,
    name,
    description,
    type_of_cancer_id
FROM cancer_study
ORDER BY cancer_study_identifier;
```

### 3. Study Information

```sql
-- Get detailed study information
SELECT
    cs.cancer_study_identifier,
    cs.name as study_name,
    cs.description,
    cs.sample_count,
    COUNT(DISTINCT p.internal_id) as patient_count
FROM cancer_study cs
LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id
WHERE
    cs.cancer_study_identifier = 'your_study_id'
GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count;
```

### 4. Find Studies by Available Data Types

Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter.

**Canonical query — studies with ALL requested data types for a cancer type:**

```sql
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'          -- ← OncoTree code from search_oncotree
  AND mutation_sample_count > 0           -- ← one condition per required data type
  AND cna_sample_count > 0
ORDER BY sample_count DESC;
```

**Data type → column** (a count of 0 means the study doesn't have that data):

| Data type in the user's question | Column (portal "Data type" filter label) |
|---|---|
| samples in the study | `sample_count` |
| mutation / mutations | `mutation_sample_count` ("Mutations") |
| copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") |
| structural variant / fusion | `structural_variant_sample_count` |
| RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" |
| specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `<study>_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq |
| specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `<study>_mrna` list |
| microRNA | `mirna_sample_count` ("miRNA") |
| protein / RPPA | `rppa_sample_count` ("RPPA") |
| mas … (7083 more chars)

▶ list_studies
{
 "search": "endometrial"
}

◀ 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)

▶ search_oncotree
{
 "search_term": "endometrial"
}

◀ 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)

◀ result
{"result":[{"code":"ESS","name":"Endometrial Stromal Sarcoma","score":60,"mainType":"Uterine Sarcoma","tissue":"Uterus","hierarchy":"UTERUS > USARC > ESS"},{"code":"UCEC","name":"Endometrial Carcinoma","score":60,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC"},{"code":"HGESS","name":"High-Grade Endometrial Stromal Sarcoma","score":50,"mainType":"Uterine Sarcoma","tissue":"Uterus","hierarchy":"UTERUS > USARC > ESS > HGESS"},{"code":"LGESS","name":"Low-Grade Endometrial Stromal Sarcoma","score":50,"mainType":"Uterine Sarcoma","tissue":"Uterus","hierarchy":"UTERUS > USARC > ESS > LGESS"},{"code":"UMEC","name":"Uterine Mixed Endometrial Carcinoma","score":50,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMEC"},{"code":"OUTT","name":"Other Uterine Tumor","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > OUTT"},{"code":"UASC","name":"Uterine Adenosquamous Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UASC"},{"code":"UCCC","name":"Uterine Clear Cell Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UCCC"},{"code":"UCS","name":"Uterine Carcinosarcoma/Uterine Malignant Mixed Mullerian Tumor","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UCS"},{"code":"UDDC","name":"Uterine Dedifferentiated Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UDDC"},{"code":"UEC","name":"Uterine Endometrioid Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UEC"},{"code":"UMC","name":"Uterine Mucinous Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMC"},{"code":"UMNC","name":"Uterine Mesonephric Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMNC"},{"code":"UNEC","name":"Uterine Neuroendocrine Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UNEC"},{"code":"UPDC","name":"Poorly Differentiated Carcinoma of the Uterus","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UPDC"},{"code":"USC","name":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > USC"},{"code":"UUC","name":"Uterine Undifferentiated Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UUC"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_name FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND (upper(attribute_name) LIKE '%HISTOLOG%' OR upper(attribute_name) LIKE '%SUBTYPE%' OR upper(attribute_name) LIKE '%MUTATION_COUNT%' OR upper(attribute_name) LIKE '%TMB%' OR upper(attribute_name) LIKE '%MSI%')"
}

◀ result
{"rows":[{"attribute_name":"ICD_O_3_HISTOLOGY"},{"attribute_name":"SUBTYPE"},{"attribute_name":"MSI_SCORE_MANTIS"},{"attribute_name":"MSI_SENSOR_SCORE"},{"attribute_name":"MUTATION_COUNT"},{"attribute_name":"TMB_NONSYNONYMOUS"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_value FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND attribute_name = 'ICD_O_3_HISTOLOGY'"
}

◀ result
{"rows":[{"attribute_value":"8380/3"},{"attribute_value":"8441/3"},{"attribute_value":"8382/3"},{"attribute_value":"8460/3"},{"attribute_value":"8461/3"},{"attribute_value":"8310/3"},{"attribute_value":"8255/3"},{"attribute_value":"8020/3"},{"attribute_value":"8140/3"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_value FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE'"
}

◀ result
{"rows":[{"attribute_value":"UCEC_POLE"},{"attribute_value":"UCEC_CN_LOW"},{"attribute_value":"UCEC_MSI"},{"attribute_value":"UCEC_CN_HIGH"},{}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_value, count() FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND attribute_name = 'CANCER_TYPE_DETAILED' GROUP BY attribute_value"
}

◀ result
{"rows":[{"attribute_value":"Uterine Mixed Endometrial Carcinoma","count()":21},{"attribute_value":"Uterine Endometrioid Carcinoma","count()":399},{"attribute_value":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","count()":109}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH sample_data AS (
    SELECT
        sample_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'MUTATION_COUNT') AS mutation_count,
        maxIf(attribute_value, attribute_name = 'CANCER_TYPE_DETAILED') AS histology,
        maxIf(attribute_value, attribute_name = 'SUBTYPE') AS molecular_subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name IN ('MUTATION_COUNT', 'CANCER_TYPE_DETAILED', 'SUBTYPE')
    GROUP BY sample_unique_id
)
SELECT
    count() AS total_samples,
    countIf(mutation_count IS NOT NULL) AS samples_with_mutation_data,
    countIf(mutation_count > 5000) AS hypermutated,
    round(100.0 * countIf(mutation_count > 5000) / countIf(mutation_count IS NOT NULL), 2) AS pct_hypermutated_of_profiled
FROM sample_data
"
}

◀ result
{"rows":[{"total_samples":530,"samples_with_mutation_data":517,"hypermutated":38,"pct_hypermutated_of_profiled":7.35}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH sample_data AS (
    SELECT
        sample_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'MUTATION_COUNT') AS mutation_count,
        maxIf(attribute_value, attribute_name = 'CANCER_TYPE_DETAILED') AS histology
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name IN ('MUTATION_COUNT', 'CANCER_TYPE_DETAILED')
    GROUP BY sample_unique_id
)
SELECT
    histology,
    count() AS n_samples,
    countIf(mutation_count IS NOT NULL) AS n_profiled,
    countIf(mutation_count > 5000) AS n_hypermutated,
    round(100.0 * countIf(mutation_count > 5000) / countIf(mutation_count IS NOT NULL), 2) AS pct_hypermutated
FROM sample_data
WHERE histology IS NOT NULL AND histology != ''
GROUP BY histology
ORDER BY n_samples DESC
"
}

◀ result
{"rows":[{"histology":"Uterine Endometrioid Carcinoma","n_samples":399,"n_profiled":388,"n_hypermutated":34,"pct_hypermutated":8.76},{"histology":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","n_samples":109,"n_profiled":108,"n_hypermutated":2,"pct_hypermutated":1.85},{"histology":"Uterine Mixed Endometrial Carcinoma","n_samples":21,"n_profiled":21,"n_hypermutated":2,"pct_hypermutated":9.52}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH sample_data AS (
    SELECT
        sample_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'MUTATION_COUNT') AS mutation_count,
        maxIf(attribute_value, attribute_name = 'SUBTYPE') AS molecular_subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name IN ('MUTATION_COUNT', 'SUBTYPE')
    GROUP BY sample_unique_id
)
SELECT
    molecular_subtype,
    count() AS n_samples,
    countIf(mutation_count IS NOT NULL) AS n_profiled,
    countIf(mutation_count > 5000) AS n_hypermutated,
    round(100.0 * countIf(mutation_count > 5000) / countIf(mutation_count IS NOT NULL), 2) AS pct_hypermutated,
    round(avg(mutation_count), 0) AS mean_mutation_count,
    round(quantile(0.5)(mutation_count), 0) AS median_mutation_count
FROM sample_data
WHERE molecular_subtype IS NOT NULL AND molecular_subtype != ''
GROUP BY molecular_subtype
ORDER BY n_samples DESC
"
}

◀ result
{"rows":[{"molecular_subtype":"UCEC_POLE","n_samples":1,"n_profiled":0,"n_hypermutated":0,"pct_hypermutated":null}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count() FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' GROUP BY attribute_value"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
{"rows":[{"count()":22},{"attribute_value":"UCEC_MSI","count()":148},{"attribute_value":"UCEC_POLE","count()":49},{"attribute_value":"UCEC_CN_HIGH","count()":163},{"attribute_value":"UCEC_CN_LOW","count()":147}]}

◀ 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 mut AS (
    SELECT sample_unique_id, patient_unique_id,
           toFloat64OrNull(attribute_value) AS mutation_count
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'MUTATION_COUNT'
),
sub AS (
    SELECT patient_unique_id, attribute_value AS molecular_subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
)
SELECT
    sub.molecular_subtype AS molecular_subtype,
    count() AS n_samples,
    countIf(mut.mutation_count IS NOT NULL) AS n_profiled,
    countIf(mut.mutation_count > 5000) AS n_hypermutated,
    round(100.0 * countIf(mut.mutation_count > 5000) / countIf(mut.mutation_count IS NOT NULL), 2) AS pct_hypermutated,
    round(avg(mut.mutation_count), 0) AS mean_mutation_count,
    round(quantile(0.5)(mut.mutation_count), 0) AS median_mutation_count
FROM mut
LEFT JOIN sub ON mut.patient_unique_id = sub.patient_unique_id
WHERE sub.molecular_subtype IS NOT NULL AND sub.molecular_subtype != ''
GROUP BY molecular_subtype
ORDER BY n_samples DESC
"
}

◀ result
{"rows":[{"molecular_subtype":"UCEC_CN_HIGH","n_samples":163,"n_profiled":163,"n_hypermutated":0,"pct_hypermutated":0,"mean_mutation_count":71,"median_mutation_count":54},{"molecular_subtype":"UCEC_MSI","n_samples":148,"n_profiled":148,"n_hypermutated":6,"pct_hypermutated":4.05,"mean_mutation_count":1020,"median_mutation_count":578},{"molecular_subtype":"UCEC_CN_LOW","n_samples":147,"n_profiled":147,"n_hypermutated":0,"pct_hypermutated":0,"mean_mutation_count":77,"median_mutation_count":50},{"molecular_subtype":"UCEC_POLE","n_samples":49,"n_profiled":49,"n_hypermutated":32,"pct_hypermutated":65.31,"mean_mutation_count":7427,"median_mutation_count":7385}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH mut AS (
    SELECT sample_unique_id, patient_unique_id,
           toFloat64OrNull(attribute_value) AS mutation_count
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'MUTATION_COUNT'
),
hist 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'
),
sub AS (
    SELECT patient_unique_id, attribute_value AS molecular_subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
)
SELECT
    hist.histology AS histology,
    sub.molecular_subtype AS molecular_subtype,
    count() AS n_samples,
    countIf(mut.mutation_count > 5000) AS n_hypermutated
FROM mut
LEFT JOIN hist ON mut.sample_unique_id = hist.sample_unique_id
LEFT JOIN sub ON mut.patient_unique_id = sub.patient_unique_id
WHERE hist.histology IS NOT NULL AND hist.histology != ''
  AND sub.molecular_subtype IS NOT NULL AND sub.molecular_subtype != ''
GROUP BY histology, molecular_subtype
ORDER BY histology, n_samples DESC
"
}

◀ result
{"rows":[{"histology":"Uterine Endometrioid Carcinoma","molecular_subtype":"UCEC_MSI","n_samples":141,"n_hypermutated":5},{"histology":"Uterine Endometrioid Carcinoma","molecular_subtype":"UCEC_CN_LOW","n_samples":140,"n_hypermutated":0},{"histology":"Uterine Endometrioid Carcinoma","molecular_subtype":"UCEC_CN_HIGH","n_samples":55,"n_hypermutated":0},{"histology":"Uterine Endometrioid Carcinoma","molecular_subtype":"UCEC_POLE","n_samples":45,"n_hypermutated":29},{"histology":"Uterine Mixed Endometrial Carcinoma","molecular_subtype":"UCEC_CN_HIGH","n_samples":11,"n_hypermutated":0},{"histology":"Uterine Mixed Endometrial Carcinoma","molecular_subtype":"UCEC_MSI","n_samples":5,"n_hypermutated":1},{"histology":"Uterine Mixed Endometrial Carcinoma","molecular_subtype":"UCEC_CN_LOW","n_samples":3,"n_hypermutated":0},{"histology":"Uterine Mixed Endometrial Carcinoma","molecular_subtype":"UCEC_POLE","n_samples":2,"n_hypermutated":1},{"histology":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","molecular_subtype":"UCEC_CN_HIGH","n_samples":97,"n_hypermutated":0},{"histology":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","molecular_subtype":"UCEC_CN_LOW","n_samples":4,"n_hypermutated":0},{"histology":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","molecular_subtype":"UCEC_MSI","n_samples":2,"n_hypermutated":0},{"histology":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","molecular_subtype":"UCEC_POLE","n_samples":2,"n_hypermutated":2}]}

▶ 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"]}}]}}

▶ get_studyviewfilter_options
{
 "studyId": "ucec_tcga_pan_can_atlas_2018",
 "attributeIds": [
  "SUBTYPE",
  "CANCER_TYPE_DETAILED",
  "MUTATION_COUNT"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"ucec_tcga_pan_can_atlas_2018","attributes":[{"attributeId":"CANCER_TYPE_DETAILED","displayName":"Cancer Type Detailed","description":"Cancer Type Detailed","datatype":"STRING","values":["Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","Uterine Mixed Endometrial Carcinoma","Uterine Endometrioid Carcinoma"]},{"attributeId":"MUTATION_COUNT","displayName":"Mutation Count","description":"Mutation Count","datatype":"NUMBER","continuous":true,"bins":[{"end":46.5,"count":129},{"start":46.5,"end":75,"count":130},{"start":75,"end":564,"count":129},{"start":564,"count":129}]},{"attributeId":"SUBTYPE","displayName":"Subtype","description":"Subtype","datatype":"STRING","values":["UCEC_MSI","UCEC_CN_HIGH","UCEC_POLE","NA","UCEC_CN_LOW"]}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "attributeId": "MUTATION_COUNT",
    "values": [
     {
      "start": 5000
     }
    ]
   }
  ]
 }
}

◀ 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%3A5000%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%3A5000%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_HIGH",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_CN_HIGH"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "CN_LOW",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_CN_LOW"
       }
      ]
     }
    ]
   }
  }
 ]
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5ba74e497aad53593c255","url":"https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5ba74e497aad53593c255","data":{"description":"Group comparison (4 custom groups)","studies":["ucec_tcga_pan_can_atlas_2018"],"totalGroups":4,"groups":[{"name":"POLE","sampleCount":49},{"name":"MSI","sampleCount":148},{"name":"CN_HIGH","sampleCount":163},{"name":"CN_LOW","sampleCount":147}],"studyViewUrl":"https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"POLE","url":"https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22UCEC_POLE%22%7D%5D%7D%5D%7D"},{"groupName":"MSI","url":"https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22UCEC_MSI%22%7D%5D%7D%5D%7D"},{"groupName":"CN_HIGH","url":"https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22UCEC_CN_HIGH%22%7D%5D%7D%5D%7D"},{"groupName":"CN_LOW","url":"https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22UCEC_CN_LOW%22%7D%5D%7D%5D%7D"}],"pageDescription":"Clinical attribute distributions compared across POLE (49) vs MSI (148) vs CN_HIGH (163) vs CN_LOW (147)."}}

═ answer (success)
## Results — TCGA UCEC PanCancer Atlas (`ucec_tcga_pan_can_atlas_2018`, n=529)

**Overall hypermutation rate**
- 38 / 517 mutation-profiled samples have >5,000 mutations → **7.35%** hypermutated

**By histological subtype** (`CANCER_TYPE_DETAILED`)

| Histology | N samples | N profiled | Hypermutated | % Hypermutated |
|---|---|---|---|---|
| Endometrioid Carcinoma | 399 | 388 | 34 | **8.76%** |
| Serous Carcinoma | 109 | 108 | 2 | **1.85%** |
| Mixed Endometrial Carcinoma | 21 | 21 | 2 | **9.52%** |

**By TCGA molecular subtype** (`SUBTYPE`) — the stronger correlate of hypermutation

| Molecular subtype | N | Hypermutated | % Hypermutated | Mean mutations | Median mutations |
|---|---|---|---|---|---|
| POLE (ultramutated) | 49 | 32 | **65.31%** | 7,427 | 7,385 |
| MSI (hypermutated) | 148 | 6 | **4.05%** | 1,020 | 578 |
| CN-high (serous-like) | 163 | 0 | **0%** | 71 | 54 |
| CN-low (endometrioid-like) | 147 | 0 | **0%** | 77 | 50 |

**Cross-tabulation** — nearly all hypermutated samples are histologically endometrioid, but this reflects that POLE mutations occur almost exclusively in endometrioid tumors (45/49 POLE cases are endometrioid; 29 of the 34 hypermutated endometrioid samples are POLE-subtype). Serous carcinoma is almost entirely CN-high (97/109) and essentially never hypermutated (only 2 of 109 serous samples exceed 5,000 mutations, both classified as POLE).

**Interpretation caveat:** Histological subtype alone is a weak predictor of hypermutation (endometrioid 8.76% vs. serous 1.85%). The correlation is much stronger with the molecular (TCGA) subtype — hypermutation is essentially a POLE-subtype phenomenon (65% of POLE tumors are hypermutated vs. ≤4% in every other molecular subtype), and POLE mutations happen to be enriched in — but not exclusive to — endometrioid histology. This is a descriptive frequency comparison; I have not run a statistical test. If you want to formally test whether hypermutation rate differs significantly by histology or molecular subtype, that's a chi-squared test (4+ groups, categorical) — happy to hand off the contingency table for that.

### Navigate

[View Hypermutated Samples (>5,000 mutations) in Study View](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%3A5000%7D%5D%7D%5D%7D)

[Compare Molecular Subtypes (POLE vs MSI vs CN-High vs CN-Low) — Clinical Comparison](https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5ba74e497aad53593c255)

Groups: POLE (n=49), MSI (n=148), CN-High (n=163), CN-Low (n=147).