Q (sonnet): What are the most frequently mutated genes in copy-number high subtype endometrial cancers compared to other subtypes?

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

◀ 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)

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

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

◀ 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
{"result":[{"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"}]}

▶ 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": "SELECT DISTINCT attribute_name FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND (upper(attribute_name) LIKE '%SUBTYPE%' OR upper(attribute_name) LIKE '%MSI%' OR upper(attribute_name) LIKE '%INTEGRAT%' OR upper(attribute_name) LIKE '%COPY%' OR upper(attribute_name) LIKE '%GENOMIC%')"
}

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

▶ clickhouse_run_select_query
{
 "query": "
WITH 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'
),
sample_subtype AS (
    SELECT cd.sample_unique_id, s.subtype
    FROM clinical_data_derived cd
    JOIN subtype s USING (patient_unique_id)
    WHERE cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
    GROUP BY cd.sample_unique_id, s.subtype
),
altered AS (
    SELECT
        if(ss.subtype = 'UCEC_CN_HIGH', 'CN_HIGH', 'OTHER') AS grp,
        ged.hugo_gene_symbol,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN sample_subtype ss USING (sample_unique_id)
    WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
      AND ged.off_panel = 0
    GROUP BY grp, ged.hugo_gene_symbol
),
profiled AS (
    SELECT
        if(ss.subtype = 'UCEC_CN_HIGH', 'CN_HIGH', 'OTHER') AS grp,
        COUNT(DISTINCT sample_unique_id) AS profiled_samples
    FROM sample_subtype ss
    WHERE ss.sample_unique_id IN (
        SELECT sample_unique_id FROM sample_to_gene_panel_derived
        WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
          AND alteration_type = 'MUTATION_EXTENDED'
    )
    GROUP BY grp
)
SELECT a.grp, a.hugo_gene_symbol, a.altered_samples, p.profiled_samples,
       round(a.altered_samples * 100.0 / p.profiled_samples, 1) AS frequency_pct
FROM altered a
JOIN profiled p USING (grp)
ORDER BY grp, altered_samples DESC
LIMIT 40
"
}

◀ result
{"rows":[{"grp":"CN_HIGH","hugo_gene_symbol":"TP53","altered_samples":141,"profiled_samples":163,"frequency_pct":86.5},{"grp":"CN_HIGH","hugo_gene_symbol":"PIK3CA","altered_samples":54,"profiled_samples":163,"frequency_pct":33.1},{"grp":"CN_HIGH","hugo_gene_symbol":"PPP2R1A","altered_samples":50,"profiled_samples":163,"frequency_pct":30.7},{"grp":"CN_HIGH","hugo_gene_symbol":"TTN","altered_samples":32,"profiled_samples":163,"frequency_pct":19.6},{"grp":"CN_HIGH","hugo_gene_symbol":"FBXW7","altered_samples":30,"profiled_samples":163,"frequency_pct":18.4},{"grp":"CN_HIGH","hugo_gene_symbol":"PTEN","altered_samples":28,"profiled_samples":163,"frequency_pct":17.2},{"grp":"CN_HIGH","hugo_gene_symbol":"CHD4","altered_samples":25,"profiled_samples":163,"frequency_pct":15.3},{"grp":"CN_HIGH","hugo_gene_symbol":"PIK3R1","altered_samples":23,"profiled_samples":163,"frequency_pct":14.1},{"grp":"CN_HIGH","hugo_gene_symbol":"CSMD3","altered_samples":20,"profiled_samples":163,"frequency_pct":12.3},{"grp":"CN_HIGH","hugo_gene_symbol":"ARHGAP35","altered_samples":18,"profiled_samples":163,"frequency_pct":11},{"grp":"CN_HIGH","hugo_gene_symbol":"MUC16","altered_samples":17,"profiled_samples":163,"frequency_pct":10.4},{"grp":"CN_HIGH","hugo_gene_symbol":"RYR2","altered_samples":13,"profiled_samples":163,"frequency_pct":8},{"grp":"CN_HIGH","hugo_gene_symbol":"SPOP","altered_samples":13,"profiled_samples":163,"frequency_pct":8},{"grp":"CN_HIGH","hugo_gene_symbol":"FOXA2","altered_samples":12,"profiled_samples":163,"frequency_pct":7.4},{"grp":"CN_HIGH","hugo_gene_symbol":"DMD","altered_samples":12,"profiled_samples":163,"frequency_pct":7.4},{"grp":"CN_HIGH","hugo_gene_symbol":"TAF1","altered_samples":12,"profiled_samples":163,"frequency_pct":7.4},{"grp":"CN_HIGH","hugo_gene_symbol":"KMT2D","altered_samples":11,"profiled_samples":163,"frequency_pct":6.7},{"grp":"CN_HIGH","hugo_gene_symbol":"ZFHX4","altered_samples":11,"profiled_samples":163,"frequency_pct":6.7},{"grp":"CN_HIGH","hugo_gene_symbol":"MUC4","altered_samples":11,"profiled_samples":163,"frequency_pct":6.7},{"grp":"CN_HIGH","hugo_gene_symbol":"SYNE1","altered_samples":10,"profiled_samples":163,"frequency_pct":6.1},{"grp":"CN_HIGH","hugo_gene_symbol":"DNAH8","altered_samples":10,"profiled_samples":163,"frequency_pct":6.1},{"grp":"CN_HIGH","hugo_gene_symbol":"FLG","altered_samples":10,"profiled_samples":163,"frequency_pct":6.1},{"grp":"CN_HIGH","hugo_gene_symbol":"AHNAK2","altered_samples":9,"profiled_samples":163,"frequency_pct":5.5},{"grp":"CN_HIGH","hugo_gene_symbol":"FAT1","altered_samples":9,"profiled_samples":163,"frequency_pct":5.5},{"grp":"CN_HIGH","hugo_gene_symbol":"FAT4","altered_samples":9,"profiled_samples":163,"frequency_pct":5.5},{"grp":"CN_HIGH","hugo_gene_symbol":"ZFHX3","altered_samples":9,"profiled_samples":163,"frequency_pct":5.5},{"grp":"CN_HIGH","hugo_gene_symbol":"ARID1A","altered_samples":8,"profiled_samples":163,"frequency_pct":4.9},{"grp":"CN_HIGH","hugo_gene_symbol":"ABCA13","altered … (1351 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH 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'
),
sample_subtype AS (
    SELECT cd.sample_unique_id, s.subtype
    FROM clinical_data_derived cd
    JOIN subtype s USING (patient_unique_id)
    WHERE cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
    GROUP BY cd.sample_unique_id, s.subtype
),
altered AS (
    SELECT
        if(ss.subtype = 'UCEC_CN_HIGH', 'CN_HIGH', 'OTHER') AS grp,
        ged.hugo_gene_symbol,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN sample_subtype ss USING (sample_unique_id)
    WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
      AND ged.off_panel = 0
    GROUP BY grp, ged.hugo_gene_symbol
),
profiled AS (
    SELECT
        if(ss.subtype = 'UCEC_CN_HIGH', 'CN_HIGH', 'OTHER') AS grp,
        COUNT(DISTINCT sample_unique_id) AS profiled_samples
    FROM sample_subtype ss
    WHERE ss.sample_unique_id IN (
        SELECT sample_unique_id FROM sample_to_gene_panel_derived
        WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
          AND alteration_type = 'MUTATION_EXTENDED'
    )
    GROUP BY grp
)
SELECT a.grp, a.hugo_gene_symbol, a.altered_samples, p.profiled_samples,
       round(a.altered_samples * 100.0 / p.profiled_samples, 1) AS frequency_pct
FROM altered a
JOIN profiled p USING (grp)
WHERE a.grp = 'OTHER'
ORDER BY altered_samples DESC
LIMIT 15
"
}

▶ clickhouse_run_select_query
{
 "query": "
WITH 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'
),
sample_subtype AS (
    SELECT cd.sample_unique_id, s.subtype
    FROM clinical_data_derived cd
    JOIN subtype s USING (patient_unique_id)
    WHERE cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
    GROUP BY cd.sample_unique_id, s.subtype
),
altered AS (
    SELECT
        ss.subtype AS grp,
        ged.hugo_gene_symbol,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN sample_subtype ss USING (sample_unique_id)
    WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
      AND ged.off_panel = 0
      AND ged.hugo_gene_symbol IN ('TP53','PIK3CA','PPP2R1A','PTEN','ARID1A','KRAS','CTNNB1','FBXW7','PIK3R1','KMT2D')
    GROUP BY grp, ged.hugo_gene_symbol
),
profiled AS (
    SELECT
        ss.subtype AS grp,
        COUNT(DISTINCT sample_unique_id) AS profiled_samples
    FROM sample_subtype ss
    WHERE ss.sample_unique_id IN (
        SELECT sample_unique_id FROM sample_to_gene_panel_derived
        WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
          AND alteration_type = 'MUTATION_EXTENDED'
    )
    GROUP BY grp
)
SELECT p.grp AS subtype, a.hugo_gene_symbol, a.altered_samples, p.profiled_samples,
       round(a.altered_samples * 100.0 / p.profiled_samples, 1) AS frequency_pct
FROM profiled p
LEFT JOIN altered a ON a.grp = p.grp AND a.hugo_gene_symbol IN ('TP53','PIK3CA','PPP2R1A','PTEN','ARID1A','KRAS','CTNNB1','FBXW7','PIK3R1','KMT2D')
ORDER BY subtype, frequency_pct DESC
"
}

◀ result
{"rows":[{"grp":"OTHER","hugo_gene_symbol":"PTEN","altered_samples":309,"profiled_samples":354,"frequency_pct":87.3},{"grp":"OTHER","hugo_gene_symbol":"ARID1A","altered_samples":219,"profiled_samples":354,"frequency_pct":61.9},{"grp":"OTHER","hugo_gene_symbol":"PIK3CA","altered_samples":205,"profiled_samples":354,"frequency_pct":57.9},{"grp":"OTHER","hugo_gene_symbol":"TTN","altered_samples":174,"profiled_samples":354,"frequency_pct":49.2},{"grp":"OTHER","hugo_gene_symbol":"PIK3R1","altered_samples":136,"profiled_samples":354,"frequency_pct":38.4},{"grp":"OTHER","hugo_gene_symbol":"KMT2D","altered_samples":132,"profiled_samples":354,"frequency_pct":37.3},{"grp":"OTHER","hugo_gene_symbol":"CTNNB1","altered_samples":128,"profiled_samples":354,"frequency_pct":36.2},{"grp":"OTHER","hugo_gene_symbol":"CTCF","altered_samples":126,"profiled_samples":354,"frequency_pct":35.6},{"grp":"OTHER","hugo_gene_symbol":"MUC16","altered_samples":125,"profiled_samples":354,"frequency_pct":35.3},{"grp":"OTHER","hugo_gene_symbol":"ZFHX3","altered_samples":114,"profiled_samples":354,"frequency_pct":32.2},{"grp":"OTHER","hugo_gene_symbol":"RYR2","altered_samples":112,"profiled_samples":354,"frequency_pct":31.6},{"grp":"OTHER","hugo_gene_symbol":"KMT2B","altered_samples":112,"profiled_samples":354,"frequency_pct":31.6},{"grp":"OTHER","hugo_gene_symbol":"OBSCN","altered_samples":112,"profiled_samples":354,"frequency_pct":31.6},{"grp":"OTHER","hugo_gene_symbol":"MUC5B","altered_samples":110,"profiled_samples":354,"frequency_pct":31.1},{"grp":"OTHER","hugo_gene_symbol":"SYNE1","altered_samples":104,"profiled_samples":354,"frequency_pct":29.4}]}

◀ result
{"rows":[{"hugo_gene_symbol":"PTEN","altered_samples":6,"profiled_samples":10,"frequency_pct":60},{"hugo_gene_symbol":"PIK3CA","altered_samples":5,"profiled_samples":10,"frequency_pct":50},{"hugo_gene_symbol":"ARID1A","altered_samples":3,"profiled_samples":10,"frequency_pct":30},{"hugo_gene_symbol":"KRAS","altered_samples":3,"profiled_samples":10,"frequency_pct":30},{"hugo_gene_symbol":"PPP2R1A","altered_samples":3,"profiled_samples":10,"frequency_pct":30},{"hugo_gene_symbol":"FBXW7","altered_samples":2,"profiled_samples":10,"frequency_pct":20},{"hugo_gene_symbol":"CTNNB1","altered_samples":2,"profiled_samples":10,"frequency_pct":20},{"hugo_gene_symbol":"TP53","altered_samples":2,"profiled_samples":10,"frequency_pct":20},{"hugo_gene_symbol":"PIK3R1","altered_samples":2,"profiled_samples":10,"frequency_pct":20},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"TP53","altered_samples":141,"profiled_samples":163,"frequency_pct":86.5},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"PIK3CA","altered_samples":54,"profiled_samples":163,"frequency_pct":33.1},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"PPP2R1A","altered_samples":50,"profiled_samples":163,"frequency_pct":30.7},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"FBXW7","altered_samples":30,"profiled_samples":163,"frequency_pct":18.4},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"PTEN","altered_samples":28,"profiled_samples":163,"frequency_pct":17.2},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"PIK3R1","altered_samples":23,"profiled_samples":163,"frequency_pct":14.1},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"KMT2D","altered_samples":11,"profiled_samples":163,"frequency_pct":6.7},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"KRAS","altered_samples":8,"profiled_samples":163,"frequency_pct":4.9},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"ARID1A","altered_samples":8,"profiled_samples":163,"frequency_pct":4.9},{"subtype":"UCEC_CN_HIGH","hugo_gene_symbol":"CTNNB1","altered_samples":5,"profiled_samples":163,"frequency_pct":3.1},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"PTEN","altered_samples":118,"profiled_samples":147,"frequency_pct":80.3},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"PIK3CA","altered_samples":74,"profiled_samples":147,"frequency_pct":50.3},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"ARID1A","altered_samples":72,"profiled_samples":147,"frequency_pct":49},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"CTNNB1","altered_samples":69,"profiled_samples":147,"frequency_pct":46.9},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"PIK3R1","altered_samples":50,"profiled_samples":147,"frequency_pct":34},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"KRAS","altered_samples":24,"profiled_samples":147,"frequency_pct":16.3},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"KMT2D","altered_samples":12,"profiled_samples":147,"frequency_pct":8.2},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"FBXW7","altered_samples":9,"profiled_samples":147,"frequency_pct":6.1},{"subtype":"UCEC_CN_LOW","hugo_gene_symbol":"TP53", … (2489 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018"
 ]
}

◀ result
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▶ get_studyviewfilter_options
{
 "studyId": "ucec_tcga_pan_can_atlas_2018",
 "attributeIds": [
  "SUBTYPE"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"ucec_tcga_pan_can_atlas_2018","attributes":[{"attributeId":"SUBTYPE","displayName":"Subtype","description":"Subtype","datatype":"STRING","values":["UCEC_MSI","UCEC_CN_HIGH","UCEC_POLE","NA","UCEC_CN_LOW"]}]}}

▶ navigate_to_group_comparison
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 "groups": [
  {
   "name": "CN-high",
   "studyViewFilter": {
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  {
   "name": "Other subtypes (MSI, CN-low, POLE)",
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       {
        "value": "UCEC_POLE"
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   }
  }
 ],
 "tab": "alterations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/alterations?comparisonId=6ab7f55ce497aad53593c5a9","url":"https://www.cbioportal.org/comparison/alterations?comparisonId=6ab7f55ce497aad53593c5a9","data":{"description":"Group comparison (2 custom groups)","studies":["ucec_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"CN-high","sampleCount":163},{"name":"Other subtypes (MSI, CN-low, POLE)","sampleCount":344}],"studyViewUrl":"https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018","groupUrls":[{"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":"Other subtypes (MSI, CN-low, 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_MSI%22%7D%2C%7B%22value%22%3A%22UCEC_CN_LOW%22%7D%2C%7B%22value%22%3A%22UCEC_POLE%22%7D%5D%7D%5D%7D"}],"pageDescription":"Comparison of genomic alteration rates across CN-high (163) vs Other subtypes (MSI, CN-low, POLE) (344)."}}

═ answer (success)
## Endometrial Cancer: CN-high vs. Other Molecular Subtypes

**Cohort:** [Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018) — 529 patients classified by TCGA's four molecular subtypes: CN-high (163), MSI (148), CN-low (147), POLE (49); 22 unclassified. Frequencies below are patient-level (altered / profiled for mutations × 100).

### CN-high subtype — top mutated genes (n=163 profiled)
| Gene | Altered | Frequency |
|---|---|---|
| **TP53** | 141 | **86.5%** |
| PIK3CA | 54 | 33.1% |
| PPP2R1A | 50 | 30.7% |
| FBXW7 | 30 | 18.4% |
| PTEN | 28 | 17.2% |
| PIK3R1 | 23 | 14.1% |
| CHD4 | 25 | 15.3% |
| KMT2D | 11 | 6.7% |
| ARID1A | 8 | 4.9% |
| CTNNB1 | 5 | 3.1% |

CN-high is dominated by **TP53** mutation — this is its defining feature, consistent with the serous-like/high-grade phenotype.

### Other subtypes for comparison (same genes, patient-level %)
| Gene | MSI (n=148) | CN-low (n=147) | POLE (n=49) |
|---|---|---|---|
| TP53 | 16.2% | 6.1% | 34.7% |
| PTEN | 93.2% | 80.3% | 95.9% |
| ARID1A | 72.3% | 49.0% | 75.5% |
| PIK3CA | 57.4% | 50.3% | 83.7% |
| CTNNB1 | 20.3% | 46.9% | 55.1% |
| KMT2D | 55.4% | 8.2% | 77.6% |
| PPP2R1A | 12.2% | 2.0% | 28.6% |
| PIK3R1 | 37.2% | 34.0% | 59.2% |
| FBXW7 | 15.5% | 6.1% | 65.3% |
| KRAS | 33.8% | 16.3% | 28.6% |

### Key contrast
- **CN-high** is essentially a **TP53-driven** subtype (86.5%, roughly double the next-highest subtype's rate), with comparatively low PTEN (17.2%) and ARID1A (4.9%) — the inverse of the other subtypes.
- **MSI and CN-low** are **PTEN/ARID1A/CTNNB1-driven** (endometrioid-type biology), with TP53 mutated in only 6–16% of cases.
- **POLE** (ultra-hypermutated) has high rates of *everything* due to its extreme mutation burden, including a secondarily elevated TP53 rate (34.7%) — this reflects overall mutational load, not subtype-specific TP53 dependence the way CN-high shows.
- Note: FBXW7, KMT2D, and PPP2R1A frequencies are inflated in POLE due to hypermutation and shouldn't be read as subtype-defining drivers there.

These are descriptive frequencies; no enrichment test (Fisher's exact) has been run to claim statistical significance of the CN-high vs. other differences — the Comparison tab below can do that directly.

[View CN-high vs. Other Subtypes — Genomic Alterations Comparison](https://www.cbioportal.org/comparison/alterations?comparisonId=6ab7f55ce497aad53593c5a9)

This opens cBioPortal's Group Comparison (Genomic Alterations tab) with CN-high (163 samples) vs. pooled MSI+CN-low+POLE (344 samples), where you can run enrichment statistics per gene directly.