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"
}

▶ read_guide
{
 "uri": "cbioportal://clinical-data-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 … (35166 more chars)

▶ 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
{"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"
}

◀ 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 attr_id, description FROM clinical_attribute_meta WHERE cancer_study_id = (SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018') AND (upper(attr_id) LIKE '%SUBTYPE%' OR upper(description) LIKE '%SUBTYPE%' OR upper(attr_id) LIKE '%COPY%')"
}

◀ result
{"rows":[{"attr_id":"SUBTYPE","description":"Subtype"}]}

▶ 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_group AS (
    SELECT DISTINCT cd.sample_unique_id, cd.patient_unique_id,
        if(s.subtype = 'UCEC_CN_HIGH', 'CN-High', 'Other') AS grp
    FROM clinical_data_derived cd
    JOIN subtype s USING (patient_unique_id)
    WHERE cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
profiled AS (
    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'
),
altered AS (
    SELECT sg.grp, ged.hugo_gene_symbol, COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN sample_group sg 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 sg.grp, ged.hugo_gene_symbol
),
grp_totals AS (
    SELECT sg.grp, COUNT(DISTINCT sg.sample_unique_id) AS profiled_samples
    FROM sample_group sg
    JOIN profiled p USING (sample_unique_id)
    GROUP BY sg.grp
)
SELECT a.grp, a.hugo_gene_symbol, a.altered_samples, gt.profiled_samples,
       ROUND(a.altered_samples * 100.0 / gt.profiled_samples, 1) AS frequency_pct
FROM altered a
JOIN grp_totals gt USING (grp)
ORDER BY a.grp, a.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":"MUC4","altered_samples":11,"profiled_samples":163,"frequency_pct":6.7},{"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":"DNAH8","altered_samples":10,"profiled_samples":163,"frequency_pct":6.1},{"grp":"CN-High","hugo_gene_symbol":"SYNE1","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":"FAT4","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":"ZFHX3","altered_samples":9,"profiled_samples":163,"frequency_pct":5.5},{"grp":"CN-High","hugo_gene_symbol":"KRAS","altered_samples":8,"profiled_samples":163,"frequency_pct":4.9},{"grp":"CN-High","hugo_gene_symbol":"ARID1A","altered_s … (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_group AS (
    SELECT DISTINCT cd.sample_unique_id, cd.patient_unique_id,
        if(s.subtype = 'UCEC_CN_HIGH', 'CN-High', 'Other') AS grp
    FROM clinical_data_derived cd
    JOIN subtype s USING (patient_unique_id)
    WHERE cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
profiled AS (
    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'
),
altered AS (
    SELECT sg.grp, ged.hugo_gene_symbol, COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN sample_group sg 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 sg.grp, ged.hugo_gene_symbol
),
grp_totals AS (
    SELECT sg.grp, COUNT(DISTINCT sg.sample_unique_id) AS profiled_samples
    FROM sample_group sg
    JOIN profiled p USING (sample_unique_id)
    GROUP BY sg.grp
)
SELECT a.grp, a.hugo_gene_symbol, a.altered_samples, gt.profiled_samples,
       ROUND(a.altered_samples * 100.0 / gt.profiled_samples, 1) AS frequency_pct
FROM altered a
JOIN grp_totals gt USING (grp)
WHERE a.grp = 'Other'
ORDER BY a.altered_samples DESC
LIMIT 12
"
}

◀ 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":"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}]}

▶ 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"
 ]
}

◀ 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_results_view
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "TP53",
  "PIK3CA",
  "PPP2R1A",
  "FBXW7",
  "PTEN",
  "CHD4",
  "PIK3R1",
  "ARID1A",
  "KMT2D",
  "CTNNB1"
 ],
 "tab": "oncoprint",
 "oncoprintClinicalTracks": [
  "SUBTYPE"
 ]
}

◀ result
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▶ navigate_to_group_comparison
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018"
 ],
 "groups": [
  {
   "name": "CN-High",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_CN_HIGH"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "Other subtypes",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_MSI"
       },
       {
        "value": "UCEC_POLE"
       },
       {
        "value": "UCEC_CN_LOW"
       }
      ]
     }
    ]
   }
  }
 ],
 "tab": "alterations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/alterations?comparisonId=6ab4a04de497aad53593bef1","url":"https://www.cbioportal.org/comparison/alterations?comparisonId=6ab4a04de497aad53593bef1","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","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","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_POLE%22%7D%2C%7B%22value%22%3A%22UCEC_CN_LOW%22%7D%5D%7D%5D%7D"}],"pageDescription":"Comparison of genomic alteration rates across CN-High (163) vs Other subtypes (344)."}}

═ answer (success)
## Mutation frequency by TCGA molecular subtype — Endometrial Cancer (TCGA, PanCancer Atlas)

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), patients are classified into 4 TCGA molecular subtypes (`SUBTYPE` clinical attribute): **Copy-Number High** (163 patients, serous-like), MSI (148), Copy-Number Low (147), and POLE (49; 22 unclassified). Sample-level mutation frequencies below use gene-specific profiled denominators (163 samples on-panel for CN-High; 354 for the pooled "Other" group).

**Top mutated genes — CN-High (163 profiled samples)**

| Gene | Altered | Frequency |
|---|---|---|
| TP53 | 141 | 86.5% |
| PIK3CA | 54 | 33.1% |
| PPP2R1A | 50 | 30.7% |
| TTN | 32 | 19.6% |
| FBXW7 | 30 | 18.4% |
| PTEN | 28 | 17.2% |
| CHD4 | 25 | 15.3% |
| PIK3R1 | 23 | 14.1% |

**Top mutated genes — Other subtypes pooled (MSI + POLE + CN-Low; 354 profiled samples)**

| Gene | Altered | Frequency |
|---|---|---|
| PTEN | 309 | 87.3% |
| ARID1A | 219 | 61.9% |
| PIK3CA | 205 | 57.9% |
| TTN | 174 | 49.2% |
| PIK3R1 | 136 | 38.4% |
| KMT2D | 132 | 37.3% |
| CTNNB1 | 128 | 36.2% |
| CTCF | 126 | 35.6% |

**Key contrast:** TP53 dominates CN-High tumors (86.5%, vs. much lower in the pooled comparator — this is the defining "serous-like" signature), whereas PTEN and ARID1A dominate the other subtypes and are comparatively rare in CN-High (17.2% and not in its top list). PIK3CA is common across both groups. Note the "Other subtypes" bucket pools three biologically distinct groups (POLE ultra-hypermutated, MSI hypermutated, CN-low), so genes like ARID1A and TTN are partly inflated by the hypermutator subtypes rather than being uniformly elevated — a per-subtype breakdown would refine this further if useful. These are descriptive frequency comparisons; no significance testing (Fisher's exact / enrichment p-values) has been run.

**Explore further:**
- [Group Comparison — Alterations Tab (CN-High vs Other subtypes)](https://www.cbioportal.org/comparison/alterations?comparisonId=6ab4a04de497aad53593bef1) — 163 vs 344 samples, statistical enrichment testing available in-browser
- [OncoPrint of top genes across all subtypes](https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=TP53%0APIK3CA%0APPP2R1A%0AFBXW7%0APTEN%0ACHD4%0APIK3R1%0AARID1A%0AKMT2D%0ACTNNB1&case_set_id=ucec_tcga_pan_can_atlas_2018_all&clinicallist=SUBTYPE) — with SUBTYPE annotation track