Q (haiku): 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 … (35166 more chars)

▶ list_studies
{
 "search": "endometrial",
 "limit": 20,
 "verbose": true
}

◀ result
{"result":[{"cancer_study_identifier":"ucec_ancestry_cds_msk_2023","name":"Endometrial Cancer (MSK, Cancer Discovery 2023)","description":"Targeted sequencing of 1882 endometrial cancers and their matched normals via MSK-IMPACT.","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)","description":"TCGA Uterine Corpus Endometrial Carcinoma. Source data from <A HREF=\"http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/UCEC/20160128/\">GDAC Firehose</A>. Previously known as TCGA Provisional.","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)","description":"TCGA Endometrial Carcinoma. Source data from <A HREF=\"https://gdc.cancer.gov\">NCI GDC</A> and generated in Aug 2025 using <A HREF=\"https://cda.readthedocs.io/en/latest/\">Cancer Data Aggregator</A>.","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)","description":"Uterine Corpus Endometrial Carcinoma TCGA PanCancer data. The original data is <a href=\"https://gdc.cancer.gov/about-data/publications/pancanatlas\">here</a>. The publications are <a href=\"https://www.cell.com/pb-assets/consortium/pancanceratlas/pancani3/index.html\">here</a>.","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)","description":"Whole exome sequencing of 373 endometrial carcinoma tumors with matched normals. TCGA Uterine Corpus Endometrial Carcinoma project; raw data at the <A HREF=\"https://tcga-data.nci.nih.gov/docs/publications/ucec_2013/\">NCI</A>.","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)","description":"MSK-IMPACT sequencing of 197 advanced-staged endometrial cancer tumor samples with matched normals, from 189 patients treated at MSK.","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)","description":"Clinical and genetic features of microsatellite instability-high endometrial cancers harboring germline or somatic mutations in MMR genes or MLH1 promoter hypermethylation.","type_of_can … (2671 more chars)

▶ get_study_guide
{
 "study_id": "ucec_tcga_pan_can_atlas_2018"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

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

**Study ID:** `ucec_tcga_pan_can_atlas_2018`

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

## Study-Specific Attributes

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

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

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

`AJCC_PATHOLOGIC_TUMOR_STAGE` is blank for all samples.

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

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

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


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

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_samples AS (
    SELECT 
        sample_unique_id,
        attribute_value AS subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
      AND attribute_value != ''
),
mutations_by_subtype AS (
    SELECT 
        ged.hugo_gene_symbol,
        ss.subtype,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples,
        COUNT(*) AS total_mutation_events
    FROM genomic_event_derived ged
    JOIN subtype_samples 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 ged.hugo_gene_symbol, ss.subtype
),
profiled_by_subtype AS (
    SELECT 
        ss.subtype,
        COUNT(DISTINCT ss.sample_unique_id) AS profiled_samples
    FROM subtype_samples ss
    GROUP BY ss.subtype
)
SELECT 
    m.hugo_gene_symbol,
    m.subtype,
    m.altered_samples,
    p.profiled_samples,
    ROUND(m.altered_samples * 100.0 / p.profiled_samples, 1) AS frequency_pct,
    m.total_mutation_events
FROM mutations_by_subtype m
JOIN profiled_by_subtype p USING (subtype)
WHERE m.subtype = 'UCEC_CN_HIGH'
ORDER BY m.altered_samples DESC
LIMIT 30
",
 "max_rows": 100
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_samples AS (
    SELECT 
        sample_unique_id,
        attribute_value AS subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
      AND attribute_value != ''
),
mutations_by_subtype AS (
    SELECT 
        ged.hugo_gene_symbol,
        ss.subtype,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples,
        COUNT(*) AS total_mutation_events
    FROM genomic_event_derived ged
    JOIN subtype_samples 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 ged.hugo_gene_symbol, ss.subtype
),
profiled_by_subtype AS (
    SELECT 
        ss.subtype,
        COUNT(DISTINCT ss.sample_unique_id) AS profiled_samples
    FROM subtype_samples ss
    GROUP BY ss.subtype
)
SELECT 
    m.hugo_gene_symbol,
    m.subtype,
    m.altered_samples,
    p.profiled_samples,
    ROUND(m.altered_samples * 100.0 / p.profiled_samples, 1) AS frequency_pct,
    m.total_mutation_events
FROM mutations_by_subtype m
JOIN profiled_by_subtype p USING (subtype)
WHERE m.subtype IN ('UCEC_CN_LOW', 'UCEC_MSI', 'UCEC_POLE')
ORDER BY m.subtype, m.altered_samples DESC
LIMIT 100
",
 "max_rows": 150
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT attribute_name, attribute_value
FROM clinical_data_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
  AND (attribute_name LIKE '%SUBTYPE%' OR attribute_name LIKE '%subtype%'
       OR attribute_name LIKE '%molecular%' OR attribute_name LIKE '%MOLECULAR%')
ORDER BY attribute_name, attribute_value
LIMIT 50
",
 "max_rows": 100
}

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

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    attribute_value AS subtype,
    COUNT(DISTINCT sample_unique_id) AS samples,
    COUNT(DISTINCT patient_unique_id) AS patients
FROM clinical_data_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
  AND attribute_name = 'SUBTYPE'
  AND attribute_value != ''
GROUP BY attribute_value
ORDER BY patients DESC
",
 "max_rows": 20
}

◀ result
{"rows":[{"subtype":"UCEC_CN_HIGH","samples":1,"patients":163},{"subtype":"UCEC_MSI","samples":1,"patients":148},{"subtype":"UCEC_CN_LOW","samples":1,"patients":147},{"subtype":"UCEC_POLE","samples":1,"patients":49}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_patients 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'
      AND attribute_value != ''
),
mutations_by_subtype AS (
    SELECT 
        ged.hugo_gene_symbol,
        sp.subtype,
        COUNT(DISTINCT ged.patient_unique_id) AS altered_patients,
        COUNT(*) AS total_mutation_events
    FROM genomic_event_derived ged
    JOIN subtype_patients sp ON ged.sample_unique_id IN (
        SELECT sample_unique_id FROM clinical_data_derived cd
        WHERE cd.patient_unique_id = sp.patient_unique_id
          AND cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
    )
    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 ged.hugo_gene_symbol, sp.subtype
),
profiled_by_subtype AS (
    SELECT 
        sp.subtype,
        COUNT(DISTINCT sp.patient_unique_id) AS profiled_patients
    FROM subtype_patients sp
    GROUP BY sp.subtype
)
SELECT 
    m.hugo_gene_symbol,
    m.subtype,
    m.altered_patients,
    p.profiled_patients,
    ROUND(m.altered_patients * 100.0 / p.profiled_patients, 1) AS frequency_pct,
    m.total_mutation_events
FROM mutations_by_subtype m
JOIN profiled_by_subtype p USING (subtype)
WHERE m.subtype = 'UCEC_CN_HIGH'
ORDER BY m.altered_patients DESC
LIMIT 30
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 1\n Code: 1. DB::Exception: Resolved identifier 'sp.patient_unique_id' in parent scope to expression 'sp.patient_unique_id' with correlated columns 'patient_unique_id' (Enable 'allow_experimental_correlated_subqueries' setting to allow correlated subqueries execution). In scope (SELECT sample_unique_id FROM clinical_data_derived AS cd WHERE (cd.patient_unique_id = sp.patient_unique_id) AND (cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018')). (UNSUPPORTED_METHOD) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_mapping AS (
    SELECT 
        sample_unique_id,
        attribute_value AS subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
      AND attribute_value != ''
)
SELECT 
    ged.hugo_gene_symbol,
    sm.subtype,
    COUNT(DISTINCT ged.sample_unique_id) AS altered_samples,
    COUNT(*) AS total_mutation_events
FROM genomic_event_derived ged
INNER JOIN subtype_mapping sm 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 sm.subtype = 'UCEC_CN_HIGH'
GROUP BY ged.hugo_gene_symbol, sm.subtype
ORDER BY altered_samples DESC
LIMIT 30
",
 "max_rows": 100
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    COUNT(DISTINCT ged.sample_unique_id) AS ged_samples,
    COUNT(DISTINCT cd.sample_unique_id) AS cd_samples
FROM genomic_event_derived ged
FULL OUTER JOIN clinical_data_derived cd USING (sample_unique_id)
WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
  AND cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
LIMIT 1
",
 "max_rows": 10
}

◀ result
{"rows":[{"ged_samples":529,"cd_samples":529}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    COUNT(*) AS total_rows,
    COUNT(DISTINCT hugo_gene_symbol) AS unique_genes,
    COUNT(DISTINCT sample_unique_id) AS unique_samples
FROM genomic_event_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
  AND variant_type = 'mutation'
  AND mutation_status != 'UNCALLED'
  AND off_panel = 0
LIMIT 1
",
 "max_rows": 10
}

◀ result
{"rows":[{"total_rows":538948,"unique_genes":19092,"unique_samples":515}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_mapping AS (
    SELECT 
        sample_unique_id,
        attribute_value AS subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
      AND attribute_value != ''
)
SELECT 
    COUNT(DISTINCT ged.sample_unique_id) AS samples_with_mutations,
    COUNT(DISTINCT sm.sample_unique_id) AS samples_with_subtype
FROM genomic_event_derived ged
INNER JOIN subtype_mapping sm 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
LIMIT 1
",
 "max_rows": 10
}

◀ result
{"rows":[{"samples_with_mutations":0,"samples_with_subtype":0}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    'genomic_event_derived' AS source,
    sample_unique_id,
    COUNT(*) AS count
FROM genomic_event_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
LIMIT 5
UNION ALL
SELECT 
    'clinical_data_derived' AS source,
    sample_unique_id,
    COUNT(*) AS count
FROM clinical_data_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
LIMIT 5
",
 "max_rows": 20
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 215\n Code: 215. DB::Exception: Column 'cbioportal_public_librechat_blue.genomic_event_derived.sample_unique_id' is not under aggregate function and not in GROUP BY keys. In query SELECT 'genomic_event_derived' AS source, sample_unique_id, COUNT(*) AS count FROM genomic_event_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' LIMIT 5. (NOT_AN_AGGREGATE) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    'genomic_event_derived' AS source,
    COUNT(DISTINCT sample_unique_id) AS distinct_samples,
    COUNT(DISTINCT substring(sample_unique_id, 1, 30)) AS first_30_chars
FROM genomic_event_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
LIMIT 5
",
 "max_rows": 10
}

◀ result
{"rows":[{"source":"genomic_event_derived","distinct_samples":529,"first_30_chars":1}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_mapping AS (
    SELECT DISTINCT
        sample_unique_id,
        attribute_value AS subtype
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
      AND attribute_value = 'UCEC_CN_HIGH'
),
mutations_cn_high AS (
    SELECT 
        ged.hugo_gene_symbol,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples,
        COUNT(*) AS total_mutation_events
    FROM genomic_event_derived ged
    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.sample_unique_id IN (SELECT sample_unique_id FROM subtype_mapping)
    GROUP BY ged.hugo_gene_symbol
)
SELECT 
    hugo_gene_symbol,
    altered_samples,
    'UCEC_CN_HIGH' AS subtype,
    163 AS profiled_patients,
    ROUND(altered_samples * 100.0 / 163, 1) AS frequency_pct,
    total_mutation_events
FROM mutations_cn_high
ORDER BY altered_samples DESC
LIMIT 30
",
 "max_rows": 100
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_samples_high AS (
    SELECT DISTINCT sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND attribute_name = 'SUBTYPE'
      AND attribute_value = 'UCEC_CN_HIGH'
)
SELECT 
    COUNT(DISTINCT ged.sample_unique_id) AS ged_samples,
    COUNT(DISTINCT ssh.sample_unique_id) AS subtype_samples,
    COUNT(DISTINCT ged.sample_unique_id) AS overlap
FROM genomic_event_derived ged
CROSS JOIN subtype_samples_high ssh
WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
LIMIT 1
",
 "max_rows": 10
}

◀ result
{"rows":[{"ged_samples":529,"subtype_samples":1,"overlap":529}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    sample_unique_id,
    attribute_name,
    attribute_value
FROM clinical_data_derived
WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
  AND attribute_name = 'SUBTYPE'
LIMIT 20
",
 "max_rows": 50
}

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

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype_patients 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'
      AND attribute_value != ''
),
patient_samples AS (
    SELECT DISTINCT
        cd.patient_unique_id,
        ged.sample_unique_id
    FROM clinical_data_derived cd
    INNER JOIN genomic_event_derived ged USING (sample_unique_id)
    WHERE cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
      AND ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
mutations_by_subtype AS (
    SELECT 
        ged.hugo_gene_symbol,
        sp.subtype,
        COUNT(DISTINCT ps.patient_unique_id) AS altered_patients,
        COUNT(*) AS total_mutation_events
    FROM genomic_event_derived ged
    INNER JOIN patient_samples ps ON ged.sample_unique_id = ps.sample_unique_id
    INNER JOIN subtype_patients sp ON ps.patient_unique_id = sp.patient_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 ged.hugo_gene_symbol, sp.subtype
)
SELECT 
    hugo_gene_symbol,
    subtype,
    altered_patients,
    CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END AS profiled_patients,
    ROUND(altered_patients * 100.0 / NULLIF(CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END, 0), 1) AS frequency_pct,
    total_mutation_events
FROM mutations_by_subtype
WHERE subtype = 'UCEC_CN_HIGH'
ORDER BY altered_patients DESC
LIMIT 30
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 207\n Code: 207. DB::Exception: JOIN  INNER JOIN ... ON ged.sample_unique_id = ps.sample_unique_id ambiguous identifier 'ged.sample_unique_id'. In scope  mutations_by_subtype. (AMBIGUOUS_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
WITH patient_to_sample AS (
    SELECT DISTINCT
        patient_unique_id,
        sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
subtype_patients 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'
      AND attribute_value = 'UCEC_CN_HIGH'
),
mutations_cn_high AS (
    SELECT 
        ged.hugo_gene_symbol,
        COUNT(DISTINCT sp.patient_unique_id) AS altered_patients,
        COUNT(*) AS total_mutation_events
    FROM genomic_event_derived ged
    INNER JOIN patient_to_sample pts ON ged.sample_unique_id = pts.sample_unique_id
    INNER JOIN subtype_patients sp ON pts.patient_unique_id = sp.patient_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 ged.hugo_gene_symbol
)
SELECT 
    hugo_gene_symbol,
    'UCEC_CN_HIGH' AS subtype,
    altered_patients,
    163 AS profiled_patients,
    ROUND(altered_patients * 100.0 / 163, 1) AS frequency_pct,
    total_mutation_events
FROM mutations_cn_high
ORDER BY altered_patients DESC
LIMIT 30
",
 "max_rows": 100
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","subtype":"UCEC_CN_HIGH","altered_patients":141,"profiled_patients":163,"frequency_pct":86.5,"total_mutation_events":146},{"hugo_gene_symbol":"PIK3CA","subtype":"UCEC_CN_HIGH","altered_patients":54,"profiled_patients":163,"frequency_pct":33.1,"total_mutation_events":58},{"hugo_gene_symbol":"PPP2R1A","subtype":"UCEC_CN_HIGH","altered_patients":50,"profiled_patients":163,"frequency_pct":30.7,"total_mutation_events":54},{"hugo_gene_symbol":"TTN","subtype":"UCEC_CN_HIGH","altered_patients":32,"profiled_patients":163,"frequency_pct":19.6,"total_mutation_events":38},{"hugo_gene_symbol":"FBXW7","subtype":"UCEC_CN_HIGH","altered_patients":30,"profiled_patients":163,"frequency_pct":18.4,"total_mutation_events":31},{"hugo_gene_symbol":"PTEN","subtype":"UCEC_CN_HIGH","altered_patients":28,"profiled_patients":163,"frequency_pct":17.2,"total_mutation_events":41},{"hugo_gene_symbol":"CHD4","subtype":"UCEC_CN_HIGH","altered_patients":25,"profiled_patients":163,"frequency_pct":15.3,"total_mutation_events":27},{"hugo_gene_symbol":"PIK3R1","subtype":"UCEC_CN_HIGH","altered_patients":23,"profiled_patients":163,"frequency_pct":14.1,"total_mutation_events":26},{"hugo_gene_symbol":"CSMD3","subtype":"UCEC_CN_HIGH","altered_patients":20,"profiled_patients":163,"frequency_pct":12.3,"total_mutation_events":22},{"hugo_gene_symbol":"ARHGAP35","subtype":"UCEC_CN_HIGH","altered_patients":18,"profiled_patients":163,"frequency_pct":11,"total_mutation_events":21},{"hugo_gene_symbol":"MUC16","subtype":"UCEC_CN_HIGH","altered_patients":17,"profiled_patients":163,"frequency_pct":10.4,"total_mutation_events":20},{"hugo_gene_symbol":"RYR2","subtype":"UCEC_CN_HIGH","altered_patients":13,"profiled_patients":163,"frequency_pct":8,"total_mutation_events":13},{"hugo_gene_symbol":"SPOP","subtype":"UCEC_CN_HIGH","altered_patients":13,"profiled_patients":163,"frequency_pct":8,"total_mutation_events":13},{"hugo_gene_symbol":"DMD","subtype":"UCEC_CN_HIGH","altered_patients":12,"profiled_patients":163,"frequency_pct":7.4,"total_mutation_events":13},{"hugo_gene_symbol":"FOXA2","subtype":"UCEC_CN_HIGH","altered_patients":12,"profiled_patients":163,"frequency_pct":7.4,"total_mutation_events":13},{"hugo_gene_symbol":"TAF1","subtype":"UCEC_CN_HIGH","altered_patients":12,"profiled_patients":163,"frequency_pct":7.4,"total_mutation_events":12},{"hugo_gene_symbol":"MUC4","subtype":"UCEC_CN_HIGH","altered_patients":11,"profiled_patients":163,"frequency_pct":6.7,"total_mutation_events":11},{"hugo_gene_symbol":"KMT2D","subtype":"UCEC_CN_HIGH","altered_patients":11,"profiled_patients":163,"frequency_pct":6.7,"total_mutation_events":11},{"hugo_gene_symbol":"ZFHX4","subtype":"UCEC_CN_HIGH","altered_patients":11,"profiled_patients":163,"frequency_pct":6.7,"total_mutation_events":13},{"hugo_gene_symbol":"SYNE1","subtype":"UCEC_CN_HIGH","altered_patients":10,"profiled_patients":163,"frequency_pct":6.1,"total_mutation_events":10},{"hugo_gene_symbol":"DNAH8","subtype":"UCEC_CN_H … (1407 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH patient_to_sample AS (
    SELECT DISTINCT
        patient_unique_id,
        sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
subtype_patients 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'
      AND attribute_value != ''
),
mutations_by_subtype AS (
    SELECT 
        ged.hugo_gene_symbol,
        sp.subtype,
        COUNT(DISTINCT sp.patient_unique_id) AS altered_patients,
        COUNT(*) AS total_mutation_events
    FROM genomic_event_derived ged
    INNER JOIN patient_to_sample pts ON ged.sample_unique_id = pts.sample_unique_id
    INNER JOIN subtype_patients sp ON pts.patient_unique_id = sp.patient_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 ged.hugo_gene_symbol, sp.subtype
)
SELECT 
    hugo_gene_symbol,
    subtype,
    altered_patients,
    CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END AS profiled_patients,
    ROUND(altered_patients * 100.0 / NULLIF(CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END, 0), 1) AS frequency_pct,
    total_mutation_events
FROM mutations_by_subtype
ORDER BY subtype, altered_patients DESC
",
 "max_rows": 200
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","subtype":"UCEC_CN_HIGH","altered_patients":141,"profiled_patients":163,"frequency_pct":86.5,"total_mutation_events":146},{"hugo_gene_symbol":"PIK3CA","subtype":"UCEC_CN_HIGH","altered_patients":54,"profiled_patients":163,"frequency_pct":33.1,"total_mutation_events":58},{"hugo_gene_symbol":"PPP2R1A","subtype":"UCEC_CN_HIGH","altered_patients":50,"profiled_patients":163,"frequency_pct":30.7,"total_mutation_events":54},{"hugo_gene_symbol":"TTN","subtype":"UCEC_CN_HIGH","altered_patients":32,"profiled_patients":163,"frequency_pct":19.6,"total_mutation_events":38},{"hugo_gene_symbol":"FBXW7","subtype":"UCEC_CN_HIGH","altered_patients":30,"profiled_patients":163,"frequency_pct":18.4,"total_mutation_events":31},{"hugo_gene_symbol":"PTEN","subtype":"UCEC_CN_HIGH","altered_patients":28,"profiled_patients":163,"frequency_pct":17.2,"total_mutation_events":41},{"hugo_gene_symbol":"CHD4","subtype":"UCEC_CN_HIGH","altered_patients":25,"profiled_patients":163,"frequency_pct":15.3,"total_mutation_events":27},{"hugo_gene_symbol":"PIK3R1","subtype":"UCEC_CN_HIGH","altered_patients":23,"profiled_patients":163,"frequency_pct":14.1,"total_mutation_events":26},{"hugo_gene_symbol":"CSMD3","subtype":"UCEC_CN_HIGH","altered_patients":20,"profiled_patients":163,"frequency_pct":12.3,"total_mutation_events":22},{"hugo_gene_symbol":"ARHGAP35","subtype":"UCEC_CN_HIGH","altered_patients":18,"profiled_patients":163,"frequency_pct":11,"total_mutation_events":21},{"hugo_gene_symbol":"MUC16","subtype":"UCEC_CN_HIGH","altered_patients":17,"profiled_patients":163,"frequency_pct":10.4,"total_mutation_events":20},{"hugo_gene_symbol":"RYR2","subtype":"UCEC_CN_HIGH","altered_patients":13,"profiled_patients":163,"frequency_pct":8,"total_mutation_events":13},{"hugo_gene_symbol":"SPOP","subtype":"UCEC_CN_HIGH","altered_patients":13,"profiled_patients":163,"frequency_pct":8,"total_mutation_events":13},{"hugo_gene_symbol":"TAF1","subtype":"UCEC_CN_HIGH","altered_patients":12,"profiled_patients":163,"frequency_pct":7.4,"total_mutation_events":12},{"hugo_gene_symbol":"DMD","subtype":"UCEC_CN_HIGH","altered_patients":12,"profiled_patients":163,"frequency_pct":7.4,"total_mutation_events":13},{"hugo_gene_symbol":"FOXA2","subtype":"UCEC_CN_HIGH","altered_patients":12,"profiled_patients":163,"frequency_pct":7.4,"total_mutation_events":13},{"hugo_gene_symbol":"KMT2D","subtype":"UCEC_CN_HIGH","altered_patients":11,"profiled_patients":163,"frequency_pct":6.7,"total_mutation_events":11},{"hugo_gene_symbol":"MUC4","subtype":"UCEC_CN_HIGH","altered_patients":11,"profiled_patients":163,"frequency_pct":6.7,"total_mutation_events":11},{"hugo_gene_symbol":"ZFHX4","subtype":"UCEC_CN_HIGH","altered_patients":11,"profiled_patients":163,"frequency_pct":6.7,"total_mutation_events":13},{"hugo_gene_symbol":"DNAH8","subtype":"UCEC_CN_HIGH","altered_patients":10,"profiled_patients":163,"frequency_pct":6.1,"total_mutation_events":10},{"hugo_gene_symbol":"SYNE1","subtype":"UCEC_CN_H … (26369 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH patient_to_sample AS (
    SELECT DISTINCT
        patient_unique_id,
        sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
subtype_patients 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'
      AND attribute_value != ''
),
top_cn_high_genes AS (
    SELECT DISTINCT hugo_gene_symbol
    FROM genomic_event_derived ged
    INNER JOIN patient_to_sample pts ON ged.sample_unique_id = pts.sample_unique_id
    INNER JOIN subtype_patients sp ON pts.patient_unique_id = sp.patient_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 sp.subtype = 'UCEC_CN_HIGH'
    ORDER BY COUNT(DISTINCT sp.patient_unique_id) DESC
    LIMIT 15
),
mutations_by_subtype AS (
    SELECT 
        ged.hugo_gene_symbol,
        sp.subtype,
        COUNT(DISTINCT sp.patient_unique_id) AS altered_patients
    FROM genomic_event_derived ged
    INNER JOIN patient_to_sample pts ON ged.sample_unique_id = pts.sample_unique_id
    INNER JOIN subtype_patients sp ON pts.patient_unique_id = sp.patient_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 (SELECT hugo_gene_symbol FROM top_cn_high_genes)
    GROUP BY ged.hugo_gene_symbol, sp.subtype
)
SELECT 
    hugo_gene_symbol,
    subtype,
    altered_patients,
    CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END AS profiled_patients,
    ROUND(altered_patients * 100.0 / NULLIF(CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END, 0), 1) AS frequency_pct
FROM mutations_by_subtype
ORDER BY hugo_gene_symbol, CASE WHEN subtype = 'UCEC_CN_HIGH' THEN 0 ELSE 1 END, altered_patients DESC
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 215\n Code: 215. DB::Exception: Column 'ged.hugo_gene_symbol' is not under aggregate function and not in GROUP BY keys. In query  top_cn_high_genes. (NOT_AN_AGGREGATE) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
WITH patient_to_sample AS (
    SELECT DISTINCT
        patient_unique_id,
        sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018'
),
subtype_patients 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'
      AND attribute_value != ''
),
top_cn_high_genes AS (
    SELECT 
        ged.hugo_gene_symbol,
        COUNT(DISTINCT sp.patient_unique_id) AS altered_count
    FROM genomic_event_derived ged
    INNER JOIN patient_to_sample pts ON ged.sample_unique_id = pts.sample_unique_id
    INNER JOIN subtype_patients sp ON pts.patient_unique_id = sp.patient_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 sp.subtype = 'UCEC_CN_HIGH'
    GROUP BY ged.hugo_gene_symbol
    ORDER BY altered_count DESC
    LIMIT 15
),
mutations_by_subtype AS (
    SELECT 
        ged.hugo_gene_symbol,
        sp.subtype,
        COUNT(DISTINCT sp.patient_unique_id) AS altered_patients
    FROM genomic_event_derived ged
    INNER JOIN patient_to_sample pts ON ged.sample_unique_id = pts.sample_unique_id
    INNER JOIN subtype_patients sp ON pts.patient_unique_id = sp.patient_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 (SELECT hugo_gene_symbol FROM top_cn_high_genes)
    GROUP BY ged.hugo_gene_symbol, sp.subtype
)
SELECT 
    hugo_gene_symbol,
    subtype,
    altered_patients,
    CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END AS profiled_patients,
    ROUND(altered_patients * 100.0 / NULLIF(CASE subtype
        WHEN 'UCEC_CN_HIGH' THEN 163
        WHEN 'UCEC_CN_LOW' THEN 147
        WHEN 'UCEC_MSI' THEN 148
        WHEN 'UCEC_POLE' THEN 49
    END, 0), 1) AS frequency_pct
FROM mutations_by_subtype
ORDER BY hugo_gene_symbol, CASE WHEN subtype = 'UCEC_CN_HIGH' THEN 0 ELSE 1 END, altered_patients DESC
",
 "max_rows": 100
}

◀ result
{"rows":[{"hugo_gene_symbol":"ARHGAP35","subtype":"UCEC_CN_HIGH","altered_patients":18,"profiled_patients":163,"frequency_pct":11},{"hugo_gene_symbol":"ARHGAP35","subtype":"UCEC_POLE","altered_patients":38,"profiled_patients":49,"frequency_pct":77.6},{"hugo_gene_symbol":"ARHGAP35","subtype":"UCEC_MSI","altered_patients":34,"profiled_patients":148,"frequency_pct":23},{"hugo_gene_symbol":"ARHGAP35","subtype":"UCEC_CN_LOW","altered_patients":13,"profiled_patients":147,"frequency_pct":8.8},{"hugo_gene_symbol":"CHD4","subtype":"UCEC_CN_HIGH","altered_patients":25,"profiled_patients":163,"frequency_pct":15.3},{"hugo_gene_symbol":"CHD4","subtype":"UCEC_POLE","altered_patients":38,"profiled_patients":49,"frequency_pct":77.6},{"hugo_gene_symbol":"CHD4","subtype":"UCEC_MSI","altered_patients":29,"profiled_patients":148,"frequency_pct":19.6},{"hugo_gene_symbol":"CHD4","subtype":"UCEC_CN_LOW","altered_patients":21,"profiled_patients":147,"frequency_pct":14.3},{"hugo_gene_symbol":"CSMD3","subtype":"UCEC_CN_HIGH","altered_patients":20,"profiled_patients":163,"frequency_pct":12.3},{"hugo_gene_symbol":"CSMD3","subtype":"UCEC_POLE","altered_patients":46,"profiled_patients":49,"frequency_pct":93.9},{"hugo_gene_symbol":"CSMD3","subtype":"UCEC_MSI","altered_patients":44,"profiled_patients":148,"frequency_pct":29.7},{"hugo_gene_symbol":"CSMD3","subtype":"UCEC_CN_LOW","altered_patients":11,"profiled_patients":147,"frequency_pct":7.5},{"hugo_gene_symbol":"DMD","subtype":"UCEC_CN_HIGH","altered_patients":12,"profiled_patients":163,"frequency_pct":7.4},{"hugo_gene_symbol":"DMD","subtype":"UCEC_POLE","altered_patients":47,"profiled_patients":49,"frequency_pct":95.9},{"hugo_gene_symbol":"DMD","subtype":"UCEC_MSI","altered_patients":37,"profiled_patients":148,"frequency_pct":25},{"hugo_gene_symbol":"DMD","subtype":"UCEC_CN_LOW","altered_patients":5,"profiled_patients":147,"frequency_pct":3.4},{"hugo_gene_symbol":"FBXW7","subtype":"UCEC_CN_HIGH","altered_patients":30,"profiled_patients":163,"frequency_pct":18.4},{"hugo_gene_symbol":"FBXW7","subtype":"UCEC_POLE","altered_patients":32,"profiled_patients":49,"frequency_pct":65.3},{"hugo_gene_symbol":"FBXW7","subtype":"UCEC_MSI","altered_patients":23,"profiled_patients":148,"frequency_pct":15.5},{"hugo_gene_symbol":"FBXW7","subtype":"UCEC_CN_LOW","altered_patients":9,"profiled_patients":147,"frequency_pct":6.1},{"hugo_gene_symbol":"MUC16","subtype":"UCEC_CN_HIGH","altered_patients":17,"profiled_patients":163,"frequency_pct":10.4},{"hugo_gene_symbol":"MUC16","subtype":"UCEC_MSI","altered_patients":67,"profiled_patients":148,"frequency_pct":45.3},{"hugo_gene_symbol":"MUC16","subtype":"UCEC_POLE","altered_patients":46,"profiled_patients":49,"frequency_pct":93.9},{"hugo_gene_symbol":"MUC16","subtype":"UCEC_CN_LOW","altered_patients":12,"profiled_patients":147,"frequency_pct":8.2},{"hugo_gene_symbol":"PIK3CA","subtype":"UCEC_CN_HIGH","altered_patients":54,"profiled_patients":163,"frequency_pct":33.1},{"hugo_gene_symbol":"PIK3CA","sub … (4093 more chars)

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

◀ result
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▶ navigate_to_study_view
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◀ result
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▶ navigate_to_results_view
{
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◀ result
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═ answer (success)
## Most Frequently Mutated Genes in CN-HIGH Subtype Endometrial Cancers

Based on analysis of **163 CN-HIGH patients** in the TCGA Uterine Corpus Endometrial Carcinoma (PanCancer Atlas), here are the top mutated genes with comparison to other molecular subtypes:

### Top 10 Genes Most Frequently Mutated in CN-HIGH Endometrial Cancers

| Gene | CN-HIGH Frequency | CN-LOW | MSI | POLE | Distinguishing Feature |
|------|-------------------|--------|-----|------|------------------------|
| **TP53** | **86.5%** (141/163) | 6.1% | 16.2% | 34.7% | **Signature mutation** – defines CN-HIGH subtype |
| **PIK3CA** | 33.1% (54/163) | 50.3% | 57.4% | 83.7% | Common across all subtypes; higher in hypermutated types |
| **PPP2R1A** | **30.7%** (50/163) | 2.0% | 12.2% | 28.6% | **Enriched in CN-HIGH** – 15-fold higher than CN-LOW |
| **FBXW7** | 18.4% (30/163) | 6.1% | 15.5% | 65.3% | Moderate enrichment in CN-HIGH |
| **PTEN** | 17.2% (28/163) | 80.3% | 93.2% | 95.9% | **Depleted in CN-HIGH** – marker of CN-LOW/MSI/POLE |
| **CHD4** | 15.3% (25/163) | 14.3% | 19.6% | 77.6% | Chromatin remodeler; frequent in all subtypes |
| **PIK3R1** | 14.1% (23/163) | 34.0% | 37.2% | 59.2% | Higher in CN-LOW and MSI |
| **TTN** | 19.6% (32/163) | 11.6% | 70.9% | 100% | **Passenger-like** – hypermutated types have it nearly universally |
| **CSMD3** | 12.3% (20/163) | 7.5% | 29.7% | 93.9% | Passenger gene; very frequent in hypermutated POLE |
| **RYR2** | 8.0% (13/163) | 9.5% | 36.5% | 89.8% | Passenger gene; hypermutated types heavily affected |

---

### Key Insight: CN-HIGH Subtype Signature

**TP53 is the hallmark mutation of CN-HIGH endometrial cancer**, with 86.5% of CN-HIGH patients mutated versus only 6.1% of CN-LOW patients. Combined with **PPP2R1A** (30.7% in CN-HIGH vs 2% in CN-LOW), these two genes define the CN-HIGH genotype.

**Contrast with other subtypes:**
- **PTEN loss is a hallmark of CN-LOW (80.3%) and MSI (93.2%)**, but only 17.2% of CN-HIGH tumors — suggesting different PI3K pathway activation mechanisms
- **Hypermutated types (MSI, POLE)** are dominated by passenger mutations (TTN, CSMD3, RYR2, DMD) at very high frequencies (70–100%), making their top genes less specific to biology
- **PIK3CA** is elevated across all subtypes (33–84%), but highest in POLE/MSI, suggesting convergent pathway activation

---

### Explore the Data Interactively

- **[View CN-HIGH cohort overview](https://www.cbioportal.org/study/summary?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)** — Clinical characteristics and mutation burden of 163 CN-HIGH samples

- **[View OncoPrint for top 5 CN-HIGH genes](https://www.cbioportal.org/results/oncoprint?session_id=6ab5afb7e497aad53593c222)** — TP53, PIK3CA, PPP2R1A, FBXW7, PTEN alterations across the CN-HIGH cohort

These queries are from **TCGA PanCancer Atlas endometrial carcinoma** (WES + comprehensive phenotyping), the largest and most deeply characterized cohort in cBioPortal.