Q (sonnet-5.5): 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"
}

▶ list_studies
{
 "search": "endometrial"
}

◀ 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)

◀ 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 attribute_name, attribute_value, count() c FROM clinical_data_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND attribute_name IN ('SUBTYPE','CANCER_TYPE_DETAILED') GROUP BY 1,2"
}

◀ result
{"rows":[{"attribute_name":"CANCER_TYPE_DETAILED","attribute_value":"Uterine Endometrioid Carcinoma","c":399},{"attribute_name":"SUBTYPE","attribute_value":"UCEC_CN_HIGH","c":163},{"attribute_name":"SUBTYPE","c":22},{"attribute_name":"CANCER_TYPE_DETAILED","attribute_value":"Uterine Mixed Endometrial Carcinoma","c":21},{"attribute_name":"CANCER_TYPE_DETAILED","attribute_value":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","c":109},{"attribute_name":"SUBTYPE","attribute_value":"UCEC_CN_LOW","c":147},{"attribute_name":"SUBTYPE","attribute_value":"UCEC_MSI","c":148},{"attribute_name":"SUBTYPE","attribute_value":"UCEC_POLE","c":49}]}

▶ clickhouse_run_select_query
{
 "query": "WITH g AS (SELECT sample_unique_id, if(attribute_value='UCEC_CN_HIGH','CNH','OTH') grp FROM clinical_data_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND attribute_name='SUBTYPE' AND attribute_value IN ('UCEC_CN_HIGH','UCEC_MSI','UCEC_CN_LOW','UCEC_POLE')),
prof AS (SELECT DISTINCT sample_unique_id FROM sample_to_gene_panel_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND alteration_type='MUTATION_EXTENDED'),
n AS (SELECT grp, count() n FROM g WHERE sample_unique_id IN (SELECT sample_unique_id FROM prof) GROUP BY grp),
m AS (SELECT e.hugo_gene_symbol gene, g.grp grp, uniqExact(e.sample_unique_id) alt FROM genomic_event_derived e JOIN g USING sample_unique_id WHERE e.cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND e.variant_type='mutation' AND e.mutation_status!='UNCALLED' AND e.off_panel=0 GROUP BY gene, grp)
SELECT gene, sumIf(alt,grp='CNH') cnh_alt, (SELECT n FROM n WHERE grp='CNH') cnh_n, round(cnh_alt*100/cnh_n,1) cnh_pct, sumIf(alt,grp='OTH') oth_alt, (SELECT n FROM n WHERE grp='OTH') oth_n, round(oth_alt*100/oth_n,1) oth_pct FROM m GROUP BY gene ORDER BY cnh_alt DESC LIMIT 20"
}

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

◀ result
{"rows":[]}

◀ 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"]}}]}}

▶ clickhouse_run_select_query
{
 "query": "WITH g AS (SELECT DISTINCT sample_unique_id, if(attribute_value='UCEC_CN_HIGH','CNH','OTH') grp FROM clinical_data_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND attribute_name='SUBTYPE' AND attribute_value IN ('UCEC_CN_HIGH','UCEC_MSI','UCEC_CN_LOW','UCEC_POLE')),
m AS (SELECT hugo_gene_symbol gene, sample_unique_id sid 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 GROUP BY gene, sid)
SELECT gene, countIf(grp='CNH') cnh_alt, round(cnh_alt*100/(SELECT count() FROM g WHERE grp='CNH'),1) cnh_pct, countIf(grp='OTH') oth_alt, round(oth_alt*100/(SELECT count() FROM g WHERE grp='OTH'),1) oth_pct, (SELECT count() FROM g WHERE grp='CNH') cnh_n, (SELECT count() FROM g WHERE grp='OTH') oth_n FROM m JOIN g ON m.sid=g.sample_unique_id GROUP BY gene ORDER BY cnh_alt DESC LIMIT 15"
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "SELECT (SELECT any(sample_unique_id) FROM clinical_data_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND attribute_name='SUBTYPE') a, (SELECT any(sample_unique_id) FROM genomic_event_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018') b, (SELECT count() FROM genomic_event_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND variant_type='mutation') c"
}

◀ result
{"rows":[{"b":"ucec_tcga_pan_can_atlas_2018_TCGA-B5-A11E-01","c":538948}]}

▶ clickhouse_run_select_query
{
 "query": "WITH g AS (SELECT DISTINCT patient_unique_id pid, if(attribute_value='UCEC_CN_HIGH','CNH','OTH') grp FROM clinical_data_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND attribute_name='SUBTYPE' AND attribute_value IN ('UCEC_CN_HIGH','UCEC_MSI','UCEC_CN_LOW','UCEC_POLE')),
sp AS (SELECT DISTINCT sample_unique_id sid, patient_unique_id pid FROM clinical_data_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND patient_unique_id IN (SELECT pid FROM g) AND sample_unique_id!=''),
prof AS (SELECT DISTINCT sample_unique_id sid FROM sample_to_gene_panel_derived WHERE cancer_study_identifier='ucec_tcga_pan_can_atlas_2018' AND alteration_type='MUTATION_EXTENDED'),
s AS (SELECT sp.sid sid, g.grp grp FROM sp JOIN g ON sp.pid=g.pid WHERE sp.sid IN (SELECT sid FROM prof)),
m AS (SELECT hugo_gene_symbol gene, sample_unique_id sid 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 GROUP BY gene, sid)
SELECT gene, countIf(grp='CNH') cnh_alt, round(cnh_alt*100/(SELECT count() FROM s WHERE grp='CNH'),1) cnh_pct, countIf(grp='OTH') oth_alt, round(oth_alt*100/(SELECT count() FROM s WHERE grp='OTH'),1) oth_pct, (SELECT count() FROM s WHERE grp='CNH') cnh_n, (SELECT count() FROM s WHERE grp='OTH') oth_n FROM m JOIN s ON m.sid=s.sid GROUP BY gene ORDER BY cnh_alt DESC LIMIT 15"
}

◀ result
{"rows":[{"gene":"TP53","cnh_alt":141,"cnh_pct":86.5,"oth_alt":50,"oth_pct":14.5,"cnh_n":163,"oth_n":344},{"gene":"PIK3CA","cnh_alt":54,"cnh_pct":33.1,"oth_alt":200,"oth_pct":58.1,"cnh_n":163,"oth_n":344},{"gene":"PPP2R1A","cnh_alt":50,"cnh_pct":30.7,"oth_alt":35,"oth_pct":10.2,"cnh_n":163,"oth_n":344},{"gene":"TTN","cnh_alt":32,"cnh_pct":19.6,"oth_alt":171,"oth_pct":49.7,"cnh_n":163,"oth_n":344},{"gene":"FBXW7","cnh_alt":30,"cnh_pct":18.4,"oth_alt":64,"oth_pct":18.6,"cnh_n":163,"oth_n":344},{"gene":"PTEN","cnh_alt":28,"cnh_pct":17.2,"oth_alt":303,"oth_pct":88.1,"cnh_n":163,"oth_n":344},{"gene":"CHD4","cnh_alt":25,"cnh_pct":15.3,"oth_alt":88,"oth_pct":25.6,"cnh_n":163,"oth_n":344},{"gene":"PIK3R1","cnh_alt":23,"cnh_pct":14.1,"oth_alt":134,"oth_pct":39,"cnh_n":163,"oth_n":344},{"gene":"CSMD3","cnh_alt":20,"cnh_pct":12.3,"oth_alt":101,"oth_pct":29.4,"cnh_n":163,"oth_n":344},{"gene":"ARHGAP35","cnh_alt":18,"cnh_pct":11,"oth_alt":85,"oth_pct":24.7,"cnh_n":163,"oth_n":344},{"gene":"MUC16","cnh_alt":17,"cnh_pct":10.4,"oth_alt":125,"oth_pct":36.3,"cnh_n":163,"oth_n":344},{"gene":"SPOP","cnh_alt":13,"cnh_pct":8,"oth_alt":42,"oth_pct":12.2,"cnh_n":163,"oth_n":344},{"gene":"RYR2","cnh_alt":13,"cnh_pct":8,"oth_alt":112,"oth_pct":32.6,"cnh_n":163,"oth_n":344},{"gene":"FOXA2","cnh_alt":12,"cnh_pct":7.4,"oth_alt":24,"oth_pct":7,"cnh_n":163,"oth_n":344},{"gene":"DMD","cnh_alt":12,"cnh_pct":7.4,"oth_alt":89,"oth_pct":25.9,"cnh_n":163,"oth_n":344}]}

▶ 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 (MSI, CN-low, POLE)",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "UCEC_MSI"
       },
       {
        "value": "UCEC_CN_LOW"
       },
       {
        "value": "UCEC_POLE"
       }
      ]
     }
    ]
   }
  }
 ],
 "tab": "mutations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/mutations?comparisonId=6ab7f55ce497aad53593c5a9","url":"https://www.cbioportal.org/comparison/mutations?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":"Mutation frequency (protein change) compared across CN-high (163) vs Other subtypes (MSI, CN-low, POLE) (344)."}}

═ answer (success)
**Cohort:** TCGA PanCancer Atlas uterine endometrial carcinoma (`ucec_tcga_pan_can_atlas_2018`). Counts are samples. The CN-high group has 163 samples. The "other subtypes" group has 344 samples (MSI, CN-low and POLE combined). I left out 22 samples with no subtype label.

**Top mutated genes in CN-high tumors, compared with the other subtypes:**

| Gene | CN-high | Other subtypes |
|---|---|---|
| **TP53** | 141 (86.5%) | 50 (14.5%) |
| **PIK3CA** | 54 (33.1%) | 200 (58.1%) |
| **PPP2R1A** | 50 (30.7%) | 35 (10.2%) |
| TTN | 32 (19.6%) | 171 (49.7%) |
| FBXW7 | 30 (18.4%) | 64 (18.6%) |
| **PTEN** | 28 (17.2%) | 303 (88.1%) |
| CHD4 | 25 (15.3%) | 88 (25.6%) |
| PIK3R1 | 23 (14.1%) | 134 (39.0%) |
| CSMD3 | 20 (12.3%) | 101 (29.4%) |
| ARHGAP35 | 18 (11.0%) | 85 (24.7%) |
| MUC16 | 17 (10.4%) | 125 (36.3%) |
| SPOP | 13 (8.0%) | 42 (12.2%) |
| RYR2 | 13 (8.0%) | 112 (32.6%) |
| FOXA2 | 12 (7.4%) | 24 (7.0%) |
| DMD | 12 (7.4%) | 89 (25.9%) |

**What stands out (descriptive, no statistical test run):**
- **TP53** is the dominant gene in CN-high tumors, mutated in about 87% versus about 15% in the other subtypes.
- **PPP2R1A** is also more frequent in CN-high, at about 31% versus about 10%.
- **PTEN, PIK3CA and PIK3R1** are less frequently mutated in CN-high tumors than in the other subtypes. PTEN is the largest gap: about 17% versus about 88%.
- **FBXW7** is about equal in both groups (18.4% vs 18.6%).
- **TTN, MUC16, RYR2, CSMD3 and DMD** are much higher in the other subtypes. These are large genes, and the MSI and POLE tumors are hypermutated, so I would treat these differences as reflecting mutation burden rather than biology.

I haven't computed p-values. The Group Comparison page below runs the enrichment statistics for you.

- [Mutation comparison: CN-high vs other subtypes](https://www.cbioportal.org/comparison/mutations?comparisonId=6ab7f55ce497aad53593c5a9)
- [Study overview](https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018)

Other endometrial cohorts in cBioPortal don't carry this TCGA copy-number-high label. Tell me if you'd like a comparison in one of them, for example using TP53 status or histology as a proxy.