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" } ▶ 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) ▶ search_oncotree { "search_term": "endometrial cancer" } ◀ 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":[{"code":"OUTT","name":"Other Uterine Tumor","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > OUTT"},{"code":"UASC","name":"Uterine Adenosquamous Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UASC"},{"code":"UCCC","name":"Uterine Clear Cell Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UCCC"},{"code":"UCEC","name":"Endometrial Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC"},{"code":"UCS","name":"Uterine Carcinosarcoma/Uterine Malignant Mixed Mullerian Tumor","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UCS"},{"code":"UDDC","name":"Uterine Dedifferentiated Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UDDC"},{"code":"UEC","name":"Uterine Endometrioid Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UEC"},{"code":"UMC","name":"Uterine Mucinous Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMC"},{"code":"UMEC","name":"Uterine Mixed Endometrial Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMEC"},{"code":"UMNC","name":"Uterine Mesonephric Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMNC"},{"code":"UNEC","name":"Uterine Neuroendocrine Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UNEC"},{"code":"UPDC","name":"Poorly Differentiated Carcinoma of the Uterus","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UPDC"},{"code":"USC","name":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > USC"},{"code":"UUC","name":"Uterine Undifferentiated Carcinoma","score":65,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UUC"}]} ▶ list_studies { "search": "endometrial UCEC", "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 GDAC Firehose. 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 NCI GDC and generated in Aug 2025 using Cancer Data Aggregator.","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 here. The publications are here.","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 NCI.","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 … (2326 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": " WITH subtype_samples AS ( SELECT sample_unique_id, patient_unique_id, cancer_study_identifier, maxIf(attribute_value, attribute_name = 'SUBTYPE') AS subtype FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' GROUP BY sample_unique_id, patient_unique_id, cancer_study_identifier ), cnh_samples AS ( SELECT sample_unique_id FROM subtype_samples WHERE subtype = 'UCEC_CN_HIGH' ), other_samples AS ( SELECT sample_unique_id FROM subtype_samples WHERE subtype IN ('UCEC_MSI', 'UCEC_CN_LOW', 'UCEC_POLE') ), cnh_mutations AS ( SELECT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) AS cnh_mutated_samples, COUNT(*) AS cnh_total_events 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 AND sample_unique_id IN (SELECT sample_unique_id FROM cnh_samples) GROUP BY hugo_gene_symbol ), other_mutations AS ( SELECT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) AS other_mutated_samples, COUNT(*) AS other_total_events 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 AND sample_unique_id IN (SELECT sample_unique_id FROM other_samples) GROUP BY hugo_gene_symbol ), cnh_profiled AS ( SELECT COUNT(DISTINCT sample_unique_id) AS total_cnh_samples FROM cnh_samples ), other_profiled AS ( SELECT COUNT(DISTINCT sample_unique_id) AS total_other_samples FROM other_samples ) SELECT c.hugo_gene_symbol, c.cnh_mutated_samples, cp.total_cnh_samples, ROUND(c.cnh_mutated_samples * 100.0 / cp.total_cnh_samples, 1) AS cnh_frequency_pct, o.other_mutated_samples, op.total_other_samples, ROUND(o.other_mutated_samples * 100.0 / op.total_other_samples, 1) AS other_frequency_pct, ROUND((c.cnh_mutated_samples * 100.0 / cp.total_cnh_samples) - (o.other_mutated_samples * 100.0 / op.total_other_samples), 1) AS frequency_difference_pct FROM cnh_mutations c LEFT JOIN other_mutations o ON c.hugo_gene_symbol = o.hugo_gene_symbol CROSS JOIN cnh_profiled cp CROSS JOIN other_profiled op WHERE c.cnh_mutated_samples >= 5 -- Filter for genes with at least 5 CNH mutations ORDER BY cnh_frequency_pct DESC LIMIT 30; ", "max_rows": 100 } ◀ result {"rows":[]} ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT attribute_value, COUNT(*) as count FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' GROUP BY attribute_value ORDER BY count DESC; ", "max_rows": 20 } ◀ result {"rows":[{"attribute_value":"UCEC_CN_HIGH","count":163},{"attribute_value":"UCEC_MSI","count":148},{"attribute_value":"UCEC_CN_LOW","count":147},{"attribute_value":"UCEC_POLE","count":49},{"count":22}]} ▶ clickhouse_run_select_query { "query": " WITH patient_subtype AS ( -- Get one subtype per patient (use maxIf to get the non-null value) SELECT patient_unique_id, cancer_study_identifier, maxIf(attribute_value, attribute_name = 'SUBTYPE' AND attribute_value != '') AS subtype FROM clinical_data_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' GROUP BY patient_unique_id, cancer_study_identifier HAVING subtype != '' ), patient_to_samples AS ( SELECT DISTINCT cd.patient_unique_id, cd.sample_unique_id, ps.subtype FROM clinical_data_derived cd JOIN patient_subtype ps ON cd.patient_unique_id = ps.patient_unique_id WHERE cd.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' ), cnh_data AS ( SELECT ged.hugo_gene_symbol, COUNT(DISTINCT ged.sample_unique_id) AS cnh_altered_samples, COUNT(*) AS cnh_total_events FROM genomic_event_derived ged JOIN patient_to_samples pts ON ged.sample_unique_id = pts.sample_unique_id WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND pts.subtype = 'UCEC_CN_HIGH' AND ged.variant_type = 'mutation' AND ged.mutation_status != 'UNCALLED' AND ged.off_panel = 0 GROUP BY ged.hugo_gene_symbol ), other_data AS ( SELECT ged.hugo_gene_symbol, COUNT(DISTINCT ged.sample_unique_id) AS other_altered_samples, COUNT(*) AS other_total_events FROM genomic_event_derived ged JOIN patient_to_samples pts ON ged.sample_unique_id = pts.sample_unique_id WHERE ged.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND pts.subtype IN ('UCEC_MSI', 'UCEC_CN_LOW', 'UCEC_POLE') AND ged.variant_type = 'mutation' AND ged.mutation_status != 'UNCALLED' AND ged.off_panel = 0 GROUP BY ged.hugo_gene_symbol ), cnh_profile_count AS ( SELECT COUNT(DISTINCT sample_unique_id) as cnh_profiled_samples FROM patient_to_samples WHERE subtype = 'UCEC_CN_HIGH' ), other_profile_count AS ( SELECT COUNT(DISTINCT sample_unique_id) as other_profiled_samples FROM patient_to_samples WHERE subtype IN ('UCEC_MSI', 'UCEC_CN_LOW', 'UCEC_POLE') ) SELECT c.hugo_gene_symbol, c.cnh_altered_samples, cp.cnh_profiled_samples, ROUND(c.cnh_altered_samples * 100.0 / cp.cnh_profiled_samples, 1) AS cnh_frequency_pct, o.other_altered_samples, op.other_profiled_samples, ROUND(COALESCE(o.other_altered_samples, 0) * 100.0 / op.other_profiled_samples, 1) AS other_frequency_pct, ROUND( c.cnh_altered_samples * 100.0 / cp.cnh_profiled_samples - COALESCE(o.other_altered_samples, 0) * 100.0 / op.other_profiled_samples, 1 ) AS enrichment_in_cnh_pct FROM cnh_data c LEFT JOIN other_data o ON c.hugo_gene_symbol = o.hugo_gene_symbol CROSS JOIN cnh_profile_count cp CROSS JOIN other_profile_count op WHERE c.cnh_altered_samples >= 5 ORDER BY cnh_frequency_pct DESC LIMIT 25; ", "max_rows": 100 } ◀ result {"rows":[{"c.hugo_gene_symbol":"TP53","cnh_altered_samples":141,"cnh_profiled_samples":164,"cnh_frequency_pct":86,"other_altered_samples":50,"other_profiled_samples":345,"other_frequency_pct":14.5,"enrichment_in_cnh_pct":71.5},{"c.hugo_gene_symbol":"PIK3CA","cnh_altered_samples":54,"cnh_profiled_samples":164,"cnh_frequency_pct":32.9,"other_altered_samples":200,"other_profiled_samples":345,"other_frequency_pct":58,"enrichment_in_cnh_pct":-25},{"c.hugo_gene_symbol":"PPP2R1A","cnh_altered_samples":50,"cnh_profiled_samples":164,"cnh_frequency_pct":30.5,"other_altered_samples":35,"other_profiled_samples":345,"other_frequency_pct":10.1,"enrichment_in_cnh_pct":20.3},{"c.hugo_gene_symbol":"TTN","cnh_altered_samples":32,"cnh_profiled_samples":164,"cnh_frequency_pct":19.5,"other_altered_samples":171,"other_profiled_samples":345,"other_frequency_pct":49.6,"enrichment_in_cnh_pct":-30.1},{"c.hugo_gene_symbol":"FBXW7","cnh_altered_samples":30,"cnh_profiled_samples":164,"cnh_frequency_pct":18.3,"other_altered_samples":64,"other_profiled_samples":345,"other_frequency_pct":18.6,"enrichment_in_cnh_pct":-0.3},{"c.hugo_gene_symbol":"PTEN","cnh_altered_samples":28,"cnh_profiled_samples":164,"cnh_frequency_pct":17.1,"other_altered_samples":303,"other_profiled_samples":345,"other_frequency_pct":87.8,"enrichment_in_cnh_pct":-70.8},{"c.hugo_gene_symbol":"CHD4","cnh_altered_samples":25,"cnh_profiled_samples":164,"cnh_frequency_pct":15.2,"other_altered_samples":88,"other_profiled_samples":345,"other_frequency_pct":25.5,"enrichment_in_cnh_pct":-10.3},{"c.hugo_gene_symbol":"PIK3R1","cnh_altered_samples":23,"cnh_profiled_samples":164,"cnh_frequency_pct":14,"other_altered_samples":134,"other_profiled_samples":345,"other_frequency_pct":38.8,"enrichment_in_cnh_pct":-24.8},{"c.hugo_gene_symbol":"CSMD3","cnh_altered_samples":20,"cnh_profiled_samples":164,"cnh_frequency_pct":12.2,"other_altered_samples":101,"other_profiled_samples":345,"other_frequency_pct":29.3,"enrichment_in_cnh_pct":-17.1},{"c.hugo_gene_symbol":"ARHGAP35","cnh_altered_samples":18,"cnh_profiled_samples":164,"cnh_frequency_pct":11,"other_altered_samples":85,"other_profiled_samples":345,"other_frequency_pct":24.6,"enrichment_in_cnh_pct":-13.7},{"c.hugo_gene_symbol":"MUC16","cnh_altered_samples":17,"cnh_profiled_samples":164,"cnh_frequency_pct":10.4,"other_altered_samples":125,"other_profiled_samples":345,"other_frequency_pct":36.2,"enrichment_in_cnh_pct":-25.9},{"c.hugo_gene_symbol":"RYR2","cnh_altered_samples":13,"cnh_profiled_samples":164,"cnh_frequency_pct":7.9,"other_altered_samples":112,"other_profiled_samples":345,"other_frequency_pct":32.5,"enrichment_in_cnh_pct":-24.5},{"c.hugo_gene_symbol":"SPOP","cnh_altered_samples":13,"cnh_profiled_samples":164,"cnh_frequency_pct":7.9,"other_altered_samples":42,"other_profiled_samples":345,"other_frequency_pct":12.2,"enrichment_in_cnh_pct":-4.2},{"c.hugo_gene_symbol":"TAF1","cnh_altered_samples":12,"cnh_profiled_samples":164,"cnh_frequency_pct":7.3,"other_altered_sample … (2497 more chars) ▶ 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 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▶ navigate_to_results_view { "studyIds": [ "ucec_tcga_pan_can_atlas_2018" ], "genes": [ "TP53", "PPP2R1A", "PIK3CA" ], "tab": "mutations", "studyViewFilter": { "clinicalDataFilters": [ { "attributeId": "SUBTYPE", "values": [ { "value": "UCEC_CN_HIGH" } ] } ] } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/mutations?session_id=6ab5ba65c2115c492d884deb","url":"https://www.cbioportal.org/results/mutations?session_id=6ab5ba65c2115c492d884deb","data":{"studyIds":["ucec_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer 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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","url":"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","data":{"studyIds":["ucec_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}} ═ answer (success) ## Summary: Most Frequently Mutated Genes in Copy-Number High Endometrial Cancers Based on analysis of the **TCGA Uterine Corpus Endometrial Carcinoma (PanCancer Atlas)** with 163 copy-number high (CNH) samples and 345 other subtype samples: ### **Top Genes Enriched in CNH Subtype:** | Gene | CNH Frequency | Other Subtypes | CNH Enrichment | |------|--------------|-----------------|----------------| | **TP53** | **86.0%** (141/164) | 14.5% (50/345) | +**71.5%** ↑ | | **PPP2R1A** | **30.5%** (50/164) | 10.1% (35/345) | +**20.3%** ↑ | | FBXW7 | 18.3% (30/164) | 18.6% (64/345) | −0.3% | | FOXA2 | 7.3% (12/164) | 7.0% (24/345) | +0.4% ↑ | ### **Most Common Genes in CNH (Overall):** 1. **TP53** — 86% (141/164 samples) — *highly specific to CNH* 2. **PIK3CA** — 32.9% (54/164) — present across all subtypes (58% in others) 3. **PPP2R1A** — 30.5% (50/164) — *enriched in CNH* 4. TTN — 19.5% (32/164) — common passenger gene, more frequent in other subtypes (49.6%) 5. FBXW7 — 18.3% (30/164) — comparable across subtypes ### **Key Finding:** **TP53 is the defining mutation of the CNH subtype**, appearing in 86% of CNH endometrial cancers versus only 14.5% in other subtypes (MSI, CN-low, POLE). This is clinically important because TP53 mutations in CNH endometrial cancers drive a serous-like phenotype with poor prognosis, regardless of histological type. --- ### **Explore the Data:** - **[View copy-number high endometrial cancer cohort](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)** — StudyView summary statistics and clinical distributions - **[View TP53, PPP2R1A, and PIK3CA mutations in CNH samples](https://www.cbioportal.org/results/mutations?session_id=6ab5ba65c2115c492d884deb)** — Detailed mutation table with protein changes and lollipop plots