Q (sonnet): Can you identify a mutations that are highly specific to a single cancer type?

▶ 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)

▶ clickhouse_run_select_query
{
 "query": "
WITH cohort AS (
    SELECT cancer_study_identifier
    FROM cancer_study_query_preferences
    WHERE preference_name = 'pan_cancer_tcga'
),
sample_cancer_type AS (
    SELECT cd.sample_unique_id, cd.attribute_value AS cancer_type, cd.cancer_study_identifier
    FROM clinical_data_derived cd
    JOIN cohort c USING (cancer_study_identifier)
    WHERE cd.attribute_name = 'CANCER_TYPE'
),
altered AS (
    SELECT ged.hugo_gene_symbol,
           sct.cancer_type,
           COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    WHERE ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
      AND ged.off_panel = 0
    GROUP BY ged.hugo_gene_symbol, sct.cancer_type
),
profiled_samples_for_gene AS (
    SELECT sample_unique_id, cancer_study_identifier
    FROM mutation_wes_coverage
),
profiled AS (
    SELECT sct.cancer_type,
           COUNT(DISTINCT p.sample_unique_id) AS profiled_samples
    FROM profiled_samples_for_gene p
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    GROUP BY sct.cancer_type
),
gene_totals AS (
    SELECT hugo_gene_symbol, SUM(altered_samples) AS total_altered
    FROM altered
    GROUP BY hugo_gene_symbol
    HAVING total_altered >= 30
),
gene_top_type AS (
    SELECT a.hugo_gene_symbol,
           a.cancer_type AS top_cancer_type,
           a.altered_samples AS top_altered,
           p.profiled_samples AS top_profiled,
           gt.total_altered
    FROM altered a
    JOIN profiled p USING (cancer_type)
    JOIN gene_totals gt USING (hugo_gene_symbol)
    QUALIFY row_number() OVER (PARTITION BY a.hugo_gene_symbol ORDER BY a.altered_samples DESC) = 1
)
SELECT hugo_gene_symbol,
       top_cancer_type,
       top_altered,
       top_profiled,
       ROUND(top_altered*100.0/top_profiled,1) AS freq_in_top_type_pct,
       total_altered,
       ROUND(top_altered*100.0/total_altered,1) AS pct_of_all_mutations_in_top_type
FROM gene_top_type
WHERE total_altered >= 30
ORDER BY pct_of_all_mutations_in_top_type DESC, top_altered DESC
LIMIT 40
",
 "max_rows": 40
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 47\n Code: 47. DB::Exception: Unknown expression identifier `hugo_gene_symbol` in scope WITH cohort AS (SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga'), sample_cancer_type AS (SELECT cd.sample_unique_id, cd.attribute_value AS cancer_type, cd.cancer_study_identifier FROM clinical_data_derived AS cd INNER JOIN cohort AS c USING (cancer_study_identifier) WHERE cd.attribute_name = 'CANCER_TYPE'), altered AS (SELECT ged.hugo_gene_symbol, sct.cancer_type, COUNTDistinct(ged.sample_unique_id) AS altered_samples FROM genomic_event_derived AS ged INNER JOIN cohort AS c USING (cancer_study_identifier) INNER JOIN sample_cancer_type AS sct USING (sample_unique_id) WHERE (ged.variant_type = 'mutation') AND (ged.mutation_status != 'UNCALLED') AND (ged.off_panel = 0) GROUP BY ged.hugo_gene_symbol, sct.cancer_type), profiled_samples_for_gene AS (SELECT sample_unique_id, cancer_study_identifier FROM mutation_wes_coverage), profiled AS (SELECT sct.cancer_type, COUNTDistinct(p.sample_unique_id) AS profiled_samples FROM profiled_samples_for_gene AS p INNER JOIN cohort AS c USING (cancer_study_identifier) INNER JOIN sample_cancer_type AS sct USING (sample_unique_id) GROUP BY sct.cancer_type), gene_totals AS (SELECT hugo_gene_symbol, SUM(altered_samples) AS total_altered FROM altered GROUP BY hugo_gene_symbol HAVING total_altered >= 30), gene_top_type AS (SELECT a.hugo_gene_symbol, a.cancer_type AS top_cancer_type, a.altered_samples AS top_altered, p.profiled_samples AS top_profiled, gt.total_altered FROM altered AS a INNER JOIN profiled AS p USING (cancer_type) INNER JOIN gene_totals AS gt USING (hugo_gene_symbol) QUALIFY row_number() OVER (PARTITION BY a.hugo_gene_symbol ORDER BY a.altered_samples DESC) = 1) SELECT hugo_gene_symbol, top_cancer_type, top_altered, top_profiled, ROUND((top_altered * 100.) / top_profiled, 1) AS freq_in_top_type_pct, total_altered, ROUND((top_altered * 100.) / total_altered, 1) AS pct_of_all_mutations_in_top_type FROM gene_top_type WHERE total_altered >= 30 ORDER BY pct_of_all_mutations_in_top_type DESC, top_altered DESC LIMIT 40. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
WITH cohort AS (
    SELECT cancer_study_identifier
    FROM cancer_study_query_preferences
    WHERE preference_name = 'pan_cancer_tcga'
),
sample_cancer_type AS (
    SELECT cd.sample_unique_id, cd.attribute_value AS cancer_type, cd.cancer_study_identifier
    FROM clinical_data_derived cd
    JOIN cohort c USING (cancer_study_identifier)
    WHERE cd.attribute_name = 'CANCER_TYPE'
),
altered AS (
    SELECT ged.hugo_gene_symbol AS gene,
           sct.cancer_type AS ctype,
           COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    WHERE ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
      AND ged.off_panel = 0
    GROUP BY gene, ctype
),
profiled_samples_for_gene AS (
    SELECT sample_unique_id, cancer_study_identifier
    FROM mutation_wes_coverage
),
profiled AS (
    SELECT sct.cancer_type AS ctype,
           COUNT(DISTINCT p.sample_unique_id) AS profiled_samples
    FROM profiled_samples_for_gene p
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    GROUP BY ctype
),
gene_totals AS (
    SELECT gene, SUM(altered_samples) AS total_altered
    FROM altered
    GROUP BY gene
    HAVING total_altered >= 30
),
joined AS (
    SELECT a.gene AS gene,
           a.ctype AS top_cancer_type,
           a.altered_samples AS top_altered,
           p.profiled_samples AS top_profiled,
           gt.total_altered AS total_altered,
           row_number() OVER (PARTITION BY a.gene ORDER BY a.altered_samples DESC) AS rn
    FROM altered a
    JOIN profiled p ON a.ctype = p.ctype
    JOIN gene_totals gt ON a.gene = gt.gene
)
SELECT gene,
       top_cancer_type,
       top_altered,
       top_profiled,
       ROUND(top_altered*100.0/top_profiled,1) AS freq_in_top_type_pct,
       total_altered,
       ROUND(top_altered*100.0/total_altered,1) AS pct_of_all_mutations_in_top_type
FROM joined
WHERE rn = 1
ORDER BY pct_of_all_mutations_in_top_type DESC, top_altered DESC
LIMIT 40
",
 "max_rows": 40
}

◀ result
{"rows":[{"gene":"VHL","top_cancer_type":"Renal Clear Cell Carcinoma","top_altered":166,"top_profiled":402,"freq_in_top_type_pct":41.3,"total_altered":199,"pct_of_all_mutations_in_top_type":83.4},{"gene":"ANKRD31","top_cancer_type":"Endometrial Cancer","top_altered":40,"top_profiled":574,"freq_in_top_type_pct":7,"total_altered":50,"pct_of_all_mutations_in_top_type":80},{"gene":"WDR97","top_cancer_type":"Endometrial Cancer","top_altered":24,"top_profiled":574,"freq_in_top_type_pct":4.2,"total_altered":31,"pct_of_all_mutations_in_top_type":77.4},{"gene":"ANKRD62","top_cancer_type":"Endometrial Cancer","top_altered":36,"top_profiled":574,"freq_in_top_type_pct":6.3,"total_altered":49,"pct_of_all_mutations_in_top_type":73.5},{"gene":"LRRC9","top_cancer_type":"Endometrial Cancer","top_altered":39,"top_profiled":574,"freq_in_top_type_pct":6.8,"total_altered":54,"pct_of_all_mutations_in_top_type":72.2},{"gene":"IDH1","top_cancer_type":"Glioma","top_altered":395,"top_profiled":514,"freq_in_top_type_pct":76.8,"total_altered":553,"pct_of_all_mutations_in_top_type":71.4},{"gene":"ERVMER34-1","top_cancer_type":"Endometrial Cancer","top_altered":23,"top_profiled":574,"freq_in_top_type_pct":4,"total_altered":33,"pct_of_all_mutations_in_top_type":69.7},{"gene":"STARD9","top_cancer_type":"Endometrial Cancer","top_altered":50,"top_profiled":574,"freq_in_top_type_pct":8.7,"total_altered":77,"pct_of_all_mutations_in_top_type":64.9},{"gene":"RAD21L1","top_cancer_type":"Endometrial Cancer","top_altered":29,"top_profiled":574,"freq_in_top_type_pct":5.1,"total_altered":46,"pct_of_all_mutations_in_top_type":63},{"gene":"ZFHX2","top_cancer_type":"Endometrial Cancer","top_altered":41,"top_profiled":574,"freq_in_top_type_pct":7.1,"total_altered":68,"pct_of_all_mutations_in_top_type":60.3},{"gene":"ZNF717","top_cancer_type":"Endometrial Cancer","top_altered":18,"top_profiled":574,"freq_in_top_type_pct":3.1,"total_altered":30,"pct_of_all_mutations_in_top_type":60},{"gene":"FAM47E","top_cancer_type":"Endometrial Cancer","top_altered":18,"top_profiled":574,"freq_in_top_type_pct":3.1,"total_altered":30,"pct_of_all_mutations_in_top_type":60},{"gene":"EFCAB8","top_cancer_type":"Endometrial Cancer","top_altered":25,"top_profiled":574,"freq_in_top_type_pct":4.4,"total_altered":42,"pct_of_all_mutations_in_top_type":59.5},{"gene":"GOLGA6L6","top_cancer_type":"Endometrial Cancer","top_altered":22,"top_profiled":574,"freq_in_top_type_pct":3.8,"total_altered":37,"pct_of_all_mutations_in_top_type":59.5},{"gene":"PATL2","top_cancer_type":"Endometrial Cancer","top_altered":19,"top_profiled":574,"freq_in_top_type_pct":3.3,"total_altered":32,"pct_of_all_mutations_in_top_type":59.4},{"gene":"NPM1","top_cancer_type":"Leukemia","top_altered":54,"top_profiled":200,"freq_in_top_type_pct":27,"total_altered":91,"pct_of_all_mutations_in_top_type":59.3},{"gene":"HMCN2","top_cancer_type":"Endometrial Cancer","top_altered":20,"top_profiled":574,"freq_in_top_type_pct":3.5,"total_altered":34,"pct_of_all_m … (4141 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT * FROM gene_mutation_frequency_by_cancer_type(preference='pan_cancer_tcga', gene='VHL') ORDER BY frequency_pct DESC
",
 "max_rows": 40
}

◀ result
{"rows":[{"cancer_type":"Renal Clear Cell Carcinoma","altered_samples":166,"profiled_samples":402,"frequency_pct":41.3},{"cancer_type":"Pheochromocytoma","altered_samples":3,"profiled_samples":147,"frequency_pct":2},{"cancer_type":"Thymic Epithelial Tumor","altered_samples":2,"profiled_samples":123,"frequency_pct":1.6},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":4,"profiled_samples":341,"frequency_pct":1.2},{"cancer_type":"Melanoma","altered_samples":5,"profiled_samples":440,"frequency_pct":1.1},{"cancer_type":"Endometrial Cancer","altered_samples":5,"profiled_samples":574,"frequency_pct":0.9},{"cancer_type":"Colorectal Cancer","altered_samples":4,"profiled_samples":534,"frequency_pct":0.7},{"cancer_type":"Prostate Cancer","altered_samples":3,"profiled_samples":494,"frequency_pct":0.6},{"cancer_type":"Bladder Cancer","altered_samples":2,"profiled_samples":410,"frequency_pct":0.5},{"cancer_type":"Esophagogastric Cancer","altered_samples":3,"profiled_samples":618,"frequency_pct":0.5},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":2,"profiled_samples":1050,"frequency_pct":0.2}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT * FROM gene_mutation_frequency_by_cancer_type(preference='pan_cancer_tcga', gene='IDH1') ORDER BY frequency_pct DESC
",
 "max_rows": 40
}

▶ clickhouse_run_select_query
{
 "query": "
SELECT * FROM gene_mutation_frequency_by_cancer_type(preference='pan_cancer_tcga', gene='NPM1') ORDER BY frequency_pct DESC
",
 "max_rows": 40
}

◀ result
{"rows":[{"cancer_type":"Glioma","altered_samples":395,"profiled_samples":514,"frequency_pct":76.8},{"cancer_type":"Leukemia","altered_samples":19,"profiled_samples":200,"frequency_pct":9.5},{"cancer_type":"Glioblastoma","altered_samples":25,"profiled_samples":397,"frequency_pct":6.3},{"cancer_type":"Melanoma","altered_samples":26,"profiled_samples":440,"frequency_pct":5.9},{"cancer_type":"Endometrial Cancer","altered_samples":20,"profiled_samples":574,"frequency_pct":3.5},{"cancer_type":"Bladder Cancer","altered_samples":9,"profiled_samples":410,"frequency_pct":2.2},{"cancer_type":"Hepatobiliary Cancer","altered_samples":8,"profiled_samples":366,"frequency_pct":2.2},{"cancer_type":"Colorectal Cancer","altered_samples":9,"profiled_samples":534,"frequency_pct":1.7},{"cancer_type":"Prostate Cancer","altered_samples":5,"profiled_samples":494,"frequency_pct":1},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":9,"profiled_samples":1050,"frequency_pct":0.9},{"cancer_type":"Head and Neck Cancer","altered_samples":4,"profiled_samples":515,"frequency_pct":0.8},{"cancer_type":"Esophagogastric Cancer","altered_samples":5,"profiled_samples":618,"frequency_pct":0.8},{"cancer_type":"Thymic Epithelial Tumor","altered_samples":1,"profiled_samples":123,"frequency_pct":0.8},{"cancer_type":"Sarcoma","altered_samples":2,"profiled_samples":255,"frequency_pct":0.8},{"cancer_type":"Pancreatic Cancer","altered_samples":1,"profiled_samples":179,"frequency_pct":0.6},{"cancer_type":"Breast Cancer","altered_samples":6,"profiled_samples":1066,"frequency_pct":0.6},{"cancer_type":"Renal Clear Cell Carcinoma","altered_samples":2,"profiled_samples":402,"frequency_pct":0.5},{"cancer_type":"Ovarian Epithelial Tumor","altered_samples":1,"profiled_samples":523,"frequency_pct":0.2}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT * FROM gene_mutation_frequency_by_cancer_type(preference='pan_cancer_tcga', gene='GNAQ') ORDER BY frequency_pct DESC
",
 "max_rows": 40
}

◀ result
{"rows":[{"cancer_type":"Leukemia","altered_samples":54,"profiled_samples":200,"frequency_pct":27},{"cancer_type":"Melanoma","altered_samples":7,"profiled_samples":440,"frequency_pct":1.6},{"cancer_type":"Endometrial Cancer","altered_samples":9,"profiled_samples":574,"frequency_pct":1.6},{"cancer_type":"Colorectal Cancer","altered_samples":6,"profiled_samples":534,"frequency_pct":1.1},{"cancer_type":"Cervical Cancer","altered_samples":2,"profiled_samples":291,"frequency_pct":0.7},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":6,"profiled_samples":1050,"frequency_pct":0.6},{"cancer_type":"Head and Neck Cancer","altered_samples":2,"profiled_samples":515,"frequency_pct":0.4},{"cancer_type":"Hepatobiliary Cancer","altered_samples":1,"profiled_samples":366,"frequency_pct":0.3},{"cancer_type":"Glioblastoma","altered_samples":1,"profiled_samples":397,"frequency_pct":0.3},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":1,"profiled_samples":341,"frequency_pct":0.3},{"cancer_type":"Glioma","altered_samples":1,"profiled_samples":514,"frequency_pct":0.2},{"cancer_type":"Esophagogastric Cancer","altered_samples":1,"profiled_samples":618,"frequency_pct":0.2}]}

◀ result
{"rows":[{"cancer_type":"Ocular Melanoma","altered_samples":40,"profiled_samples":80,"frequency_pct":50},{"cancer_type":"Melanoma","altered_samples":15,"profiled_samples":440,"frequency_pct":3.4},{"cancer_type":"Endometrial Cancer","altered_samples":16,"profiled_samples":574,"frequency_pct":2.8},{"cancer_type":"Seminoma","altered_samples":1,"profiled_samples":63,"frequency_pct":1.6},{"cancer_type":"Colorectal Cancer","altered_samples":8,"profiled_samples":534,"frequency_pct":1.5},{"cancer_type":"Esophagogastric Cancer","altered_samples":5,"profiled_samples":618,"frequency_pct":0.8},{"cancer_type":"Bladder Cancer","altered_samples":3,"profiled_samples":410,"frequency_pct":0.7},{"cancer_type":"Cervical Cancer","altered_samples":2,"profiled_samples":291,"frequency_pct":0.7},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":7,"profiled_samples":1050,"frequency_pct":0.7},{"cancer_type":"Pancreatic Cancer","altered_samples":1,"profiled_samples":179,"frequency_pct":0.6},{"cancer_type":"Hepatobiliary Cancer","altered_samples":1,"profiled_samples":366,"frequency_pct":0.3},{"cancer_type":"Breast Cancer","altered_samples":3,"profiled_samples":1066,"frequency_pct":0.3},{"cancer_type":"Glioblastoma","altered_samples":1,"profiled_samples":397,"frequency_pct":0.3}]}

▶ resolve_and_route
{
 "studyIds": [
  "uvm_tcga_pan_can_atlas_2018",
  "lgg_tcga_pan_can_atlas_2018",
  "kirc_tcga_pan_can_atlas_2018",
  "laml_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 4 matching studies. Pick the best match (prefer TCGA → prefer PanCancer Atlas), use its metadata to call the appropriate navigation tool(s) and generate URLs immediately. Present other studies as alternatives.","data":{"totalCount":4,"studiesWithMetadata":[{"studyId":"uvm_tcga_pan_can_atlas_2018","name":"Uveal Melanoma (TCGA, PanCancer Atlas)","sampleCount":80,"studyViewUrl":"https://www.cbioportal.org/study?id=uvm_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_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","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"],"molecularProfileIds":["uvm_tcga_pan_can_atlas_2018_gistic","uvm_tcga_pan_can_atlas_2018_log2CNA","uvm_tcga_pan_can_atlas_2018_mutations","uvm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","uvm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","uvm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","uvm_tcga_pan_can_atlas_2018_rppa","uvm_tcga_pan_can_atlas_2018_rppa_Zscores","uvm_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["uvm_tcga_pan_can_atlas_2018_armlevel_cna","uvm_tcga_pan_can_atlas_2018_genetic_ancestry","uvm_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","uvm_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["uvm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","uvm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","uvm_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"]}},{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514,"studyViewUrl":"https://www.cbioportal.org/study?id=lgg_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_S … (6960 more chars)

▶ navigate_to_results_view
{
 "studyIds": [
  "uvm_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "GNAQ",
  "GNA11"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=uvm_tcga_pan_can_atlas_2018&gene_list=GNAQ%0AGNA11&case_set_id=uvm_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=uvm_tcga_pan_can_atlas_2018&gene_list=GNAQ%0AGNA11&case_set_id=uvm_tcga_pan_can_atlas_2018_all","data":{"studyIds":["uvm_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"uvm_tcga_pan_can_atlas_2018","name":"Uveal Melanoma (TCGA, PanCancer Atlas)","sampleCount":80}],"genes":["GNAQ","GNA11"],"caseSetId":"uvm_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "IDH1"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=IDH1&case_set_id=lgg_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=IDH1&case_set_id=lgg_tcga_pan_can_atlas_2018_all","data":{"studyIds":["lgg_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514}],"genes":["IDH1"],"caseSetId":"lgg_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "kirc_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "VHL"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=kirc_tcga_pan_can_atlas_2018&gene_list=VHL&case_set_id=kirc_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=kirc_tcga_pan_can_atlas_2018&gene_list=VHL&case_set_id=kirc_tcga_pan_can_atlas_2018_all","data":{"studyIds":["kirc_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"kirc_tcga_pan_can_atlas_2018","name":"Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)","sampleCount":512}],"genes":["VHL"],"caseSetId":"kirc_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "laml_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "NPM1"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=laml_tcga_pan_can_atlas_2018&gene_list=NPM1&case_set_id=laml_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=laml_tcga_pan_can_atlas_2018&gene_list=NPM1&case_set_id=laml_tcga_pan_can_atlas_2018_all","data":{"studyIds":["laml_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"laml_tcga_pan_can_atlas_2018","name":"Acute Myeloid Leukemia (TCGA, PanCancer Atlas)","sampleCount":200}],"genes":["NPM1"],"caseSetId":"laml_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
Explore each in the OncoPrint view:

- **[GNAQ/GNA11 in Uveal Melanoma](https://www.cbioportal.org/results/oncoprint?cancer_study_list=uvm_tcga_pan_can_atlas_2018&gene_list=GNAQ%0AGNA11&case_set_id=uvm_tcga_pan_can_atlas_2018_all)** (80 samples) — the two together typically account for the majority of driver events in this rare melanoma subtype
- **[IDH1 in Lower-Grade Glioma](https://www.cbioportal.org/results/oncoprint?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=IDH1&case_set_id=lgg_tcga_pan_can_atlas_2018_all)** (514 samples)
- **[VHL in Clear Cell Renal Carcinoma](https://www.cbioportal.org/results/oncoprint?cancer_study_list=kirc_tcga_pan_can_atlas_2018&gene_list=VHL&case_set_id=kirc_tcga_pan_can_atlas_2018_all)** (512 samples)
- **[NPM1 in Acute Myeloid Leukemia](https://www.cbioportal.org/results/oncoprint?cancer_study_list=laml_tcga_pan_can_atlas_2018&gene_list=NPM1&case_set_id=laml_tcga_pan_can_atlas_2018_all)** (200 samples)

Let me know if you'd like me to run this same cross-cancer specificity scan restricted to a particular gene panel, driver-annotated mutations only, or a non-TCGA cohort (e.g., MSK-IMPACT) for broader/rarer subtype coverage.