Q (haiku): Which cancer types show the highest frequency of BRAF V600E mutations across all TCGA Pan-Cancer Atlas studies?

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-guide"
}

▶ read_guide
{
 "uri": "cbioportal://common-pitfalls#16"
}

◀ 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
### 16. 🚨 SILENT QUERY SUBSTITUTION ("did you mean...")

When the user's wording differs from canonical terminology (e.g. "V600V" looks like "V600E" with a typo, or "point mutation" sounds like "missense"), it is forbidden to silently rewrite the question and answer the rewritten version. Doing so produces an answer that looks confident but is for a different question — the user cannot tell what was changed.

#### ❌ Wrong: silently substitute

> User: *"Find patients in colorectal cancer with the V600V alteration in BRAF"*
> Agent: *(internally treats this as V600E)* "I found 412 samples with BRAF V600E in colorectal studies..."

> User: *"What is the most prevalent TP53 mutation in uterine cancer that is not a point mutation?"*
> Agent: *(internally treats "point mutation" = "missense", silently excludes only missense)* "The most prevalent non-missense TP53 mutation is..."

#### ✅ Correct: answer the literal question, flag any normalization

For an unusual-looking variant the user may have typed deliberately:
- Query for what was asked, literally.
- If 0 rows come back, **explain *why* zero is the expected answer** before suggesting a likely-intended alternative. For synonymous variants (e.g. BRAF V600V, TP53 R175R), the explanation is: *cBioPortal's mutation tables filter out synonymous (silent) variants in most studies, so 0 hits means "filtered upstream", not "no such variant exists in any patient"*. Then ask: *"Did you mean V600E (the canonical activating variant)? Or would you like me to look for V600V in the studies that do retain synonymous calls?"*
- If the wording is ambiguous (e.g. "point mutation"), ask the user which definition they meant before querying — do not pick one silently.

#### Mutation-type terminology mapping (use this to disambiguate)

| User says | Canonical definition | `mutation_type` filter |
|---|---|---|
| "point mutation" | Any SNV (single-nucleotide variant) — includes missense, nonsense, synonymous, splice-site SNVs | `mutation_type IN ('Missense_Mutation','Nonsense_Mutation','Silent','Splice_Site')` — **but ask the user to confirm scope first** |
| "missense" | Single amino-acid substitution that changes the protein | `mutation_type = 'Missense_Mutation'` |
| "nonsense" / "stop-gain" | Premature stop codon | `mutation_type = 'Nonsense_Mutation'` |
| "synonymous" / "silent" | Nucleotide change with no amino-acid change | `mutation_type = 'Silent'` (**often filtered out of public datasets** — see below) |
| "splice site" | Mutation in canonical splice acceptor/donor | `mutation_type = 'Splice_Site'` |
| "frameshift" | Indel changing reading frame | `mutation_type IN ('Frame_Shift_Ins','Frame_Shift_Del')` |
| "indel" / "in-frame" | In-frame insertion or deletion | `mutation_type IN ('In_Frame_Ins','In_Frame_Del')` |
| "truncating" | Anything that disrupts the protein early | `mutation_type IN ('Nonsense_Mutation','Frame_Shift_Ins','Frame_Shift_Del','Splice_Site','Nonstop_Mutation')` |

**Synonymous-variant filter. … (785 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT *
FROM gene_mutation_frequency_by_cancer_type(
    preference = 'pan_cancer_tcga',
    gene       = 'BRAF'
)
WHERE mutation_variant = 'V600E'
ORDER BY frequency_pct DESC;
",
 "max_rows": 50
}

◀ 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 or function identifier `mutation_variant` in scope SELECT * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'BRAF') WHERE mutation_variant = 'V600E' ORDER BY frequency_pct DESC. (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
    FROM clinical_data_derived cd
    JOIN cohort c USING (cancer_study_identifier)
    WHERE cd.attribute_name = 'CANCER_TYPE'
),
altered AS (
    SELECT 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.hugo_gene_symbol = 'BRAF'
      AND ged.mutation_variant = 'V600E'
      AND ged.off_panel = 0
    GROUP BY sct.cancer_type
),
profiled_samples_for_gene AS (
    SELECT sample_unique_id, cancer_study_identifier
    FROM mutation_panel_gene_coverage
    WHERE hugo_gene_symbol = 'BRAF'
    UNION ALL
    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
)
SELECT a.cancer_type,
       a.altered_samples,
       p.profiled_samples,
       ROUND(a.altered_samples * 100.0 / NULLIF(p.profiled_samples, 0), 1) AS frequency_pct
FROM altered a
JOIN profiled p USING (cancer_type)
WHERE p.profiled_samples >= 50
ORDER BY frequency_pct DESC;
",
 "max_rows": 50
}

◀ result
{"rows":[{"cancer_type":"Thyroid Cancer","altered_samples":284,"profiled_samples":490,"frequency_pct":58},{"cancer_type":"Melanoma","altered_samples":158,"profiled_samples":440,"frequency_pct":35.9},{"cancer_type":"Colorectal Cancer","altered_samples":48,"profiled_samples":534,"frequency_pct":9},{"cancer_type":"Glioblastoma","altered_samples":5,"profiled_samples":397,"frequency_pct":1.3},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":9,"profiled_samples":1050,"frequency_pct":0.9},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":2,"profiled_samples":341,"frequency_pct":0.6},{"cancer_type":"Glioma","altered_samples":1,"profiled_samples":514,"frequency_pct":0.2},{"cancer_type":"Bladder Cancer","altered_samples":1,"profiled_samples":410,"frequency_pct":0.2},{"cancer_type":"Head and Neck Cancer","altered_samples":1,"profiled_samples":515,"frequency_pct":0.2}]}

▶ resolve_and_route
{
 "studyIds": [
  "thca_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018",
  "gbm_tcga_pan_can_atlas_2018",
  "luad_tcga_pan_can_atlas_2018",
  "kirp_tcga_pan_can_atlas_2018",
  "lgggbm_tcga_pan_can_atlas_2018",
  "blca_tcga_pan_can_atlas_2018",
  "hnsc_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":false,"error":"Invalid study ID(s): lgggbm_tcga_pan_can_atlas_2018","details":{"invalidIds":["lgggbm_tcga_pan_can_atlas_2018"],"providedIds":["thca_tcga_pan_can_atlas_2018","skcm_tcga_pan_can_atlas_2018","coadread_tcga_pan_can_atlas_2018","gbm_tcga_pan_can_atlas_2018","luad_tcga_pan_can_atlas_2018","kirp_tcga_pan_can_atlas_2018","lgggbm_tcga_pan_can_atlas_2018","blca_tcga_pan_can_atlas_2018","hnsc_tcga_pan_can_atlas_2018"]}}

▶ resolve_and_route
{
 "studyIds": [
  "thca_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 3 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":3,"studiesWithMetadata":[{"studyId":"thca_tcga_pan_can_atlas_2018","name":"Thyroid Carcinoma (TCGA, PanCancer Atlas)","sampleCount":500,"studyViewUrl":"https://www.cbioportal.org/study?id=thca_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":["thca_tcga_pan_can_atlas_2018_gistic","thca_tcga_pan_can_atlas_2018_log2CNA","thca_tcga_pan_can_atlas_2018_mutations","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","thca_tcga_pan_can_atlas_2018_rppa","thca_tcga_pan_can_atlas_2018_rppa_Zscores","thca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["thca_tcga_pan_can_atlas_2018_armlevel_cna","thca_tcga_pan_can_atlas_2018_genetic_ancestry","thca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","thca_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","thca_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":"skcm_tcga_pan_can_atlas_2018","name":"Skin Cutaneous Melan … (5328 more chars)

▶ navigate_to_results_view
{
 "studyIds": [
  "thca_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "BRAF"
 ],
 "tab": "mutations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/mutations?cancer_study_list=thca_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018&gene_list=BRAF&case_set_id=all","url":"https://www.cbioportal.org/results/mutations?cancer_study_list=thca_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018&gene_list=BRAF&case_set_id=all","data":{"studyIds":["thca_tcga_pan_can_atlas_2018","skcm_tcga_pan_can_atlas_2018","coadread_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"thca_tcga_pan_can_atlas_2018","name":"Thyroid Carcinoma (TCGA, PanCancer Atlas)","sampleCount":500},{"studyId":"skcm_tcga_pan_can_atlas_2018","name":"Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)","sampleCount":448},{"studyId":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594}],"genes":["BRAF"],"caseSetId":"all","pageDescription":"Detailed mutation table with lollipop plot showing mutation positions, types, and frequencies for queried genes."}}

═ answer (success)
## BRAF V600E Mutation Frequency Across TCGA Pan-Cancer Atlas Studies

**Top 3 cancer types with highest BRAF V600E mutation frequency:**

| Cancer Type | V600E Samples | Profiled Samples | Frequency |
|---|---|---|---|
| **Thyroid Cancer** | 284 | 490 | **58.0%** |
| **Melanoma** | 158 | 440 | **35.9%** |
| **Colorectal Cancer** | 48 | 534 | **9.0%** |

BRAF V600E dominates in **thyroid cancer**, where it appears in more than half of all sequenced samples. This is the defining mutation in papillary thyroid carcinoma and other thyroid malignancies in these cohorts. **Melanoma** is the second-highest, reflecting V600E as a major BRAF-mutant subtype in skin tumors. **Colorectal cancer** carries the mutation at lower frequency (9%), typically in subset of microsatellite-unstable or specific adenocarcinoma subtypes.

The mutation is rare in lung, brain, kidney, and head/neck cancers (0.2–0.9%).

### Explore the Data

[View BRAF mutations in top three cancer types](https://www.cbioportal.org/results/mutations?cancer_study_list=thca_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018&gene_list=BRAF&case_set_id=all) — Mutations tab shows the full variant spectrum and lollipop plot for each cancer type.