Q (haiku): In MSS colorectal cancer, what is the frequency of BRAF oncogenic mutations (as defined by OncoKB)? Use the 2017 MSK study for this. Can you give me a table of all these mutations, with frequency, count and denominator?

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

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

▶ list_studies
{
 "search": "MSK colorectal 2017"
}

◀ 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)

◀ 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)

◀ result
{"result":[{"cancer_study_identifier":"crc_msk_2017","name":"Metastatic Colorectal Cancer (MSK, Cancer Cell 2018)","type_of_cancer_id":"coadread","sample_count":1134,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_msk_2017"}]}

▶ get_study_guide
{
 "study_id": "crc_msk_2017"
}

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-guide"
}

◀ result
# Study Guide: Metastatic Colorectal Cancer (MSK, Cancer Cell 2018)

**Study ID:** `crc_msk_2017`
**Cancer Type:** coadread
**Description:** Targeted sequencing of 1134 metastatic colorectal tumor/normal pairs.

## Cohort Statistics
- **Patients:** 1,099
- **Samples:** 1,135

## Available Data Types

- **COPY_NUMBER_ALTERATION**: Putative copy-number alterations from GISTIC
- **MUTATION_EXTENDED**: Mutations
- **STRUCTURAL_VARIANT**: Structural variants

## Gene Panels

- **IMPACT410**: 911 samples
- **IMPACT341**: 214 samples
- **IMPACT468**: 9 samples

## Available Clinical Attributes

| Attribute | Samples with Data |
|-----------|------------------|
| TIME_FROM_MET_DX_SEQUENCING | 1,134 |
| METASTASES_SITE_FIRST_LIVER | 1,134 |
| PRIMARY_SITE | 1,134 |
| METASTATIC_BIOPSY_SITE | 1,134 |
| MUTATION_COUNT | 1,134 |
| MSI_STATUS | 1,134 |
| SOMATIC_STATUS | 1,134 |
| METASTASES_SITE_FIRST_PERITONEUM_OMENTUM_ABDOMEN | 1,134 |
| METASTASES_SITE_FIRST_BONE | 1,134 |
| METASTASES_SITE_FIRST_GYN | 1,134 |
| METASTASES_SITE_FIRST_LN | 1,134 |
| TIME_TO_METASTASIS_MONTHS | 1,134 |
| FRACTION_GENOME_ALTERED | 1,134 |
| TMB_NONSYNONYMOUS | 1,134 |
| SPECIMEN_TYPE | 1,134 |
| METASTASES_SITE_FIRST_LUNG | 1,134 |
| STAGE_AT_DIAGNOSIS | 1,134 |
| FIRST_SITE_OF_METS | 1,134 |
| CHEMO_EXP_SEQ_SPECIMEN | 1,134 |
| METASTASES_SITE_FIRST_BRAIN | 1,134 |

## Top Mutated Genes

| Gene | Altered Samples |
|------|----------------|
| APC | 867 |
| TP53 | 824 |
| KRAS | 495 |
| PIK3CA | 229 |
| SMAD4 | 173 |
| FBXW7 | 145 |
| BRAF | 133 |
| TCF7L2 | 124 |
| SOX9 | 107 |
| KMT2D | 107 |

## Sample Types

- **Primary**: 601 samples
- **Metastasis**: 533 samples

## Query Tips for crc_msk_2017

```sql
-- Get all samples in this study
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'crc_msk_2017';

-- Get mutations for a specific gene
SELECT sample_unique_id, hugo_gene_symbol, mutation_variant, mutation_type
FROM genomic_event_derived
WHERE cancer_study_identifier = 'crc_msk_2017'
    AND hugo_gene_symbol = 'TP53'
    AND variant_type = 'mutation';

-- Get clinical data for specific attributes
SELECT sample_unique_id, attribute_name, attribute_value
FROM clinical_data_derived
WHERE cancer_study_identifier = 'crc_msk_2017'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'OS_MONTHS');
```


◀ result
# Sample and Study Filtering Guide

## Overview
Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria.

## Study-Level Filtering

### 1. Always Filter by Study
Every query should specify a study to ensure consistent results:

```sql
-- Always include study filtering
SELECT *
FROM your_table
WHERE
    cancer_study_identifier = 'your_study_id'
    -- Additional filters...
```

### 2. Find Available Studies

```sql
-- Discover available studies
SELECT
    cancer_study_identifier,
    name,
    description,
    type_of_cancer_id
FROM cancer_study
ORDER BY cancer_study_identifier;
```

### 3. Study Information

```sql
-- Get detailed study information
SELECT
    cs.cancer_study_identifier,
    cs.name as study_name,
    cs.description,
    cs.sample_count,
    COUNT(DISTINCT p.internal_id) as patient_count
FROM cancer_study cs
LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id
WHERE
    cs.cancer_study_identifier = 'your_study_id'
GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count;
```

### 4. Find Studies by Available Data Types

Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter.

**Canonical query — studies with ALL requested data types for a cancer type:**

```sql
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'          -- ← OncoTree code from search_oncotree
  AND mutation_sample_count > 0           -- ← one condition per required data type
  AND cna_sample_count > 0
ORDER BY sample_count DESC;
```

**Data type → column** (a count of 0 means the study doesn't have that data):

| Data type in the user's question | Column (portal "Data type" filter label) |
|---|---|
| samples in the study | `sample_count` |
| mutation / mutations | `mutation_sample_count` ("Mutations") |
| copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") |
| structural variant / fusion | `structural_variant_sample_count` |
| RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" |
| specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `<study>_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq |
| specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `<study>_mrna` list |
| microRNA | `mirna_sample_count` ("miRNA") |
| protein / RPPA | `rppa_sample_count` ("RPPA") |
| mas … (7083 more chars)

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"hugo_gene_symbol","type":"String","comment":"HUGO gene symbol (e.g., TP53, KRAS, BRAF). Use for gene-specific queries."},{"name":"entrez_gene_id","type":"Int32"},{"name":"gene_panel_stable_id","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"genetic_profile_stable_id","type":"LowCardinality(String)"},{"name":"variant_type","type":"LowCardinality(String)","comment":"Type of genomic event: \"mutation\" for SNVs/indels, \"cna\" for copy number alterations, \"structural_variant\" for SVs. Always filter by this."},{"name":"mutation_variant","type":"String","comment":"Protein change notation (e.g., p.V600E, p.R175H). Use for specific variant queries. \"NA\" for non-mutation events."},{"name":"mutation_type","type":"LowCardinality(String)"},{"name":"mutation_status","type":"LowCardinality(String)","comment":"For mutations: Somatic, Germline, UNKNOWN, or UNCALLED. Filter mutation_status != \"UNCALLED\" to exclude uncertain calls. Include all other statuses."},{"name":"driver_filter","type":"LowCardinality(String)"},{"name":"driver_filter_annotation","type":"String"},{"name":"driver_tiers_filter","type":"LowCardinality(String)"},{"name":"driver_tiers_filter_annotation","type":"String"},{"name":"cna_alteration","type":"Nullable(Int8)","comment":"Copy number alteration: only 2 = amplification (AMP) and -2 = deep deletion (HOMDEL) are stored. Shallow deletion (-1), diploid (0) and gain (1) are not in this table; query genetic_alteration_derived WHERE profile_type = 'gistic' (alteration_value is a String, e.g. '-1'). NULL for non-CNA events."},{"name":"cna_cytoband","type":"String"},{"name":"sv_event_info","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"off_panel","type":"Bool","comment":"Boolean: 1 = mutation outside gene panel coverage (off-panel), 0 = within panel (on-panel). Filter off_panel = 0 for reliable frequency calculations."}]}

◀ 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": "
SELECT DISTINCT attribute_value AS msi_status
FROM clinical_data_derived
WHERE cancer_study_identifier = 'crc_msk_2017'
  AND attribute_name = 'MSI_STATUS'
ORDER BY attribute_value
"
}

◀ result
{"rows":[{},{"msi_status":"Inconclusive"},{"msi_status":"MSI"},{"msi_status":"MSS"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH mss_samples AS (
  SELECT DISTINCT sample_unique_id
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'crc_msk_2017'
    AND attribute_name = 'MSI_STATUS'
    AND attribute_value = 'MSS'
),
braf_mutations AS (
  SELECT 
    ged.mutation_variant,
    ged.mutation_type,
    ged.driver_filter,
    ged.driver_filter_annotation,
    COUNT(DISTINCT ged.sample_unique_id) AS sample_count
  FROM genomic_event_derived ged
  WHERE ged.cancer_study_identifier = 'crc_msk_2017'
    AND ged.hugo_gene_symbol = 'BRAF'
    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 mss_samples)
    AND ged.driver_filter != ''  -- OncoKB-annotated driver mutations
  GROUP BY ged.mutation_variant, ged.mutation_type, ged.driver_filter, ged.driver_filter_annotation
  ORDER BY sample_count DESC
)
SELECT * FROM braf_mutations
",
 "max_rows": 100
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check MSS sample count and BRAF mutations (all)
WITH mss_samples AS (
  SELECT DISTINCT sample_unique_id
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'crc_msk_2017'
    AND attribute_name = 'MSI_STATUS'
    AND attribute_value = 'MSS'
)
SELECT 
  'MSS_samples' AS query_type,
  COUNT(*) AS count
FROM mss_samples
UNION ALL
SELECT 
  'BRAF_mutations_in_MSS',
  COUNT(DISTINCT sample_unique_id)
FROM genomic_event_derived ged
WHERE ged.cancer_study_identifier = 'crc_msk_2017'
  AND ged.hugo_gene_symbol = 'BRAF'
  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 mss_samples)
UNION ALL
SELECT 
  'BRAF_driver_annotated_in_study',
  COUNT(DISTINCT sample_unique_id)
FROM genomic_event_derived ged
WHERE ged.cancer_study_identifier = 'crc_msk_2017'
  AND ged.hugo_gene_symbol = 'BRAF'
  AND ged.variant_type = 'mutation'
  AND ged.mutation_status != 'UNCALLED'
  AND ged.off_panel = 0
  AND ged.driver_filter != ''
"
}

◀ result
{"rows":[{"query_type":"BRAF_mutations_in_MSS","count":78},{"query_type":"MSS_samples","count":701},{"query_type":"BRAF_driver_annotated_in_study","count":0}]}

▶ clickhouse_run_select_query
{
 "query": "
-- All BRAF mutations in MSS samples (regardless of driver status)
WITH mss_samples AS (
  SELECT DISTINCT sample_unique_id
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'crc_msk_2017'
    AND attribute_name = 'MSI_STATUS'
    AND attribute_value = 'MSS'
)
SELECT 
  COALESCE(ged.mutation_variant, 'NA') AS mutation_variant,
  ged.mutation_type,
  COALESCE(ged.driver_filter, 'Not annotated') AS driver_filter,
  COUNT(DISTINCT ged.sample_unique_id) AS sample_count
FROM genomic_event_derived ged
WHERE ged.cancer_study_identifier = 'crc_msk_2017'
  AND ged.hugo_gene_symbol = 'BRAF'
  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 mss_samples)
GROUP BY mutation_variant, mutation_type, driver_filter
ORDER BY sample_count DESC
",
 "max_rows": 50
}

◀ result
{"rows":[{"mutation_variant":"V600E","mutation_type":"Missense_Mutation","sample_count":46},{"mutation_variant":"D594G","mutation_type":"Missense_Mutation","sample_count":7},{"mutation_variant":"D594N","mutation_type":"Missense_Mutation","sample_count":3},{"mutation_variant":"T599delinsIP","mutation_type":"In_Frame_Ins","sample_count":2},{"mutation_variant":"N581S","mutation_type":"Missense_Mutation","sample_count":2},{"mutation_variant":"G469E","mutation_type":"Missense_Mutation","sample_count":2},{"mutation_variant":"G469A","mutation_type":"Missense_Mutation","sample_count":2},{"mutation_variant":"F294L","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"K601E","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"G596R","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"P277Hfs*2","mutation_type":"Frame_Shift_Del","sample_count":1},{"mutation_variant":"S602Y","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"E501K","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"D284Efs*4","mutation_type":"Frame_Shift_Ins","sample_count":1},{"mutation_variant":"D594V","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"R354*","mutation_type":"Nonsense_Mutation","sample_count":1},{"mutation_variant":"L312P","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"Y633C","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"A404Cfs*9","mutation_type":"Frame_Shift_Ins","sample_count":1},{"mutation_variant":"Q356K","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"G466V","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"H574Y","mutation_type":"Missense_Mutation","sample_count":1},{"mutation_variant":"N581I","mutation_type":"Missense_Mutation","sample_count":1}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get profiled samples for BRAF in MSS samples
WITH mss_samples AS (
  SELECT DISTINCT sample_unique_id
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'crc_msk_2017'
    AND attribute_name = 'MSI_STATUS'
    AND attribute_value = 'MSS'
),
-- BRAF-profiled samples in MSS cohort (targeted panel or WES)
braf_profiled AS (
  SELECT DISTINCT p.sample_unique_id
  FROM mutation_panel_gene_coverage p
  WHERE p.cancer_study_identifier = 'crc_msk_2017'
    AND p.hugo_gene_symbol = 'BRAF'
    AND p.sample_unique_id IN (SELECT sample_unique_id FROM mss_samples)
  UNION ALL
  SELECT DISTINCT p.sample_unique_id
  FROM mutation_wes_coverage p
  WHERE p.cancer_study_identifier = 'crc_msk_2017'
    AND p.sample_unique_id IN (SELECT sample_unique_id FROM mss_samples)
)
SELECT COUNT(DISTINCT sample_unique_id) AS braf_profiled_mss
FROM braf_profiled
"
}

◀ result
{"rows":[{"braf_profiled_mss":701}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Final comprehensive query for BRAF mutations in MSS samples
WITH mss_samples AS (
  SELECT DISTINCT sample_unique_id
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'crc_msk_2017'
    AND attribute_name = 'MSI_STATUS'
    AND attribute_value = 'MSS'
),
braf_mutations_all AS (
  SELECT 
    ged.mutation_variant,
    ged.mutation_type,
    COALESCE(ged.driver_filter, 'Not annotated') AS driver_status,
    CASE WHEN ged.driver_filter != '' THEN 1 ELSE 0 END AS is_oncokb_driver,
    COUNT(DISTINCT ged.sample_unique_id) AS sample_count
  FROM genomic_event_derived ged
  WHERE ged.cancer_study_identifier = 'crc_msk_2017'
    AND ged.hugo_gene_symbol = 'BRAF'
    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 mss_samples)
  GROUP BY ged.mutation_variant, ged.mutation_type, ged.driver_filter
)
SELECT 
  mutation_variant,
  mutation_type,
  sample_count,
  701 AS profiled_denominator,
  ROUND(sample_count * 100.0 / 701, 2) AS frequency_percent,
  driver_status,
  is_oncokb_driver
FROM braf_mutations_all
ORDER BY sample_count DESC, mutation_variant
",
 "max_rows": 100
}

◀ result
{"rows":[{"mutation_variant":"V600E","mutation_type":"Missense_Mutation","sample_count":46,"profiled_denominator":701,"frequency_percent":6.56,"is_oncokb_driver":0},{"mutation_variant":"D594G","mutation_type":"Missense_Mutation","sample_count":7,"profiled_denominator":701,"frequency_percent":1,"is_oncokb_driver":0},{"mutation_variant":"D594N","mutation_type":"Missense_Mutation","sample_count":3,"profiled_denominator":701,"frequency_percent":0.43,"is_oncokb_driver":0},{"mutation_variant":"G469A","mutation_type":"Missense_Mutation","sample_count":2,"profiled_denominator":701,"frequency_percent":0.29,"is_oncokb_driver":0},{"mutation_variant":"G469E","mutation_type":"Missense_Mutation","sample_count":2,"profiled_denominator":701,"frequency_percent":0.29,"is_oncokb_driver":0},{"mutation_variant":"N581S","mutation_type":"Missense_Mutation","sample_count":2,"profiled_denominator":701,"frequency_percent":0.29,"is_oncokb_driver":0},{"mutation_variant":"T599delinsIP","mutation_type":"In_Frame_Ins","sample_count":2,"profiled_denominator":701,"frequency_percent":0.29,"is_oncokb_driver":0},{"mutation_variant":"A404Cfs*9","mutation_type":"Frame_Shift_Ins","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"D284Efs*4","mutation_type":"Frame_Shift_Ins","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"D594V","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"E501K","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"F294L","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"G466V","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"G596R","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"H574Y","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"K601E","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"L312P","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"N581I","mutation_type":"Missense_Mutation","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"P277Hfs*2","mutation_type":"Frame_Shift_Del","sample_count":1,"profiled_denominator":701,"frequency_percent":0.14,"is_oncokb_driver":0},{"mutation_variant":"Q356K","mutation_ty … (581 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "crc_msk_2017"
 ]
}

◀ result
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▶ navigate_to_study_view
{
 "studyIds": [
  "crc_msk_2017"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "attributeId": "MSI_STATUS",
    "values": [
     {
      "value": "MSS"
     }
    ]
   }
  ],
  "geneFilters": [
   {
    "molecularProfileIds": [
     "crc_msk_2017_mutations"
    ],
    "geneQueries": [
     [
      {
       "hugoGeneSymbol": "BRAF"
      }
     ]
    ]
   }
  ]
 }
}

◀ result
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═ answer (success)
Perfect! Now let me provide a comprehensive answer with all the data:

---

## BRAF Oncogenic Mutations in MSS Colorectal Cancer (MSK 2017 Study)

### Key Finding: **No OncoKB-Annotated BRAF Oncogenic Mutations**

Unfortunately, **the MSK 2017 colorectal cancer study does NOT have OncoKB driver annotations** for any BRAF mutations. The `driver_filter` field is empty (not annotated) for all BRAF mutations in this cohort.

However, I can provide you with the complete inventory of **all BRAF mutations found in MSS samples** (701 profiled for BRAF):

### Overall BRAF Mutation Frequency in MSS CRC:
- **Total BRAF-mutant MSS samples:** 78 / 701 profiled = **11.13%**

### Detailed Mutation Breakdown

| Mutation Variant | Mutation Type | Count | Profiled | Frequency (%) | OncoKB Driver Status |
|---|---|---|---|---|---|
| V600E | Missense | 46 | 701 | 6.56% | Not annotated |
| D594G | Missense | 7 | 701 | 1.00% | Not annotated |
| D594N | Missense | 3 | 701 | 0.43% | Not annotated |
| G469A | Missense | 2 | 701 | 0.29% | Not annotated |
| G469E | Missense | 2 | 701 | 0.29% | Not annotated |
| N581S | Missense | 2 | 701 | 0.29% | Not annotated |
| T599delinsIP | In-Frame Insertion | 2 | 701 | 0.29% | Not annotated |
| A404Cfs*9 | Frame Shift Insertion | 1 | 701 | 0.14% | Not annotated |
| D284Efs*4 | Frame Shift Insertion | 1 | 701 | 0.14% | Not annotated |
| D594V | Missense | 1 | 701 | 0.14% | Not annotated |
| E501K | Missense | 1 | 701 | 0.14% | Not annotated |
| F294L | Missense | 1 | 701 | 0.14% | Not annotated |
| G466V | Missense | 1 | 701 | 0.14% | Not annotated |
| G596R | Missense | 1 | 701 | 0.14% | Not annotated |
| H574Y | Missense | 1 | 701 | 0.14% | Not annotated |
| K601E | Missense | 1 | 701 | 0.14% | Not annotated |
| L312P | Missense | 1 | 701 | 0.14% | Not annotated |
| N581I | Missense | 1 | 701 | 0.14% | Not annotated |
| P277Hfs*2 | Frame Shift Deletion | 1 | 701 | 0.14% | Not annotated |
| Q356K | Missense | 1 | 701 | 0.14% | Not annotated |
| R354* | Nonsense | 1 | 701 | 0.14% | Not annotated |
| S602Y | Missense | 1 | 701 | 0.14% | Not annotated |
| Y633C | Missense | 1 | 701 | 0.14% | Not annotated |

**Note:** V600E is the canonical BRAF oncogenic hotspot variant (activation loop mutation). Although biologically it is known to be oncogenic, **this study's database does not carry OncoKB annotation metadata** for BRAF.

### Access the Filtered Cohort

[View MSS colorectal cancer samples with BRAF mutations in cBioPortal](https://www.cbioportal.org/study/summary?id=crc_msk_2017#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22MSI_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22MSS%22%7D%5D%7D%5D%2C%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22BRAF%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22crc_msk_2017_mutations%22%5D%7D%5D%7D)