Q (sonnet): 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://sample-filtering-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 … (36467 more chars)

◀ 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 … (7866 more chars)

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

▶ search_oncotree
{
 "search_term": "colorectal"
}

◀ result
{"result":[{"cancer_study_identifier":"msk_impact_2017","name":"MSK-IMPACT Clinical Sequencing Cohort (MSK, Nat Med 2017)","type_of_cancer_id":"mixed","sample_count":10945,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_impact_2017"},{"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"},{"cancer_study_identifier":"lung_msk_2017","name":"Non-Small Cell Cancer (MSK, Cancer Discov 2017)","type_of_cancer_id":"luad","sample_count":915,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_2017"},{"cancer_study_identifier":"prad_mskcc_2017","name":"Prostate Cancer (MSK, JCO Precis Oncol 2017)","type_of_cancer_id":"prad","sample_count":504,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=prad_mskcc_2017"},{"cancer_study_identifier":"egc_msk_2017","name":"Metastatic Esophagogastric Cancer (MSK, Cancer Discovery 2017)","type_of_cancer_id":"egc","sample_count":341,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=egc_msk_2017"},{"cancer_study_identifier":"brca_msk_erbb2_2026","name":"HER2-Low and HER2-Null Metastatic Breast Cancers treated with T-DXd (MSK, Clinical Cancer Research 2026)","type_of_cancer_id":"breast","sample_count":272,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brca_msk_erbb2_2026"},{"cancer_study_identifier":"mel_iatlas_riaz_nivolumab_2017","name":"Melanoma (MSK, Cell 2017) - iAtlas Harmonized","type_of_cancer_id":"mel","sample_count":107,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=mel_iatlas_riaz_nivolumab_2017"},{"cancer_study_identifier":"blca_nmibc_2017","name":"Nonmuscle Invasive Bladder Cancer (MSK Eur Urol 2017)","type_of_cancer_id":"blca","sample_count":105,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=blca_nmibc_2017"},{"cancer_study_identifier":"skcm_vanderbilt_mskcc_2015","name":"Metastatic Melanoma (MSK, JCO Precis Oncol 2017)","type_of_cancer_id":"skcm","sample_count":66,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=skcm_vanderbilt_mskcc_2015"},{"cancer_study_identifier":"odg_msk_2017","name":"Anaplastic Oligodendroglioma and Anaplastic Oligoastrocytoma (MSK, Neuro Oncol 2017)","type_of_cancer_id":"difg","sample_count":22,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=odg_msk_2017"}]}

◀ result
{"result":[{"code":"COADREAD","name":"Colorectal Adenocarcinoma","score":60,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD"},{"code":"CAIS","name":"Colon Adenocarcinoma In Situ","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > CAIS"},{"code":"CMC","name":"Medullary Carcinoma of the Colon","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > CMC"},{"code":"COAD","name":"Colon Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > COAD"},{"code":"MACR","name":"Mucinous Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > MACR"},{"code":"READ","name":"Rectal Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > READ"},{"code":"SRCCR","name":"Signet Ring Cell Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > SRCCR"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_name, count(DISTINCT attribute_value) AS n_values, count() AS n_rows
FROM clinical_data_derived
WHERE cancer_study_identifier = 'crc_msk_2017'
  AND (lower(attribute_name) LIKE '%msi%' OR lower(attribute_name) LIKE '%microsat%' OR lower(attribute_name) LIKE '%stab%')
GROUP BY attribute_name"
}

◀ result
{"rows":[{"attribute_name":"MSI_SCORE","n_values":368,"n_rows":1134},{"attribute_name":"MSI_STATUS","n_values":4,"n_rows":1134}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT driver_filter, count() AS mutations
FROM genomic_event_derived
WHERE cancer_study_identifier = 'crc_msk_2017' AND variant_type = 'mutation' AND hugo_gene_symbol = 'BRAF'
GROUP BY driver_filter"
}

◀ result
{"rows":[{"mutations":142}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(DISTINCT sample_unique_id) AS n_samples
FROM clinical_data_derived
WHERE cancer_study_identifier = 'crc_msk_2017' AND attribute_name = 'MSI_STATUS'
GROUP BY attribute_value ORDER BY n_samples DESC"
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"rows":[{"attribute_value":"MSS","n_samples":701},{"n_samples":327},{"attribute_value":"MSI","n_samples":105},{"attribute_value":"Inconclusive","n_samples":1}]}

◀ 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."}]}

▶ 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'
),
profiled AS (
    SELECT COUNT(DISTINCT stgp.sample_unique_id) AS n
    FROM sample_to_gene_panel_derived stgp
    WHERE stgp.cancer_study_identifier = 'crc_msk_2017'
      AND stgp.alteration_type = 'MUTATION_EXTENDED'
      AND stgp.sample_unique_id IN (SELECT sample_unique_id FROM mss_samples)
)
SELECT
    ged.mutation_variant AS protein_change,
    ged.mutation_type,
    COUNT(DISTINCT ged.sample_unique_id) AS altered_samples,
    COUNT(*) AS total_events,
    (SELECT n FROM profiled) AS profiled_samples,
    ROUND(COUNT(DISTINCT ged.sample_unique_id) * 100.0 / (SELECT n FROM profiled), 2) AS frequency_pct
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 protein_change, mutation_type
ORDER BY altered_samples DESC
"
}

◀ result
{"rows":[{"protein_change":"V600E","mutation_type":"Missense_Mutation","altered_samples":46,"total_events":46,"profiled_samples":701,"frequency_pct":6.56},{"protein_change":"D594G","mutation_type":"Missense_Mutation","altered_samples":7,"total_events":7,"profiled_samples":701,"frequency_pct":1},{"protein_change":"D594N","mutation_type":"Missense_Mutation","altered_samples":3,"total_events":3,"profiled_samples":701,"frequency_pct":0.43},{"protein_change":"T599delinsIP","mutation_type":"In_Frame_Ins","altered_samples":2,"total_events":2,"profiled_samples":701,"frequency_pct":0.29},{"protein_change":"N581S","mutation_type":"Missense_Mutation","altered_samples":2,"total_events":2,"profiled_samples":701,"frequency_pct":0.29},{"protein_change":"G469E","mutation_type":"Missense_Mutation","altered_samples":2,"total_events":2,"profiled_samples":701,"frequency_pct":0.29},{"protein_change":"G469A","mutation_type":"Missense_Mutation","altered_samples":2,"total_events":2,"profiled_samples":701,"frequency_pct":0.29},{"protein_change":"H574Y","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"N581I","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"P277Hfs*2","mutation_type":"Frame_Shift_Del","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"G466V","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"L312P","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"Q356K","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"Y633C","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"A404Cfs*9","mutation_type":"Frame_Shift_Ins","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"K601E","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"R354*","mutation_type":"Nonsense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"F294L","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"G596R","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"E501K","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profiled_samples":701,"frequency_pct":0.14},{"protein_change":"S602Y","mutation_type":"Missense_Mutation","altered_samples":1,"total_events":1,"profil … (329 more chars)

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

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"crc_msk_2017","name":"Metastatic Colorectal Cancer (MSK, Cancer Cell 2018)","sampleCount":1134,"studyViewUrl":"https://www.cbioportal.org/study?id=crc_msk_2017","metadata":{"clinicalAttributeIds":["AGE_AT_DIAGNOSIS","CANCER_TYPE","CANCER_TYPE_DETAILED","CHEMO_EXP_SEQ_SPECIMEN","FIRST_SITE_OF_METS","FRACTION_GENOME_ALTERED","GENE_PANEL","MCRC_TYPE","METASTASECTOMY","METASTASECTOMY_SITE","METASTASES_SITE_FIRST_BONE","METASTASES_SITE_FIRST_BRAIN","METASTASES_SITE_FIRST_GYN","METASTASES_SITE_FIRST_LIVER","METASTASES_SITE_FIRST_LN","METASTASES_SITE_FIRST_LUNG","METASTASES_SITE_FIRST_PELVIS","METASTASES_SITE_FIRST_PERITONEUM_OMENTUM_ABDOMEN","METASTATIC_BIOPSY_SITE","MOLECULAR_SUBTYPE","MSI_SCORE","MSI_STATUS","MUTATION_COUNT","ONCOTREE_CODE","OS_MONTHS","OS_MONTHS_ADDITIONAL","OS_STATUS","OTHER_SITES_OF_METS","PATIENT_GRADE","PRIMARY_SITE","PRIMARY_TUMOR_LOCATION","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SPECIMEN_TYPE","STAGE_AT_DIAGNOSIS","TIME_FROM_MET_DX_SEQUENCING","TIME_TO_METASTASIS_MONTHS","TMB_NONSYNONYMOUS","TUMOR_SAMPLE_HISTOLOGY","VITAL_STATUS"],"molecularProfileIds":["crc_msk_2017_gistic","crc_msk_2017_mutations","crc_msk_2017_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}}]}}

▶ get_studyviewfilter_options
{
 "studyId": "crc_msk_2017",
 "attributeIds": [
  "MSI_STATUS"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"crc_msk_2017","attributes":[{"attributeId":"MSI_STATUS","displayName":"MSI Status","description":"MSI status.","datatype":"STRING","values":["MSI","NA","Inconclusive","MSS"]}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "crc_msk_2017"
 ],
 "genes": [
  "BRAF: MUT_DRIVER"
 ],
 "tab": "oncoprint",
 "studyViewFilter": {
  "clinicalDataFilters": [
   {
    "attributeId": "MSI_STATUS",
    "values": [
     {
      "value": "MSS"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?session_id=6ab433b8c2115c492d884974","url":"https://www.cbioportal.org/results/oncoprint?session_id=6ab433b8c2115c492d884974","data":{"studyIds":["crc_msk_2017"],"studies":[{"studyId":"crc_msk_2017","name":"Metastatic Colorectal Cancer (MSK, Cancer Cell 2018)","sampleCount":1134}],"genes":["BRAF"],"filteredSampleCount":701,"caseSetId":"-1","sessionId":"6ab433b8c2115c492d884974","studyViewUrl":"https://www.cbioportal.org/study?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%7D","pageDescription":"Visual summary of alterations in queried genes across samples."}}

▶ 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
{"success":true,"message":"Navigating to 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","url":"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","data":{"studyIds":["crc_msk_2017"],"studies":[{"studyId":"crc_msk_2017","name":"Metastatic Colorectal Cancer (MSK, Cancer Cell 2018)","sampleCount":1134}],"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)
Here are the links to view this live with OncoKB oncogenicity applied:

**[OncoPrint: BRAF oncogenic drivers (OncoKB), MSS samples only](https://www.cbioportal.org/results/oncoprint?session_id=6ab433b8c2115c492d884974)** — this restricts to the 701 MSS samples and applies the `MUT_DRIVER` OQL filter, which uses the portal's live OncoKB annotation to show only oncogenic/likely-oncogenic BRAF mutations. Cross-reference the Mutations tab there against the table above to see exactly which of the 23 variant types are flagged oncogenic.

**[Study overview: MSS cohort, BRAF filter](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)** — cohort-level view of the same filter (701 MSS samples).

**Summary:** In the MSS subset (701/1134 samples) of the MSK 2017 metastatic colorectal cancer cohort, BRAF is mutated in 71 samples (10.13%) across 23 distinct protein changes, dominated by V600E (46 samples, 6.56%). Since this database doesn't persist OncoKB calls, I can't state which of the remaining 22 rarer variants (D594G/N/V, kinase-dead variants like G466V/G469A/E, truncating changes, etc.) are OncoKB-oncogenic without you (or the OncoPrint link above) checking the live annotation — some of these are known oncogenic Class II/III BRAF alleles, others (e.g., isolated frameshifts/nonsense far from the kinase domain) are less likely to be annotated oncogenic, but I won't assert either way without confirming through OncoKB itself.