Q (haiku): What are the most frequently altered genes in KRAS wild-type lung adenocarcinoma patients?

▶ 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 … (35166 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 … (7083 more chars)

▶ search_oncotree
{
 "search_term": "lung adenocarcinoma"
}

◀ result
{"result":[{"code":"LUAD","name":"Lung Adenocarcinoma","score":80,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAD"},{"code":"LAIS","name":"Lung Adenocarcinoma In Situ","score":60,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LAIS"}]}

▶ list_studies
{
 "search": "lung adenocarcinoma",
 "limit": 10
}

◀ result
{"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","type_of_cancer_id":"mixed","sample_count":25775,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_met_2021"},{"cancer_study_identifier":"luad_mskcc_2023_met_organotropism","name":"Lung Adenocarcinoma Met Organotropism (MSK, Cancer Cell 2023)","type_of_cancer_id":"luad","sample_count":2653,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2023_met_organotropism"},{"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":"luad_mskcc_2020","name":"Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)","type_of_cancer_id":"luad","sample_count":604,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2020"},{"cancer_study_identifier":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"luad","sample_count":586,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga"},{"cancer_study_identifier":"luad_tcga_gdc","name":"Lung Adenocarcinoma (TCGA GDC, 2025)","type_of_cancer_id":"luad","sample_count":571,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_gdc"},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"luad","sample_count":566,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"luad_msk_npjpo_2021","name":"Lung Adenocarcinoma (MSK, NPJ Precision Oncology 2021)","type_of_cancer_id":"luad","sample_count":426,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_msk_npjpo_2021"},{"cancer_study_identifier":"luad_oncosg_2020","name":"Lung Adenocarcinoma (OncoSG, Nat Genet 2020)","type_of_cancer_id":"luad","sample_count":305,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_oncosg_2020"},{"cancer_study_identifier":"lung_msk_mind_2020","name":"Lung Adenocarcinoma (MSK Mind,Nature Cancer 2022)","type_of_cancer_id":"lung","sample_count":247,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_mind_2020"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get KRAS wild-type samples first
WITH kras_wt AS (
    SELECT DISTINCT s.sample_unique_id, s.cancer_study_identifier
    FROM genomic_event_derived s
    -- Left join to find KRAS mutations
    LEFT JOIN genomic_event_derived kras_mut 
        ON s.sample_unique_id = kras_mut.sample_unique_id 
        AND kras_mut.cancer_study_identifier = s.cancer_study_identifier
        AND kras_mut.hugo_gene_symbol = 'KRAS'
        AND kras_mut.variant_type = 'mutation'
        AND kras_mut.mutation_status != 'UNCALLED'
    WHERE s.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        -- Get samples profiled for KRAS
        AND EXISTS (
            SELECT 1 FROM sample_to_gene_panel_derived sgp
            WHERE sgp.sample_unique_id = s.sample_unique_id
            AND sgp.cancer_study_identifier = s.cancer_study_identifier
            AND sgp.alteration_type = 'MUTATION_EXTENDED'
            AND EXISTS (
                SELECT 1 FROM gene_panel gp
                JOIN gene_panel_list gpl ON gp.internal_id = gpl.internal_id
                JOIN gene g ON gpl.gene_id = g.entrez_gene_id
                WHERE gp.stable_id = sgp.gene_panel_id AND g.hugo_gene_symbol = 'KRAS'
            )
        )
    -- Only include samples with no KRAS mutation (wild-type)
    AND kras_mut.sample_unique_id IS NULL
),
-- Get altered gene counts in KRAS WT samples
altered_genes AS (
    SELECT 
        ged.hugo_gene_symbol,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples,
        COUNT(*) AS total_events
    FROM genomic_event_derived ged
    JOIN kras_wt wt ON ged.sample_unique_id = wt.sample_unique_id
        AND ged.cancer_study_identifier = wt.cancer_study_identifier
    WHERE ged.variant_type = 'mutation'
        AND ged.mutation_status != 'UNCALLED'
        AND ged.off_panel = 0
    GROUP BY ged.hugo_gene_symbol
),
-- Get profiled counts for each gene in KRAS WT cohort
profiled_genes AS (
    SELECT 
        g.hugo_gene_symbol,
        COUNT(DISTINCT sgp.sample_unique_id) AS profiled_samples
    FROM sample_to_gene_panel_derived sgp
    JOIN gene_panel gp ON gp.stable_id = sgp.gene_panel_id
    JOIN gene_panel_list gpl ON gp.internal_id = gpl.internal_id
    JOIN gene g ON gpl.gene_id = g.entrez_gene_id
    JOIN kras_wt wt ON sgp.sample_unique_id = wt.sample_unique_id
        AND sgp.cancer_study_identifier = wt.cancer_study_identifier
    WHERE sgp.alteration_type = 'MUTATION_EXTENDED'
    GROUP BY g.hugo_gene_symbol
)
SELECT 
    a.hugo_gene_symbol,
    a.altered_samples,
    p.profiled_samples,
    ROUND(a.altered_samples * 100.0 / p.profiled_samples, 1) AS frequency_pct,
    a.total_events
FROM altered_genes a
JOIN profiled_genes p ON a.hugo_gene_symbol = p.hugo_gene_symbol
ORDER BY frequency_pct DESC
LIMIT 30;
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 1\n Code: 1. DB::Exception: Resolved identifier 's.sample_unique_id' in parent scope to expression 's.sample_unique_id' with correlated columns 'sample_unique_id' (Enable 'allow_experimental_correlated_subqueries' setting to allow correlated subqueries execution). In scope (SELECT _CAST(1, 'UInt64') FROM (SELECT 1 FROM cbioportal_public_librechat_blue.sample_to_gene_panel_derived AS sgp WHERE (sgp.sample_unique_id = s.sample_unique_id) AND (sgp.cancer_study_identifier = s.cancer_study_identifier) AND (sgp.alteration_type = 'MUTATION_EXTENDED') AND exists((SELECT 1 FROM gene_panel AS gp ALL INNER JOIN gene_panel_list AS gpl ON gp.internal_id = gpl.internal_id ALL INNER JOIN gene AS g ON gpl.gene_id = g.entrez_gene_id WHERE (gp.stable_id = sgp.gene_panel_id) AND (g.hugo_gene_symbol = 'KRAS')))) LIMIT _CAST(1, 'UInt64')). (UNSUPPORTED_METHOD) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
-- First, identify KRAS mutations in TCGA LUAD
WITH kras_mutant_samples AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND hugo_gene_symbol = 'KRAS'
        AND variant_type = 'mutation'
        AND mutation_status != 'UNCALLED'
),
-- Get all samples profiled for mutations in LUAD
all_profiled_samples AS (
    SELECT DISTINCT sample_unique_id
    FROM sample_to_gene_panel_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND alteration_type = 'MUTATION_EXTENDED'
),
-- KRAS wild-type = profiled for mutations but NOT in kras_mutant list
kras_wt_samples AS (
    SELECT sample_unique_id
    FROM all_profiled_samples
    WHERE sample_unique_id NOT IN (SELECT sample_unique_id FROM kras_mutant_samples)
),
-- Get altered gene counts in KRAS WT samples
altered_genes AS (
    SELECT 
        ged.hugo_gene_symbol,
        COUNT(DISTINCT ged.sample_unique_id) AS altered_samples,
        COUNT(*) AS total_events
    FROM genomic_event_derived ged
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND ged.sample_unique_id IN (SELECT sample_unique_id FROM kras_wt_samples)
        AND ged.variant_type = 'mutation'
        AND ged.mutation_status != 'UNCALLED'
        AND ged.off_panel = 0
    GROUP BY ged.hugo_gene_symbol
),
-- Get profiled counts for each gene in KRAS WT cohort using panel coverage
profiled_genes AS (
    SELECT 
        g.hugo_gene_symbol,
        COUNT(DISTINCT sgp.sample_unique_id) AS profiled_samples
    FROM sample_to_gene_panel_derived sgp
    JOIN gene_panel gp ON gp.stable_id = sgp.gene_panel_id
    JOIN gene_panel_list gpl ON gp.internal_id = gpl.internal_id
    JOIN gene g ON gpl.gene_id = g.entrez_gene_id
    WHERE sgp.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND sgp.sample_unique_id IN (SELECT sample_unique_id FROM kras_wt_samples)
        AND sgp.alteration_type = 'MUTATION_EXTENDED'
    GROUP BY g.hugo_gene_symbol
)
SELECT 
    a.hugo_gene_symbol,
    a.altered_samples,
    p.profiled_samples,
    ROUND(a.altered_samples * 100.0 / p.profiled_samples, 1) AS frequency_pct,
    a.total_events
FROM altered_genes a
JOIN profiled_genes p ON a.hugo_gene_symbol = p.hugo_gene_symbol
ORDER BY frequency_pct DESC
LIMIT 30;
",
 "max_rows": 100
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check basic data availability
SELECT 
    COUNT(DISTINCT sample_unique_id) as total_samples,
    COUNT(DISTINCT CASE WHEN variant_type = 'mutation' THEN sample_unique_id END) as mutation_samples,
    COUNT(*) as total_events
FROM genomic_event_derived
WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018';
",
 "max_rows": 10
}

◀ result
{"rows":[{"total_samples":566,"mutation_samples":561,"total_events":342717}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check KRAS mutations
SELECT 
    COUNT(DISTINCT sample_unique_id) as kras_mutant_samples,
    COUNT(*) as kras_events
FROM genomic_event_derived
WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'KRAS'
    AND variant_type = 'mutation'
    AND mutation_status != 'UNCALLED';
",
 "max_rows": 10
}

◀ result
{"rows":[{"kras_mutant_samples":168,"kras_events":172}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get top mutated genes in KRAS wild-type samples
-- First, get all KRAS mutant samples
WITH kras_mutant AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND hugo_gene_symbol = 'KRAS'
        AND variant_type = 'mutation'
        AND mutation_status != 'UNCALLED'
),
-- Get gene frequencies in KRAS WT cohort
gene_freqs AS (
    SELECT 
        hugo_gene_symbol,
        COUNT(DISTINCT sample_unique_id) as altered_samples,
        COUNT(*) as total_mutation_events
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND variant_type = 'mutation'
        AND mutation_status != 'UNCALLED'
        AND off_panel = 0
        AND sample_unique_id NOT IN (SELECT sample_unique_id FROM kras_mutant)
    GROUP BY hugo_gene_symbol
)
SELECT 
    gene_freqs.hugo_gene_symbol,
    gene_freqs.altered_samples,
    gene_freqs.total_mutation_events,
    (SELECT COUNT(DISTINCT sample_unique_id) FROM genomic_event_derived 
     WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' 
     AND variant_type = 'mutation'
     AND sample_unique_id NOT IN (SELECT sample_unique_id FROM kras_mutant)) AS wt_cohort_size
FROM gene_freqs
ORDER BY gene_freqs.altered_samples DESC
LIMIT 30;
",
 "max_rows": 100
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","altered_samples":233,"total_mutation_events":247,"wt_cohort_size":393},{"hugo_gene_symbol":"TTN","altered_samples":190,"total_mutation_events":501,"wt_cohort_size":393},{"hugo_gene_symbol":"MUC16","altered_samples":158,"total_mutation_events":284,"wt_cohort_size":393},{"hugo_gene_symbol":"CSMD3","altered_samples":157,"total_mutation_events":276,"wt_cohort_size":393},{"hugo_gene_symbol":"RYR2","altered_samples":147,"total_mutation_events":289,"wt_cohort_size":393},{"hugo_gene_symbol":"LRP1B","altered_samples":132,"total_mutation_events":227,"wt_cohort_size":393},{"hugo_gene_symbol":"ZFHX4","altered_samples":122,"total_mutation_events":185,"wt_cohort_size":393},{"hugo_gene_symbol":"USH2A","altered_samples":120,"total_mutation_events":202,"wt_cohort_size":393},{"hugo_gene_symbol":"XIRP2","altered_samples":107,"total_mutation_events":143,"wt_cohort_size":393},{"hugo_gene_symbol":"FLG","altered_samples":105,"total_mutation_events":161,"wt_cohort_size":393},{"hugo_gene_symbol":"SPTA1","altered_samples":104,"total_mutation_events":143,"wt_cohort_size":393},{"hugo_gene_symbol":"COL11A1","altered_samples":89,"total_mutation_events":115,"wt_cohort_size":393},{"hugo_gene_symbol":"NAV3","altered_samples":88,"total_mutation_events":116,"wt_cohort_size":393},{"hugo_gene_symbol":"PCDH15","altered_samples":88,"total_mutation_events":132,"wt_cohort_size":393},{"hugo_gene_symbol":"FAT3","altered_samples":85,"total_mutation_events":123,"wt_cohort_size":393},{"hugo_gene_symbol":"MUC17","altered_samples":83,"total_mutation_events":118,"wt_cohort_size":393},{"hugo_gene_symbol":"ZNF536","altered_samples":79,"total_mutation_events":111,"wt_cohort_size":393},{"hugo_gene_symbol":"PCLO","altered_samples":77,"total_mutation_events":102,"wt_cohort_size":393},{"hugo_gene_symbol":"CSMD1","altered_samples":77,"total_mutation_events":106,"wt_cohort_size":393},{"hugo_gene_symbol":"TNR","altered_samples":76,"total_mutation_events":95,"wt_cohort_size":393},{"hugo_gene_symbol":"RYR3","altered_samples":75,"total_mutation_events":105,"wt_cohort_size":393},{"hugo_gene_symbol":"ANK2","altered_samples":73,"total_mutation_events":93,"wt_cohort_size":393},{"hugo_gene_symbol":"DNAH9","altered_samples":73,"total_mutation_events":94,"wt_cohort_size":393},{"hugo_gene_symbol":"ZNF804A","altered_samples":69,"total_mutation_events":89,"wt_cohort_size":393},{"hugo_gene_symbol":"APOB","altered_samples":69,"total_mutation_events":86,"wt_cohort_size":393},{"hugo_gene_symbol":"EGFR","altered_samples":69,"total_mutation_events":85,"wt_cohort_size":393},{"hugo_gene_symbol":"SI","altered_samples":69,"total_mutation_events":91,"wt_cohort_size":393},{"hugo_gene_symbol":"ADAMTS12","altered_samples":68,"total_mutation_events":88,"wt_cohort_size":393},{"hugo_gene_symbol":"ADGRG4","altered_samples":67,"total_mutation_events":78,"wt_cohort_size":393},{"hugo_gene_symbol":"CDH10","altered_samples":67,"total_mutation_events":83,"wt_cohort_size":393}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get combined mutation + CNA alterations in KRAS wild-type samples
WITH kras_mutant AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND hugo_gene_symbol = 'KRAS'
        AND variant_type = 'mutation'
        AND mutation_status != 'UNCALLED'
),
-- KRAS wild-type samples profiled for mutations
wt_samples AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND variant_type = 'mutation'
        AND sample_unique_id NOT IN (SELECT sample_unique_id FROM kras_mutant)
),
-- Combined alterations (mutations + CNA)
combined_alterations AS (
    SELECT 
        hugo_gene_symbol,
        COUNT(DISTINCT sample_unique_id) as altered_samples,
        COUNT(*) as total_events,
        SUM(CASE WHEN variant_type = 'mutation' THEN 1 ELSE 0 END) as mutation_events,
        SUM(CASE WHEN variant_type = 'cna' THEN 1 ELSE 0 END) as cna_events
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
        AND sample_unique_id IN (SELECT sample_unique_id FROM wt_samples)
        AND ((variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND off_panel = 0)
             OR (variant_type = 'cna' AND cna_alteration IN (2, -2)))
    GROUP BY hugo_gene_symbol
)
SELECT 
    hugo_gene_symbol,
    altered_samples,
    total_events,
    mutation_events,
    cna_events,
    ROUND(altered_samples * 100.0 / 393, 1) AS frequency_pct
FROM combined_alterations
ORDER BY altered_samples DESC
LIMIT 30;
",
 "max_rows": 100
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","altered_samples":233,"total_events":251,"mutation_events":247,"cna_events":4,"frequency_pct":59.3},{"hugo_gene_symbol":"TTN","altered_samples":190,"total_events":504,"mutation_events":501,"cna_events":3,"frequency_pct":48.3},{"hugo_gene_symbol":"CSMD3","altered_samples":164,"total_events":287,"mutation_events":276,"cna_events":11,"frequency_pct":41.7},{"hugo_gene_symbol":"MUC16","altered_samples":159,"total_events":287,"mutation_events":284,"cna_events":3,"frequency_pct":40.5},{"hugo_gene_symbol":"RYR2","altered_samples":152,"total_events":299,"mutation_events":289,"cna_events":10,"frequency_pct":38.7},{"hugo_gene_symbol":"LRP1B","altered_samples":139,"total_events":241,"mutation_events":227,"cna_events":14,"frequency_pct":35.4},{"hugo_gene_symbol":"ZFHX4","altered_samples":133,"total_events":200,"mutation_events":185,"cna_events":15,"frequency_pct":33.8},{"hugo_gene_symbol":"FLG","altered_samples":129,"total_events":192,"mutation_events":161,"cna_events":31,"frequency_pct":32.8},{"hugo_gene_symbol":"USH2A","altered_samples":127,"total_events":210,"mutation_events":202,"cna_events":8,"frequency_pct":32.3},{"hugo_gene_symbol":"SPTA1","altered_samples":120,"total_events":163,"mutation_events":143,"cna_events":20,"frequency_pct":30.5},{"hugo_gene_symbol":"XIRP2","altered_samples":108,"total_events":145,"mutation_events":143,"cna_events":2,"frequency_pct":27.5},{"hugo_gene_symbol":"CSMD1","altered_samples":95,"total_events":131,"mutation_events":106,"cna_events":25,"frequency_pct":24.2},{"hugo_gene_symbol":"NAV3","altered_samples":94,"total_events":122,"mutation_events":116,"cna_events":6,"frequency_pct":23.9},{"hugo_gene_symbol":"COL11A1","altered_samples":94,"total_events":120,"mutation_events":115,"cna_events":5,"frequency_pct":23.9},{"hugo_gene_symbol":"ADAMTS12","altered_samples":93,"total_events":124,"mutation_events":88,"cna_events":36,"frequency_pct":23.7},{"hugo_gene_symbol":"PCDH15","altered_samples":91,"total_events":137,"mutation_events":132,"cna_events":5,"frequency_pct":23.2},{"hugo_gene_symbol":"ZNF536","altered_samples":91,"total_events":124,"mutation_events":111,"cna_events":13,"frequency_pct":23.2},{"hugo_gene_symbol":"PTPRD","altered_samples":91,"total_events":116,"mutation_events":84,"cna_events":32,"frequency_pct":23.2},{"hugo_gene_symbol":"FAT3","altered_samples":89,"total_events":128,"mutation_events":123,"cna_events":5,"frequency_pct":22.6},{"hugo_gene_symbol":"MUC17","altered_samples":88,"total_events":126,"mutation_events":118,"cna_events":8,"frequency_pct":22.4},{"hugo_gene_symbol":"CDH10","altered_samples":88,"total_events":116,"mutation_events":83,"cna_events":33,"frequency_pct":22.4},{"hugo_gene_symbol":"DNAH5","altered_samples":84,"total_events":109,"mutation_events":63,"cna_events":46,"frequency_pct":21.4},{"hugo_gene_symbol":"TNR","altered_samples":84,"total_events":104,"mutation_events":95,"cna_events":9,"frequency_pct":21.4},{"hugo_gene_symbol":"CDKN2A","altered_samples":84,"tota … (830 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ]
}

◀ result
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▶ get_studyviewfilter_options
{
 "studyId": "luad_tcga_pan_can_atlas_2018",
 "geneSpecificQueries": [
  {
   "hugoGeneSymbol": "KRAS",
   "profileType": "mutations"
  }
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"luad_tcga_pan_can_atlas_2018","geneSpecificCounts":[{"hugoGeneSymbol":"KRAS","profileType":"mutations","counts":[{"value":"Missense_Mutation","label":"Missense Mutation","count":171},{"value":"Nonsense_Mutation","label":"Nonsense Mutation","count":1}]}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ],
 "filterJson": {
  "mutationDataFilters": [
   {
    "hugoGeneSymbol": "KRAS",
    "profileType": "mutations",
    "categorization": "MUTATED",
    "values": [
     [
      {
       "value": "NOT_MUTATED"
      }
     ]
    ]
   }
  ]
 }
}

◀ result
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═ answer (success)
Perfect! Now let me present the results comprehensively.

## Most Frequently Altered Genes in KRAS Wild-Type Lung Adenocarcinoma

I analyzed **393 KRAS wild-type samples** from the TCGA Lung Adenocarcinoma PanCancer Atlas study (566 total samples; 168 KRAS-mutant samples excluded).

### Top 20 Most Frequently Altered Genes (Mutation + CNA)

| Gene | Altered Samples | Frequency | Alterations |
|------|-----------------|-----------|------------|
| **TP53** | 233 | 59.3% | 247 mutations, 4 CNAs |
| **TTN** | 190 | 48.3% | 501 mutations, 3 CNAs |
| **CSMD3** | 164 | 41.7% | 276 mutations, 11 CNAs |
| **MUC16** | 159 | 40.5% | 284 mutations, 3 CNAs |
| **RYR2** | 152 | 38.7% | 289 mutations, 10 CNAs |
| **LRP1B** | 139 | 35.4% | 227 mutations, 14 CNAs |
| **ZFHX4** | 133 | 33.8% | 185 mutations, 15 CNAs |
| **FLG** | 129 | 32.8% | 161 mutations, 31 CNAs |
| **USH2A** | 127 | 32.3% | 202 mutations, 8 CNAs |
| **SPTA1** | 120 | 30.5% | 143 mutations, 20 CNAs |
| **XIRP2** | 108 | 27.5% | 143 mutations, 2 CNAs |
| **CSMD1** | 95 | 24.2% | 106 mutations, 25 CNAs |
| **NAV3** | 94 | 23.9% | 116 mutations, 6 CNAs |
| **COL11A1** | 94 | 23.9% | 115 mutations, 5 CNAs |
| **ADAMTS12** | 93 | 23.7% | 88 mutations, 36 CNAs |
| **PCDH15** | 91 | 23.2% | 132 mutations, 5 CNAs |
| **ZNF536** | 91 | 23.2% | 111 mutations, 13 CNAs |
| **PTPRD** | 91 | 23.2% | 84 mutations, 32 CNAs |
| **FAT3** | 89 | 22.6% | 123 mutations, 5 CNAs |
| **MUC17** | 88 | 22.4% | 118 mutations, 8 CNAs |

### Key Observations

1. **TP53 dominates** at 59.3% frequency — the most consistently altered gene in KRAS wild-type adenocarcinomas.

2. **TTN is also highly frequent** (48.3%), typical of lung adenocarcinoma where large genes accumulate passenger mutations due to the hypermutated phenotype.

3. **Cancer-driver genes enriched:** Beyond TP53, clinically relevant genes include:
   - **EGFR** (69 samples, 17.6% frequency among all mutations) — key driver in KRAS-WT adenocarcinomas
   - **CDKN2A** (84 samples, 21.4%) — predominantly copy-number deletions (64 of 87 events)

4. **Many genes are large/genomic passengers** (TTN, MUC16, CSMD3, RYR2, LRP1B, etc.), as expected in a hypermutated cancer type.

5. **Copy-number alterations** are less common than mutations overall but contribute significantly for tumor suppressors like CDKN2A (64 homozygous deletions out of 87 total alterations).

### Explore the Data

[View KRAS wild-type LUAD cohort in StudyView](https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22mutationDataFilters%22%3A%5B%7B%22categorization%22%3A%22MUTATED%22%2C%22hugoGeneSymbol%22%3A%22KRAS%22%2C%22profileType%22%3A%22mutations%22%2C%22values%22%3A%5B%5B%7B%22value%22%3A%22NOT_MUTATED%22%7D%5D%5D%7D%5D%7D)

This link opens the TCGA LUAD study pre-filtered to KRAS wild-type samples, where you can explore mutation and copy-number distributions interactively.