Q (haiku): List the top 20 mutated genes in study nbl_msk_2023.

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

▶ get_study_guide
{
 "study_id": "nbl_msk_2023"
}

◀ 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
# Study Guide: Pediatric Neuroblastoma (MSK, Nat Genet 2023)

**Study ID:** `nbl_msk_2023`
**Cancer Type:** nbl
**Description:** Targeted sequencing of 223 pediatric neuroblastoma tumor/normal pairs via MSK-IMPACT.

## Cohort Statistics
- **Patients:** 352
- **Samples:** 224

## Available Data Types

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

## Gene Panels

- **IMPACT468**: 165 samples
- **IMPACT410**: 52 samples
- **IMPACT341**: 3 samples
- **IMPACT505**: 2 samples
- **IMPACT-HEME-400**: 1 samples

## Available Clinical Attributes

| Attribute | Samples with Data |
|-----------|------------------|
| SAMPLE_TYPE | 223 |
| CANCER_TYPE_DETAILED | 223 |
| MSI_COMMENT | 223 |
| AGE_AT_SEQ_REPORTED_YEARS | 223 |
| PRIMARY_SITE | 223 |
| MUTATION_COUNT | 223 |
| SOMATIC_STATUS | 223 |
| FRACTION_GENOME_ALTERED | 223 |
| SAMPLE_CODE | 223 |
| TUMOR_PLOIDY | 223 |
| TMB_NONSYNONYMOUS | 223 |
| SAMPLE_CLASS | 223 |
| MEDIAN_COVERAGE | 223 |
| CANCER_TYPE | 223 |
| INSTITUTE | 223 |
| MSI_TYPE | 223 |
| SAMPLE_COVERAGE | 223 |
| METASTATIC_SITE | 223 |
| GENE_PANEL | 223 |
| TUMOR_PURITY | 223 |

## Top Mutated Genes

| Gene | Altered Samples |
|------|----------------|
| ALK | 33 |
| ATRX | 24 |
| SMARCA4 | 9 |
| BRAF | 8 |
| BRCA2 | 8 |
| TERT | 7 |
| KMT2D | 6 |
| CREBBP | 6 |
| KRAS | 6 |
| PTPN11 | 6 |

## Sample Types

- **Primary**: 103 samples
- **Recurrence**: 75 samples
- **Metastasis**: 44 samples
- **Unknown**: 1 samples

## Query Tips for nbl_msk_2023

```sql
-- Get all samples in this study
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'nbl_msk_2023';

-- 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 = 'nbl_msk_2023'
    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 = 'nbl_msk_2023'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'OS_MONTHS');
```


▶ clickhouse_run_select_query
{
 "query": "
SELECT hugo_gene_symbol, altered_samples, profiled_samples, frequency_pct, total_mutation_events
FROM top_mutated_genes_in_study(study = 'nbl_msk_2023', top_n = 20)
ORDER BY altered_samples DESC
",
 "max_rows": 20
}

◀ result
{"rows":[{"hugo_gene_symbol":"ALK","altered_samples":33,"profiled_samples":223,"frequency_pct":14.8,"total_mutation_events":34},{"hugo_gene_symbol":"ATRX","altered_samples":24,"profiled_samples":223,"frequency_pct":10.8,"total_mutation_events":24},{"hugo_gene_symbol":"SMARCA4","altered_samples":9,"profiled_samples":223,"frequency_pct":4,"total_mutation_events":9},{"hugo_gene_symbol":"BRAF","altered_samples":8,"profiled_samples":223,"frequency_pct":3.6,"total_mutation_events":8},{"hugo_gene_symbol":"BRCA2","altered_samples":8,"profiled_samples":223,"frequency_pct":3.6,"total_mutation_events":8},{"hugo_gene_symbol":"TERT","altered_samples":7,"profiled_samples":223,"frequency_pct":3.1,"total_mutation_events":7},{"hugo_gene_symbol":"CREBBP","altered_samples":6,"profiled_samples":223,"frequency_pct":2.7,"total_mutation_events":8},{"hugo_gene_symbol":"FAT1","altered_samples":6,"profiled_samples":223,"frequency_pct":2.7,"total_mutation_events":6},{"hugo_gene_symbol":"KMT2D","altered_samples":6,"profiled_samples":223,"frequency_pct":2.7,"total_mutation_events":9},{"hugo_gene_symbol":"KRAS","altered_samples":6,"profiled_samples":223,"frequency_pct":2.7,"total_mutation_events":6},{"hugo_gene_symbol":"PTPN11","altered_samples":6,"profiled_samples":223,"frequency_pct":2.7,"total_mutation_events":6},{"hugo_gene_symbol":"ROS1","altered_samples":6,"profiled_samples":223,"frequency_pct":2.7,"total_mutation_events":6},{"hugo_gene_symbol":"CD79B","altered_samples":5,"profiled_samples":223,"frequency_pct":2.2,"total_mutation_events":5},{"hugo_gene_symbol":"IRS2","altered_samples":5,"profiled_samples":223,"frequency_pct":2.2,"total_mutation_events":5},{"hugo_gene_symbol":"KMT2B","altered_samples":5,"profiled_samples":168,"frequency_pct":3,"total_mutation_events":5},{"hugo_gene_symbol":"NCOR1","altered_samples":5,"profiled_samples":223,"frequency_pct":2.2,"total_mutation_events":5},{"hugo_gene_symbol":"NF1","altered_samples":5,"profiled_samples":223,"frequency_pct":2.2,"total_mutation_events":6},{"hugo_gene_symbol":"PDGFRA","altered_samples":5,"profiled_samples":223,"frequency_pct":2.2,"total_mutation_events":5},{"hugo_gene_symbol":"ARID1A","altered_samples":4,"profiled_samples":223,"frequency_pct":1.8,"total_mutation_events":4},{"hugo_gene_symbol":"AXL","altered_samples":4,"profiled_samples":223,"frequency_pct":1.8,"total_mutation_events":4}]}

═ answer (success)
Perfect! Here are the **top 20 mutated genes in the Pediatric Neuroblastoma (MSK, 2023) study**:

| Gene | Altered Samples | Sample % | Profiled Samples | Total Mutations |
|------|-----------------|----------|------------------|-----------------|
| ALK | 33 | 14.8% | 223 | 34 |
| ATRX | 24 | 10.8% | 223 | 24 |
| SMARCA4 | 9 | 4.0% | 223 | 9 |
| BRAF | 8 | 3.6% | 223 | 8 |
| BRCA2 | 8 | 3.6% | 223 | 8 |
| TERT | 7 | 3.1% | 223 | 7 |
| CREBBP | 6 | 2.7% | 223 | 8 |
| FAT1 | 6 | 2.7% | 223 | 6 |
| KMT2D | 6 | 2.7% | 223 | 9 |
| KRAS | 6 | 2.7% | 223 | 6 |
| PTPN11 | 6 | 2.7% | 223 | 6 |
| ROS1 | 6 | 2.7% | 223 | 6 |
| CD79B | 5 | 2.2% | 223 | 5 |
| IRS2 | 5 | 2.2% | 223 | 5 |
| KMT2B | 5 | 3.0% | 168 | 5 |
| NCOR1 | 5 | 2.2% | 223 | 5 |
| NF1 | 5 | 2.2% | 223 | 6 |
| PDGFRA | 5 | 2.2% | 223 | 5 |
| ARID1A | 4 | 1.8% | 223 | 4 |
| AXL | 4 | 1.8% | 223 | 4 |

**Key findings:**
- **ALK** is the most frequently mutated gene in this cohort (33/223 samples, 14.8%), reflecting its established importance in neuroblastoma pathogenesis.
- **ATRX** is the second most common (24 samples, 10.8%), another known NBL driver.
- The cohort includes 224 primary and relapsed/metastatic tumors profiled via MSK-IMPACT gene panels.
- Note: **KMT2B** was only profiled in 168 samples (not all 223), likely on a subset of the gene panels; its frequency of 3.0% reflects this smaller profiled denominator.