Q (haiku): What are the top 10 most frequently mutated genes across all cancer types in the MSK-CHORD Study?

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

▶ list_studies
{
 "search": "MSK-CHORD"
}

◀ 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
{"result":[{"cancer_study_identifier":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","type_of_cancer_id":"mixed","sample_count":25040,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024"}]}

▶ get_study_guide
{
 "study_id": "msk_chord_2024"
}

◀ result
# MSK-CHORD (MSK, Nature 2024)

**Study ID:** `msk_chord_2024`

## Overview
Targeted sequencing via MSK-IMPACT panels. Clinical annotations include some derived from natural language processing (denoted NLP).

**Exactly five cancer types** (`CANCER_TYPE`, patients): Non-Small Cell Lung Cancer 7,809, Colorectal Cancer 5,543, Breast Cancer 5,368, Prostate Cancer 3,211, Pancreatic Cancer 3,109. There is **no melanoma** or any other cancer type; say so up front if asked, instead of substituting another type.

**No therapy-response variable.** There is no RECIST, objective response, or best-response attribute or event. For treatment-outcome questions (e.g. immunotherapy response), say this first; the only proxies are `OS_MONTHS`/`OS_STATUS`, or NLP radiology progression events (`Diagnosis` events with `SUBTYPE = 'Progression'`, key `PROGRESSION` = Y/N/Indeterminate), in patients with `Treatment` events of the relevant `SUBTYPE` (e.g. `Immuno`: 3,341 patients). Hand off the comparison to cBioPortal group comparison / survival.

**Nearly one sample per patient: 24,950 patients / 25,040 samples.** Only 90 patients have more than one sample, and all 90 have samples from two different cancer types (second primaries); only 26 have both a `Primary` and a `Metastasis` sample. There is no meaningful same-patient (paired) primary-vs-metastasis cohort. For "same patient" / paired questions, say this up front, then offer the **unpaired** comparison of all `Primary` vs `Metastasis` samples (`SAMPLE_TYPE`), labelled as unpaired.
```sql
SELECT countIf(n > 1) AS multi_sample_patients,         -- 90
       countIf(has_p AND has_m) AS primary_and_met      -- 26
FROM (SELECT patient_unique_id, count() AS n,
             has(groupArray(attribute_value), 'Primary') AS has_p,
             has(groupArray(attribute_value), 'Metastasis') AS has_m
      FROM clinical_data_derived
      WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'SAMPLE_TYPE'
      GROUP BY patient_unique_id);
```

## Gene Panels
This study uses multiple MSK-IMPACT panel versions:
- **IMPACT341**: Earlier version, 341 genes
- **IMPACT410**: 410 genes
- **IMPACT468**: 468 genes  
- **IMPACT505**: Latest version, 505 genes

**Important:** Different samples may have different gene coverage. Always use gene-specific denominators when calculating mutation frequencies.

## Clinical Attributes - Semantic Guide

### Cancer Classification
| Attribute | Description | Values |
|-----------|-------------|--------|
| `CANCER_TYPE` | Broad cancer category | e.g., "Non-Small Cell Lung Cancer", "Breast Cancer" |
| `CANCER_TYPE_DETAILED` | Specific subtype | e.g., "Lung Adenocarcinoma", "Invasive Ductal Carcinoma" |
| `ONCOTREE_CODE` | OncoTree classification code | Standardized cancer type codes |

### Sample Information
| Attribute | Description | Values |
|-----------|-------------|--------|
| `SAMPLE_TYPE` | Sample origin | Primary, Metastasis, Local Recurrence, Unknown |
| `SAMPLE_CLASS` | Sample … (5439 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT *
FROM top_mutated_genes_in_study(
    study = 'msk_chord_2024',
    top_n = 10
);
",
 "max_rows": 20
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","altered_samples":13124,"profiled_samples":25040,"frequency_pct":52.4,"total_mutation_events":13876},{"hugo_gene_symbol":"KRAS","altered_samples":7128,"profiled_samples":25040,"frequency_pct":28.5,"total_mutation_events":7231},{"hugo_gene_symbol":"APC","altered_samples":4777,"profiled_samples":25040,"frequency_pct":19.1,"total_mutation_events":7187},{"hugo_gene_symbol":"PIK3CA","altered_samples":3708,"profiled_samples":25040,"frequency_pct":14.8,"total_mutation_events":4178},{"hugo_gene_symbol":"EGFR","altered_samples":2159,"profiled_samples":25040,"frequency_pct":8.6,"total_mutation_events":2556},{"hugo_gene_symbol":"ARID1A","altered_samples":1843,"profiled_samples":25040,"frequency_pct":7.4,"total_mutation_events":2205},{"hugo_gene_symbol":"SMAD4","altered_samples":1796,"profiled_samples":25040,"frequency_pct":7.2,"total_mutation_events":1925},{"hugo_gene_symbol":"KMT2D","altered_samples":1783,"profiled_samples":25040,"frequency_pct":7.1,"total_mutation_events":2390},{"hugo_gene_symbol":"KMT2C","altered_samples":1629,"profiled_samples":25040,"frequency_pct":6.5,"total_mutation_events":1979},{"hugo_gene_symbol":"ATM","altered_samples":1388,"profiled_samples":25040,"frequency_pct":5.5,"total_mutation_events":1681}]}

▶ resolve_and_route
{
 "studyIds": [
  "msk_chord_2024"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024","metadata":{"clinicalAttributeIds":["ADRENAL_GLANDS","BONE","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_GROUP","CLINICAL_SUMMARY","CNS_BRAIN","CURRENT_AGE_DEID","DIAGNOSIS_DESCRIPTION","ETHNICITY","FRACTION_GENOME_ALTERED","GENDER","GENE_PANEL","GLEASON_FIRST_REPORTED","GLEASON_HIGHEST_REPORTED","GLEASON_SAMPLE_LEVEL","HER2","HISTORY_OF_PDL1","HR","ICD_O_HISTOLOGY_DESCRIPTION","INTRA_ABDOMINAL","LIVER","LUNG","LYMPH_NODES","METASTATIC_SITE","MSI_COMMENT","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","NUM_ICDO_DX","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER","PATHOLOGICAL_GROUP","PDL1_POSITIVE","PLEURA","PRIMARY_SITE","PRIOR_MED_TO_MSK","RACE","REPRODUCTIVE_ORGANS","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SMOKING_PREDICTIONS_3_CLASSES","SOMATIC_STATUS","STAGE_HIGHEST_RECORDED","TMB_NONSYNONYMOUS","TUMOR_PURITY"],"molecularProfileIds":["msk_chord_2024_cna","msk_chord_2024_mutations","msk_chord_2024_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "msk_chord_2024"
 ],
 "genes": [
  "TP53",
  "KRAS",
  "APC",
  "PIK3CA",
  "EGFR",
  "ARID1A",
  "SMAD4",
  "KMT2D",
  "KMT2C",
  "ATM"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=msk_chord_2024&gene_list=TP53%0AKRAS%0AAPC%0APIK3CA%0AEGFR%0AARID1A%0ASMAD4%0AKMT2D%0AKMT2C%0AATM&case_set_id=msk_chord_2024_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=msk_chord_2024&gene_list=TP53%0AKRAS%0AAPC%0APIK3CA%0AEGFR%0AARID1A%0ASMAD4%0AKMT2D%0AKMT2C%0AATM&case_set_id=msk_chord_2024_all","data":{"studyIds":["msk_chord_2024"],"studies":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040}],"genes":["TP53","KRAS","APC","PIK3CA","EGFR","ARID1A","SMAD4","KMT2D","KMT2C","ATM"],"caseSetId":"msk_chord_2024_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
## Top 10 Most Frequently Mutated Genes in MSK-CHORD Study

The **MSK-CHORD Study** (MSK, Nature 2024) analyzed 25,040 samples across five cancer types: Non-Small Cell Lung Cancer, Colorectal Cancer, Breast Cancer, Prostate Cancer, and Pancreatic Cancer.

### Results (Sample-Level Frequencies)

| Rank | Gene | Altered Samples | Profiled Samples | Frequency | Mutations |
|------|------|-----------------|------------------|-----------|-----------|
| 1 | **TP53** | 13,124 | 25,040 | **52.4%** | 13,876 |
| 2 | **KRAS** | 7,128 | 25,040 | **28.5%** | 7,231 |
| 3 | **APC** | 4,777 | 25,040 | **19.1%** | 7,187 |
| 4 | **PIK3CA** | 3,708 | 25,040 | **14.8%** | 4,178 |
| 5 | **EGFR** | 2,159 | 25,040 | **8.6%** | 2,556 |
| 6 | **ARID1A** | 1,843 | 25,040 | **7.4%** | 2,205 |
| 7 | **SMAD4** | 1,796 | 25,040 | **7.2%** | 1,925 |
| 8 | **KMT2D** | 1,783 | 25,040 | **7.1%** | 2,390 |
| 9 | **KMT2C** | 1,629 | 25,040 | **6.5%** | 1,979 |
| 10 | **ATM** | 1,388 | 25,040 | **5.5%** | 1,681 |

### Key Findings

- **TP53** is the most commonly mutated gene, affecting more than half of all samples (52.4%), reflecting its role as the most frequently altered tumor suppressor across cancer types.
- **KRAS** is the second most common (28.5%), driven heavily by its high frequency in colorectal and lung cancers.
- **APC** is the third most frequently mutated (19.1%), reflecting the high proportion of colorectal cancer samples in the cohort, where APC mutations are a hallmark.
- These top genes are characteristic drivers of the five major cancer types represented in this cohort.

### Explore the Data

[View OncoPrint of top 10 mutated genes](https://www.cbioportal.org/results/oncoprint?cancer_study_list=msk_chord_2024&gene_list=TP53%0AKRAS%0AAPC%0APIK3CA%0AEGFR%0AARID1A%0ASMAD4%0AKMT2D%0AKMT2C%0AATM&case_set_id=msk_chord_2024_all)

This visualization displays the mutations and copy-number alterations across all samples and allows you to explore patterns, co-occurrences, and sample-level details.