Q (sonnet): What are the top 5 most frequently mutated genes in the MSK-CHORD Study?

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-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)

▶ clickhouse_run_select_query
{
 "query": "SELECT hugo_gene_symbol, total_mutation_events AS mutations, altered_samples, profiled_samples, frequency_pct
FROM top_mutated_genes_in_study(study = 'msk_chord_2024', top_n = 5)
ORDER BY altered_samples DESC"
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","mutations":13876,"altered_samples":13124,"profiled_samples":25040,"frequency_pct":52.4},{"hugo_gene_symbol":"KRAS","mutations":7231,"altered_samples":7128,"profiled_samples":25040,"frequency_pct":28.5},{"hugo_gene_symbol":"APC","mutations":7187,"altered_samples":4777,"profiled_samples":25040,"frequency_pct":19.1},{"hugo_gene_symbol":"PIK3CA","mutations":4178,"altered_samples":3708,"profiled_samples":25040,"frequency_pct":14.8},{"hugo_gene_symbol":"EGFR","mutations":2556,"altered_samples":2159,"profiled_samples":25040,"frequency_pct":8.6}]}

▶ 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"
 ],
 "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&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&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"],"caseSetId":"msk_chord_2024_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
## Top 5 Most Frequently Mutated Genes — MSK-CHORD (msk_chord_2024)

Sample-level frequencies, using gene-specific profiled denominators (25,040 total profiled samples across this WES/panel-based cohort):

| Rank | Gene | Altered Samples | Profiled Samples | Frequency (%) | Total Mutation Events |
|------|------|------------------|-------------------|----------------|--------------------------|
| 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 |

Note: MSK-CHORD is a pan-cancer, multi–tumor-type cohort (lung, colorectal, breast, prostate, pancreatic, etc.), so these frequencies reflect an aggregate across many cancer types rather than one histology — APC and KRAS prevalence, for instance, is heavily driven by colorectal cancer representation in the cohort.

[View OncoPrint for TP53, KRAS, APC, PIK3CA, EGFR in MSK-CHORD](https://www.cbioportal.org/results/oncoprint?cancer_study_list=msk_chord_2024&gene_list=TP53%0AKRAS%0AAPC%0APIK3CA%0AEGFR&case_set_id=msk_chord_2024_all)

Visual summary of alterations in queried genes across samples.