Q (sonnet): What are the top 5 most frequently mutated genes in the Osteosarcoma study from TARGET?

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

▶ list_studies
{
 "search": "osteosarcoma TARGET"
}

◀ 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":"os_target_gdc","name":"Osteosarcoma (TARGET GDC, 2025)","type_of_cancer_id":"os","sample_count":159,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=os_target_gdc"}]}

▶ 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 = 'os_target_gdc', top_n = 5)"
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","mutations":33,"altered_samples":32,"profiled_samples":143,"frequency_pct":22.4},{"hugo_gene_symbol":"MUC16","mutations":17,"altered_samples":16,"profiled_samples":143,"frequency_pct":11.2},{"hugo_gene_symbol":"TTN","mutations":18,"altered_samples":16,"profiled_samples":143,"frequency_pct":11.2},{"hugo_gene_symbol":"ATRX","mutations":11,"altered_samples":11,"profiled_samples":143,"frequency_pct":7.7},{"hugo_gene_symbol":"DNAH9","mutations":10,"altered_samples":10,"profiled_samples":143,"frequency_pct":7}]}

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

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"os_target_gdc","name":"Osteosarcoma (TARGET GDC, 2025)","sampleCount":159,"studyViewUrl":"https://www.cbioportal.org/study?id=os_target_gdc","metadata":{"clinicalAttributeIds":["AGE","CANCER_TYPE","CANCER_TYPE_DETAILED","DAYS_TO_BIRTH","DAYS_TO_DEATH","DISEASE_TYPE","ETHNICITY","MUTATION_COUNT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PRIMARY_DIAGNOSIS","PRIMARY_SITE_PATIENT","PROJECT_ID","RACE","SAMPLE_COUNT","SEX","SPECIMEN_TYPE","TMB_NONSYNONYMOUS","VITAL_STATUS"],"molecularProfileIds":["os_target_gdc_cna","os_target_gdc_mrna_seq_fpkm","os_target_gdc_mrna_seq_fpkm_Zscores","os_target_gdc_mrna_seq_read_counts_Zscores","os_target_gdc_mrna_seq_tpm","os_target_gdc_mrna_seq_tpm_Zscores","os_target_gdc_mutations","os_target_gdc_rna_seq_mrna"],"heatmapProfileIds":["os_target_gdc_mrna_seq_fpkm_Zscores","os_target_gdc_mrna_seq_read_counts_Zscores","os_target_gdc_mrna_seq_tpm_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "os_target_gdc"
 ],
 "genes": [
  "TP53",
  "MUC16",
  "TTN",
  "ATRX",
  "DNAH9"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=os_target_gdc&gene_list=TP53%0AMUC16%0ATTN%0AATRX%0ADNAH9&case_set_id=os_target_gdc_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=os_target_gdc&gene_list=TP53%0AMUC16%0ATTN%0AATRX%0ADNAH9&case_set_id=os_target_gdc_all","data":{"studyIds":["os_target_gdc"],"studies":[{"studyId":"os_target_gdc","name":"Osteosarcoma (TARGET GDC, 2025)","sampleCount":159}],"genes":["TP53","MUC16","TTN","ATRX","DNAH9"],"caseSetId":"os_target_gdc_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
## Top 5 Mutated Genes — Osteosarcoma (TARGET GDC, 2025)

This is a whole-exome sequenced cohort with 143 samples profiled for mutations. Frequencies below are sample-level (altered/profiled × 100):

| Rank | Gene | Mutated Samples | Profiled Samples | Frequency |
|------|------|------------------|-------------------|-----------|
| 1 | **TP53** | 32 | 143 | 22.4% |
| 2 | **MUC16** | 16 | 143 | 11.2% |
| 2 | **TTN** | 16 | 143 | 11.2% |
| 4 | **ATRX** | 11 | 143 | 7.7% |
| 5 | **DNAH9** | 10 | 143 | 7.0% |

MUC16 and TTN are tied at 16 altered samples each. Note MUC16 and TTN are very large genes and are frequently mutated across many cancer types largely as passenger mutations due to their size, rather than necessarily reflecting osteosarcoma-specific driver biology. TP53 is the classic recurrently-altered driver in osteosarcoma.

[View OncoPrint for these 5 genes](https://www.cbioportal.org/results/oncoprint?cancer_study_list=os_target_gdc&gene_list=TP53%0AMUC16%0ATTN%0AATRX%0ADNAH9&case_set_id=os_target_gdc_all)

[View study cohort overview](https://www.cbioportal.org/study?id=os_target_gdc)