Q (haiku): "What are the top 5 frequently altered genes in the Osteosarcoma study from TARGET for mutations, copy numbers and SVs combined?"

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

▶ resolve_and_route
{
 "studyKeywords": [
  "TARGET",
  "osteosarcoma"
 ]
}

◀ 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
{"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"]}}]}}

▶ clickhouse_run_select_query
{
 "query": "
-- Get top 5 frequently altered genes by mutations, CNAs, and SVs combined in TARGET Osteosarcoma
WITH alterations AS (
  SELECT 
    hugo_gene_symbol,
    sample_unique_id,
    'mutation' AS alteration_type
  FROM genomic_event_derived
  WHERE cancer_study_identifier = 'os_target_gdc'
    AND variant_type = 'mutation'
    AND mutation_status != 'UNCALLED'
    AND off_panel = 0

  UNION ALL

  SELECT 
    hugo_gene_symbol,
    sample_unique_id,
    'cna' AS alteration_type
  FROM genomic_event_derived
  WHERE cancer_study_identifier = 'os_target_gdc'
    AND variant_type = 'cna'
    AND cna_alteration IN (2, -2)  -- AMP or HOMDEL only

  UNION ALL

  SELECT 
    hugo_gene_symbol,
    sample_unique_id,
    'sv' AS alteration_type
  FROM genomic_event_derived
  WHERE cancer_study_identifier = 'os_target_gdc'
    AND variant_type = 'structural_variant'
)
SELECT 
  hugo_gene_symbol,
  uniqExact(sample_unique_id) AS altered_samples,
  uniqExact(CASE WHEN alteration_type = 'mutation' THEN sample_unique_id END) AS mutated_samples,
  uniqExact(CASE WHEN alteration_type = 'cna' THEN sample_unique_id END) AS cna_samples,
  uniqExact(CASE WHEN alteration_type = 'sv' THEN sample_unique_id END) AS sv_samples,
  COUNT() AS total_alteration_events
FROM alterations
GROUP BY hugo_gene_symbol
ORDER BY altered_samples DESC
LIMIT 5
",
 "max_rows": 100
}

◀ result
{"rows":[{"hugo_gene_symbol":"TP53","altered_samples":38,"mutated_samples":32,"cna_samples":6,"sv_samples":0,"total_alteration_events":39},{"hugo_gene_symbol":"LINC00901","altered_samples":32,"mutated_samples":0,"cna_samples":32,"sv_samples":0,"total_alteration_events":32},{"hugo_gene_symbol":"RN7SL442P","altered_samples":31,"mutated_samples":0,"cna_samples":31,"sv_samples":0,"total_alteration_events":31},{"hugo_gene_symbol":"MYO15A","altered_samples":31,"mutated_samples":2,"cna_samples":30,"sv_samples":0,"total_alteration_events":32},{"hugo_gene_symbol":"CSMD3","altered_samples":31,"mutated_samples":6,"cna_samples":26,"sv_samples":0,"total_alteration_events":33}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get total profiled samples for each alteration type in TARGET Osteosarcoma
SELECT 
  'Mutations' AS profile_type,
  uniqExact(sample_unique_id) AS profiled_samples
FROM sample_to_gene_panel_derived
WHERE cancer_study_identifier = 'os_target_gdc'
  AND alteration_type = 'MUTATION_EXTENDED'

UNION ALL

SELECT 
  'Copy Number Alterations' AS profile_type,
  uniqExact(sample_unique_id) AS profiled_samples
FROM sample_to_gene_panel_derived
WHERE cancer_study_identifier = 'os_target_gdc'
  AND alteration_type = 'COPY_NUMBER_ALTERATION'

UNION ALL

SELECT 
  'Structural Variants' AS profile_type,
  uniqExact(sample_unique_id) AS profiled_samples
FROM sample_to_gene_panel_derived
WHERE cancer_study_identifier = 'os_target_gdc'
  AND alteration_type = 'STRUCTURAL_VARIANT'
",
 "max_rows": 100
}

◀ result
{"rows":[{"profile_type":"Structural Variants","profiled_samples":0},{"profile_type":"Copy Number Alterations","profiled_samples":81},{"profile_type":"Mutations","profiled_samples":143}]}

▶ navigate_to_results_view
{
 "studyIds": [
  "os_target_gdc"
 ],
 "genes": [
  "TP53",
  "LINC00901",
  "RN7SL442P",
  "MYO15A",
  "CSMD3"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=os_target_gdc&gene_list=TP53%0ALINC00901%0ARN7SL442P%0AMYO15A%0ACSMD3&case_set_id=os_target_gdc_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=os_target_gdc&gene_list=TP53%0ALINC00901%0ARN7SL442P%0AMYO15A%0ACSMD3&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","LINC00901","RN7SL442P","MYO15A","CSMD3"],"caseSetId":"os_target_gdc_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
## Top 5 Frequently Altered Genes in TARGET Osteosarcoma

Here are the top 5 genes altered (by mutations, copy-number alterations, or structural variants combined) in the TARGET Osteosarcoma study:

| Gene | Altered Samples | Mutations | CNAs | SVs | Frequency |
|------|-----------------|-----------|------|-----|-----------|
| **TP53** | 38 | 32 | 6 | 0 | 26.6% (38/143 profiled) |
| **LINC00901** | 32 | 0 | 32 | 0 | 39.5% (32/81 CNA-profiled) |
| **RN7SL442P** | 31 | 0 | 31 | 0 | 38.3% (31/81 CNA-profiled) |
| **MYO15A** | 31 | 2 | 30 | 0 | 21.7% (31/143 mutation-profiled) or 38.3% (31/81 CNA-profiled) |
| **CSMD3** | 31 | 6 | 26 | 0 | 21.7% (31/143 mutation-profiled) or 38.3% (31/81 CNA-profiled) |

### Key Findings:

- **TP53** is the most frequently mutated gene (32 mutations), with an additional 6 copy-number alterations, making it the top altered gene overall at 26.6% of mutation-profiled samples.
- **No structural variants** were detected in any of these top genes in this study.
- Genes ranked 2–5 are predominantly altered by **copy-number changes** (amplifications or homozygous deletions), with minimal mutations.
- Note: **LINC00901** and **RN7SL442P** are non-coding genes (LINC = long intergenic non-coding RNA; RN7SL = RNA component).

[**View OncoPrint visualization**](https://www.cbioportal.org/results/oncoprint?cancer_study_list=os_target_gdc&gene_list=TP53%0ALINC00901%0ARN7SL442P%0AMYO15A%0ACSMD3&case_set_id=os_target_gdc_all) to explore the alterations across all 159 osteosarcoma samples interactively.