Q (sonnet): How many patients have a shallow deletion for SMARCA4 in the POG study?

▶ list_studies
{
 "search": "POG"
}

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

◀ result
{"result":[{"cancer_study_identifier":"pog570_bcgsc_2020","name":"Pan-cancer Analysis of Advanced and Metastatic Tumors (BCGSC, Nature Cancer 2020)","type_of_cancer_id":"mixed","sample_count":570,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pog570_bcgsc_2020"}]}

◀ 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 alteration_value, count() AS samples
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'pog570_bcgsc_2020' AND hugo_gene_symbol = 'SMARCA4'
  AND profile_type = 'gistic' AND alteration_value NOT IN ('', 'NA')
GROUP BY alteration_value"
}

◀ result
{"rows":[{"alteration_value":"2","samples":3},{"alteration_value":"0","samples":394},{"alteration_value":"1","samples":58},{"alteration_value":"-1","samples":111},{"alteration_value":"-2","samples":4}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT count(DISTINCT sample_unique_id) AS samples, count(DISTINCT patient_unique_id) AS patients
FROM genetic_alteration_derived gad
JOIN (SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived
      WHERE cancer_study_identifier = 'pog570_bcgsc_2020') cd USING sample_unique_id
WHERE gad.cancer_study_identifier = 'pog570_bcgsc_2020'
  AND gad.hugo_gene_symbol = 'SMARCA4'
  AND gad.profile_type = 'gistic'
  AND gad.alteration_value = '-1'"
}

◀ result
{"rows":[{"samples":111,"patients":111}]}

▶ resolve_and_route
{
 "studyIds": [
  "pog570_bcgsc_2020"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"pog570_bcgsc_2020","name":"Pan-cancer Analysis of Advanced and Metastatic Tumors (BCGSC, Nature Cancer 2020)","sampleCount":570,"studyViewUrl":"https://www.cbioportal.org/study?id=pog570_bcgsc_2020","metadata":{"clinicalAttributeIds":["AGE","ANALYSIS_COHORT","BIOPSY_COHORT","BIOPSY_SITE","CANCER_TYPE","CANCER_TYPE_DETAILED","GENDER","HRD_SCORE","ICI","ICI_BEST_RESPONSE","ICI_DURABLE_CLINICAL_BENEFIT","ICI_MUTATION_CATEGORY","MSI_SCORE","MUTATION_COUNT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PRIMARY_SITE","SAMPLE_COUNT","SAMPLE_TYPE","SOMATIC_STATUS","TMB_NONSYNONYMOUS","TREATED_WITH_ICI","TREATMENT_CATEGORY","TUMOR_CONTENT","T_CELLS_CD4_MEMORY_ACTIVATED","T_CELLS_CD4_MEMORY_RESTING","T_CELLS_CD4_NAIVE","T_CELLS_CD8","T_CELLS_FOLLICULAR_HELPER","T_CELLS_GAMMA_DELTA","T_CELLS_REGULATORY_TREGS"],"molecularProfileIds":["pog570_bcgsc_2020_gistic","pog570_bcgsc_2020_mutations","pog570_bcgsc_2020_rna_seq_mrna","pog570_bcgsc_2020_rna_seq_mrna_median_all_sample_Zscores","pog570_bcgsc_2020_structural_variants"],"genericAssayProfiles":["pog570_bcgsc_2020_immune_cell_signature"],"heatmapProfileIds":["pog570_bcgsc_2020_rna_seq_mrna_median_all_sample_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","generic_assay_immune_cell_signature"]}}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "pog570_bcgsc_2020"
 ],
 "filterJson": {
  "genomicDataFilters": [
   {
    "hugoGeneSymbol": "SMARCA4",
    "profileType": "gistic",
    "values": [
     {
      "value": "-1"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=pog570_bcgsc_2020#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22SMARCA4%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%22-1%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=pog570_bcgsc_2020#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22SMARCA4%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%22-1%22%7D%5D%7D%5D%7D","data":{"studyIds":["pog570_bcgsc_2020"],"studies":[{"studyId":"pog570_bcgsc_2020","name":"Pan-cancer Analysis of Advanced and Metastatic Tumors (BCGSC, Nature Cancer 2020)","sampleCount":570}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}}

═ answer (success)
You can explore this filtered cohort directly here:

[View SMARCA4 shallow deletion patients — POG study](https://www.cbioportal.org/study/summary?id=pog570_bcgsc_2020#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22SMARCA4%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%22-1%22%7D%5D%7D%5D%7D)

This opens the Pan-cancer Analysis of Advanced and Metastatic Tumors (BCGSC, Nature Cancer 2020) study (`pog570_bcgsc_2020`, 570 samples) filtered to the 111 samples/patients with a SMARCA4 shallow deletion (GISTIC value −1).