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

▶ list_studies
{
 "search": "POG"
}

◀ 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"}]}

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

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-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 … (36467 more chars)

◀ result
# Sample and Study Filtering Guide

## Overview
Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria.

## Study-Level Filtering

### 1. Always Filter by Study
Every query should specify a study to ensure consistent results:

```sql
-- Always include study filtering
SELECT *
FROM your_table
WHERE
    cancer_study_identifier = 'your_study_id'
    -- Additional filters...
```

### 2. Find Available Studies

```sql
-- Discover available studies
SELECT
    cancer_study_identifier,
    name,
    description,
    type_of_cancer_id
FROM cancer_study
ORDER BY cancer_study_identifier;
```

### 3. Study Information

```sql
-- Get detailed study information
SELECT
    cs.cancer_study_identifier,
    cs.name as study_name,
    cs.description,
    cs.sample_count,
    COUNT(DISTINCT p.internal_id) as patient_count
FROM cancer_study cs
LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id
WHERE
    cs.cancer_study_identifier = 'your_study_id'
GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count;
```

### 4. Find Studies by Available Data Types

Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter.

**Canonical query — studies with ALL requested data types for a cancer type:**

```sql
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'          -- ← OncoTree code from search_oncotree
  AND mutation_sample_count > 0           -- ← one condition per required data type
  AND cna_sample_count > 0
ORDER BY sample_count DESC;
```

**Data type → column** (a count of 0 means the study doesn't have that data):

| Data type in the user's question | Column (portal "Data type" filter label) |
|---|---|
| samples in the study | `sample_count` |
| mutation / mutations | `mutation_sample_count` ("Mutations") |
| copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") |
| structural variant / fusion | `structural_variant_sample_count` |
| RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" |
| specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `<study>_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq |
| specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `<study>_mrna` list |
| microRNA | `mirna_sample_count` ("miRNA") |
| protein / RPPA | `rppa_sample_count` ("RPPA") |
| mas … (7866 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT profile_type, alteration_value, count() AS samples
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'pog570_bcgsc_2020'
  AND hugo_gene_symbol = 'SMARCA4'
GROUP BY profile_type, alteration_value
ORDER BY profile_type, alteration_value
"
}

◀ result
{"rows":[{"profile_type":"gistic","alteration_value":"-1","samples":111},{"profile_type":"gistic","alteration_value":"-2","samples":4},{"profile_type":"gistic","alteration_value":"0","samples":394},{"profile_type":"gistic","alteration_value":"1","samples":58},{"profile_type":"gistic","alteration_value":"2","samples":3},{"profile_type":"rna_seq_mrna","alteration_value":"0.2641","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"0.5447","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"0.5689","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"1.0218","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"1.3562","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"1.4979","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"1.5261","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"1.7784","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"1.8345","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"1.9753","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.031","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.0647","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.071","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.1535","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.1539","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.1559","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.1633","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.1713","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.1728","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.2087","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.2272","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.2613","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.2934","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.3038","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.3261","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.3288","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.3812","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.4197","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.4464","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.4662","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.4739","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.499","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.5376","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.5814","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.5816","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.6932","samples":1},{"profile_type":"rna_seq_mrna","alteration_value":"10.7156","sam … (4533 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    uniqExact(gad.sample_unique_id) AS shallow_del_samples,
    uniqExact(cd.patient_unique_id) AS shallow_del_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
  ON gad.sample_unique_id = cd.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":[{"shallow_del_samples":111,"shallow_del_patients":111}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT count() AS profiled_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')
"
}

◀ result
{"rows":[{"profiled_samples":570}]}

▶ 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)
[View SMARCA4 shallow deletion cohort in POG570](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 filters the study cohort to the 111 patients with SMARCA4 shallow deletion, letting you explore their clinical and genomic characteristics interactively.