Q (sonnet-5.5): How many patients have a shallow deletion for SMARCA4 in the POG study?

▶ list_studies
{
 "search": "POG"
}

▶ read_guide
{
 "uri": "cbioportal://gene-expression-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
# Gene Expression Analysis Guide

This guide covers continuous-value genomic data: gene **expression**, **copy number** values, **methylation**, and related profile types. Mutation/CNA/SV *frequency* analysis lives in `cbioportal://mutation-frequency-guide`.

## Where this data lives

Continuous per-sample-per-gene values are stored in `genetic_alteration_derived`:

| Column | Description |
|---|---|
| `sample_unique_id` | `<study>_<sample_stable_id>` |
| `cancer_study_identifier` | study scope |
| `hugo_gene_symbol` | gene |
| `profile_type` | which assay/normalization (see below) |
| `alteration_value` | the actual value — stored as Nullable(String); cast with `toFloat64OrNull` |

`alteration_value` is a string because the same column hosts many different value scales. The `''` and `'NA'` sentinels mean "missing"; always filter them out and use `toFloat64OrNull(alteration_value) IS NOT NULL` for downstream math.

## Discovering profile types for a study

Different studies expose different profile types depending on what assays were run and how the data was normalized. Always check what a specific study supports before picking one:

```sql
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'brca_metabric'
ORDER BY profile_type;
```

Common values across the public portal:

| Family | Profile types |
|---|---|
| mRNA expression | `rna_seq_v2_mrna`, `rna_seq_v2_mrna_median_Zscores`, `rna_seq_v2_mrna_median_all_sample_Zscores` (TCGA PanCancer Atlas), `mrna`, `mrna_median_Zscores`, `mrna_seq_v2_rsem`, `mrna_seq_v2_rsem_Zscores`, `mrna_seq_cpm`, `mrna_seq_fpkm`, `mrna_U133`, `mrna_outliers` |
| Copy number (continuous) | `cna`, `linear_CNA`, `log2CNA`, `cna_consensus`, `cna_rae`, `gistic` |
| Methylation | `methylation_hm27`, `methylation_hm450`, `methylation_epic`, `methylation_promoters_rrbs` |
| miRNA | `mirna`, `mirna_median_Zscores` |
| Protein | `protein_quantification`, `protein_level`, `RPPA` |

**Z-score vs raw choice.** When the user asks "is X correlated with Y", either works for Spearman (rank-based) — Pearson would care. Default to the non-Z-score variant if both exist, and call out which one in the response.

## Canonical recipe — Spearman correlation between two genes

```sql
SELECT *
FROM gene_pair_coexpression(
    study        = 'brca_metabric',
    gene_a       = 'TP53',
    gene_b       = 'MYC',
    profile_type = 'mrna'
);
```

Returns one row: `(gene_a, gene_b, profile_type, spearman_correlation, num_samples)`.

- `spearman_correlation` in [−1, 1]; `NULL` when fewer than 3 valid paired samples.
- Mirrors cbioportal-backend's `ClickhouseCoExpressionMapper.getCoExpressions`, simplified to a pair lookup (the backend computes one ref gene vs ALL other genes for the coexpression page; here the agent asks about a specific pair).

### Verified examples

| Study | gene_a | gene_b | profile_type | spearman | n |
|---|---|---|---|---|---|
| `brca_metabric` | TP53 | MYC | `mrna` | 0.118 | 1980 |
|  … (4931 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT profile_type, alteration_value, count() n, uniqExact(sample_unique_id) s FROM genetic_alteration_derived WHERE cancer_study_identifier='pog570_bcgsc_2020' AND hugo_gene_symbol='SMARCA4' AND profile_type IN ('gistic','cna','linear_CNA','log2CNA') GROUP BY 1,2 ORDER BY 1,2"
}

▶ clickhouse_list_table_columns
{
 "table": "sample_derived"
}

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

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"sample_unique_id_base64","type":"String"},{"name":"sample_stable_id","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"patient_unique_id_base64","type":"String"},{"name":"patient_stable_id","type":"String"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"internal_id","type":"Int32"},{"name":"patient_internal_id","type":"Int32"},{"name":"sample_type","type":"String"},{"name":"sequenced","type":"Int32"},{"name":"copy_number_segment_present","type":"Int32"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT uniqExact(s.patient_unique_id) pts, count() smp, (SELECT uniqExact(patient_unique_id) FROM sample_derived WHERE cancer_study_identifier='pog570_bcgsc_2020' AND sample_unique_id IN (SELECT sample_unique_id FROM genetic_alteration_derived WHERE cancer_study_identifier='pog570_bcgsc_2020' AND hugo_gene_symbol='SMARCA4' AND profile_type='gistic')) profiled_pts FROM sample_derived s WHERE cancer_study_identifier='pog570_bcgsc_2020' AND sample_unique_id IN (SELECT sample_unique_id FROM genetic_alteration_derived WHERE cancer_study_identifier='pog570_bcgsc_2020' AND hugo_gene_symbol='SMARCA4' AND profile_type='gistic' AND alteration_value='-1')"
}

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

◀ result
{"rows":[{"pts":111,"smp":111,"profiled_pts":570}]}

◀ 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)
In the POG570 study (*Pan-cancer Analysis of Advanced and Metastatic Tumors*, BCGSC, Nature Cancer 2020), **111 patients** have a shallow deletion of SMARCA4. That is 111 of 570 patients profiled for copy number, or about 19.5%.

- Shallow deletion is GISTIC level −1.
- Each patient has one sample, so the patient and sample counts are the same.
- For comparison, 4 patients have a deep deletion (−2), 58 have a gain (+1), and 3 have an amplification (+2). The other 394 are diploid.

[View these patients in the 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)