Q (sonnet-5.5): calculate median expression of ceacam5, itgb6, muc2, tpgb and muc1 mRNA in pancreatic cancer

▶ read_guide
{
 "uri": "cbioportal://gene-expression-guide"
}

▶ list_studies
{
 "search": "pancreatic",
 "limit": 50
}

◀ 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)

◀ result
{"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","type_of_cancer_id":"mixed","sample_count":25775,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_met_2021"},{"cancer_study_identifier":"pdac_msk_2024","name":"Pancreatic Adenocarcinoma (MSK, Nat Med 2024)","type_of_cancer_id":"pancreas","sample_count":2336,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pdac_msk_2024"},{"cancer_study_identifier":"paad_qcmg_uq_2016","name":"Pancreatic Adenocarcinoma (QCMG, Nature 2016)","type_of_cancer_id":"paad","sample_count":456,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=paad_qcmg_uq_2016"},{"cancer_study_identifier":"pancreas_ctdna_msk_2025","name":"Pancreatic Cancer cfDNA (MSK, J Natl Cancer Inst 2025)","type_of_cancer_id":"paad","sample_count":412,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancreas_ctdna_msk_2025"},{"cancer_study_identifier":"pancreas_msk_2024","name":"Pancreatic Cancer (MSK, Cancer Cell 2024)","type_of_cancer_id":"pancreas","sample_count":395,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancreas_msk_2024"},{"cancer_study_identifier":"paad_tcga_gdc","name":"Pancreatic Adenocarcinoma (TCGA GDC, 2025)","type_of_cancer_id":"paad","sample_count":186,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=paad_tcga_gdc"},{"cancer_study_identifier":"paad_tcga","name":"Pancreatic Adenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"paad","sample_count":186,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=paad_tcga"},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018","name":"Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"paad","sample_count":184,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=paad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"pancreas_cptac_gdc","name":"Pancreatic Cancer (CPTAC GDC, 2025)","type_of_cancer_id":"pancreas","sample_count":183,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancreas_cptac_gdc"},{"cancer_study_identifier":"paad_cptac_2021","name":"Pancreatic Ductal Adenocarcinoma (CPTAC, Cell 2021)","type_of_cancer_id":"paad","sample_count":140,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=paad_cptac_2021"},{"cancer_study_identifier":"paad_utsw_2015","name":"Pancreatic Cancer (UTSW, Nat Commun 2015)","type_of_cancer_id":"paad","sample_count":109,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=paad_utsw_2015"},{"cancer_study_identifier":"paad_icgc","name":"Pancreatic Adenocarcinoma (ICGC, Nature 2012)","type_of_cancer_id":"paad","sample_count":99,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=paad_icgc"},{"cancer_study_identifier":"panet_arcnet_2017","name":"Pancreatic Neuroendocrine Tumors (Multi-Institute, Nature 2017)","type_of_cancer_id":"panet","sample_count":98,"has_gui … (1828 more chars)

═ answer (success)
Several pancreatic cancer studies in cBioPortal could answer this, so I need you to pick one before I calculate anything. Expression values from different studies use different assays and normalizations, so pooling them isn't valid.

**Pancreatic adenocarcinoma (PDAC), studies with mRNA data are likely among these:**
- [Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=paad_tcga_pan_can_atlas_2018) (`paad_tcga_pan_can_atlas_2018`, 184 samples)
- [Pancreatic Adenocarcinoma (TCGA, Firehose Legacy)](https://www.cbioportal.org/study/summary?id=paad_tcga) (`paad_tcga`, 186)
- [Pancreatic Adenocarcinoma (TCGA GDC, 2025)](https://www.cbioportal.org/study/summary?id=paad_tcga_gdc) (`paad_tcga_gdc`, 186)
- [Pancreatic Ductal Adenocarcinoma (CPTAC, Cell 2021)](https://www.cbioportal.org/study/summary?id=paad_cptac_2021) (`paad_cptac_2021`, 140)
- [Pancreatic Cancer (CPTAC GDC, 2025)](https://www.cbioportal.org/study/summary?id=pancreas_cptac_gdc) (`pancreas_cptac_gdc`, 183)
- [Pancreatic Adenocarcinoma (QCMG, Nature 2016)](https://www.cbioportal.org/study/summary?id=paad_qcmg_uq_2016) (`paad_qcmg_uq_2016`, 456)
- [Pancreatic Cancer (UTSW, Nat Commun 2015)](https://www.cbioportal.org/study/summary?id=paad_utsw_2015) (`paad_utsw_2015`, 109)
- [Pancreatic Adenocarcinoma (ICGC, Nature 2012)](https://www.cbioportal.org/study/summary?id=paad_icgc) (`paad_icgc`, 99)
- Others: MSK studies, which are mostly targeted DNA sequencing and may have no mRNA data, and the PRINCE trial cohort.

**Other pancreatic tumor types:** neuroendocrine tumors (`panet_*`) and acinar cell carcinoma (`paac_*`).

Questions for you:
1. Which study, or studies, should I use? I can report each one separately. If you have no preference, I'd suggest the TCGA PanCancer Atlas study.
2. Which value do you want: raw expression (RSEM/FPKM, as available) or z-scores?
3. Did you mean **TPBG** (trophoblast glycoprotein) rather than "TPGB"? I'll assume TPBG unless you say otherwise.