Q (sonnet-5.5): Can you analyze thyroid hormone gene expression by diagnosis in the Pediatric Brain Tumor Atlas (PBTA, Provisional) cohort?

▶ list_studies
{
 "search": "pediatric brain tumor atlas"
}

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

◀ result
{"result":[{"note":"No studies matched 'pediatric brain tumor atlas'. Before saying the study isn't available, read cbioportal://study-resolution-guide: names can omit centers or consortia (HTAN centers appear only as atlas codes in study ids, e.g. hta9 = OHSU), try fewer words, or it may be on another cBioPortal instance."}]}

◀ 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)

▶ list_studies
{
 "search": "PBTA"
}

▶ read_guide
{
 "uri": "cbioportal://study-resolution-guide"
}

◀ result
{"result":[{"note":"No studies matched 'PBTA'. Before saying the study isn't available, read cbioportal://study-resolution-guide: names can omit centers or consortia (HTAN centers appear only as atlas codes in study ids, e.g. hta9 = OHSU), try fewer words, or it may be on another cBioPortal instance."}]}

◀ result
# Study Resolution Guide

Use this guide when the user names a study, cohort, portal, or data source that may not exist in the connected cBioPortal deployment.

## Routing Triggers

Read this guide when the user mentions:

- PBTA, Pediatric Brain Tumor Atlas, pediatric cBioPortal, Kids First
- GENIE, AACR GENIE, MSK private cohorts, institutional cohorts
- "download study", "which study", "find cohort", "data from [portal]"
- a named cohort that `list_studies(search=...)` does not find
- HTAN, or an HTAN center (OHSU, MSK, Vanderbilt, ...)

## Core Rules

1. Resolve the requested study before substituting another study.
2. If the requested study is not in this deployment, say so explicitly.
3. Do not silently analyze a substitute cohort.
4. If the user agrees to a substitute, keep a one-line scope caveat when reporting numbers.

## Known External cBioPortal Instances

These are not necessarily queryable from this MCP server, but they are useful redirects:

| User wording | Likely external instance | Scope |
|---|---|---|
| PBTA, Pediatric Brain Tumor Atlas, pediatric brain tumors | https://pedcbioportal.kidsfirstdrc.org/ | Pediatric cancer studies, including pediatric brain tumor cohorts |
| GENIE | https://genie.cbioportal.org/ | AACR GENIE data access, depending on release and permissions |
| MSK private / institutional cohorts | private institutional cBioPortal deployments | Not queryable from public cBioPortal unless exported to the public database |

## HTAN Studies

HTAN study ids carry the atlas code (`hta9`, `hta8`, ...), and the names don't always name the center, so `list_studies(search="OHSU HTAN")` finds nothing. Search `list_studies(search="HTAN")` and match the center by atlas code:

| Atlas code | Center | Study |
|---|---|---|
| `hta8` | MSK | `crc_hta8_htan_2024` |
| `hta9` | OHSU | `brca_hta9_htan_2022` ("Breast Cancer (HTAN, 2022)") |
| `hta11` | Vanderbilt | `crc_hta11_htan_2021` |

For HTAN imaging (Minerva, MxIF, H&E), continue with the external-resources-guide.

## Study Resolution Workflow

1. Call `list_studies(search=...)` with the user's exact study/cohort phrase and close variants.
2. If a cancer type is mentioned, call `search_oncotree(search_term)` before disease-level study discovery.
3. If no matching study is found, check known external instances above before declaring the study absent.
4. If proposing a substitute, describe why it is a substitute and how its scope differs.

## Substitute-Cohort Answer Pattern

> I cannot query PBTA from this cBioPortal deployment. PBTA is typically accessed through pediatric cBioPortal at https://pedcbioportal.kidsfirstdrc.org/. I can analyze `[substitute_study_id]` here, but its results should not be interpreted as PBTA results.

When reporting numbers from a substitute:

> Scope note: these counts are from `[substitute_study_id]` in this deployment, not from the requested PBTA cohort.

## Do Not

- Do not answer a PBTA question with `brain_cptac_2020` numbers without a scope wa … (169 more chars)

═ answer (success)
I can't analyze the Pediatric Brain Tumor Atlas (PBTA, Provisional) cohort here. It isn't in the cBioPortal deployment I'm connected to. I searched for "pediatric brain tumor atlas" and "PBTA" and found no matching study.

PBTA is usually hosted on the Kids First pediatric cBioPortal: https://pedcbioportal.kidsfirstdrc.org/. To look at thyroid hormone gene expression by diagnosis there, you could:
- Open the PBTA study and use the **Plots** tab. Set one axis to mRNA expression for your gene of interest and the other axis to a clinical attribute such as cancer type or diagnosis.
- Query the genes together in the **Results View** and compare expression across diagnosis groups with **Group Comparison**.

I haven't substituted another cohort, because that wouldn't give PBTA results. Other pediatric or brain tumor studies are available here, for example CPTAC brain or TARGET. I can run the analysis on one of those if you'd like, and I'd label the results clearly as not being PBTA.

To go ahead, I need two things from you:
1. **Genes:** Which genes count as "thyroid hormone" genes? Examples include the receptors (THRA, THRB), the deiodinases (DIO1, DIO2, DIO3) and the transporters (SLC16A2/MCT8, SLCO1C1). I can use this set or your own list.
2. **Cohort:** Which substitute study should I use, if any?