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

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

▶ list_studies
{
 "search": "PBTA",
 "verbose": true
}

◀ 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":[]}

▶ list_studies
{
 "search": "pediatric brain",
 "verbose": true
}

◀ result
{"result":[{"cancer_study_identifier":"brain_cptac_2020","name":"Pediatric Brain Cancer (CPTAC/CHOP, Cell 2020)","description":"Integrated proteogenomic characterization across major histological types of pediatric brain cancer . We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglio-glioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data.","type_of_cancer_id":"brain","sample_count":218,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brain_cptac_2020"}]}

▶ list_studies
{
 "search": "brain tumor atlas",
 "verbose": true,
 "limit": 10
}

◀ result
{"result":[]}

▶ list_studies
{
 "limit": 50,
 "verbose": false
}

◀ result
{"result":[{"cancer_study_identifier":"msk_impact_50k_2026","name":"MSK-IMPACT 50K Clinical Sequencing Cohort (MSK, Cancer Cell 2026)","type_of_cancer_id":"mixed","sample_count":54331,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_impact_50k_2026"},{"cancer_study_identifier":"msk_ch_2023","name":"Cancer Therapy and Clonal Hematopoiesis (MSK, Blood Adv 2023)","type_of_cancer_id":"mixed","sample_count":42714,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_ch_2023"},{"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":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","type_of_cancer_id":"mixed","sample_count":25040,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024"},{"cancer_study_identifier":"msk_ch_2020","name":"Cancer Therapy and Clonal Hematopoiesis (MSK, Nat Genet 2020)","type_of_cancer_id":"mixed","sample_count":24146,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_ch_2020"},{"cancer_study_identifier":"msk_impact_2017","name":"MSK-IMPACT Clinical Sequencing Cohort (MSK, Nat Med 2017)","type_of_cancer_id":"mixed","sample_count":10945,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_impact_2017"},{"cancer_study_identifier":"pan_origimed_2020","name":"China Pan-cancer (OrigiMed, Nature 2022)","type_of_cancer_id":"mixed","sample_count":10194,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pan_origimed_2020"},{"cancer_study_identifier":"sarcoma_msk_2022","name":"Soft Tissue and Bone Sarcoma (MSK, Nat Commun 2022)","type_of_cancer_id":"soft_tissue","sample_count":7494,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=sarcoma_msk_2022"},{"cancer_study_identifier":"crc_msk_2026","name":"Colorectal Adenocarcinoma (MSK, 2026)","type_of_cancer_id":"read","sample_count":7237,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_msk_2026"},{"cancer_study_identifier":"breast_msk_2026","name":"CCNE1 Amplifications in Breast Cancer (MSK, 2026)","type_of_cancer_id":"breast","sample_count":6318,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=breast_msk_2026"},{"cancer_study_identifier":"pancan_pdmr_2025","name":"NCI Patient-Derived Models Repository (PDMR, 2025)","type_of_cancer_id":"mixed","sample_count":6272,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancan_pdmr_2025"},{"cancer_study_identifier":"msk_ctdna_vte_2024","name":"MSK ctDNA Sequencing Cohort (MSK, Nature Medicine 2024)","type_of_cancer_id":"mixed","sample_count":5567,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_ctdna_vte_2024"},{"cancer_study_identifier":"pancan_mimsi_msk_2024","name":"Mixed Tumors - MiMSI Cohort (MSK, Nat Commun 2024)","type_of_ … (9056 more chars)

═ answer (success)
Unfortunately, I cannot find the **Pediatric Brain Tumor Atlas (PBTA)** cohort in this cBioPortal instance. The PBTA is typically a provisional/external dataset that may not be hosted in this particular deployment.

**Available alternatives I found:**

1. **[Pediatric Brain Cancer (CPTAC/CHOP, Cell 2020)](https://www.cbioportal.org/study/summary?id=brain_cptac_2020)** — 218 pediatric brain tumors across 7 histological types (low-grade glioma, ependymoma, high-grade glioma, medulloblastoma, ganglio-glioma, craniopharyngioma, atypical teratoid rhabdoid tumor), with RNA-seq, proteomics, and phosphoproteomics.

This study would allow thyroid hormone gene expression analysis by diagnosis, as it has RNA sequencing data across multiple brain tumor types.

**For the PBTA cohort specifically**, I recommend:
- Checking if your institution has a local/private cBioPortal instance hosting PBTA
- Visiting the [Kids First DRC Portal](https://www.kidsfirstdrc.org/) or [Open Pediatric Brain Tumor Atlas](https://opendata.cavatica.sbg.us/) where PBTA data may be hosted
- Using the cBioPortal REST API directly if PBTA exists at a different deployment URL

Would you like me to analyze thyroid hormone gene expression in the **CPTAC/CHOP pediatric brain cancer study** instead, or do you have more specific information about where the PBTA cohort is hosted?