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 Pediatric Brain Tumor", "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` | `_` | | `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 | `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 | | `brca_metabric` | ESR1 | PGR | `mrna` | 0.487 | 1980 | ESR1↔PGR is the textbook breast-cancer estrogen-receptor coregula … (2785 more chars) ◀ result {"result":[]} ▶ list_studies { "search": "PBTA", "verbose": true } ◀ result {"result":[]} ▶ list_studies { "search": "pediatric", "limit": 20, "verbose": true } ▶ list_studies { "search": "brain tumor", "limit": 20, "verbose": true } ◀ result {"result":[{"cancer_study_identifier":"all_phase2_target_2018_pub","name":"Pediatric Acute Lymphoid Leukemia - Phase II (TARGET, 2018)","description":"Whole genome or whole exome sequencing on 298 Pediatric Acute Lymphoid Leukemia - Phase II samples. Comprehensive profiling of ALL Phase 2 samples.

TARGET data is intended exclusively for biomedical research using pediatric data (i.e., the research objectives cannot be accomplished using data from adults) that focus on the development of more effective treatments, diagnostic tests, or prognostic markers for childhood cancers. Moreover, TARGET data can be used for research relevant to the biology, causes, treatment and late complications of treatment of pediatric cancers, but is not intended for the sole purposes of methods and/or tool development (please see Using TARGET Data section of the OCG website). If you are interested in using TARGET data for publication or other research purposes, you must follow the TARGET Publication Guidelines.TARGET data is intended exclusively for biomedical research using pediatric data (i.e., the research objectives cannot be accomplished using data from adults) that focus on the development of more effective treatments, diagnostic tests, or prognostic markers for childhood cancers. Moreover, TARGET data can be used for research relevant to the biology, causes, treatment and late complications of treatment of pediatric cancers, but is not intended for the sole purposes of methods and/or tool development (please see Using TARGET Data section of the OCG website). If you are interested in using TARGET data for publication or other research purposes, you must follow the TARGET Publication Guidelines.

","type_of_cancer_id":"nbl","sample_count":1089,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=nbl_target_2018_pub"},{"cancer_study_identifier":"aml_target_2018_pub","name":"Pediatric Acute Myeloid Leukemia (TARGET, 2018)","description":"Whole genome or whole exome sequencing on 333 TARGET-AML samples. Comprehensive profiling of TARGET-AML samples.

TARGET data is intended exclusively for biomedical research using pediatric data (i.e., the research objectives cannot be accomplished using data from adults) that focus on the development of more effective treatments, diagnost … (9828 more chars) ◀ result {"result":[{"cancer_study_identifier":"gbm_mayo_pdx_sarkaria_2019","name":"Brain Tumor PDXs (Mayo Clinic, Clin Cancer Res 2020)","description":"WES on a total of 106 PDXs and 80 PDXs have the matched germline (blood) samples sequenced. A total of 66 PDXs have RNASeq available","type_of_cancer_id":"difg","sample_count":106,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_mayo_pdx_sarkaria_2019"},{"cancer_study_identifier":"past_dkfz_heidelberg_2013","name":"Pilocytic Astrocytoma (ICGC, Nature Genetics 2013)","description":"Whole-genome sequencing of 96 pilocytic astrocytoma tumor/normal pairs. Conducted by the International Cancer Genome Consortium (ICGC) PedBrain Tumor Project.","type_of_cancer_id":"past","sample_count":96,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=past_dkfz_heidelberg_2013"},{"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"}]} ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT cancer_study_identifier, cancer_study.name FROM cancer_study WHERE LOWER(cancer_study_identifier) LIKE '%pbta%' OR LOWER(cancer_study.name) LIKE '%pbta%' OR LOWER(cancer_study.name) LIKE '%brain tumor atlas%' ORDER BY cancer_study_identifier LIMIT 20 " } ◀ result {"rows":[]} ▶ read_guide { "uri": "cbioportal://study-resolution-guide" } ◀ 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 ## 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 | ## 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 warning. - Do not let later turns drop the substitute-cohort warning. - Do not claim a study does not exist globally; say it is not available in the connected deployment. ═ answer (success) ## Summary The **Pediatric Brain Tumor Atlas (PBTA, Provisional)** cohort is **not available in this cBioPortal deployment**. ### Where to Find PBTA Data PBTA is typically accessed through the **Kids First pediatric cBioPortal** at: - **https://pedcbioportal.kidsfirstdrc.org/** This specialized portal hosts the comprehensive Pediatric Brain Tumor Atlas along with other pediatric cancer cohorts from the Kids First Data Resource Center. ### Alternative: Pediatric Brain Tumor Data Here If you'd like to analyze thyroid hormone gene expression in a pediatric brain tumor cohort available in this deployment, I can offer: - **[Pediatric Brain Cancer (CPTAC/CHOP, Cell 2020)](https://www.cbioportal.org/study/summary?id=brain_cptac_2020)** — 218 samples across 7 pediatric brain cancer histologies: - Low-grade glioma (93 samples) - High-grade glioma (25) - Ependymoma (32) - Medulloblastoma (22) - Ganglio-glioma (18) - Craniopharyngioma (16) - Atypical teratoid/rhabdoid tumor (12) This study includes **RNA-seq expression data** and allows analysis by histological diagnosis. Would you like me to analyze thyroid hormone gene expression in the **CPTAC/CHOP brain cancer study**, or would you prefer to work with PBTA data directly through the Kids First portal?