Q (sonnet): Help me rank TCGA cancer studies based on median CD3 expression

▶ list_guides
{}

◀ result
{"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

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

◀ result
# Gene Resolution Guide

Use this guide before querying gene expression, mutation, copy-number, methylation, or structural-variant data when the user's gene term may be ambiguous.

## Routing Triggers

Read this guide when the user mentions:

- a gene family shorthand: `CD3`, `HLA`, `KRT`, `MUC`, `MT-`, `IGH`, `IGK`, `IGL`
- a marker name that may refer to multiple genes or proteins
- a gene alias, old symbol, or informal name
- a wildcard-like term such as "all CD3 genes"

These examples are not exhaustive. Apply this guide to any gene term that may resolve to multiple symbols, aliases, paralogs, family members, or marker genes.

## Core Rule

Do not silently aggregate multiple genes when the user names an ambiguous symbol. Either ask for clarification or choose a clearly standard marker and state the choice.

For example, "CD3 expression" can refer to `CD3D`, `CD3E`, or `CD3G`; in many immune-marker contexts `CD3E` is the standard marker, but the agent must not average all CD3 genes unless the user asks for a combined signature.

## Gene Discovery Query

After validating the gene table exists, search exact symbols first, then prefix/alias-like matches:

```sql
SELECT
    hugo_gene_symbol,
    entrez_gene_id
FROM gene
WHERE upper(hugo_gene_symbol) = upper('CD3')
   OR upper(hugo_gene_symbol) LIKE upper('CD3%')
ORDER BY hugo_gene_symbol
LIMIT 50;
```

If aliases are available in this deployment, inspect the relevant alias table before assuming no match. If no alias table exists, state that alias resolution is limited to available gene symbols.

## Answer Pattern

If multiple plausible genes are found:

> "CD3" is ambiguous in cBioPortal gene-symbol terms. I found `CD3D`, `CD3E`, and `CD3G`. Did you mean `CD3E` as a T-cell marker, or should I analyze all three separately?

If the user clearly asks for a combined family/signature:

- report each gene separately by default
- only compute an average/signature if the user explicitly requests it
- state exactly how the combined value was calculated

## Do Not

- Do not average multiple genes into one expression value without explicit permission.
- Do not rewrite an ambiguous symbol to a single gene without telling the user.
- Do not treat a prefix match as a validated gene symbol.


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

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-guide"
}

◀ 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 | `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
# Sample and Study Filtering Guide

## Overview
Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria.

## Study-Level Filtering

### 1. Always Filter by Study
Every query should specify a study to ensure consistent results:

```sql
-- Always include study filtering
SELECT *
FROM your_table
WHERE
    cancer_study_identifier = 'your_study_id'
    -- Additional filters...
```

### 2. Find Available Studies

```sql
-- Discover available studies
SELECT
    cancer_study_identifier,
    name,
    description,
    type_of_cancer_id
FROM cancer_study
ORDER BY cancer_study_identifier;
```

### 3. Study Information

```sql
-- Get detailed study information
SELECT
    cs.cancer_study_identifier,
    cs.name as study_name,
    cs.description,
    cs.sample_count,
    COUNT(DISTINCT p.internal_id) as patient_count
FROM cancer_study cs
LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id
WHERE
    cs.cancer_study_identifier = 'your_study_id'
GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count;
```

### 4. Find Studies by Available Data Types

Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter.

**Canonical query — studies with ALL requested data types for a cancer type:**

```sql
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'          -- ← OncoTree code from search_oncotree
  AND mutation_sample_count > 0           -- ← one condition per required data type
  AND cna_sample_count > 0
ORDER BY sample_count DESC;
```

**Data type → column** (a count of 0 means the study doesn't have that data):

| Data type in the user's question | Column (portal "Data type" filter label) |
|---|---|
| samples in the study | `sample_count` |
| mutation / mutations | `mutation_sample_count` ("Mutations") |
| copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") |
| structural variant / fusion | `structural_variant_sample_count` |
| RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" |
| specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `<study>_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq |
| specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `<study>_mrna` list |
| microRNA | `mirna_sample_count` ("miRNA") |
| protein / RPPA | `rppa_sample_count` ("RPPA") |
| mas … (7083 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT hugo_gene_symbol, entrez_gene_id FROM gene WHERE upper(hugo_gene_symbol) IN ('CD3D','CD3E','CD3G') ORDER BY hugo_gene_symbol"
}

◀ result
{"rows":[{"hugo_gene_symbol":"CD3D","entrez_gene_id":915},{"hugo_gene_symbol":"CD3E","entrez_gene_id":916},{"hugo_gene_symbol":"CD3G","entrez_gene_id":917}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier, profile_type, count() AS n
FROM genetic_alteration_derived
WHERE cancer_study_identifier LIKE '%tcga_pan_can_atlas_2018%'
  AND profile_type IN ('mrna_median_Zscores','rna_seq_v2_mrna_median_Zscores','mrna_seq_v2_rsem_Zscores','mrna_seq_v2_rsem')
  AND hugo_gene_symbol = 'CD3E'
GROUP BY cancer_study_identifier, profile_type
ORDER BY cancer_study_identifier"
}

◀ result
{"rows":[{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":78},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":407},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":1082},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":294},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":36},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":592},{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":48},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":181},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":160},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":515},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":65},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":510},{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":283},{"cancer_study_identifier":"laml_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":173},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":514},{"cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":366},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":510},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":484},{"cancer_study_identifier":"meso_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":87},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":300},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":177},{"cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":178},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":493},{"cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":253},{"cancer_study_identifier":"skcm_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":443},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","n":412},{"canc … (681 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    cancer_study_identifier,
    count() AS n_samples,
    median(toFloat64OrNull(alteration_value)) AS median_cd3e_zscore
FROM genetic_alteration_derived
WHERE cancer_study_identifier LIKE '%tcga_pan_can_atlas_2018%'
  AND profile_type = 'rna_seq_v2_mrna_median_Zscores'
  AND hugo_gene_symbol = 'CD3E'
  AND alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(alteration_value) IS NOT NULL
GROUP BY cancer_study_identifier
ORDER BY median_cd3e_zscore DESC
",
 "max_rows": 40
}

◀ result
{"rows":[{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","n_samples":65,"median_cd3e_zscore":-0.1426},{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018","n_samples":283,"median_cd3e_zscore":-0.1857},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018","n_samples":493,"median_cd3e_zscore":-0.2348},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","n_samples":36,"median_cd3e_zscore":-0.24225000000000002},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018","n_samples":294,"median_cd3e_zscore":-0.25035},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018","n_samples":181,"median_cd3e_zscore":-0.2547},{"cancer_study_identifier":"meso_tcga_pan_can_atlas_2018","n_samples":87,"median_cd3e_zscore":-0.2755},{"cancer_study_identifier":"tgct_tcga_pan_can_atlas_2018","n_samples":149,"median_cd3e_zscore":-0.2759},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018","n_samples":514,"median_cd3e_zscore":-0.28115},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018","n_samples":160,"median_cd3e_zscore":-0.28335},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","n_samples":592,"median_cd3e_zscore":-0.3076},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018","n_samples":177,"median_cd3e_zscore":-0.3196},{"cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018","n_samples":366,"median_cd3e_zscore":-0.32020000000000004},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","n_samples":1082,"median_cd3e_zscore":-0.32715000000000005},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018","n_samples":498,"median_cd3e_zscore":-0.3305},{"cancer_study_identifier":"laml_tcga_pan_can_atlas_2018","n_samples":173,"median_cd3e_zscore":-0.3382},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","n_samples":527,"median_cd3e_zscore":-0.3499},{"cancer_study_identifier":"ucs_tcga_pan_can_atlas_2018","n_samples":57,"median_cd3e_zscore":-0.355},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","n_samples":484,"median_cd3e_zscore":-0.35975},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","n_samples":510,"median_cd3e_zscore":-0.36195},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","n_samples":300,"median_cd3e_zscore":-0.36435},{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018","n_samples":48,"median_cd3e_zscore":-0.36575},{"cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018","n_samples":253,"median_cd3e_zscore":-0.3701},{"cancer_study_identifier":"thym_tcga_pan_can_atlas_2018","n_samples":119,"median_cd3e_zscore":-0.376},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018","n_samples":510,"median_cd3e_zscore":-0.381},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018","n_samples":412,"median_cd3e_zscore":-0.38639999999999997},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018","n_samples":515,"median_cd3e_zscore":-0.3924},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","n_samples":407,"median_cd3e_zscore":-0.3965},{"cancer_study_identifier":" … (385 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    cancer_study_identifier,
    count() AS n_samples,
    median(toFloat64OrNull(alteration_value)) AS median_cd3e_rsem
FROM genetic_alteration_derived
WHERE cancer_study_identifier IN ('skcm_tcga_pan_can_atlas_2018','kich_tcga_pan_can_atlas_2018','brca_tcga_pan_can_atlas_2018','dlbc_tcga_pan_can_atlas_2018')
  AND profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores'
  AND hugo_gene_symbol = 'CD3E'
  AND alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(alteration_value) IS NOT NULL
GROUP BY cancer_study_identifier
ORDER BY median_cd3e_rsem DESC
"
}

◀ result
{"rows":[{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018","n_samples":48,"median_cd3e_rsem":0.21935000000000002},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","n_samples":65,"median_cd3e_rsem":0.117},{"cancer_study_identifier":"skcm_tcga_pan_can_atlas_2018","n_samples":443,"median_cd3e_rsem":0.0705},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","n_samples":1082,"median_cd3e_rsem":0.057}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    cancer_study_identifier,
    count() AS n_samples,
    median(toFloat64OrNull(alteration_value)) AS median_cd3e_zscore
FROM genetic_alteration_derived
WHERE cancer_study_identifier LIKE '%tcga_pan_can_atlas_2018%'
  AND profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores'
  AND hugo_gene_symbol = 'CD3E'
  AND alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(alteration_value) IS NOT NULL
GROUP BY cancer_study_identifier
ORDER BY median_cd3e_zscore DESC
",
 "max_rows": 40
}

◀ result
{"rows":[{"cancer_study_identifier":"thym_tcga_pan_can_atlas_2018","n_samples":119,"median_cd3e_zscore":0.2484},{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018","n_samples":48,"median_cd3e_zscore":0.21935000000000002},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","n_samples":300,"median_cd3e_zscore":0.14450000000000002},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","n_samples":65,"median_cd3e_zscore":0.117},{"cancer_study_identifier":"tgct_tcga_pan_can_atlas_2018","n_samples":149,"median_cd3e_zscore":0.106},{"cancer_study_identifier":"meso_tcga_pan_can_atlas_2018","n_samples":87,"median_cd3e_zscore":0.0884},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018","n_samples":412,"median_cd3e_zscore":0.08760000000000001},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","n_samples":510,"median_cd3e_zscore":0.07655},{"cancer_study_identifier":"skcm_tcga_pan_can_atlas_2018","n_samples":443,"median_cd3e_zscore":0.0705},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018","n_samples":160,"median_cd3e_zscore":0.0684},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018","n_samples":177,"median_cd3e_zscore":0.0653},{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018","n_samples":283,"median_cd3e_zscore":0.0588},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","n_samples":1082,"median_cd3e_zscore":0.057},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","n_samples":592,"median_cd3e_zscore":0.0529},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018","n_samples":515,"median_cd3e_zscore":0.0486},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","n_samples":484,"median_cd3e_zscore":0.04725},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","n_samples":36,"median_cd3e_zscore":0.04555},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018","n_samples":493,"median_cd3e_zscore":0.0418},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","n_samples":527,"median_cd3e_zscore":0.0374},{"cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018","n_samples":178,"median_cd3e_zscore":0.0371},{"cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018","n_samples":366,"median_cd3e_zscore":0.0326},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018","n_samples":294,"median_cd3e_zscore":0.032100000000000004},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","n_samples":407,"median_cd3e_zscore":0.0281},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018","n_samples":181,"median_cd3e_zscore":0.0216},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018","n_samples":498,"median_cd3e_zscore":0.013},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018","n_samples":510,"median_cd3e_zscore":-0.007299999999999999},{"cancer_study_identifier":"laml_tcga_pan_can_atlas_2018","n_samples":173,"median_cd3e_zscore":-0.0147},{"cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018","n_samples":253,"median_cd3e_zscore":-0.0175},{"cancer_study_identifier":"ucs_tcga_pan … (385 more chars)

═ answer (success)
This ranking is much more biologically coherent (THYM — thymic tumors with T-cell rich stroma — and DLBC, a lymphoma, top the list; LGG, ACC, UVM at the bottom, consistent with "cold" immune microenvironments).

Note: since this analysis spans all 32 studies, individual gene-level navigation isn't practical for a single ResultsView call, so I'll link to a StudyView/ResultsView on a couple of key studies of interest is not ideal either. I'll instead provide the data table (primary deliverable) and link to the cBioPortal comparison across all these studies isn't a native single-view feature — I'll note that and offer per-study links are not scalable. Given the nature of this cross-study aggregate ranking, the most useful navigation is not available as a single "cross study" plot in the standard tools, so I will skip Navigate for this one and focus on the query result as the deliverable, per the guidance that Query-only responses are appropriate for aggregate cross-cancer-type comparisons.

## CD3E Expression Ranking Across TCGA PanCancer Atlas Studies

**Note on gene selection:** "CD3" is ambiguous — it resolves to three genes (CD3D, CD3E, CD3G), all subunits of the T-cell receptor complex. I used **CD3E**, the standard T-cell marker in immune-profiling literature. Let me know if you'd like CD3D or CD3G instead, or all three reported separately.

**Note on normalization:** I used `mrna_median_all_sample_Zscores` (RNA-Seq V2 RSEM, Z-scored against **all samples** in each study) rather than the diploid-normalized variant, which is confounded by each study's CNA profile and produced a biologically implausible ranking (e.g., melanoma ranking near the bottom). All 32 TCGA PanCancer Atlas studies have this profile type, making values reasonably comparable across studies.

| Rank | Study | Median CD3E Z-score | n samples |
|---|---|---|---|
| 1 | THYM (Thymoma) | 0.248 | 119 |
| 2 | DLBC (Diffuse Large B-Cell Lymphoma) | 0.219 | 48 |
| 3 | OV (Ovarian) | 0.145 | 300 |
| 4 | KICH (Kidney Chromophobe) | 0.117 | 65 |
| 5 | TGCT (Testicular Germ Cell) | 0.106 | 149 |
| 6 | MESO (Mesothelioma) | 0.088 | 87 |
| 7 | STAD (Stomach) | 0.088 | 412 |
| 8 | LUAD (Lung Adenocarcinoma) | 0.077 | 510 |
| 9 | SKCM (Melanoma) | 0.071 | 443 |
| 10 | GBM (Glioblastoma) | 0.068 | 160 |
| ... | (23 more studies) | ... | ... |
| 30 | UVM (Uveal Melanoma) | −0.154 | 80 |
| 31 | ACC (Adrenocortical) | −0.185 | 78 |
| 32 | LGG (Low-Grade Glioma) | −0.201 | 514 |

Full 32-study table available on request. THYM and DLBC top the ranking (consistent with lymphocyte-rich tissue of origin/tumor microenvironment); LGG, ACC, and UVM show the lowest CD3E expression (consistent with immunologically "cold" tumor types).

Since this is an aggregate ranking across 32 studies, there isn't a single cBioPortal page that visualizes it directly. If you'd like, I can generate a **Plots tab** link for any specific pair of studies, or a **Group Comparison** of CD3E expression between a subset of studies (e.g., top 5 vs bottom 5) — let me know which comparison would be most useful.