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

▶ 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 | `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
# 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 … (7866 more chars)

▶ clickhouse_run_select_query
{
 "query": "WITH cohort AS (
    SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga'
),
ct AS (
    SELECT sample_unique_id, attribute_value AS cancer_type
    FROM clinical_data_derived
    WHERE attribute_name = 'CANCER_TYPE' AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM cohort)
)
SELECT ct.cancer_type,
       count() AS samples,
       round(median(toFloat64OrNull(g.alteration_value)), 2) AS median_rsem,
       round(median(log2(toFloat64OrNull(g.alteration_value) + 1)), 2) AS median_log2_rsem
FROM genetic_alteration_derived g
JOIN ct USING (sample_unique_id)
WHERE g.cancer_study_identifier IN (SELECT cancer_study_identifier FROM cohort)
  AND g.hugo_gene_symbol = 'CD3D'
  AND g.profile_type = 'rna_seq_v2_mrna'
  AND g.alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(g.alteration_value) IS NOT NULL
GROUP BY ct.cancer_type
ORDER BY median_rsem DESC;",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_type":"Thymic Epithelial Tumor","samples":119,"median_rsem":6846.47,"median_log2_rsem":12.74},{"cancer_type":"Mature B-Cell Neoplasms","samples":48,"median_rsem":1076.02,"median_log2_rsem":10.07},{"cancer_type":"Seminoma","samples":63,"median_rsem":413.72,"median_log2_rsem":8.7},{"cancer_type":"Renal Clear Cell Carcinoma","samples":510,"median_rsem":140.31,"median_log2_rsem":7.14},{"cancer_type":"Non-Seminomatous Germ Cell Tumor","samples":86,"median_rsem":123.13,"median_log2_rsem":6.96},{"cancer_type":"Cervical Cancer","samples":294,"median_rsem":122.35,"median_log2_rsem":6.95},{"cancer_type":"Non-Small Cell Lung Cancer","samples":994,"median_rsem":122.29,"median_log2_rsem":6.95},{"cancer_type":"Esophagogastric Cancer","samples":593,"median_rsem":113.23,"median_log2_rsem":6.84},{"cancer_type":"Head and Neck Cancer","samples":515,"median_rsem":105.24,"median_log2_rsem":6.73},{"cancer_type":"Pancreatic Cancer","samples":177,"median_rsem":99.58,"median_log2_rsem":6.65},{"cancer_type":"Leukemia","samples":173,"median_rsem":96.04,"median_log2_rsem":6.6},{"cancer_type":"Pleural Mesothelioma","samples":87,"median_rsem":87.82,"median_log2_rsem":6.47},{"cancer_type":"Melanoma","samples":443,"median_rsem":85.67,"median_log2_rsem":6.44},{"cancer_type":"Breast Cancer","samples":1082,"median_rsem":67.87,"median_log2_rsem":6.11},{"cancer_type":"Hepatobiliary Cancer","samples":366,"median_rsem":63.08,"median_log2_rsem":6},{"cancer_type":"Colorectal Cancer","samples":592,"median_rsem":60.55,"median_log2_rsem":5.94},{"cancer_type":"Cholangiocarcinoma","samples":36,"median_rsem":59.62,"median_log2_rsem":5.92},{"cancer_type":"Bladder Cancer","samples":407,"median_rsem":57.83,"median_log2_rsem":5.88},{"cancer_type":"Endometrial Cancer","samples":584,"median_rsem":46.07,"median_log2_rsem":5.56},{"cancer_type":"Prostate Cancer","samples":493,"median_rsem":42.6,"median_log2_rsem":5.45},{"cancer_type":"Thyroid Cancer","samples":498,"median_rsem":36.12,"median_log2_rsem":5.21},{"cancer_type":"Sarcoma","samples":253,"median_rsem":35.62,"median_log2_rsem":5.19},{"cancer_type":"Renal Non-Clear Cell Carcinoma","samples":348,"median_rsem":34.84,"median_log2_rsem":5.16},{"cancer_type":"Ovarian Epithelial Tumor","samples":300,"median_rsem":30.28,"median_log2_rsem":4.97},{"cancer_type":"Glioblastoma","samples":160,"median_rsem":13.51,"median_log2_rsem":3.86},{"cancer_type":"Pheochromocytoma","samples":147,"median_rsem":12.74,"median_log2_rsem":3.78},{"cancer_type":"Miscellaneous Neuroepithelial Tumor","samples":31,"median_rsem":11.57,"median_log2_rsem":3.65},{"cancer_type":"Ocular Melanoma","samples":80,"median_rsem":5.09,"median_log2_rsem":2.61},{"cancer_type":"Adrenocortical Carcinoma","samples":78,"median_rsem":3.11,"median_log2_rsem":2.04},{"cancer_type":"Glioma","samples":514,"median_rsem":1.33,"median_log2_rsem":1.22}]}

▶ clickhouse_run_select_query
{
 "query": "WITH cohort AS (
    SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga'
),
ct AS (
    SELECT sample_unique_id, attribute_value AS cancer_type
    FROM clinical_data_derived
    WHERE attribute_name = 'CANCER_TYPE' AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM cohort)
)
SELECT ct.cancer_type,
       g.hugo_gene_symbol,
       count() AS samples,
       round(median(toFloat64OrNull(g.alteration_value)), 2) AS median_rsem
FROM genetic_alteration_derived g
JOIN ct USING (sample_unique_id)
WHERE g.cancer_study_identifier IN (SELECT cancer_study_identifier FROM cohort)
  AND g.hugo_gene_symbol IN ('CD3E','CD3G')
  AND g.profile_type = 'rna_seq_v2_mrna'
  AND g.alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(g.alteration_value) IS NOT NULL
GROUP BY ct.cancer_type, g.hugo_gene_symbol
ORDER BY g.hugo_gene_symbol, median_rsem DESC;",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_type":"Thymic Epithelial Tumor","hugo_gene_symbol":"CD3E","samples":119,"median_rsem":8500.2},{"cancer_type":"Mature B-Cell Neoplasms","hugo_gene_symbol":"CD3E","samples":48,"median_rsem":1734.14},{"cancer_type":"Seminoma","hugo_gene_symbol":"CD3E","samples":63,"median_rsem":954.16},{"cancer_type":"Renal Clear Cell Carcinoma","hugo_gene_symbol":"CD3E","samples":510,"median_rsem":419.11},{"cancer_type":"Non-Small Cell Lung Cancer","hugo_gene_symbol":"CD3E","samples":994,"median_rsem":319.79},{"cancer_type":"Non-Seminomatous Germ Cell Tumor","hugo_gene_symbol":"CD3E","samples":86,"median_rsem":296.4},{"cancer_type":"Cervical Cancer","hugo_gene_symbol":"CD3E","samples":294,"median_rsem":271.18},{"cancer_type":"Leukemia","hugo_gene_symbol":"CD3E","samples":173,"median_rsem":263.64},{"cancer_type":"Pancreatic Cancer","hugo_gene_symbol":"CD3E","samples":177,"median_rsem":258.9},{"cancer_type":"Melanoma","hugo_gene_symbol":"CD3E","samples":443,"median_rsem":256.82},{"cancer_type":"Pleural Mesothelioma","hugo_gene_symbol":"CD3E","samples":87,"median_rsem":254.92},{"cancer_type":"Head and Neck Cancer","hugo_gene_symbol":"CD3E","samples":515,"median_rsem":250.1},{"cancer_type":"Esophagogastric Cancer","hugo_gene_symbol":"CD3E","samples":593,"median_rsem":247.62},{"cancer_type":"Breast Cancer","hugo_gene_symbol":"CD3E","samples":1082,"median_rsem":192.82},{"cancer_type":"Cholangiocarcinoma","hugo_gene_symbol":"CD3E","samples":36,"median_rsem":150.97},{"cancer_type":"Endometrial Cancer","hugo_gene_symbol":"CD3E","samples":584,"median_rsem":137.76},{"cancer_type":"Prostate Cancer","hugo_gene_symbol":"CD3E","samples":493,"median_rsem":134.52},{"cancer_type":"Hepatobiliary Cancer","hugo_gene_symbol":"CD3E","samples":366,"median_rsem":134.23},{"cancer_type":"Colorectal Cancer","hugo_gene_symbol":"CD3E","samples":592,"median_rsem":131.59},{"cancer_type":"Bladder Cancer","hugo_gene_symbol":"CD3E","samples":407,"median_rsem":125.35},{"cancer_type":"Thyroid Cancer","hugo_gene_symbol":"CD3E","samples":498,"median_rsem":114.12},{"cancer_type":"Renal Non-Clear Cell Carcinoma","hugo_gene_symbol":"CD3E","samples":348,"median_rsem":114.09},{"cancer_type":"Sarcoma","hugo_gene_symbol":"CD3E","samples":253,"median_rsem":105},{"cancer_type":"Ovarian Epithelial Tumor","hugo_gene_symbol":"CD3E","samples":300,"median_rsem":99.78},{"cancer_type":"Pheochromocytoma","hugo_gene_symbol":"CD3E","samples":147,"median_rsem":34.71},{"cancer_type":"Glioblastoma","hugo_gene_symbol":"CD3E","samples":160,"median_rsem":32.52},{"cancer_type":"Miscellaneous Neuroepithelial Tumor","hugo_gene_symbol":"CD3E","samples":31,"median_rsem":27.98},{"cancer_type":"Ocular Melanoma","hugo_gene_symbol":"CD3E","samples":80,"median_rsem":25.45},{"cancer_type":"Adrenocortical Carcinoma","hugo_gene_symbol":"CD3E","samples":78,"median_rsem":14.91},{"cancer_type":"Glioma","hugo_gene_symbol":"CD3E","samples":514,"median_rsem":6.91},{"cancer_type":"Thymic Epithelial Tumor","hugo_gene_symbol":"CD3G … (2829 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "acc_tcga_pan_can_atlas_2018",
  "blca_tcga_pan_can_atlas_2018",
  "brca_tcga_pan_can_atlas_2018",
  "cesc_tcga_pan_can_atlas_2018",
  "chol_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018",
  "dlbc_tcga_pan_can_atlas_2018",
  "esca_tcga_pan_can_atlas_2018",
  "gbm_tcga_pan_can_atlas_2018",
  "hnsc_tcga_pan_can_atlas_2018",
  "kich_tcga_pan_can_atlas_2018",
  "kirc_tcga_pan_can_atlas_2018",
  "kirp_tcga_pan_can_atlas_2018",
  "laml_tcga_pan_can_atlas_2018",
  "lgg_tcga_pan_can_atlas_2018",
  "lihc_tcga_pan_can_atlas_2018",
  "luad_tcga_pan_can_atlas_2018",
  "lusc_tcga_pan_can_atlas_2018",
  "meso_tcga_pan_can_atlas_2018",
  "ov_tcga_pan_can_atlas_2018",
  "paad_tcga_pan_can_atlas_2018",
  "pcpg_tcga_pan_can_atlas_2018",
  "prad_tcga_pan_can_atlas_2018",
  "sarc_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "stad_tcga_pan_can_atlas_2018",
  "tgct_tcga_pan_can_atlas_2018",
  "thca_tcga_pan_can_atlas_2018",
  "thym_tcga_pan_can_atlas_2018",
  "ucec_tcga_pan_can_atlas_2018",
  "ucs_tcga_pan_can_atlas_2018",
  "uvm_tcga_pan_can_atlas_2018"
 ]
}

◀ result
Error: result (81,891 characters across 1 line) exceeds maximum allowed tokens. Output has been saved to /home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-5mhnjm0m/bf9c3fee-6860-4359-b5dd-537bdbabb4d1/tool-results/mcp-navigator-resolve_and_route-1790441403018.txt.
Format: Plain text
- For targeted searches (find a string): use grep on the file directly.
- For analysis or summarization that requires reading the full content: the file's lines are too long for Read's offset/limit. Slice by character range via Bash instead — e.g. python3 -c 'print(open("/home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-5mhnjm0m/bf9c3fee-6860-4359-b5dd-537bdbabb4d1/tool-results/mcp-navigator-resolve_and_route-1790441403018.txt").read()[A:B])' in ~80,000-char spans until you have read 100% of it.
- If the Agent tool is available, do this inside a subagent so the full output stays out of your main context. Give it the instruction above verbatim, and be explicit about what it must return — e.g. "Slice /home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-5mhnjm0m/bf9c3fee-6860-4359-b5dd-537bdbabb4d1/tool-results/mcp-navigator-resolve_and_route-1790441403018.txt in ~80,000-char spans via python (read()[A:B]) until you have read all 81,891 characters, then summarize and quote any key findings verbatim." A vague "summarize this" may lose detail.


▶ resolve_and_route
{
 "studyIds": [
  "thym_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"thym_tcga_pan_can_atlas_2018","name":"Thymoma (TCGA, PanCancer Atlas)","sampleCount":123,"studyViewUrl":"https://www.cbioportal.org/study?id=thym_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT"],"molecularProfileIds":["thym_tcga_pan_can_atlas_2018_gistic","thym_tcga_pan_can_atlas_2018_log2CNA","thym_tcga_pan_can_atlas_2018_mutations","thym_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","thym_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","thym_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","thym_tcga_pan_can_atlas_2018_rppa","thym_tcga_pan_can_atlas_2018_rppa_Zscores","thym_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["thym_tcga_pan_can_atlas_2018_armlevel_cna","thym_tcga_pan_can_atlas_2018_genetic_ancestry","thym_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","thym_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["thym_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","thym_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","thym_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}}]}}

▶ resolve_and_route
{
 "studyIds": [
  "gbm_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592,"studyViewUrl":"https://www.cbioportal.org/study?id=gbm_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","RAGNUM_HYPOXIA_SCORE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT","WINTER_HYPOXIA_SCORE"],"molecularProfileIds":["gbm_tcga_pan_can_atlas_2018_gistic","gbm_tcga_pan_can_atlas_2018_log2CNA","gbm_tcga_pan_can_atlas_2018_mutations","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_tcga_pan_can_atlas_2018_rppa","gbm_tcga_pan_can_atlas_2018_rppa_Zscores","gbm_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["gbm_tcga_pan_can_atlas_2018_armlevel_cna","gbm_tcga_pan_can_atlas_2018_genetic_ancestry","gbm_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","gbm_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "thym_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "CD3D"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "clinical_attribute",
  "selectedDataSourceOption": "CANCER_TYPE"
 },
 "plotsVertSelection": {
  "dataType": "mrna",
  "selectedGeneOption": "CD3D",
  "selectedDataSourceOption": "rna_seq_v2_mrna"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=thym_tcga_pan_can_atlas_2018&gene_list=CD3D&case_set_id=thym_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22CANCER_TYPE%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%22915%22%2C%22dataType%22%3A%22mrna%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=thym_tcga_pan_can_atlas_2018&gene_list=CD3D&case_set_id=thym_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22CANCER_TYPE%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%22915%22%2C%22dataType%22%3A%22mrna%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna%22%7D","data":{"studyIds":["thym_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"thym_tcga_pan_can_atlas_2018","name":"Thymoma (TCGA, PanCancer Atlas)","sampleCount":123}],"genes":["CD3D"],"caseSetId":"thym_tcga_pan_can_atlas_2018_all","pageDescription":"Scatter/box plots: CD3D mrna (y-axis) by cancer type (x-axis)."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "gbm_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "CD3D"
 ],
 "tab": "plots"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=CD3D&case_set_id=gbm_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/plots?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=CD3D&case_set_id=gbm_tcga_pan_can_atlas_2018_all","data":{"studyIds":["gbm_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592}],"genes":["CD3D"],"caseSetId":"gbm_tcga_pan_can_atlas_2018_all","pageDescription":"Scatter/box plots comparing any two data elements."}}

═ answer (success)
Explore the highest and lowest-ranked cohorts directly in the portal:

- [Thymoma (TCGA, PanCancer Atlas) — CD3D expression](https://www.cbioportal.org/results/plots?cancer_study_list=thym_tcga_pan_can_atlas_2018&gene_list=CD3D&case_set_id=thym_tcga_pan_can_atlas_2018_all) — the top-ranked cohort (123 samples), reflecting the T-cell-rich thymic microenvironment.
- [Glioblastoma (TCGA, PanCancer Atlas) — CD3D expression](https://www.cbioportal.org/results/plots?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=CD3D&case_set_id=gbm_tcga_pan_can_atlas_2018_all) — one of the lowest-ranked cohorts (592 samples), consistent with GBM's classically "cold" immune microenvironment.

To browse any other cancer type from the table above in the same way, replace the study ID in the URL (e.g. `brca_tcga_pan_can_atlas_2018` for breast cancer) — or let me know which cohort you'd like next and I'll generate the link directly.