Q (sonnet-5.5): Which cancer type has the highest average tumor mutational burden across all studies?

▶ read_guide
{
 "uri": "cbioportal://clinical-data-guide"
}

◀ result
# Clinical Data Query Guide

## Overview
Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis.

## Data Organization

### Patient-Level vs Sample-Level Data
- **Patient-level**: Demographics, overall survival, disease stage (stored once per patient)
- **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient)

### Key Tables
- `clinical_patient`: Patient-level clinical attributes
- `clinical_sample`: Sample-level clinical attributes
- `clinical_data_derived`: Pre-joined view combining both levels
- `clinical_attribute_meta`: Metadata about available clinical attributes

## Recommended Approach: Use clinical_data_derived

The `clinical_data_derived` table is pre-joined and optimized for most queries:

```sql
-- Get clinical data for specific attributes
SELECT
    sample_unique_id,
    patient_unique_id,
    attribute_name,
    attribute_value
FROM clinical_data_derived
WHERE
    cancer_study_identifier = 'your_study_id'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS');
```

## Clinical Attribute Discovery

### Use clinical_attribute_meta for Discovering Available Attributes
**Always start here** to see what clinical attributes are available for a specific study:

```sql
-- Discover available clinical attributes for a study
SELECT
    attr_id,
    description,
    patient_attribute,
    cancer_study_id
FROM clinical_attribute_meta
WHERE
    cancer_study_id = (
        SELECT cancer_study_id
        FROM cancer_study
        WHERE cancer_study_identifier = 'your_study_id'
    )
ORDER BY patient_attribute, attr_id;
```

**Key Fields:**
- **attr_id**: matches attr_id in clinical_sample/clinical_patient tables
- **description**: human-readable description of the attribute
- **patient_attribute**: true = patient attribute, false = sample attribute
- **cancer_study_id**: links to cancer_study table (filter by study)

## Attribute Semantics and Matching

### Case-Insensitive Matching for Attribute Values

Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant.

When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study:

```sql
-- Correct: case-insensitive clinical value filter
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'your_study_id'
  AND attribute_name = 'MUTATION_STATUS'
  AND upper(attribute_value) = 'GERMLINE';
```

Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study.

### Query the Requested Attribute, Not a Proxy

Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier AS study, count(v) AS n, round(avg(v),2) AS mean_tmb, round(quantile(0.5)(v),2) AS median_tmb
FROM (SELECT cancer_study_identifier, toFloat64OrNull(attribute_value) AS v FROM clinical_data_derived
WHERE attribute_name='TMB_NONSYNONYMOUS' AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name='pan_cancer_tcga'))
GROUP BY study ORDER BY mean_tmb DESC LIMIT 8"
}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name='pan_cancer_tcga' ORDER BY cancer_study_identifier",
 "max_rows": 100
}

◀ result
{"rows":[{"study":"ucec_tcga_pan_can_atlas_2018","n":517,"mean_tmb":35.66,"median_tmb":2.57},{"study":"skcm_tcga_pan_can_atlas_2018","n":440,"mean_tmb":25.91,"median_tmb":14.88},{"study":"coadread_tcga_pan_can_atlas_2018","n":534,"mean_tmb":13.64,"median_tmb":3.5},{"study":"stad_tcga_pan_can_atlas_2018","n":436,"mean_tmb":12.69,"median_tmb":3.9},{"study":"luad_tcga_pan_can_atlas_2018","n":566,"mean_tmb":10.02,"median_tmb":6.68},{"study":"lusc_tcga_pan_can_atlas_2018","n":484,"mean_tmb":9.43,"median_tmb":7.67},{"study":"blca_tcga_pan_can_atlas_2018","n":410,"mean_tmb":8.44,"median_tmb":5.77},{"study":"cesc_tcga_pan_can_atlas_2018","n":291,"mean_tmb":6.48,"median_tmb":2.83}]}

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

▶ resolve_and_route
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 2 matching studies. Pick the best match (prefer TCGA → prefer PanCancer Atlas), use its metadata to call the appropriate navigation tool(s) and generate URLs immediately. Present other studies as alternatives.","data":{"totalCount":2,"studiesWithMetadata":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529,"studyViewUrl":"https://www.cbioportal.org/study?id=ucec_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":["ucec_tcga_pan_can_atlas_2018_gistic","ucec_tcga_pan_can_atlas_2018_log2CNA","ucec_tcga_pan_can_atlas_2018_mutations","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_tcga_pan_can_atlas_2018_rppa","ucec_tcga_pan_can_atlas_2018_rppa_Zscores","ucec_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["ucec_tcga_pan_can_atlas_2018_armlevel_cna","ucec_tcga_pan_can_atlas_2018_genetic_ancestry","ucec_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","ucec_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_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"]}},{"studyId":"skcm_tcga_pan_can_atlas_2018","name":"S … (2429 more chars)

═ answer (success)
Uterine Corpus Endometrial Carcinoma (UCEC) has the highest average tumor mutational burden. Averaging TMB across every study in the database would mix incompatible pipelines and panels. So I used one consistently processed cohort, the 32 TCGA PanCancer Atlas studies, with one study per cancer type. The measure is nonsynonymous TMB in mutations per Mb, counted per sample.

| Cancer type (TCGA PanCancer Atlas) | Samples | Mean TMB | Median TMB |
|---|---|---|---|
| **Uterine Corpus Endometrial Carcinoma** | 517 | **35.66** | 2.57 |
| Skin Cutaneous Melanoma | 440 | 25.91 | 14.88 |
| Colorectal (COADREAD) | 534 | 13.64 | 3.50 |
| Stomach Adenocarcinoma | 436 | 12.69 | 3.90 |
| Lung Adenocarcinoma | 566 | 10.02 | 6.68 |
| Lung Squamous Cell Carcinoma | 484 | 9.43 | 7.67 |
| Bladder Urothelial Carcinoma | 410 | 8.44 | 5.77 |
| Cervical Cancer | 291 | 6.48 | 2.83 |

**The ranking depends on the statistic.** Most uterine tumors have a low TMB (median 2.57). A small group of very highly mutated tumors pulls the mean up, and that group is probably the hypermutated (POLE-mutant or mismatch-repair-deficient) subset. That subset is my inference and I didn't check it in the data. By median, melanoma is highest (14.88), followed by lung squamous (7.67) and lung adenocarcinoma (6.68). Melanoma is the cancer type with a consistently high TMB.

[Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018) · [Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study?id=skcm_tcga_pan_can_atlas_2018)