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

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

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-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)

◀ result
# Mutation Frequency Analysis Guide

## IMPORTANT: Reporting Mutation Frequencies
- **ALWAYS report frequencies as percentages**, not raw counts: `frequency = (altered_samples / total_profiled_samples) × 100`
- For quick frequency lookups, **prefer the TCGA Pan-Cancer Atlas study first**, then offer to expand to other studies
- When reporting across multiple studies, show **ranges** (e.g., "TP53 is mutated in 30–60% of samples") rather than a single average
- **NEVER** sum mutation events across studies to compute an aggregate frequency — this can exceed 100% due to double-counting
- Warn users that samples may overlap across cohorts (e.g., MSK studies may share patients)
- **Choose and state the counting unit**: use patient-level frequencies for prevalence/rate questions unless the user explicitly asks for samples; use sample-level frequencies when the user asks about samples.
- **For "across cancer types" questions**, jump to the [Cross-Cancer-Type Mutation Frequency](#cross-cancer-type-mutation-frequency) section below — there is one correct recipe and several common wrong ones.

## Counting Unit: Samples vs Patients

Before answering any mutation count or frequency question, decide whether the unit is samples or patients and state that choice in the answer.

| User wording | Counting unit |
|--------------|---------------|
| "prevalence", "rate", "fraction of patients", "patients with", "how common is" | Patient-level: `COUNT(DISTINCT patient_unique_id)` |
| "samples", "specimens", "biopsies", sample-level cohort composition | Sample-level: `COUNT(DISTINCT sample_unique_id)` |
| Ambiguous | Ask, or default to patient-level for prevalence/rate language and say so |

### Cross-study sample-count caveat

When an answer touches more than one study and reports a sample count, prepend a one-line caveat:

> Sample IDs are unique within cBioPortal study prefixes, not guaranteed biological-sample identifiers across studies; overlapping cohorts can count the same patient/sample more than once.

Prefer one of these safer approaches:

- Use a shipped `cancer_study_query_preferences` cohort such as `pan_cancer_tcga` or `all_studies_non_redundant`.
- Restrict to one named study.
- Aggregate by `patient_unique_id` when the biological question is patient prevalence.

## STOP rule: a frequency above 100% means your query is wrong

If your query returns a frequency over 100%, **do not try to debug or explain the data inconsistency to the user**. The cause is always one of these query bugs:

- Summing mutation events instead of `COUNT(DISTINCT sample_unique_id)` for the numerator
- Using a study-wide sample count as the denominator instead of the gene-specific profiled count
- Cross-study aggregation where the same biological sample appears under multiple `sample_unique_id` values (e.g., MSK-IMPACT and MSK-CHORD share patients)
- **Joining the profiled CTE through `gene_panel` / `gene_panel_list` without a WES branch.** `gene_panel_id = 'WES'` is *not* a row in … (35166 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')
)
WHERE v IS NOT NULL
GROUP BY study
ORDER BY mean_tmb DESC",
 "max_rows": 40
}

◀ 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},{"study":"hnsc_tcga_pan_can_atlas_2018","n":515,"mean_tmb":5.41,"median_tmb":3.53},{"study":"esca_tcga_pan_can_atlas_2018","n":182,"mean_tmb":4.87,"median_tmb":3.53},{"study":"ucs_tcga_pan_can_atlas_2018","n":57,"mean_tmb":4.28,"median_tmb":1.57},{"study":"dlbc_tcga_pan_can_atlas_2018","n":41,"mean_tmb":3.96,"median_tmb":3.6},{"study":"gbm_tcga_pan_can_atlas_2018","n":397,"mean_tmb":3.89,"median_tmb":1.7},{"study":"paad_tcga_pan_can_atlas_2018","n":179,"mean_tmb":3.89,"median_tmb":1.17},{"study":"lihc_tcga_pan_can_atlas_2018","n":366,"mean_tmb":3.52,"median_tmb":2.83},{"study":"acc_tcga_pan_can_atlas_2018","n":91,"mean_tmb":2.78,"median_tmb":0.9},{"study":"brca_tcga_pan_can_atlas_2018","n":1066,"mean_tmb":2.73,"median_tmb":1.3},{"study":"kirp_tcga_pan_can_atlas_2018","n":276,"mean_tmb":2.73,"median_tmb":2.23},{"study":"chol_tcga_pan_can_atlas_2018","n":36,"mean_tmb":2.32,"median_tmb":1.45},{"study":"ov_tcga_pan_can_atlas_2018","n":523,"mean_tmb":2.31,"median_tmb":1.87},{"study":"sarc_tcga_pan_can_atlas_2018","n":255,"mean_tmb":2.31,"median_tmb":1.3},{"study":"kirc_tcga_pan_can_atlas_2018","n":402,"mean_tmb":1.77,"median_tmb":1.67},{"study":"lgg_tcga_pan_can_atlas_2018","n":514,"mean_tmb":1.77,"median_tmb":0.93},{"study":"prad_tcga_pan_can_atlas_2018","n":494,"mean_tmb":1.56,"median_tmb":0.9},{"study":"laml_tcga_pan_can_atlas_2018","n":200,"mean_tmb":1.28,"median_tmb":0.53},{"study":"kich_tcga_pan_can_atlas_2018","n":65,"mean_tmb":1.09,"median_tmb":0.67},{"study":"meso_tcga_pan_can_atlas_2018","n":86,"mean_tmb":0.98,"median_tmb":0.88},{"study":"thym_tcga_pan_can_atlas_2018","n":123,"mean_tmb":0.74,"median_tmb":0.47},{"study":"uvm_tcga_pan_can_atlas_2018","n":80,"mean_tmb":0.56,"median_tmb":0.4},{"study":"thca_tcga_pan_can_atlas_2018","n":490,"mean_tmb":0.54,"median_tmb":0.33},{"study":"tgct_tcga_pan_can_atlas_2018","n":149,"mean_tmb":0.49,"median_tmb":0.4},{"study":"pcpg_tcga_pan_can_atlas_2018","n":178,"mean_tmb":0.34,"median_tmb":0.3}]}

▶ resolve_and_route
{
 "studyIds": [
  "ucec_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018",
  "luad_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 4 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":4,"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 … (7989 more chars)

═ answer (success)
Explore the two leading cancer types directly:

- [Uterine Corpus Endometrial Carcinoma — StudyView (highest mean TMB)](https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018) — 529 samples
- [Skin Cutaneous Melanoma — StudyView (highest median TMB)](https://www.cbioportal.org/study?id=skcm_tcga_pan_can_atlas_2018) — 448 samples

Both StudyView pages let you plot the `TMB_NONSYNONYMOUS` distribution directly (Summary tab → Mutation Count/TMB chart) to see the skew for yourself.

**Summary:** "Highest average TMB" depends on whether you mean mean or median. **UCEC (endometrial cancer)** has the highest mean (35.7 mut/Mb) because a minority of hypermutated (POLE-mutant/MSI) tumors pull the average way up — its median is only 2.6. **Melanoma (SKCM)** has the highest median (14.9 mut/Mb) and is the more defensible answer if "average" is meant to reflect a typical tumor in that type, since UV-driven mutagenesis elevates TMB broadly across the cohort rather than in a small subset.