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 … (36467 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
LIMIT 10"
}

◀ 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}]}

═ answer (success)
## Highest Average TMB: Uterine Corpus Endometrial Carcinoma (UCEC)

Using the **TCGA PanCancer Atlas** cohort — the standard, consistently-processed reference dataset for comparing tumor mutational burden (TMB) across cancer types — [Uterine Corpus Endometrial Carcinoma (UCEC)](https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018) has the highest **mean** TMB:

| Cancer Type | n (samples) | Mean TMB (mut/Mb) | Median TMB (mut/Mb) |
|---|---|---|---|
| **Uterine Endometrial Carcinoma** | 517 | **35.66** | 2.57 |
| Melanoma (SKCM) | 440 | 25.91 | 14.88 |
| Colorectal (COADREAD) | 534 | 13.64 | 3.50 |
| Stomach (STAD) | 436 | 12.69 | 3.90 |
| Lung Adenocarcinoma (LUAD) | 566 | 10.02 | 6.68 |

**Important caveat on the mean vs. median discrepancy:** UCEC's very high mean is driven by a subset of hypermutated tumors (POLE-mutant and microsatellite-instable endometrial cancers), which pull the average up dramatically — its median TMB (2.57) is actually one of the lowest in this list. If ranking by the **typical (median)** patient's mutational burden instead, **Melanoma (SKCM)** is the clear leader at 14.88 mut/Mb, followed by Lung Squamous (7.67) and Lung Adenocarcinoma (6.68).

So the answer depends on which statistic you mean:
- **By mean:** Uterine Endometrial Carcinoma (UCEC)
- **By median (more representative of a typical patient):** Melanoma (SKCM)

This uses `TMB_NONSYNONYMOUS` (mutations/Mb) across all 32 TCGA PanCancer Atlas studies — one consistent processing pipeline, so results are comparable. TMB isn't available identically across *all* cBioPortal studies (panels/pipelines differ), so this reflects the standard TCGA reference cohort rather than every study in the database.

You can explore the distribution yourself, including the hypermutator outliers driving UCEC's mean:

[View TMB by Cancer Type in TCGA PanCancer Atlas (Plots tab)](https://www.cbioportal.org/study/plots?id=ucec_tcga_pan_can_atlas_2018)

For a side-by-side comparison across all these cancer types with box plots showing the hypermutator spread, I'd recommend using the Group Comparison feature — let me know if you'd like a direct link to that view comparing specific cancer types.