Q (sonnet-5.5): Which cancer types have the highest aneuploidy scores and how does this correlate with mutation burden across TCGA Pan-Cancer studies?

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

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-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
Statistical Test Selection Guide
================================

Purpose
-------
This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults.

HARD RULES — NEVER FABRICATE A STATISTIC
----------------------------------------
ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically:

1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."*
2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead.
3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median".
4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do.
5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison.

If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number.

Ambiguous-Term Clarification (before any analysis)
--------------------------------------------------
If the user's question uses an ambiguous term, ask before computing anything. Examples:

- **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier AS study, count() AS n, round(avg(a),2) AS mean_as, round(quantile(0.5)(a),1) AS median_as, round(avg(t),2) AS mean_tmb, round(quantile(0.5)(t),2) AS median_tmb, round(rankCorr(a,t),2) AS spearman
FROM (
 SELECT cancer_study_identifier, sample_unique_id,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='ANEUPLOIDY_SCORE') AS a,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='TMB_NONSYNONYMOUS') AS t,
  countIf(attribute_name='ANEUPLOIDY_SCORE' AND toFloat64OrNull(attribute_value) IS NOT NULL) AS ha,
  countIf(attribute_name='TMB_NONSYNONYMOUS' AND toFloat64OrNull(attribute_value) IS NOT NULL) AS ht
 FROM clinical_data_derived
 WHERE attribute_name IN ('ANEUPLOIDY_SCORE','TMB_NONSYNONYMOUS')
  AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name='pan_cancer_tcga')
 GROUP BY cancer_study_identifier, sample_unique_id
 HAVING ha>0 AND ht>0)
GROUP BY study ORDER BY median_as DESC",
 "max_rows": 50
}

◀ result
{"rows":[{"study":"ucs_tcga_pan_can_atlas_2018","n":56,"mean_as":17.21,"median_as":19,"mean_tmb":4.32,"median_tmb":1.55,"spearman":-0.18},{"study":"tgct_tcga_pan_can_atlas_2018","n":149,"mean_as":18.83,"median_as":19,"mean_tmb":0.49,"median_tmb":0.4,"spearman":0.03},{"study":"acc_tcga_pan_can_atlas_2018","n":89,"mean_as":18.29,"median_as":17,"mean_tmb":2.83,"median_tmb":0.9,"spearman":0.15},{"study":"luad_tcga_pan_can_atlas_2018","n":501,"mean_as":15.04,"median_as":16,"mean_tmb":10.24,"median_tmb":6.87,"spearman":0.26},{"study":"lusc_tcga_pan_can_atlas_2018","n":479,"mean_as":16.21,"median_as":16,"mean_tmb":9.51,"median_tmb":7.73,"spearman":0.17},{"study":"esca_tcga_pan_can_atlas_2018","n":162,"mean_as":14.57,"median_as":14.5,"mean_tmb":5.08,"median_tmb":3.65,"spearman":0.03},{"study":"kich_tcga_pan_can_atlas_2018","n":65,"mean_as":14.86,"median_as":14,"mean_tmb":1.09,"median_tmb":0.67,"spearman":0.21},{"study":"blca_tcga_pan_can_atlas_2018","n":400,"mean_as":13.58,"median_as":14,"mean_tmb":8.57,"median_tmb":5.9,"spearman":0.25},{"study":"ov_tcga_pan_can_atlas_2018","n":504,"mean_as":14,"median_as":13,"mean_tmb":2.26,"median_tmb":1.9,"spearman":-0.07},{"study":"coadread_tcga_pan_can_atlas_2018","n":529,"mean_as":12.32,"median_as":12,"mean_tmb":13.02,"median_tmb":3.5,"spearman":-0.31},{"study":"brca_tcga_pan_can_atlas_2018","n":1024,"mean_as":12.2,"median_as":11,"mean_tmb":2.76,"median_tmb":1.33,"spearman":0.25},{"study":"skcm_tcga_pan_can_atlas_2018","n":431,"mean_as":13.01,"median_as":11,"mean_tmb":23.85,"median_tmb":15.3,"spearman":0.01},{"study":"stad_tcga_pan_can_atlas_2018","n":423,"mean_as":11.17,"median_as":10,"mean_tmb":12.87,"median_tmb":4,"spearman":-0.11},{"study":"hnsc_tcga_pan_can_atlas_2018","n":502,"mean_as":11.41,"median_as":10,"mean_tmb":5.35,"median_tmb":3.58,"spearman":0.13},{"study":"sarc_tcga_pan_can_atlas_2018","n":244,"mean_as":10.86,"median_as":9.5,"mean_tmb":2.38,"median_tmb":1.33,"spearman":0.48},{"study":"chol_tcga_pan_can_atlas_2018","n":36,"mean_as":10.42,"median_as":9,"mean_tmb":2.32,"median_tmb":1.45,"spearman":0.23},{"study":"cesc_tcga_pan_can_atlas_2018","n":288,"mean_as":10.26,"median_as":8,"mean_tmb":6.42,"median_tmb":2.83,"spearman":0.09},{"study":"lihc_tcga_pan_can_atlas_2018","n":355,"mean_as":10.45,"median_as":8,"mean_tmb":3.54,"median_tmb":2.83,"spearman":0.18},{"study":"kirp_tcga_pan_can_atlas_2018","n":274,"mean_as":8.26,"median_as":8,"mean_tmb":2.75,"median_tmb":2.23,"spearman":-0.02},{"study":"gbm_tcga_pan_can_atlas_2018","n":378,"mean_as":8.31,"median_as":7,"mean_tmb":4.03,"median_tmb":1.73,"spearman":0.03},{"study":"paad_tcga_pan_can_atlas_2018","n":162,"mean_as":9.45,"median_as":7,"mean_tmb":4.25,"median_tmb":1.2,"spearman":0.4},{"study":"meso_tcga_pan_can_atlas_2018","n":81,"mean_as":8.16,"median_as":6,"mean_tmb":1.03,"median_tmb":0.93,"spearman":0.17},{"study":"dlbc_tcga_pan_can_atlas_2018","n":41,"mean_as":6.78,"median_as":5,"mean_tmb":3.96,"median_tmb":3.6,"spearman":0.23},{"study":"uvm_tcga_pan_ … (1120 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT count() AS n, round(rankCorr(a,t),3) AS spearman_all, round(corr(a,t),3) AS pearson_all FROM (
 SELECT sample_unique_id,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='ANEUPLOIDY_SCORE') AS a,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='TMB_NONSYNONYMOUS') AS t,
  countIf(attribute_name='ANEUPLOIDY_SCORE' AND toFloat64OrNull(attribute_value) IS NOT NULL) AS ha,
  countIf(attribute_name='TMB_NONSYNONYMOUS' AND toFloat64OrNull(attribute_value) IS NOT NULL) AS ht
 FROM clinical_data_derived
 WHERE attribute_name IN ('ANEUPLOIDY_SCORE','TMB_NONSYNONYMOUS')
  AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name='pan_cancer_tcga')
 GROUP BY sample_unique_id HAVING ha>0 AND ht>0)"
}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name='pan_cancer_tcga' ORDER BY 1",
 "max_rows": 50
}

◀ result
{"rows":[{"n":9975,"spearman_all":0.311,"pearson_all":-0.046}]}

◀ 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": [
  "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-pqyf1b1k/c8dc35ff-dc6f-4549-80f3-e08c6d381cbf/tool-results/mcp-navigator-resolve_and_route-1790656974102.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-pqyf1b1k/c8dc35ff-dc6f-4549-80f3-e08c6d381cbf/tool-results/mcp-navigator-resolve_and_route-1790656974102.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-pqyf1b1k/c8dc35ff-dc6f-4549-80f3-e08c6d381cbf/tool-results/mcp-navigator-resolve_and_route-1790656974102.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.


▶ navigate_to_study_view
{
 "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"
 ],
 "tab": "summary"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018","url":"https://www.cbioportal.org/study/summary?id=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018","data":{"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"," … (4218 more chars)

═ answer (success)
**Cohort:** the 32 TCGA PanCancer Atlas studies. Only samples with both an aneuploidy score and a TMB value are counted (9,975 samples in total). Aneuploidy score (AS) is the number of altered chromosome arms, from 0 to 39. TMB is nonsynonymous mutations per Mb. Correlations are Spearman rank correlations (ρ).

## Highest aneuploidy scores

| Rank | Cancer type | n | Median AS | Mean AS | Median TMB | ρ (AS vs TMB) |
|---|---|---|---|---|---|---|
| 1 | Uterine Carcinosarcoma (UCS) | 56 | 19 | 17.2 | 1.55 | −0.18 |
| 2 | Testicular Germ Cell (TGCT) | 149 | 19 | 18.8 | 0.40 | 0.03 |
| 3 | Adrenocortical (ACC) | 89 | 17 | 18.3 | 0.90 | 0.15 |
| 4 | Lung Adenocarcinoma (LUAD) | 501 | 16 | 15.0 | 6.87 | 0.26 |
| 5 | Lung Squamous (LUSC) | 479 | 16 | 16.2 | 7.73 | 0.17 |
| 6 | Esophageal (ESCA) | 162 | 14.5 | 14.6 | 3.65 | 0.03 |
| 7 | Kidney Chromophobe (KICH) | 65 | 14 | 14.9 | 0.67 | 0.21 |
| 8 | Bladder (BLCA) | 400 | 14 | 13.6 | 5.90 | 0.25 |
| 9 | Ovarian (OV) | 504 | 13 | 14.0 | 1.90 | −0.07 |
| 10 | Colorectal (COADREAD) | 529 | 12 | 12.3 | 3.50 | −0.31 |

**Lowest:** thyroid (THCA), AML (LAML) and thymoma (THYM) all have a median AS of 0. Prostate (median 1), low-grade glioma (2), uterine endometrial (3), and pheochromocytoma/paraganglioma, kidney clear cell and uveal melanoma (4 each) are also low.

## Correlation with mutation burden

- **Pan-cancer:** ρ = 0.31 across all 9,975 samples. The Pearson correlation is about −0.05, which reflects a few extreme hypermutated tumors.
- **Across cancer types, the two measures mostly do not track each other.**
  - The top-AS cancers are not the high-TMB cancers. Uterine carcinosarcoma, testicular germ cell and adrenocortical tumors have high AS and low median TMB.
  - Melanoma has the highest median TMB (15.3) but only a mid-range AS (median 11).
  - Uterine endometrial (UCEC) has the highest mean TMB (35.6, driven by hypermutated tumors) but a low median AS (3).
- **Within cancer types, the direction varies.**
  - Positive: sarcoma (ρ 0.48), low-grade glioma (0.47), thymoma (0.45), pancreatic (0.40), thyroid and AML (0.34 each), prostate (0.30), LUAD (0.26), BLCA and BRCA (0.25 each).
  - Negative: uterine endometrial (−0.40) and colorectal (−0.31). Both contain hypermutated, microsatellite-unstable tumors that are typically chromosomally stable.
  - Near zero: melanoma, testicular, esophageal, kidney papillary, uveal melanoma and glioblastoma.

I have not computed significance for these correlations or run any multiple-testing correction. The per-study ρ values are descriptive. You can test them in cBioPortal or in R/Python, for example with `cor.test(method="spearman")`.

**Links**
- [Open all 32 TCGA PanCancer Atlas studies](https://www.cbioportal.org/study/summary?id=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018)
- In the study summary, open the "Charts" menu, add "Aneuploidy Score" and "TMB (nonsynonymous)", and use the scatter view to see the relationship.

The link opens all 32 studies together. Samples in the pooled cohort are unique to each study, so they are not double-counted here.