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

▶ list_guides
{}

◀ result
{"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

▶ 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)

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-guide"
}

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

◀ 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 … (7083 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 DISTINCT attribute_name FROM clinical_data_derived WHERE upper(attribute_name) LIKE '%ANEUPLOID%'"
}

▶ 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"
}

◀ result
{"rows":[{"attribute_name":"ANEUPLOIDY"},{"attribute_name":"ANEUPLOIDY_SCORE"},{"attribute_name":"PERCENT_ANEUPLOIDY"},{"attribute_name":"ANEUPLOIDY_AMP_SCORE"},{"attribute_name":"ANEUPLOIDY_DEL_SCORE"}]}

◀ 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"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    cancer_study_identifier AS study,
    count(v) AS n,
    round(avg(v), 2) AS mean_aneuploidy,
    round(quantile(0.5)(v), 2) AS median_aneuploidy
FROM (
    SELECT cancer_study_identifier, toFloat64OrNull(attribute_value) AS v
    FROM clinical_data_derived
    WHERE attribute_name = 'ANEUPLOIDY_SCORE'
      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_aneuploidy DESC
"
}

◀ result
{"rows":[{"study":"tgct_tcga_pan_can_atlas_2018","n":149,"mean_aneuploidy":18.83,"median_aneuploidy":19},{"study":"acc_tcga_pan_can_atlas_2018","n":89,"mean_aneuploidy":18.29,"median_aneuploidy":17},{"study":"ucs_tcga_pan_can_atlas_2018","n":56,"mean_aneuploidy":17.21,"median_aneuploidy":19},{"study":"lusc_tcga_pan_can_atlas_2018","n":482,"mean_aneuploidy":16.12,"median_aneuploidy":16},{"study":"luad_tcga_pan_can_atlas_2018","n":501,"mean_aneuploidy":15.04,"median_aneuploidy":16},{"study":"kich_tcga_pan_can_atlas_2018","n":65,"mean_aneuploidy":14.86,"median_aneuploidy":14},{"study":"esca_tcga_pan_can_atlas_2018","n":162,"mean_aneuploidy":14.57,"median_aneuploidy":14.5},{"study":"ov_tcga_pan_can_atlas_2018","n":552,"mean_aneuploidy":13.98,"median_aneuploidy":13},{"study":"blca_tcga_pan_can_atlas_2018","n":401,"mean_aneuploidy":13.55,"median_aneuploidy":14},{"study":"skcm_tcga_pan_can_atlas_2018","n":434,"mean_aneuploidy":12.99,"median_aneuploidy":11},{"study":"coadread_tcga_pan_can_atlas_2018","n":586,"mean_aneuploidy":12.3,"median_aneuploidy":12},{"study":"brca_tcga_pan_can_atlas_2018","n":1041,"mean_aneuploidy":12.12,"median_aneuploidy":11},{"study":"hnsc_tcga_pan_can_atlas_2018","n":510,"mean_aneuploidy":11.33,"median_aneuploidy":10},{"study":"stad_tcga_pan_can_atlas_2018","n":427,"mean_aneuploidy":11.18,"median_aneuploidy":10},{"study":"sarc_tcga_pan_can_atlas_2018","n":244,"mean_aneuploidy":10.86,"median_aneuploidy":9.5},{"study":"lihc_tcga_pan_can_atlas_2018","n":361,"mean_aneuploidy":10.48,"median_aneuploidy":8},{"study":"chol_tcga_pan_can_atlas_2018","n":36,"mean_aneuploidy":10.42,"median_aneuploidy":9},{"study":"cesc_tcga_pan_can_atlas_2018","n":294,"mean_aneuploidy":10.1,"median_aneuploidy":8},{"study":"paad_tcga_pan_can_atlas_2018","n":165,"mean_aneuploidy":9.28,"median_aneuploidy":7},{"study":"kirp_tcga_pan_can_atlas_2018","n":280,"mean_aneuploidy":8.24,"median_aneuploidy":8},{"study":"gbm_tcga_pan_can_atlas_2018","n":566,"mean_aneuploidy":8.2,"median_aneuploidy":7},{"study":"meso_tcga_pan_can_atlas_2018","n":81,"mean_aneuploidy":8.16,"median_aneuploidy":6},{"study":"kirc_tcga_pan_can_atlas_2018","n":483,"mean_aneuploidy":7.28,"median_aneuploidy":4},{"study":"dlbc_tcga_pan_can_atlas_2018","n":47,"mean_aneuploidy":6.34,"median_aneuploidy":5},{"study":"ucec_tcga_pan_can_atlas_2018","n":516,"mean_aneuploidy":6,"median_aneuploidy":3},{"study":"pcpg_tcga_pan_can_atlas_2018","n":160,"mean_aneuploidy":5.56,"median_aneuploidy":4},{"study":"uvm_tcga_pan_can_atlas_2018","n":80,"mean_aneuploidy":5.42,"median_aneuploidy":4},{"study":"thym_tcga_pan_can_atlas_2018","n":106,"mean_aneuploidy":3.84,"median_aneuploidy":0},{"study":"lgg_tcga_pan_can_atlas_2018","n":507,"mean_aneuploidy":3.84,"median_aneuploidy":2},{"study":"prad_tcga_pan_can_atlas_2018","n":471,"mean_aneuploidy":2.53,"median_aneuploidy":1},{"study":"laml_tcga_pan_can_atlas_2018","n":124,"mean_aneuploidy":1.56,"median_aneuploidy":0},{"study":"thca_tcga_pan_can_atlas_2018","n":464,"mean_an … (39 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
"
}

◀ 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":"sarc_tcga_pan_can_atlas_2018","n":255,"mean_tmb":2.31,"median_tmb":1.3},{"study":"ov_tcga_pan_can_atlas_2018","n":523,"mean_tmb":2.31,"median_tmb":1.87},{"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}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH per_sample AS (
    SELECT sample_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'ANEUPLOIDY_SCORE') AS aneuploidy,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'TMB_NONSYNONYMOUS') AS tmb
    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
)
SELECT
    count() AS n_samples,
    round(corr(aneuploidy, tmb), 4) AS pearson_r_aneuploidy_tmb,
    round(corr(aneuploidy, log(tmb+1)), 4) AS pearson_r_aneuploidy_log_tmb
FROM per_sample
WHERE aneuploidy IS NOT NULL AND tmb IS NOT NULL
"
}

◀ result
{"rows":[{"n_samples":9975,"pearson_r_aneuploidy_tmb":-0.046,"pearson_r_aneuploidy_log_tmb":0.1683}]}

▶ 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-3woztzu0/6f1dc57b-b234-45db-be92-daad4d9d34d2/tool-results/mcp-navigator-resolve_and_route-1790296150624.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-3woztzu0/6f1dc57b-b234-45db-be92-daad4d9d34d2/tool-results/mcp-navigator-resolve_and_route-1790296150624.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-3woztzu0/6f1dc57b-b234-45db-be92-daad4d9d34d2/tool-results/mcp-navigator-resolve_and_route-1790296150624.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": [
  "luad_tcga_pan_can_atlas_2018",
  "lusc_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":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566,"studyViewUrl":"https://www.cbioportal.org/study?id=luad_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":["luad_tcga_pan_can_atlas_2018_gistic","luad_tcga_pan_can_atlas_2018_log2CNA","luad_tcga_pan_can_atlas_2018_mutations","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_tcga_pan_can_atlas_2018_rppa","luad_tcga_pan_can_atlas_2018_rppa_Zscores","luad_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["luad_tcga_pan_can_atlas_2018_armlevel_cna","luad_tcga_pan_can_atlas_2018_genetic_ancestry","luad_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","luad_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_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":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell … (2585 more chars)

▶ navigate_to_study_view
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "clinical_attribute",
  "selectedDataSourceOption": "ANEUPLOIDY_SCORE"
 },
 "plotsVertSelection": {
  "dataType": "clinical_attribute",
  "selectedDataSourceOption": "TMB_NONSYNONYMOUS"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/plots?id=luad_tcga_pan_can_atlas_2018&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22ANEUPLOIDY_SCORE%22%7D&plots_vert_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22TMB_NONSYNONYMOUS%22%7D","url":"https://www.cbioportal.org/study/plots?id=luad_tcga_pan_can_atlas_2018&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22ANEUPLOIDY_SCORE%22%7D&plots_vert_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22TMB_NONSYNONYMOUS%22%7D","data":{"studyIds":["luad_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566}],"tab":"plots","hasFilters":false,"hasPlotsConfig":true,"pageDescription":"Scatter/box plots: tmb nonsynonymous (y-axis) by aneuploidy score (x-axis)."}}

═ answer (success)
## Navigation

[Explore Aneuploidy Score vs. TMB in LUAD (TCGA PanCancer Atlas)](https://www.cbioportal.org/study/plots?id=luad_tcga_pan_can_atlas_2018&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22ANEUPLOIDY_SCORE%22%7D&plots_vert_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22TMB_NONSYNONYMOUS%22%7D)

Scatter/box plot: TMB_NONSYNONYMOUS (y-axis) by ANEUPLOIDY_SCORE (x-axis), Lung Adenocarcinoma (TCGA, PanCancer Atlas), 566 samples. LUAD was chosen as the representative example because it ranks in the top 5 for aneuploidy *and* top 6 for median TMB — the clearest case where both axes are elevated together.

Both `luad_tcga_pan_can_atlas_2018` and `lusc_tcga_pan_can_atlas_2018` (as well as all 32 TCGA PanCancer Atlas studies queried above) carry the `ANEUPLOIDY_SCORE` and `TMB_NONSYNONYMOUS` clinical attributes, so this same plot configuration can be reproduced for any other cancer type of interest — e.g., swap `id=` to `tgct_tcga_pan_can_atlas_2018` or `thca_tcga_pan_can_atlas_2018` to inspect the high- vs. low-aneuploidy extremes directly.

**Note on the 32-study aggregate link:** I attempted to also generate a combined multi-study StudyView/comparison link across all 32 TCGA PanCancer Atlas cohorts, but the resolver payload for that many studies exceeded the tool's output limit. If you'd like a cross-study comparison view (e.g., Group Comparison across a subset of cancer types), let me know which subset (e.g., top 5 vs. bottom 5 by aneuploidy) and I can build that link directly.