Q (sonnet-5.5): In the TCGA PanCancer Atlas breast cancer study, what is the average tumor mutational burden of patients who have at least two of the five most frequently mutated genes and how does this compare to the average TMB of all patients?

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-guide"
}

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

◀ 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)

▶ list_studies
{
 "search": "breast TCGA PanCancer Atlas"
}

◀ 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
{"result":[{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"brca","sample_count":1084,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018"}]}

▶ clickhouse_run_select_query
{
 "query": "WITH top AS (SELECT hugo_gene_symbol g, altered_samples FROM top_mutated_genes_in_study(study='brca_tcga_pan_can_atlas_2018', top_n=6)),
pm AS (SELECT DISTINCT patient_unique_id, hugo_gene_symbol g FROM genomic_event_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND variant_type='mutation' AND mutation_status!='UNCALLED' AND off_panel=0 AND hugo_gene_symbol IN (SELECT g FROM top LIMIT 5)),
multi AS (SELECT patient_unique_id FROM pm GROUP BY patient_unique_id HAVING count()>=2),
tmb AS (SELECT patient_unique_id, avg(toFloat64OrNull(attribute_value)) v FROM clinical_data_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND attribute_name='TMB_NONSYNONYMOUS' AND toFloat64OrNull(attribute_value) IS NOT NULL GROUP BY patient_unique_id)
SELECT (SELECT groupArray((g,altered_samples)) FROM top) tops,
 count() n_all, round(avg(v),3) mean_all, round(median(v),3) med_all,
 countIf(patient_unique_id IN (SELECT patient_unique_id FROM multi)) n_multi,
 round(avgIf(v, patient_unique_id IN (SELECT patient_unique_id FROM multi)),3) mean_multi,
 round(medianIf(v, patient_unique_id IN (SELECT patient_unique_id FROM multi)),3) med_multi,
 round(avgIf(v, patient_unique_id NOT IN (SELECT patient_unique_id FROM multi)),3) mean_other
FROM tmb"
}

▶ resolve_and_route
{
 "studyIds": "[\"brca_tcga_pan_can_atlas_2018\"]"
}

◀ result
{"rows":[{"tops":[["PIK3CA",347],["TP53",347],["TTN",187],["CDH1",130],["GATA3",127],["MUC16",109]],"n_all":1066,"mean_all":2.734,"med_all":1.3,"n_multi":296,"mean_multi":5.534,"med_multi":2.033,"mean_other":1.658}]}

✗ error
<tool_use_error>Error: No such tool available: mcp__claude_ai_cBioPortal_MCP__resolve_and_route</tool_use_error>

▶ resolve_and_route
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_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":["brca_tcga_pan_can_atlas_2018_gistic","brca_tcga_pan_can_atlas_2018_log2CNA","brca_tcga_pan_can_atlas_2018_mutations","brca_tcga_pan_can_atlas_2018_protein_quantification","brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa","brca_tcga_pan_can_atlas_2018_rppa_Zscores","brca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["brca_tcga_pan_can_atlas_2018_armlevel_cna","brca_tcga_pan_can_atlas_2018_genetic_ancestry","brca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","brca_tcga_pan_can_atlas_2018_methylation_hm450","brca_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_anc … (83 more chars)

═ answer (success)
Patients with mutations in at least two of the five most frequently mutated genes have a higher average TMB than all patients. The average is about 5.5 mutations/Mb in those patients, against about 2.7 mutations/Mb across the whole cohort. This is for Breast Invasive Carcinoma (TCGA, PanCancer Atlas). I counted patients and used TMB from the nonsynonymous TMB attribute, averaging across samples for any patient with more than one.

**Five most frequently mutated genes** (mutated samples): PIK3CA (347), TP53 (347), TTN (187), CDH1 (130), GATA3 (127). MUC16 is sixth, with 109.

| Group | Patients with TMB | Mean TMB (mut/Mb) | Median TMB (mut/Mb) |
|---|---|---|---|
| ≥2 of the top-5 genes mutated | 296 | **5.53** | 2.03 |
| All patients | 1,066 | **2.73** | 1.30 |
| Remaining patients (0–1 top-5 genes) | 770 | 1.66 | not computed |

- **Size of the difference:** the mean for the multi-mutated group is roughly 2× the all-patient mean. The medians (2.03 vs 1.30) differ by less, which suggests a few high-TMB tumors pull the group mean up. I did not run a statistical test, so I can't report significance.
- **Caveat:** mutating several genes, especially a very long gene like TTN, goes hand in hand with a higher mutation count. The higher TMB in this group is therefore partly expected. It does not by itself show a biological link between these genes.

[Open the study in cBioPortal](https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018)