Q (sonnet): What is the average tumor mutational burden of breast cancer 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"
}

▶ list_studies
{
 "search": "breast TCGA pan can atlas"
}

◀ 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)

◀ 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": "SELECT * FROM top_mutated_genes_in_study(study = 'brca_tcga_pan_can_atlas_2018', top_n = 5)"
}

◀ result
{"rows":[{"hugo_gene_symbol":"PIK3CA","altered_samples":347,"profiled_samples":1066,"frequency_pct":32.6,"total_mutation_events":387},{"hugo_gene_symbol":"TP53","altered_samples":347,"profiled_samples":1066,"frequency_pct":32.6,"total_mutation_events":353},{"hugo_gene_symbol":"TTN","altered_samples":187,"profiled_samples":1066,"frequency_pct":17.5,"total_mutation_events":311},{"hugo_gene_symbol":"CDH1","altered_samples":130,"profiled_samples":1066,"frequency_pct":12.2,"total_mutation_events":132},{"hugo_gene_symbol":"GATA3","altered_samples":127,"profiled_samples":1066,"frequency_pct":11.9,"total_mutation_events":130}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH top5 AS (
    SELECT DISTINCT patient_unique_id, hugo_gene_symbol
    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 ('PIK3CA','TP53','TTN','CDH1','GATA3')
),
gene_counts AS (
    SELECT patient_unique_id, count(DISTINCT hugo_gene_symbol) AS n_genes
    FROM top5
    GROUP BY patient_unique_id
),
tmb AS (
    SELECT patient_unique_id, toFloat64OrNull(attribute_value) AS tmb
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND attribute_name = 'TMB_NONSYNONYMOUS'
)
SELECT
    'At least 2 of top5 genes mutated' AS group_name,
    count() AS n_patients,
    round(avg(t.tmb), 3) AS mean_tmb,
    round(quantile(0.5)(t.tmb), 3) AS median_tmb
FROM gene_counts gc
JOIN tmb t USING (patient_unique_id)
WHERE gc.n_genes >= 2
  AND t.tmb IS NOT NULL

UNION ALL

SELECT
    'All patients (TMB available)' AS group_name,
    count() AS n_patients,
    round(avg(t.tmb), 3) AS mean_tmb,
    round(quantile(0.5)(t.tmb), 3) AS median_tmb
FROM tmb t
WHERE t.tmb IS NOT NULL
"
}

◀ result
{"rows":[{"group_name":"All patients (TMB available)","n_patients":1066,"mean_tmb":2.734,"median_tmb":1.3},{"group_name":"At least 2 of top5 genes mutated","n_patients":296,"mean_tmb":5.534,"median_tmb":2.033}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH top5 AS (
    SELECT DISTINCT patient_unique_id, hugo_gene_symbol
    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 ('PIK3CA','TP53','TTN','CDH1','GATA3')
),
gene_counts AS (
    SELECT patient_unique_id, count(DISTINCT hugo_gene_symbol) AS n_genes
    FROM top5
    GROUP BY patient_unique_id
),
all_tmb_patients AS (
    SELECT DISTINCT patient_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND attribute_name = 'TMB_NONSYNONYMOUS'
),
tmb AS (
    SELECT patient_unique_id, toFloat64OrNull(attribute_value) AS tmb
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND attribute_name = 'TMB_NONSYNONYMOUS'
)
SELECT
    '<2 of top5 genes mutated' AS group_name,
    count() AS n_patients,
    round(avg(t.tmb), 3) AS mean_tmb,
    round(quantile(0.5)(t.tmb), 3) AS median_tmb
FROM all_tmb_patients p
LEFT JOIN gene_counts gc USING (patient_unique_id)
JOIN tmb t USING (patient_unique_id)
WHERE (gc.n_genes IS NULL OR gc.n_genes < 2)
  AND t.tmb IS NOT NULL
"
}

◀ result
{"rows":[{"group_name":"<2 of top5 genes mutated","n_patients":770,"mean_tmb":1.658,"median_tmb":1.167}]}

▶ 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)

▶ navigate_to_group_comparison
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "groups": [
  {
   "name": "≥2 of PIK3CA/TP53/TTN/CDH1/GATA3 mutated",
   "studyViewFilter": {
    "geneFilters": [
     {
      "molecularProfileIds": [
       "brca_tcga_pan_can_atlas_2018_mutations"
      ],
      "geneQueries": [
       [
        {
         "hugoGeneSymbol": "PIK3CA"
        }
       ],
       [
        {
         "hugoGeneSymbol": "TP53"
        }
       ],
       [
        {
         "hugoGeneSymbol": "TTN"
        }
       ],
       [
        {
         "hugoGeneSymbol": "CDH1"
        }
       ],
       [
        {
         "hugoGeneSymbol": "GATA3"
        }
       ]
      ]
     }
    ]
   }
  },
  {
   "name": "Rest of cohort",
   "isUnselected": true
  }
 ],
 "tab": "clinical"
}

◀ result
{"success":false,"error":"No samples found for group \"≥2 of PIK3CA/TP53/TTN/CDH1/GATA3 mutated\" — filter may be too restrictive","details":{}}

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "PIK3CA",
  "TP53",
  "TTN",
  "CDH1",
  "GATA3"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=PIK3CA%0ATP53%0ATTN%0ACDH1%0AGATA3&case_set_id=brca_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=PIK3CA%0ATP53%0ATTN%0ACDH1%0AGATA3&case_set_id=brca_tcga_pan_can_atlas_2018_all","data":{"studyIds":["brca_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084}],"genes":["PIK3CA","TP53","TTN","CDH1","GATA3"],"caseSetId":"brca_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
The oncoprint filter approach (union of any single gene) doesn't support "≥2 genes" logic directly via geneFilters — that structure represents an OR condition (any one gene) not a co-occurrence count. This is a structural limitation of the study-view filter, so I'll present the OncoPrint link (which shows all 5 genes and lets users visually assess co-mutation) alongside the query results, rather than force an inaccurate group-comparison link.

## Results

**Cohort:** Breast Invasive Carcinoma (TCGA, PanCancer Atlas) — 1,066 patients with TMB data (`TMB_NONSYNONYMOUS`).

**Top 5 most frequently mutated genes** (patient-level, of 1,066 profiled): PIK3CA (32.6%), TP53 (32.6%), TTN (17.5%), CDH1 (12.2%), GATA3 (11.9%).

| Group | Patients | Mean TMB (mut/Mb) | Median TMB |
|---|---|---|---|
| ≥2 of the top 5 genes mutated | 296 | **5.53** | 2.03 |
| Fewer than 2 of the top 5 mutated | 770 | 1.66 | 1.17 |
| **All patients (overall cohort)** | 1,066 | **2.73** | 1.30 |

Patients carrying mutations in at least two of these five genes have a mean TMB roughly **2× higher** than the overall cohort average (5.53 vs. 2.73 mutations/Mb), and over 3× higher than patients with fewer than two of these genes mutated (5.53 vs. 1.66). The median shows the same direction but a smaller gap (2.03 vs. 1.30 overall), consistent with a right-skewed TMB distribution where a subset of higher-mutation-count tumors pulls the mean up more than the median. This pattern is expected mechanically: TTN in particular is a large gene whose mutation likelihood scales with overall mutation burden, so tumors already carrying more mutations are more likely to hit TTN (and the other frequently mutated genes) by chance as well as by biology — this is descriptive co-occurrence, not a tested statistical association.

[View gene co-mutation OncoPrint (PIK3CA, TP53, TTN, CDH1, GATA3)](https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=PIK3CA%0ATP53%0ATTN%0ACDH1%0AGATA3&case_set_id=brca_tcga_pan_can_atlas_2018_all)

[Explore the full cohort in Study View](https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018)

If you'd like a formal statistical comparison (e.g., a t-test or rank-sum test on TMB between these two groups), that would need to be run outside cBioPortal (R/Python) using the counts above, since the database itself doesn't compute significance tests.