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?

▶ 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://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 … (35166 more chars)

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-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
# 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)

▶ clickhouse_run_select_query
{
 "query": "SELECT hugo_gene_symbol, total_mutation_events AS mutations, altered_samples, profiled_samples, frequency_pct
FROM top_mutated_genes_in_study(study = 'brca_tcga_pan_can_atlas_2018', top_n = 5)"
}

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

▶ clickhouse_run_select_query
{
 "query": "
WITH top5 AS (
    SELECT arrayJoin(['PIK3CA','TP53','TTN','CDH1','GATA3']) AS gene
),
sample_gene_counts AS (
    SELECT sample_unique_id, COUNT(DISTINCT hugo_gene_symbol) AS n_genes_mutated
    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 gene FROM top5)
    GROUP BY sample_unique_id
),
qualifying_samples AS (
    SELECT sample_unique_id FROM sample_gene_counts WHERE n_genes_mutated >= 2
),
tmb_all AS (
    SELECT sample_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
    (SELECT count() FROM qualifying_samples) AS n_qualifying_samples,
    (SELECT round(avg(tmb),3) FROM tmb_all WHERE sample_unique_id IN (SELECT sample_unique_id FROM qualifying_samples) AND tmb IS NOT NULL) AS avg_tmb_qualifying,
    (SELECT count() FROM tmb_all WHERE sample_unique_id IN (SELECT sample_unique_id FROM qualifying_samples) AND tmb IS NOT NULL) AS n_qualifying_with_tmb,
    (SELECT round(avg(tmb),3) FROM tmb_all WHERE tmb IS NOT NULL) AS avg_tmb_all,
    (SELECT count() FROM tmb_all WHERE tmb IS NOT NULL) AS n_all_with_tmb
"
}

◀ result
{"rows":[{"n_qualifying_samples":296,"avg_tmb_qualifying":5.534,"n_qualifying_with_tmb":296,"avg_tmb_all":2.734,"n_all_with_tmb":1066}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH top5 AS (
    SELECT arrayJoin(['PIK3CA','TP53','TTN','CDH1','GATA3']) AS gene
),
sample_gene_counts AS (
    SELECT sample_unique_id, COUNT(DISTINCT hugo_gene_symbol) AS n_genes_mutated
    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 gene FROM top5)
    GROUP BY sample_unique_id
),
qualifying_samples AS (
    SELECT sample_unique_id FROM sample_gene_counts WHERE n_genes_mutated >= 2
),
tmb_all AS (
    SELECT sample_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
    (SELECT round(quantile(0.5)(tmb),3) FROM tmb_all WHERE sample_unique_id IN (SELECT sample_unique_id FROM qualifying_samples) AND tmb IS NOT NULL) AS median_tmb_qualifying,
    (SELECT round(quantile(0.5)(tmb),3) FROM tmb_all WHERE tmb IS NOT NULL) AS median_tmb_all
"
}

◀ result
{"rows":[{"median_tmb_qualifying":2.033,"median_tmb_all":1.3}]}

▶ 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"
        }
       ]
      ]
     },
     {
      "molecularProfileIds": [
       "brca_tcga_pan_can_atlas_2018_mutations"
      ],
      "geneQueries": [
       [
        {
         "hugoGeneSymbol": "TP53"
        }
       ]
      ]
     }
    ]
   }
  },
  {
   "name": "All other samples",
   "isUnselected": true
  }
 ],
 "tab": "clinical"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5c0ebc2115c492d884df7","url":"https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5c0ebc2115c492d884df7","data":{"description":"Group comparison (2 custom groups)","studies":["brca_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"≥2 of PIK3CA/TP53/TTN/CDH1/GATA3 mutated","sampleCount":81},{"name":"All other samples","sampleCount":1003}],"studyViewUrl":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"≥2 of PIK3CA/TP53/TTN/CDH1/GATA3 mutated","url":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22molecularProfileIds%22%3A%5B%22brca_tcga_pan_can_atlas_2018_mutations%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22PIK3CA%22%7D%5D%5D%7D%2C%7B%22molecularProfileIds%22%3A%5B%22brca_tcga_pan_can_atlas_2018_mutations%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22TP53%22%7D%5D%5D%7D%5D%7D"}],"pageDescription":"Clinical attribute distributions compared across ≥2 of PIK3CA/TP53/TTN/CDH1/GATA3 mutated (81) vs All other samples (1003)."}}

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

◀ 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&clinicallist=TMB_NONSYNONYMOUS","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&clinicallist=TMB_NONSYNONYMOUS","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)
### Visualizations

- **[OncoPrint: PIK3CA, TP53, TTN, CDH1, GATA3 with TMB track](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&clinicallist=TMB_NONSYNONYMOUS)** — visual summary of alterations in the top 5 genes across all 1,084 samples, annotated with TMB_NONSYNONYMOUS. Sort/inspect samples with ≥2 mutated genes here to visually confirm the pattern.

- **[Study Overview: Breast Invasive Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018)** — full cohort (1,084 samples).

⚠️ Note: The Group Comparison link I generated compares PIK3CA-**and**-TP53 co-mutated samples (n=81) vs. all others — this does **not** match the SQL-derived "≥2 of any 5 genes" group (n=296), because cBioPortal's StudyView gene-filter UI doesn't support arbitrary "k-of-n" gene logic. I'm omitting that mismatched link to avoid confusion; the OncoPrint above is the best available visual proxy — use it in combination with the table above, which reflects the correct SQL-computed ≥2-of-5 group.