Q (haiku): "Are mutations in DNA repair pathway genes (BRCA1, BRCA2, ATM, CHEK2) enriched in specific cancer types across TCGA Pan-Cancer Atlas?"

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

◀ 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 *
FROM gene_mutation_frequency_by_cancer_type(
    preference = 'pan_cancer_tcga',
    gene = 'BRCA1'
)
ORDER BY frequency_pct DESC;
",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_type":"Endometrial Cancer","altered_samples":47,"profiled_samples":574,"frequency_pct":8.2},{"cancer_type":"Melanoma","altered_samples":28,"profiled_samples":440,"frequency_pct":6.4},{"cancer_type":"Bladder Cancer","altered_samples":21,"profiled_samples":410,"frequency_pct":5.1},{"cancer_type":"Cervical Cancer","altered_samples":14,"profiled_samples":291,"frequency_pct":4.8},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":41,"profiled_samples":1050,"frequency_pct":3.9},{"cancer_type":"Ovarian Epithelial Tumor","altered_samples":18,"profiled_samples":523,"frequency_pct":3.4},{"cancer_type":"Colorectal Cancer","altered_samples":16,"profiled_samples":534,"frequency_pct":3},{"cancer_type":"Esophagogastric Cancer","altered_samples":18,"profiled_samples":618,"frequency_pct":2.9},{"cancer_type":"Breast Cancer","altered_samples":27,"profiled_samples":1066,"frequency_pct":2.5},{"cancer_type":"Head and Neck Cancer","altered_samples":11,"profiled_samples":515,"frequency_pct":2.1},{"cancer_type":"Glioblastoma","altered_samples":6,"profiled_samples":397,"frequency_pct":1.5},{"cancer_type":"Pancreatic Cancer","altered_samples":2,"profiled_samples":179,"frequency_pct":1.1},{"cancer_type":"Hepatobiliary Cancer","altered_samples":4,"profiled_samples":366,"frequency_pct":1.1},{"cancer_type":"Renal Clear Cell Carcinoma","altered_samples":4,"profiled_samples":402,"frequency_pct":1},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":3,"profiled_samples":341,"frequency_pct":0.9},{"cancer_type":"Glioma","altered_samples":2,"profiled_samples":514,"frequency_pct":0.4},{"cancer_type":"Thyroid Cancer","altered_samples":1,"profiled_samples":490,"frequency_pct":0.2},{"cancer_type":"Prostate Cancer","altered_samples":1,"profiled_samples":494,"frequency_pct":0.2}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT *
FROM gene_mutation_frequency_by_cancer_type(
    preference = 'pan_cancer_tcga',
    gene = 'BRCA2'
)
ORDER BY frequency_pct DESC;
",
 "max_rows": 100
}

▶ clickhouse_run_select_query
{
 "query": "
SELECT *
FROM gene_mutation_frequency_by_cancer_type(
    preference = 'pan_cancer_tcga',
    gene = 'ATM'
)
ORDER BY frequency_pct DESC;
",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_type":"Endometrial Cancer","altered_samples":83,"profiled_samples":574,"frequency_pct":14.5},{"cancer_type":"Melanoma","altered_samples":51,"profiled_samples":440,"frequency_pct":11.6},{"cancer_type":"Bladder Cancer","altered_samples":40,"profiled_samples":410,"frequency_pct":9.8},{"cancer_type":"Esophagogastric Cancer","altered_samples":45,"profiled_samples":618,"frequency_pct":7.3},{"cancer_type":"Colorectal Cancer","altered_samples":38,"profiled_samples":534,"frequency_pct":7.1},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":60,"profiled_samples":1050,"frequency_pct":5.7},{"cancer_type":"Cervical Cancer","altered_samples":13,"profiled_samples":291,"frequency_pct":4.5},{"cancer_type":"Head and Neck Cancer","altered_samples":21,"profiled_samples":515,"frequency_pct":4.1},{"cancer_type":"Ovarian Epithelial Tumor","altered_samples":15,"profiled_samples":523,"frequency_pct":2.9},{"cancer_type":"Breast Cancer","altered_samples":29,"profiled_samples":1066,"frequency_pct":2.7},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":7,"profiled_samples":341,"frequency_pct":2.1},{"cancer_type":"Renal Clear Cell Carcinoma","altered_samples":8,"profiled_samples":402,"frequency_pct":2},{"cancer_type":"Hepatobiliary Cancer","altered_samples":7,"profiled_samples":366,"frequency_pct":1.9},{"cancer_type":"Prostate Cancer","altered_samples":8,"profiled_samples":494,"frequency_pct":1.6},{"cancer_type":"Thymic Epithelial Tumor","altered_samples":2,"profiled_samples":123,"frequency_pct":1.6},{"cancer_type":"Sarcoma","altered_samples":4,"profiled_samples":255,"frequency_pct":1.6},{"cancer_type":"Glioblastoma","altered_samples":6,"profiled_samples":397,"frequency_pct":1.5},{"cancer_type":"Glioma","altered_samples":7,"profiled_samples":514,"frequency_pct":1.4},{"cancer_type":"Ocular Melanoma","altered_samples":1,"profiled_samples":80,"frequency_pct":1.2},{"cancer_type":"Pancreatic Cancer","altered_samples":2,"profiled_samples":179,"frequency_pct":1.1},{"cancer_type":"Leukemia","altered_samples":2,"profiled_samples":200,"frequency_pct":1},{"cancer_type":"Pheochromocytoma","altered_samples":1,"profiled_samples":147,"frequency_pct":0.7},{"cancer_type":"Thyroid Cancer","altered_samples":2,"profiled_samples":490,"frequency_pct":0.4}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT *
FROM gene_mutation_frequency_by_cancer_type(
    preference = 'pan_cancer_tcga',
    gene = 'CHEK2'
)
ORDER BY frequency_pct DESC;
",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_type":"Endometrial Cancer","altered_samples":101,"profiled_samples":574,"frequency_pct":17.6},{"cancer_type":"Bladder Cancer","altered_samples":55,"profiled_samples":410,"frequency_pct":13.4},{"cancer_type":"Colorectal Cancer","altered_samples":70,"profiled_samples":534,"frequency_pct":13.1},{"cancer_type":"Melanoma","altered_samples":42,"profiled_samples":440,"frequency_pct":9.5},{"cancer_type":"Esophagogastric Cancer","altered_samples":51,"profiled_samples":618,"frequency_pct":8.3},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":76,"profiled_samples":1050,"frequency_pct":7.2},{"cancer_type":"Adrenocortical Carcinoma","altered_samples":5,"profiled_samples":91,"frequency_pct":5.5},{"cancer_type":"Pancreatic Cancer","altered_samples":8,"profiled_samples":179,"frequency_pct":4.5},{"cancer_type":"Prostate Cancer","altered_samples":20,"profiled_samples":494,"frequency_pct":4},{"cancer_type":"Cervical Cancer","altered_samples":11,"profiled_samples":291,"frequency_pct":3.8},{"cancer_type":"Hepatobiliary Cancer","altered_samples":13,"profiled_samples":366,"frequency_pct":3.6},{"cancer_type":"Head and Neck Cancer","altered_samples":17,"profiled_samples":515,"frequency_pct":3.3},{"cancer_type":"Renal Clear Cell Carcinoma","altered_samples":13,"profiled_samples":402,"frequency_pct":3.2},{"cancer_type":"Sarcoma","altered_samples":7,"profiled_samples":255,"frequency_pct":2.7},{"cancer_type":"Breast Cancer","altered_samples":25,"profiled_samples":1066,"frequency_pct":2.3},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":8,"profiled_samples":341,"frequency_pct":2.3},{"cancer_type":"Ovarian Epithelial Tumor","altered_samples":10,"profiled_samples":523,"frequency_pct":1.9},{"cancer_type":"Glioblastoma","altered_samples":7,"profiled_samples":397,"frequency_pct":1.8},{"cancer_type":"Non-Seminomatous Germ Cell Tumor","altered_samples":1,"profiled_samples":86,"frequency_pct":1.2},{"cancer_type":"Pleural Mesothelioma","altered_samples":1,"profiled_samples":86,"frequency_pct":1.2},{"cancer_type":"Thyroid Cancer","altered_samples":6,"profiled_samples":490,"frequency_pct":1.2},{"cancer_type":"Glioma","altered_samples":4,"profiled_samples":514,"frequency_pct":0.8},{"cancer_type":"Leukemia","altered_samples":1,"profiled_samples":200,"frequency_pct":0.5}]}

◀ result
{"rows":[{"cancer_type":"Endometrial Cancer","altered_samples":36,"profiled_samples":574,"frequency_pct":6.3},{"cancer_type":"Bladder Cancer","altered_samples":10,"profiled_samples":410,"frequency_pct":2.4},{"cancer_type":"Adrenocortical Carcinoma","altered_samples":2,"profiled_samples":91,"frequency_pct":2.2},{"cancer_type":"Colorectal Cancer","altered_samples":9,"profiled_samples":534,"frequency_pct":1.7},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":17,"profiled_samples":1050,"frequency_pct":1.6},{"cancer_type":"Esophagogastric Cancer","altered_samples":9,"profiled_samples":618,"frequency_pct":1.5},{"cancer_type":"Cervical Cancer","altered_samples":4,"profiled_samples":291,"frequency_pct":1.4},{"cancer_type":"Melanoma","altered_samples":5,"profiled_samples":440,"frequency_pct":1.1},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":3,"profiled_samples":341,"frequency_pct":0.9},{"cancer_type":"Head and Neck Cancer","altered_samples":4,"profiled_samples":515,"frequency_pct":0.8},{"cancer_type":"Hepatobiliary Cancer","altered_samples":3,"profiled_samples":366,"frequency_pct":0.8},{"cancer_type":"Ovarian Epithelial Tumor","altered_samples":4,"profiled_samples":523,"frequency_pct":0.8},{"cancer_type":"Breast Cancer","altered_samples":9,"profiled_samples":1066,"frequency_pct":0.8},{"cancer_type":"Thyroid Cancer","altered_samples":4,"profiled_samples":490,"frequency_pct":0.8},{"cancer_type":"Thymic Epithelial Tumor","altered_samples":1,"profiled_samples":123,"frequency_pct":0.8},{"cancer_type":"Pancreatic Cancer","altered_samples":1,"profiled_samples":179,"frequency_pct":0.6},{"cancer_type":"Glioma","altered_samples":2,"profiled_samples":514,"frequency_pct":0.4},{"cancer_type":"Sarcoma","altered_samples":1,"profiled_samples":255,"frequency_pct":0.4},{"cancer_type":"Renal Clear Cell Carcinoma","altered_samples":1,"profiled_samples":402,"frequency_pct":0.2}]}

▶ resolve_and_route
{
 "studyKeywords": [
  "TCGA",
  "pan",
  "cancer"
 ]
}

◀ result
{"success":true,"message":"Found 35 matching studies (top 5 with full metadata, rest with basic info). 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":35,"studiesWithMetadata":[{"studyId":"pancan_pcawg_2020","name":"Pan-cancer analysis of whole genomes (ICGC/TCGA, Nature 2020)","sampleCount":2922,"studyViewUrl":"https://www.cbioportal.org/study?id=pancan_pcawg_2020","metadata":{"clinicalAttributeIds":["AGE","ALCOHOL","ALCOHOL_HISTORY_INTENSITY","ANCESTRY_PRIMARY","CANCER_TYPE","CANCER_TYPE_DETAILED","CELLULARITY","FIRST THERAPY_RESPONSE","FIRST_THERAPY","GRADE","HISTOLOGY","HISTOLOGY_ABBREVIATION","HISTOLOGY_TIER1","HISTOLOGY_TIER2","HISTOLOGY_TIER3","HISTOLOGY_TIER4","ICD_10","ICGC_SAMPLE_ID","MUTATION_COUNT","ONCOTREE_CODE","ORGAN_SYSTEM","OS_MONTHS","OS_STATUS","PLOIDY","PROJECT_CODE","PURITY","PURITY_CONFUGURATION","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_TYPE","SEQUENCING_TYPE","SEX","STAGE","TBL_SCORE","TMB_NONSYNONYMOUS","TOBACCO_SMOKING_HISTORY_INDICATOR","TOBACCO_SMOKING_INTENSITY","TUMOR_SAMPLE_HISTOLOGY_CODE","WGD"],"molecularProfileIds":["pancan_pcawg_2020_cna","pancan_pcawg_2020_mirna","pancan_pcawg_2020_mirna_median_Zscores","pancan_pcawg_2020_mrna_seq_fpkm_capture","pancan_pcawg_2020_mrna_seq_fpkm_capture_all_sample_Zscores","pancan_pcawg_2020_mutations"],"genericAssayProfiles":["pancan_pcawg_2020_mutational_signatures_contribution_DBS","pancan_pcawg_2020_mutational_signatures_contribution_ID","pancan_pcawg_2020_mutational_signatures_contribution_SBS","pancan_pcawg_2020_mutational_signatures_counts_DBS","pancan_pcawg_2020_mutational_signatures_counts_ID","pancan_pcawg_2020_mutational_signatures_counts_SBS"],"heatmapProfileIds":["pancan_pcawg_2020_mirna_median_Zscores","pancan_pcawg_2020_mrna_seq_fpkm_capture_all_sample_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","generic_assay_mutational_signature"]}},{"studyId":"nsclc_tcga_broad_2016","name":"Pan-Lung Cancer (TCGA, Nat Genet 2016)","sampleCount":1144,"studyViewUrl":"https://www.cbioportal.org/study?id=nsclc_tcga_broad_2016","metadata":{"clinicalAttributeIds":["AGE","AGE_AT_SURGERY","CANCER_TYPE","CANCER_TYPE_DETAILED","DAYS_TO_DEATH","DAYS_TO_LAST_FOLLOWUP","FRACTION_GENOME_ALTERED","HISTORY_NEOADJUVANT_TRTYN","HISTORY_OTHER_MALIGNANCY","MUTATION_COUNT","M_STAGE","N_STAGE","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","SAMPLE_COUNT","SEX","SMOKING_HISTORY","SMOKING_PACK_YEARS","SOMATIC_STATUS","STAGE","TMB_NONSYNONYMOUS","T_STAGE"],"molecularProfileIds":["nsclc_tcga_broad_2016_cna","nsclc_tcga_broad_2016_mutations","nsclc_tcga_broad_2016_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}},{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084,"studyVi … (20545 more chars)

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018",
  "gbm_tcga_pan_can_atlas_2018",
  "ov_tcga_pan_can_atlas_2018",
  "luad_tcga_pan_can_atlas_2018",
  "ucec_tcga_pan_can_atlas_2018",
  "hnsc_tcga_pan_can_atlas_2018",
  "lgg_tcga_pan_can_atlas_2018",
  "kirc_tcga_pan_can_atlas_2018",
  "thca_tcga_pan_can_atlas_2018",
  "prad_tcga_pan_can_atlas_2018",
  "lusc_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "stad_tcga_pan_can_atlas_2018",
  "blca_tcga_pan_can_atlas_2018",
  "lihc_tcga_pan_can_atlas_2018",
  "cesc_tcga_pan_can_atlas_2018",
  "kirp_tcga_pan_can_atlas_2018",
  "sarc_tcga_pan_can_atlas_2018",
  "laml_tcga_pan_can_atlas_2018",
  "paad_tcga_pan_can_atlas_2018",
  "esca_tcga_pan_can_atlas_2018",
  "pcpg_tcga_pan_can_atlas_2018",
  "tgct_tcga_pan_can_atlas_2018",
  "thym_tcga_pan_can_atlas_2018",
  "acc_tcga_pan_can_atlas_2018",
  "meso_tcga_pan_can_atlas_2018",
  "uvm_tcga_pan_can_atlas_2018",
  "kich_tcga_pan_can_atlas_2018",
  "ucs_tcga_pan_can_atlas_2018",
  "dlbc_tcga_pan_can_atlas_2018",
  "chol_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "BRCA1",
  "BRCA2",
  "ATM",
  "CHEK2"
 ],
 "tab": "oncoprint"
}

◀ result
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═ answer (success)
## DNA Repair Pathway Mutations Across TCGA Pan-Cancer Atlas

Yes, mutations in DNA repair pathway genes show **clear enrichment** in specific cancer types across TCGA Pan-Cancer Atlas. Here's the breakdown by gene:

### **BRCA2** — Highest Overall Burden (Most Enriched)
BRCA2 shows the most dramatic enrichment:

| Cancer Type | Mutation Frequency | Altered/Profiled |
|---|---|---|
| **Endometrial Cancer** | **14.5%** | 83/574 |
| **Melanoma** | **11.6%** | 51/440 |
| **Bladder Cancer** | **9.8%** | 40/410 |
| Esophagogastric Cancer | 7.3% | 45/618 |
| Colorectal Cancer | 7.1% | 38/534 |
| Non-Small Cell Lung Cancer | 5.7% | 60/1050 |
| Breast Cancer | 2.7% | 29/1066 |

---

### **ATM** — Second Most Enriched (Moderate Burden)
ATM shows substantial enrichment in DNA-repair-deficient cancers:

| Cancer Type | Mutation Frequency | Altered/Profiled |
|---|---|---|
| **Endometrial Cancer** | **17.6%** | 101/574 |
| **Bladder Cancer** | **13.4%** | 55/410 |
| **Colorectal Cancer** | **13.1%** | 70/534 |
| Melanoma | 9.5% | 42/440 |
| Esophagogastric Cancer | 8.3% | 51/618 |
| Non-Small Cell Lung Cancer | 7.2% | 76/1050 |
| Breast Cancer | 2.3% | 25/1066 |

---

### **BRCA1** — Lowest Overall Burden (Selective Enrichment)
BRCA1 shows the narrowest distribution, concentrated in a few types:

| Cancer Type | Mutation Frequency | Altered/Profiled |
|---|---|---|
| **Endometrial Cancer** | **8.2%** | 47/574 |
| **Melanoma** | **6.4%** | 28/440 |
| **Bladder Cancer** | **5.1%** | 21/410 |
| Cervical Cancer | 4.8% | 14/291 |
| Non-Small Cell Lung Cancer | 3.9% | 41/1050 |
| Breast Cancer | 2.5% | 27/1066 |

---

### **CHEK2** — Rarest (Very Low Frequency)
CHEK2 is minimally altered across most types:

| Cancer Type | Mutation Frequency | Altered/Profiled |
|---|---|---|
| **Endometrial Cancer** | **6.3%** | 36/574 |
| Bladder Cancer | 2.4% | 10/410 |
| Adrenocortical Carcinoma | 2.2% | 2/91 |
| All others | <2% | — |

---

## Key Patterns of Enrichment

1. **Endometrial cancer** is the clear hotspot — all four genes are enriched here (ATM 17.6%, BRCA2 14.5%, BRCA1 8.2%, CHEK2 6.3%). This reflects the high microsatellite instability (MSI) rate in UCEC.

2. **Hypermutated cancers show selective enrichment**:
   - **BRCA2 and ATM** peak in melanoma (11.6%, 9.5%), bladder (9.8%, 13.4%), and colorectal (7.1%, 13.1%) — cancers with high mutation burdens.
   - **BRCA1** also shows this pattern but is less frequent overall.

3. **Breast cancer** — despite BRCA1/2's clinical significance in hereditary breast cancer — shows relatively modest frequencies (BRCA2 2.7%, BRCA1 2.5% in TCGA BRCA), suggesting TCGA breast samples are enriched for somatic rather than germline BRCA mutations.

4. **CHEK2** is uniformly rare (<2% in most types), suggesting it's not a common somatic target across TCGA except in endometrial cancer.

---

## Interactive Visualization

[**View OncoPrint of all four DNA repair genes across TCGA PanCancer**](https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cacc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2%0AATM%0ACHEK2&case_set_id=all) — shows sample-by-sample mutation status across all cancer types.

---

## Interpretation Note

**Important caveat:** Frequently mutated does not mean functionally important. These patterns reflect (1) shared mutational processes (e.g., deficient mismatch repair in endometrial cancer), (2) large gene size (BRCA2, ATM, CHEK2 are big targets for random mutations), and (3) study-specific sequencing depth. To determine whether these mutations have clinical significance, you would need to assess OncoKB annotation status, pathway impact, or run a statistical test comparing survival or treatment response by mutation status using the cBioPortal Group Comparison tab.