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"
}

▶ read_guide
{
 "uri": "cbioportal://common-pitfalls#16"
}

◀ 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
### 16. 🚨 SILENT QUERY SUBSTITUTION ("did you mean...")

When the user's wording differs from canonical terminology (e.g. "V600V" looks like "V600E" with a typo, or "point mutation" sounds like "missense"), it is forbidden to silently rewrite the question and answer the rewritten version. Doing so produces an answer that looks confident but is for a different question — the user cannot tell what was changed.

#### ❌ Wrong: silently substitute

> User: *"Find patients in colorectal cancer with the V600V alteration in BRAF"*
> Agent: *(internally treats this as V600E)* "I found 412 samples with BRAF V600E in colorectal studies..."

> User: *"What is the most prevalent TP53 mutation in uterine cancer that is not a point mutation?"*
> Agent: *(internally treats "point mutation" = "missense", silently excludes only missense)* "The most prevalent non-missense TP53 mutation is..."

#### ✅ Correct: answer the literal question, flag any normalization

For an unusual-looking variant the user may have typed deliberately:
- Query for what was asked, literally.
- If 0 rows come back, **explain *why* zero is the expected answer** before suggesting a likely-intended alternative. For synonymous variants (e.g. BRAF V600V, TP53 R175R), the explanation is: *cBioPortal's mutation tables filter out synonymous (silent) variants in most studies, so 0 hits means "filtered upstream", not "no such variant exists in any patient"*. Then ask: *"Did you mean V600E (the canonical activating variant)? Or would you like me to look for V600V in the studies that do retain synonymous calls?"*
- If the wording is ambiguous (e.g. "point mutation"), ask the user which definition they meant before querying — do not pick one silently.

#### Mutation-type terminology mapping (use this to disambiguate)

| User says | Canonical definition | `mutation_type` filter |
|---|---|---|
| "point mutation" | Any SNV (single-nucleotide variant) — includes missense, nonsense, synonymous, splice-site SNVs | `mutation_type IN ('Missense_Mutation','Nonsense_Mutation','Silent','Splice_Site')` — **but ask the user to confirm scope first** |
| "missense" | Single amino-acid substitution that changes the protein | `mutation_type = 'Missense_Mutation'` |
| "nonsense" / "stop-gain" | Premature stop codon | `mutation_type = 'Nonsense_Mutation'` |
| "synonymous" / "silent" | Nucleotide change with no amino-acid change | `mutation_type = 'Silent'` (**often filtered out of public datasets** — see below) |
| "splice site" | Mutation in canonical splice acceptor/donor | `mutation_type = 'Splice_Site'` |
| "frameshift" | Indel changing reading frame | `mutation_type IN ('Frame_Shift_Ins','Frame_Shift_Del')` |
| "indel" / "in-frame" | In-frame insertion or deletion | `mutation_type IN ('In_Frame_Ins','In_Frame_Del')` |
| "truncating" | Anything that disrupts the protein early | `mutation_type IN ('Nonsense_Mutation','Frame_Shift_Ins','Frame_Shift_Del','Splice_Site','Nonstop_Mutation')` |

**Synonymous-variant filter. … (785 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
}

▶ 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":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       = 'CHEK2'
)
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":"Sarcoma","altered_samples":4,"profiled_samples":255,"frequency_pct":1.6},{"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":"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}]}

◀ 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":"Renal Non-Clear Cell Carcinoma","altered_samples":8,"profiled_samples":341,"frequency_pct":2.3},{"cancer_type":"Breast Cancer","altered_samples":25,"profiled_samples":1066,"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":"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":"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":"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}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT cancer_study_identifier
FROM cancer_study_query_preferences
WHERE preference_name = 'pan_cancer_tcga'
ORDER BY cancer_study_identifier;
",
 "max_rows": 100
}

◀ 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"}]}

▶ 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/de9a9997-e9c4-4b66-ba3f-28b8677388a6/tool-results/mcp-navigator-resolve_and_route-1790296122817.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/de9a9997-e9c4-4b66-ba3f-28b8677388a6/tool-results/mcp-navigator-resolve_and_route-1790296122817.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/de9a9997-e9c4-4b66-ba3f-28b8677388a6/tool-results/mcp-navigator-resolve_and_route-1790296122817.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.


▶ navigate_to_results_view
{
 "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"
 ],
 "genes": [
  "BRCA1",
  "BRCA2",
  "ATM",
  "CHEK2"
 ],
 "tab": "oncoprint"
}

◀ result
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═ answer (success)
Perfect! Now let me create a comprehensive summary with visualizations:

## DNA Repair Gene Mutations Across TCGA Pan-Cancer Atlas

**Counting unit:** Sample-level mutation frequencies (altered samples / mutation-profiled samples × 100%)

---

### **Key Finding: Clear Cancer-Type Enrichment**

Yes, mutations in DNA repair pathway genes show marked **enrichment in specific cancer types**, with different patterns for each gene:

---

## **BRCA1 Mutation Distribution**

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

**Top enriched:** Endometrial, melanoma, bladder, and cervical cancers (5–8.2%). Surprisingly modest in breast cancer (2.5%).

---

## **BRCA2 Mutation Distribution**

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

**Top enriched:** Endometrial cancer (14.5%) shows the strongest BRCA2 enrichment, followed by melanoma (11.6%) and bladder cancer (9.8%). Again, breast cancer is lower than the most enriched types.

---

## **ATM Mutation Distribution**

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

**Top enriched:** Endometrial cancer leads at 17.6%, followed by bladder (13.4%) and colorectal (13.1%). ATM is substantially more mutated in endometrial, gastrointestinal, and bladder cancers compared to breast cancer.

---

## **CHEK2 Mutation Distribution**

| Cancer Type | Altered | Profiled | Frequency |
|---|---|---|---|
| **Endometrial Cancer** | 36 | 574 | **6.3%** |
| **Bladder Cancer** | 10 | 410 | 2.4% |
| **Adrenocortical** | 2 | 91 | 2.2% |
| **Colorectal Cancer** | 9 | 534 | 1.7% |
| **Lung Cancer** | 17 | 1050 | 1.6% |

**Top enriched:** CHEK2 shows the most dramatic enrichment in endometrial cancer (6.3%) compared to other types (all <2.5%). The pattern is highly distinct from the other three genes.

---

## **Summary: Gene-Specific Enrichment Patterns**

| Gene | Most Enriched Cancer Type | Frequency | Notable Pattern |
|---|---|---|---|
| **BRCA1** | Endometrial | 8.2% | Modest overall; balanced across types |
| **BRCA2** | Endometrial | 14.5% | Strong enrichment in gynecologic + GI + melanoma |
| **ATM** | Endometrial | 17.6% | **Highest absolute frequency**; strong in GI/bladder |
| **CHEK2** | Endometrial | 6.3% | Dramatic endometrial-specific enrichment |

**Hypermutation context:** The high frequencies in endometrial, colorectal, and bladder cancers reflect their **high mutation burdens** (often due to MSI, POLE mutations, environmental exposures). Large genes like ATM accumulate mutations more frequently in hypermutated backgrounds—this is a sampling effect, not necessarily selective advantage.

---

## **Interactive Visualization**

[View DNA repair genes (BRCA1, BRCA2, ATM, CHEK2) across TCGA Pan-Cancer Atlas](https://www.cbioportal.org/results/oncoprint?cancer_study_list=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2%0AATM%0ACHEK2&case_set_id=all)

This OncoPrint visualization displays mutation profiles across all 32 TCGA cancer types, allowing you to inspect sample-level alteration patterns and co-occurrence within tumor samples.