Q (sonnet): "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)

▶ clickhouse_run_select_query
{
 "query": "SELECT 'BRCA1' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'BRCA1')
UNION ALL
SELECT 'BRCA2' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'BRCA2')
UNION ALL
SELECT 'ATM' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'ATM')
UNION ALL
SELECT 'CHEK2' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'CHEK2')
ORDER BY gene, frequency_pct DESC",
 "max_rows": 200
}

◀ result
{"rows":[{"gene":"BRCA2","cancer_type":"Glioblastoma","altered_samples":6,"profiled_samples":397,"frequency_pct":1.5},{"gene":"BRCA2","cancer_type":"Sarcoma","altered_samples":4,"profiled_samples":255,"frequency_pct":1.6},{"gene":"BRCA2","cancer_type":"Bladder Cancer","altered_samples":40,"profiled_samples":410,"frequency_pct":9.8},{"gene":"BRCA2","cancer_type":"Head and Neck Cancer","altered_samples":21,"profiled_samples":515,"frequency_pct":4.1},{"gene":"BRCA2","cancer_type":"Ocular Melanoma","altered_samples":1,"profiled_samples":80,"frequency_pct":1.2},{"gene":"BRCA2","cancer_type":"Pancreatic Cancer","altered_samples":2,"profiled_samples":179,"frequency_pct":1.1},{"gene":"BRCA2","cancer_type":"Non-Small Cell Lung Cancer","altered_samples":60,"profiled_samples":1050,"frequency_pct":5.7},{"gene":"BRCA2","cancer_type":"Glioma","altered_samples":7,"profiled_samples":514,"frequency_pct":1.4},{"gene":"BRCA2","cancer_type":"Leukemia","altered_samples":2,"profiled_samples":200,"frequency_pct":1},{"gene":"BRCA2","cancer_type":"Cervical Cancer","altered_samples":13,"profiled_samples":291,"frequency_pct":4.5},{"gene":"BRCA2","cancer_type":"Hepatobiliary Cancer","altered_samples":7,"profiled_samples":366,"frequency_pct":1.9},{"gene":"BRCA2","cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":7,"profiled_samples":341,"frequency_pct":2.1},{"gene":"BRCA2","cancer_type":"Thymic Epithelial Tumor","altered_samples":2,"profiled_samples":123,"frequency_pct":1.6},{"gene":"BRCA2","cancer_type":"Renal Clear Cell Carcinoma","altered_samples":8,"profiled_samples":402,"frequency_pct":2},{"gene":"BRCA2","cancer_type":"Ovarian Epithelial Tumor","altered_samples":15,"profiled_samples":523,"frequency_pct":2.9},{"gene":"BRCA2","cancer_type":"Prostate Cancer","altered_samples":8,"profiled_samples":494,"frequency_pct":1.6},{"gene":"BRCA2","cancer_type":"Thyroid Cancer","altered_samples":2,"profiled_samples":490,"frequency_pct":0.4},{"gene":"BRCA2","cancer_type":"Breast Cancer","altered_samples":29,"profiled_samples":1066,"frequency_pct":2.7},{"gene":"BRCA2","cancer_type":"Colorectal Cancer","altered_samples":38,"profiled_samples":534,"frequency_pct":7.1},{"gene":"BRCA2","cancer_type":"Esophagogastric Cancer","altered_samples":45,"profiled_samples":618,"frequency_pct":7.3},{"gene":"BRCA2","cancer_type":"Melanoma","altered_samples":51,"profiled_samples":440,"frequency_pct":11.6},{"gene":"BRCA2","cancer_type":"Endometrial Cancer","altered_samples":83,"profiled_samples":574,"frequency_pct":14.5},{"gene":"BRCA2","cancer_type":"Pheochromocytoma","altered_samples":1,"profiled_samples":147,"frequency_pct":0.7},{"gene":"BRCA1","cancer_type":"Glioblastoma","altered_samples":6,"profiled_samples":397,"frequency_pct":1.5},{"gene":"BRCA1","cancer_type":"Glioma","altered_samples":2,"profiled_samples":514,"frequency_pct":0.4},{"gene":"BRCA1","cancer_type":"Bladder Cancer","altered_samples":21,"profiled_samples":410,"frequency_pct":5.1},{"gene":"BRCA1","cancer_type":"Head and N … (6505 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ORDER BY cancer_study_identifier",
 "max_rows": 50
}

◀ 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-ly8cg8ra/06b3cb2d-f0d8-49be-aa9e-ba8723d85718/tool-results/mcp-navigator-resolve_and_route-1790300246373.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-ly8cg8ra/06b3cb2d-f0d8-49be-aa9e-ba8723d85718/tool-results/mcp-navigator-resolve_and_route-1790300246373.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-ly8cg8ra/06b3cb2d-f0d8-49be-aa9e-ba8723d85718/tool-results/mcp-navigator-resolve_and_route-1790300246373.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: MUT",
  "BRCA2: MUT",
  "ATM: MUT",
  "CHEK2: MUT"
 ],
 "tab": "cancerTypesSummary"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/cancerTypesSummary?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%3A+MUT%0ABRCA2%3A+MUT%0AATM%3A+MUT%0ACHEK2%3A+MUT&case_set_id=all","url":"https://www.cbioportal.org/results/cancerTypesSummary?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%3A+MUT%0ABRCA2%3A+MUT%0AATM%3A+MUT%0ACHEK2%3A+MUT&case_set_id=all","data":{"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 … (4300 more chars)

═ answer (success)
## Mutation frequencies of DNA repair genes across TCGA PanCancer Atlas (32 studies, sample-level, WES-profiled denominators)

**Important framing note:** the numbers below are descriptive frequencies from the database, not statistical enrichment tests. cBioPortal/ClickHouse cannot compute a p-value or Fisher's exact test for "enrichment" — to formally test whether a gene's mutation rate differs across cancer types you'd need the Comparison/Mutual Exclusivity tools (linked below) or external statistical software. Also note: large genes (ATM, BRCA2) show inflated mutation counts in hypermutated tumor types (endometrial, colorectal — POLE/MSI-driven), which is a passenger-mutation burden effect, not necessarily a driver-enrichment signal.

### ATM (largest gene, most sensitive to hypermutation burden)
| Cancer type | Altered/Profiled | % |
|---|---|---|
| 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% |
| Non-Small Cell Lung Cancer | 76/1050 | 7.2% |
| *(remaining 20 types: 0.5–5.5%)* | | |

### BRCA2
| Cancer type | Altered/Profiled | % |
|---|---|---|
| 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% |
| Non-Small Cell Lung Cancer | 60/1050 | 5.7% |
| *(remaining: 0.4–4.5%, incl. Breast Cancer 2.7%, Ovarian 2.9%)* | | |

### BRCA1
| Cancer type | Altered/Profiled | % |
|---|---|---|
| Endometrial Cancer | 47/574 | **8.2%** |
| Melanoma | 28/440 | 6.4% |
| Bladder Cancer | 21/410 | 5.1% |
| Cervical Cancer | 14/291 | 4.8% |
| Non-Small Cell Lung Cancer | 41/1050 | 3.9% |
| *(remaining: 0.2–3.4%, incl. Breast Cancer 2.5%, Ovarian 3.4%)* | | |

### CHEK2 (smaller gene, lowest overall rates)
| Cancer type | Altered/Profiled | % |
|---|---|---|
| Endometrial Cancer | 36/574 | **6.3%** |
| Bladder Cancer | 10/410 | 2.4% |
| Adrenocortical Carcinoma | 2/91 | 2.2% |
| Colorectal Cancer | 9/534 | 1.7% |
| *(remaining: 0.2–1.6%, incl. Breast Cancer 0.8%)* | | |

### Pattern across all four genes
All four genes peak in **Endometrial Cancer** (6–18%), followed by **Bladder Cancer, Melanoma, Colorectal Cancer, and Esophagogastric Cancer** — the classic hypermutated/high-TMB TCGA cancer types (driven largely by POLE-ultramutated and MSI-high endometrial tumors, UV-signature melanoma, and MSI colorectal/gastric tumors). This co-clustering is consistent with a **mutation-burden effect** rather than necessarily reflecting pathway-specific selection — large genes like ATM and BRCA2 accumulate more passenger mutations simply by being bigger, in proportion to a tumor's overall mutation rate. Breast and ovarian cancer — where BRCA1/2 germline/somatic alterations are clinically best known — actually show only modest **somatic** mutation frequencies here (BRCA1 2.5%/3.4%, BRCA2 2.7%/2.9%); this reflects that clinically significant BRCA1/2 alterations in breast/ovarian cancer are more often germline or driven by non-mutation mechanisms (promoter methylation, large deletions), which this frequency table does not capture.

To formally test enrichment (e.g., "is ATM significantly enriched in endometrial cancer vs. the rest of the cohort") you can use cBioPortal's built-in comparison test — link below.

---

### Explore interactively

[Alteration frequencies by cancer type — BRCA1, BRCA2, ATM, CHEK2 (TCGA PanCancer Atlas, 32 studies)](https://www.cbioportal.org/results/cancerTypesSummary?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%3A+MUT%0ABRCA2%3A+MUT%0AATM%3A+MUT%0ACHEK2%3A+MUT&case_set_id=all)

This opens the Cancer Types Summary tab showing all 32 TCGA PanCancer Atlas studies (Adrenocortical through Uveal Melanoma) with mutation frequencies plotted per cancer type for all four genes simultaneously. From there you can switch to the Mutual Exclusivity tab for co-occurrence statistics, or the Comparison tab to formally test whether any single cancer type's mutation rate differs significantly from the rest of the cohort.