Q (sonnet-5.5): "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 … (36467 more chars)

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

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name='pan_cancer_tcga' ORDER BY 1",
 "max_rows": 40
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 47\n Code: 47. DB::Exception: Unknown expression identifier `g` in scope SELECT 'CHEK2', * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'CHEK2') ORDER BY g ASC, frequency_pct DESC. (UNKNOWN_IDENTIFIER) (version 26.4.1.2509 (official build))\n"}

◀ 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"}]}

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

◀ result
{"rows":[{"g":"ATM","cancer_type":"Endometrial Cancer","altered_samples":101,"profiled_samples":574,"frequency_pct":17.6},{"g":"ATM","cancer_type":"Bladder Cancer","altered_samples":55,"profiled_samples":410,"frequency_pct":13.4},{"g":"ATM","cancer_type":"Colorectal Cancer","altered_samples":70,"profiled_samples":534,"frequency_pct":13.1},{"g":"ATM","cancer_type":"Melanoma","altered_samples":42,"profiled_samples":440,"frequency_pct":9.5},{"g":"ATM","cancer_type":"Esophagogastric Cancer","altered_samples":51,"profiled_samples":618,"frequency_pct":8.3},{"g":"ATM","cancer_type":"Non-Small Cell Lung Cancer","altered_samples":76,"profiled_samples":1050,"frequency_pct":7.2},{"g":"ATM","cancer_type":"Adrenocortical Carcinoma","altered_samples":5,"profiled_samples":91,"frequency_pct":5.5},{"g":"ATM","cancer_type":"Pancreatic Cancer","altered_samples":8,"profiled_samples":179,"frequency_pct":4.5},{"g":"ATM","cancer_type":"Prostate Cancer","altered_samples":20,"profiled_samples":494,"frequency_pct":4},{"g":"ATM","cancer_type":"Cervical Cancer","altered_samples":11,"profiled_samples":291,"frequency_pct":3.8},{"g":"ATM","cancer_type":"Hepatobiliary Cancer","altered_samples":13,"profiled_samples":366,"frequency_pct":3.6},{"g":"ATM","cancer_type":"Head and Neck Cancer","altered_samples":17,"profiled_samples":515,"frequency_pct":3.3},{"g":"ATM","cancer_type":"Renal Clear Cell Carcinoma","altered_samples":13,"profiled_samples":402,"frequency_pct":3.2},{"g":"ATM","cancer_type":"Sarcoma","altered_samples":7,"profiled_samples":255,"frequency_pct":2.7},{"g":"ATM","cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":8,"profiled_samples":341,"frequency_pct":2.3},{"g":"ATM","cancer_type":"Breast Cancer","altered_samples":25,"profiled_samples":1066,"frequency_pct":2.3},{"g":"ATM","cancer_type":"Ovarian Epithelial Tumor","altered_samples":10,"profiled_samples":523,"frequency_pct":1.9},{"g":"ATM","cancer_type":"Glioblastoma","altered_samples":7,"profiled_samples":397,"frequency_pct":1.8},{"g":"ATM","cancer_type":"Non-Seminomatous Germ Cell Tumor","altered_samples":1,"profiled_samples":86,"frequency_pct":1.2},{"g":"ATM","cancer_type":"Thyroid Cancer","altered_samples":6,"profiled_samples":490,"frequency_pct":1.2},{"g":"ATM","cancer_type":"Pleural Mesothelioma","altered_samples":1,"profiled_samples":86,"frequency_pct":1.2},{"g":"ATM","cancer_type":"Glioma","altered_samples":4,"profiled_samples":514,"frequency_pct":0.8},{"g":"ATM","cancer_type":"Leukemia","altered_samples":1,"profiled_samples":200,"frequency_pct":0.5},{"g":"BRCA1","cancer_type":"Endometrial Cancer","altered_samples":47,"profiled_samples":574,"frequency_pct":8.2},{"g":"BRCA1","cancer_type":"Melanoma","altered_samples":28,"profiled_samples":440,"frequency_pct":6.4},{"g":"BRCA1","cancer_type":"Bladder Cancer","altered_samples":21,"profiled_samples":410,"frequency_pct":5.1},{"g":"BRCA1","cancer_type":"Cervical Cancer","altered_samples":14,"profiled_samples":291,"frequency_pct":4.8},{"g":"BRCA1","canc … (6256 more chars)

▶ 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-pqyf1b1k/22a72d7a-99c9-4e8a-8c24-da33c6daa992/tool-results/mcp-navigator-resolve_and_route-1790656977153.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-pqyf1b1k/22a72d7a-99c9-4e8a-8c24-da33c6daa992/tool-results/mcp-navigator-resolve_and_route-1790656977153.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-pqyf1b1k/22a72d7a-99c9-4e8a-8c24-da33c6daa992/tool-results/mcp-navigator-resolve_and_route-1790656977153.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",
 "profileFilter": "mutations"
}

◀ 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&profileFilter=mutations","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&profileFilter=mutations","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_p … (4348 more chars)

═ answer (success)
**Short answer:** The mutation frequencies differ a lot by cancer type, and the same few types lead for all four genes. I can't say "enriched" in the statistical sense, because I didn't run a significance test.

**Cohort and unit:** TCGA PanCancer Atlas, 32 studies. Counts are samples with a non-synonymous mutation divided by samples profiled for that gene (whole-exome, so the denominators are the same for every gene). Cancer types are the study-level labels, so lung adenocarcinoma and squamous carcinoma are merged as "Non-Small Cell Lung Cancer". Cancer types with fewer than 50 profiled samples are omitted.

**Top cancer types by mutation frequency (altered / profiled)**

| Gene | Highest | Lowest (among types with any hits) |
|---|---|---|
| ATM | Endometrial 101/574 (17.6%), Bladder 55/410 (13.4%), Colorectal 70/534 (13.1%), Melanoma 42/440 (9.5%), Esophagogastric 51/618 (8.3%) | Glioma 0.8%, Leukemia 0.5% |
| BRCA2 | Endometrial 83/574 (14.5%), Melanoma 51/440 (11.6%), Bladder 40/410 (9.8%), Esophagogastric 45/618 (7.3%), Colorectal 38/534 (7.1%) | Thyroid 0.4% |
| BRCA1 | Endometrial 47/574 (8.2%), Melanoma 28/440 (6.4%), Bladder 21/410 (5.1%), Cervical 14/291 (4.8%), NSCLC 41/1050 (3.9%) | Prostate and thyroid 0.2% |
| CHEK2 | Endometrial 36/574 (6.3%), Bladder 10/410 (2.4%), Adrenocortical 2/91 (2.2%), Colorectal 9/534 (1.7%) | Renal clear cell 0.2% |

**Patterns**
- Endometrial cancer ranks first for all four genes. Bladder, melanoma, colorectal and esophagogastric cancers also rank near the top.
- Breast, ovarian, prostate and pancreatic cancers have low somatic mutation rates for these genes: BRCA1 is 2.5% in breast and 3.4% in ovarian, and BRCA2 is 2.7% and 2.9%. They are the cancers best known for germline BRCA-related risk.
- Endometrial, colorectal, melanoma, bladder and esophagogastric tumors often carry very high overall mutation burdens (for example POLE or MSI in endometrial and colorectal). Large genes like ATM and BRCA2 pick up many passenger mutations in those tumors, so the high frequencies may reflect mutation burden rather than pathway-specific selection. This interpretation comes from general knowledge, not from the data I queried. Checking it would mean comparing against a background such as TMB.
- These counts include all non-synonymous mutation types. I didn't separate truncating from missense, and the TCGA data has no driver annotation, so I can't say which mutations are functional.

**Next step for a formal test:** the Group Comparison and Mutual Exclusivity tools on cBioPortal can compare cancer types statistically. You could also compare against a hypermutation-adjusted background.

[View BRCA1/BRCA2/ATM/CHEK2 alteration frequencies by cancer type (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&profileFilter=mutations)

Alteration frequencies for queried genes by cancer type.