Q (sonnet): Which cancer types show the highest frequency of BRAF V600E mutations across all TCGA Pan-Cancer Atlas studies? ▶ 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": " WITH cohort AS ( SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ), sample_cancer_type AS ( SELECT cd.sample_unique_id, cd.attribute_value AS cancer_type FROM clinical_data_derived cd JOIN cohort c USING (cancer_study_identifier) WHERE cd.attribute_name = 'CANCER_TYPE' ), altered AS ( SELECT sct.cancer_type, COUNT(DISTINCT ged.sample_unique_id) AS altered_samples FROM genomic_event_derived ged JOIN cohort c USING (cancer_study_identifier) JOIN sample_cancer_type sct USING (sample_unique_id) WHERE ged.variant_type = 'mutation' AND ged.mutation_status != 'UNCALLED' AND ged.hugo_gene_symbol = 'BRAF' AND ged.mutation_variant = 'V600E' AND ged.off_panel = 0 GROUP BY sct.cancer_type ), profiled_samples_for_gene AS ( SELECT sample_unique_id, cancer_study_identifier FROM mutation_panel_gene_coverage WHERE hugo_gene_symbol = 'BRAF' UNION ALL SELECT sample_unique_id, cancer_study_identifier FROM mutation_wes_coverage ), profiled AS ( SELECT sct.cancer_type, COUNT(DISTINCT p.sample_unique_id) AS profiled_samples FROM profiled_samples_for_gene p JOIN cohort c USING (cancer_study_identifier) JOIN sample_cancer_type sct USING (sample_unique_id) GROUP BY sct.cancer_type ) SELECT a.cancer_type, a.altered_samples, p.profiled_samples, ROUND(a.altered_samples * 100.0 / NULLIF(p.profiled_samples, 0), 1) AS frequency_pct FROM altered a JOIN profiled p USING (cancer_type) WHERE p.profiled_samples >= 50 ORDER BY frequency_pct DESC; ", "max_rows": 50 } ◀ result {"rows":[{"cancer_type":"Thyroid Cancer","altered_samples":284,"profiled_samples":490,"frequency_pct":58},{"cancer_type":"Melanoma","altered_samples":158,"profiled_samples":440,"frequency_pct":35.9},{"cancer_type":"Colorectal Cancer","altered_samples":48,"profiled_samples":534,"frequency_pct":9},{"cancer_type":"Glioblastoma","altered_samples":5,"profiled_samples":397,"frequency_pct":1.3},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":9,"profiled_samples":1050,"frequency_pct":0.9},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":2,"profiled_samples":341,"frequency_pct":0.6},{"cancer_type":"Bladder Cancer","altered_samples":1,"profiled_samples":410,"frequency_pct":0.2},{"cancer_type":"Head and Neck Cancer","altered_samples":1,"profiled_samples":515,"frequency_pct":0.2},{"cancer_type":"Glioma","altered_samples":1,"profiled_samples":514,"frequency_pct":0.2}]} ▶ 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/f4c17c0d-1b1f-4a0f-9512-0b4f51aacd71/tool-results/mcp-navigator-resolve_and_route-1790300179865.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/f4c17c0d-1b1f-4a0f-9512-0b4f51aacd71/tool-results/mcp-navigator-resolve_and_route-1790300179865.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/f4c17c0d-1b1f-4a0f-9512-0b4f51aacd71/tool-results/mcp-navigator-resolve_and_route-1790300179865.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. ▶ resolve_and_route { "studyIds": [ "thca_tcga_pan_can_atlas_2018", "skcm_tcga_pan_can_atlas_2018" ] } ◀ result {"success":true,"message":"Found 2 matching studies. 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":2,"studiesWithMetadata":[{"studyId":"thca_tcga_pan_can_atlas_2018","name":"Thyroid Carcinoma (TCGA, PanCancer Atlas)","sampleCount":500,"studyViewUrl":"https://www.cbioportal.org/study?id=thca_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","RAGNUM_HYPOXIA_SCORE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT","WINTER_HYPOXIA_SCORE"],"molecularProfileIds":["thca_tcga_pan_can_atlas_2018_gistic","thca_tcga_pan_can_atlas_2018_log2CNA","thca_tcga_pan_can_atlas_2018_mutations","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","thca_tcga_pan_can_atlas_2018_rppa","thca_tcga_pan_can_atlas_2018_rppa_Zscores","thca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["thca_tcga_pan_can_atlas_2018_armlevel_cna","thca_tcga_pan_can_atlas_2018_genetic_ancestry","thca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","thca_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","thca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","thca_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}},{"studyId":"skcm_tcga_pan_can_atlas_2018","name":"Skin Cutaneous Melan … (2410 more chars) ▶ navigate_to_results_view { "studyIds": [ "thca_tcga_pan_can_atlas_2018" ], "genes": [ "BRAF: V600E" ], "tab": "mutations" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/mutations?cancer_study_list=thca_tcga_pan_can_atlas_2018&gene_list=BRAF%3A+V600E&case_set_id=thca_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/mutations?cancer_study_list=thca_tcga_pan_can_atlas_2018&gene_list=BRAF%3A+V600E&case_set_id=thca_tcga_pan_can_atlas_2018_all","data":{"studyIds":["thca_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"thca_tcga_pan_can_atlas_2018","name":"Thyroid Carcinoma (TCGA, PanCancer Atlas)","sampleCount":500}],"genes":["BRAF"],"caseSetId":"thca_tcga_pan_can_atlas_2018_all","pageDescription":"Detailed mutation table with lollipop plot showing mutation positions, types, and frequencies for queried genes."}} ▶ navigate_to_results_view { "studyIds": [ "skcm_tcga_pan_can_atlas_2018" ], "genes": [ "BRAF: V600E" ], "tab": "mutations" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/mutations?cancer_study_list=skcm_tcga_pan_can_atlas_2018&gene_list=BRAF%3A+V600E&case_set_id=skcm_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/mutations?cancer_study_list=skcm_tcga_pan_can_atlas_2018&gene_list=BRAF%3A+V600E&case_set_id=skcm_tcga_pan_can_atlas_2018_all","data":{"studyIds":["skcm_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"skcm_tcga_pan_can_atlas_2018","name":"Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)","sampleCount":448}],"genes":["BRAF"],"caseSetId":"skcm_tcga_pan_can_atlas_2018_all","pageDescription":"Detailed mutation table with lollipop plot showing mutation positions, types, and frequencies for queried genes."}} ═ answer (success) ## BRAF V600E Frequency Across TCGA PanCancer Atlas Cancer Types Using the 32-study TCGA PanCancer Atlas cohort (sample-level frequency, restricted to cancer types with ≥50 samples profiled for BRAF), BRAF V600E specifically (not all BRAF mutations) ranks as follows: | Cancer Type | Altered Samples | Profiled Samples | Frequency | |---|---|---|---| | **Thyroid Cancer** | 284 | 490 | **58.0%** | | **Melanoma** (Skin Cutaneous) | 158 | 440 | **35.9%** | | Colorectal Cancer | 48 | 534 | 9.0% | | Glioblastoma | 5 | 397 | 1.3% | | Non-Small Cell Lung Cancer | 9 | 1,050 | 0.9% | | Renal Non-Clear Cell Carcinoma | 2 | 341 | 0.6% | | Bladder Cancer | 1 | 410 | 0.2% | | Head and Neck Cancer | 1 | 515 | 0.2% | | Glioma (low-grade) | 1 | 514 | 0.2% | **Thyroid carcinoma** shows the highest V600E frequency by a wide margin (58.0% of profiled samples), followed by **melanoma** (35.9%). All other TCGA cancer types show V600E in fewer than 10% of samples. Note colorectal cancer here pools MSI/hypermutated and non-hypermutated tumors together (TCGA's `coadread` grouping). Direct links to the mutation lollipop plots (position/frequency detail) for the top two cancer types: - [Thyroid Carcinoma — BRAF V600E Mutations](https://www.cbioportal.org/results/mutations?cancer_study_list=thca_tcga_pan_can_atlas_2018&gene_list=BRAF%3A+V600E&case_set_id=thca_tcga_pan_can_atlas_2018_all) - [Skin Cutaneous Melanoma — BRAF V600E Mutations](https://www.cbioportal.org/results/mutations?cancer_study_list=skcm_tcga_pan_can_atlas_2018&gene_list=BRAF%3A+V600E&case_set_id=skcm_tcga_pan_can_atlas_2018_all)