Q (sonnet): What are the most frequently altered genes in KRAS wild-type lung adenocarcinoma patients? ▶ 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) ▶ read_guide { "uri": "cbioportal://sample-filtering-guide" } ▶ list_studies { "search": "lung adenocarcinoma" } ◀ result # Sample and Study Filtering Guide ## Overview Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria. ## Study-Level Filtering ### 1. Always Filter by Study Every query should specify a study to ensure consistent results: ```sql -- Always include study filtering SELECT * FROM your_table WHERE cancer_study_identifier = 'your_study_id' -- Additional filters... ``` ### 2. Find Available Studies ```sql -- Discover available studies SELECT cancer_study_identifier, name, description, type_of_cancer_id FROM cancer_study ORDER BY cancer_study_identifier; ``` ### 3. Study Information ```sql -- Get detailed study information SELECT cs.cancer_study_identifier, cs.name as study_name, cs.description, cs.sample_count, COUNT(DISTINCT p.internal_id) as patient_count FROM cancer_study cs LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id WHERE cs.cancer_study_identifier = 'your_study_id' GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count; ``` ### 4. Find Studies by Available Data Types Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter. **Canonical query — studies with ALL requested data types for a cancer type:** ```sql SELECT cancer_study_identifier, name, sample_count, mutation_sample_count, cna_sample_count FROM cancer_study WHERE type_of_cancer_id = 'luad' -- ← OncoTree code from search_oncotree AND mutation_sample_count > 0 -- ← one condition per required data type AND cna_sample_count > 0 ORDER BY sample_count DESC; ``` **Data type → column** (a count of 0 means the study doesn't have that data): | Data type in the user's question | Column (portal "Data type" filter label) | |---|---| | samples in the study | `sample_count` | | mutation / mutations | `mutation_sample_count` ("Mutations") | | copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") | | structural variant / fusion | `structural_variant_sample_count` | | RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" | | specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq | | specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `_mrna` list | | microRNA | `mirna_sample_count` ("miRNA") | | protein / RPPA | `rppa_sample_count` ("RPPA") | | mas … (7866 more chars) ◀ result {"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","type_of_cancer_id":"mixed","sample_count":25775,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_met_2021"},{"cancer_study_identifier":"luad_mskcc_2023_met_organotropism","name":"Lung Adenocarcinoma Met Organotropism (MSK, Cancer Cell 2023)","type_of_cancer_id":"luad","sample_count":2653,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2023_met_organotropism"},{"cancer_study_identifier":"lung_msk_2017","name":"Non-Small Cell Cancer (MSK, Cancer Discov 2017)","type_of_cancer_id":"luad","sample_count":915,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_2017"},{"cancer_study_identifier":"luad_mskcc_2020","name":"Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)","type_of_cancer_id":"luad","sample_count":604,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2020"},{"cancer_study_identifier":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"luad","sample_count":586,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga"},{"cancer_study_identifier":"luad_tcga_gdc","name":"Lung Adenocarcinoma (TCGA GDC, 2025)","type_of_cancer_id":"luad","sample_count":571,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_gdc"},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"luad","sample_count":566,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"luad_msk_npjpo_2021","name":"Lung Adenocarcinoma (MSK, NPJ Precision Oncology 2021)","type_of_cancer_id":"luad","sample_count":426,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_msk_npjpo_2021"},{"cancer_study_identifier":"luad_oncosg_2020","name":"Lung Adenocarcinoma (OncoSG, Nat Genet 2020)","type_of_cancer_id":"luad","sample_count":305,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_oncosg_2020"},{"cancer_study_identifier":"lung_msk_mind_2020","name":"Lung Adenocarcinoma (MSK Mind,Nature Cancer 2022)","type_of_cancer_id":"lung","sample_count":247,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_mind_2020"},{"cancer_study_identifier":"luad_cptac_gdc","name":"Lung Adenocarcinoma (CPTAC GDC, 2025)","type_of_cancer_id":"luad","sample_count":241,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_cptac_gdc"},{"cancer_study_identifier":"luad_tcga_pub","name":"Lung Adenocarcinoma (TCGA, Nature 2014)","type_of_cancer_id":"luad","sample_count":230,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pub"},{"cancer_study_identifier":"luad_mskimpact_2021","name":"Lung Adenocarcinoma (MSK, 2021)","type_of_cancer_id":"luad","sample_count":186,"has_guide":false, … (1174 more chars) ▶ clickhouse_run_select_query { "query": "SELECT * FROM co_altered_genes_in_study(study = 'luad_tcga_pan_can_atlas_2018', gene = 'KRAS', top_n = 20)" } ▶ clickhouse_run_select_query { "query": "SELECT * FROM top_mutated_genes_in_study(study = 'luad_tcga_pan_can_atlas_2018', top_n = 20)" } ◀ result {"rows":[{"hugo_gene_symbol":"TP53","mutant_altered":62,"mutant_profiled":168,"mutant_pct":36.9,"wildtype_altered":233,"wildtype_profiled":398,"wildtype_pct":58.5,"pct_difference":-21.6},{"hugo_gene_symbol":"EGFR","mutant_altered":1,"mutant_profiled":168,"mutant_pct":0.6,"wildtype_altered":69,"wildtype_profiled":398,"wildtype_pct":17.3,"pct_difference":-16.7},{"hugo_gene_symbol":"STK11","mutant_altered":38,"mutant_profiled":168,"mutant_pct":22.6,"wildtype_altered":37,"wildtype_profiled":398,"wildtype_pct":9.3,"pct_difference":13.3},{"hugo_gene_symbol":"ATM","mutant_altered":28,"mutant_profiled":168,"mutant_pct":16.7,"wildtype_altered":19,"wildtype_profiled":398,"wildtype_pct":4.8,"pct_difference":11.9},{"hugo_gene_symbol":"MUC16","mutant_altered":84,"mutant_profiled":168,"mutant_pct":50,"wildtype_altered":158,"wildtype_profiled":398,"wildtype_pct":39.7,"pct_difference":10.3},{"hugo_gene_symbol":"NF1","mutant_altered":8,"mutant_profiled":168,"mutant_pct":4.8,"wildtype_altered":58,"wildtype_profiled":398,"wildtype_pct":14.6,"pct_difference":-9.8},{"hugo_gene_symbol":"CSMD1","mutant_altered":48,"mutant_profiled":168,"mutant_pct":28.6,"wildtype_altered":77,"wildtype_profiled":398,"wildtype_pct":19.3,"pct_difference":9.3},{"hugo_gene_symbol":"CNTNAP2","mutant_altered":32,"mutant_profiled":168,"mutant_pct":19,"wildtype_altered":39,"wildtype_profiled":398,"wildtype_pct":9.8,"pct_difference":9.2},{"hugo_gene_symbol":"RIMS2","mutant_altered":32,"mutant_profiled":168,"mutant_pct":19,"wildtype_altered":40,"wildtype_profiled":398,"wildtype_pct":10.1,"pct_difference":8.9},{"hugo_gene_symbol":"LRP1B","mutant_altered":69,"mutant_profiled":168,"mutant_pct":41.1,"wildtype_altered":132,"wildtype_profiled":398,"wildtype_pct":33.2,"pct_difference":7.9},{"hugo_gene_symbol":"LRRC7","mutant_altered":34,"mutant_profiled":168,"mutant_pct":20.2,"wildtype_altered":49,"wildtype_profiled":398,"wildtype_pct":12.3,"pct_difference":7.9},{"hugo_gene_symbol":"TNR","mutant_altered":19,"mutant_profiled":168,"mutant_pct":11.3,"wildtype_altered":76,"wildtype_profiled":398,"wildtype_pct":19.1,"pct_difference":-7.8},{"hugo_gene_symbol":"BRAF","mutant_altered":3,"mutant_profiled":168,"mutant_pct":1.8,"wildtype_altered":38,"wildtype_profiled":398,"wildtype_pct":9.5,"pct_difference":-7.7},{"hugo_gene_symbol":"RP1L1","mutant_altered":37,"mutant_profiled":168,"mutant_pct":22,"wildtype_altered":60,"wildtype_profiled":398,"wildtype_pct":15.1,"pct_difference":6.9},{"hugo_gene_symbol":"ZFHX4","mutant_altered":63,"mutant_profiled":168,"mutant_pct":37.5,"wildtype_altered":122,"wildtype_profiled":398,"wildtype_pct":30.7,"pct_difference":6.8},{"hugo_gene_symbol":"HECTD4","mutant_altered":18,"mutant_profiled":168,"mutant_pct":10.7,"wildtype_altered":16,"wildtype_profiled":398,"wildtype_pct":4,"pct_difference":6.7},{"hugo_gene_symbol":"KMT2C","mutant_altered":15,"mutant_profiled":168,"mutant_pct":8.9,"wildtype_altered":62,"wildtype_profiled":398,"wildtype_pct":15.6,"pct_difference":-6.7},{"hugo_gene_ … (513 more chars) ◀ result {"rows":[{"hugo_gene_symbol":"TP53","altered_samples":295,"profiled_samples":566,"frequency_pct":52.1,"total_mutation_events":314},{"hugo_gene_symbol":"TTN","altered_samples":272,"profiled_samples":566,"frequency_pct":48.1,"total_mutation_events":679},{"hugo_gene_symbol":"MUC16","altered_samples":242,"profiled_samples":566,"frequency_pct":42.8,"total_mutation_events":424},{"hugo_gene_symbol":"CSMD3","altered_samples":226,"profiled_samples":566,"frequency_pct":39.9,"total_mutation_events":394},{"hugo_gene_symbol":"RYR2","altered_samples":217,"profiled_samples":566,"frequency_pct":38.3,"total_mutation_events":395},{"hugo_gene_symbol":"LRP1B","altered_samples":201,"profiled_samples":566,"frequency_pct":35.5,"total_mutation_events":338},{"hugo_gene_symbol":"ZFHX4","altered_samples":185,"profiled_samples":566,"frequency_pct":32.7,"total_mutation_events":276},{"hugo_gene_symbol":"USH2A","altered_samples":177,"profiled_samples":566,"frequency_pct":31.3,"total_mutation_events":286},{"hugo_gene_symbol":"KRAS","altered_samples":168,"profiled_samples":566,"frequency_pct":29.7,"total_mutation_events":172},{"hugo_gene_symbol":"XIRP2","altered_samples":150,"profiled_samples":566,"frequency_pct":26.5,"total_mutation_events":202},{"hugo_gene_symbol":"SPTA1","altered_samples":148,"profiled_samples":566,"frequency_pct":26.1,"total_mutation_events":198},{"hugo_gene_symbol":"FLG","altered_samples":145,"profiled_samples":566,"frequency_pct":25.6,"total_mutation_events":229},{"hugo_gene_symbol":"CSMD1","altered_samples":125,"profiled_samples":566,"frequency_pct":22.1,"total_mutation_events":164},{"hugo_gene_symbol":"NAV3","altered_samples":123,"profiled_samples":566,"frequency_pct":21.7,"total_mutation_events":155},{"hugo_gene_symbol":"FAT3","altered_samples":121,"profiled_samples":566,"frequency_pct":21.4,"total_mutation_events":171},{"hugo_gene_symbol":"COL11A1","altered_samples":120,"profiled_samples":566,"frequency_pct":21.2,"total_mutation_events":161},{"hugo_gene_symbol":"PCDH15","altered_samples":118,"profiled_samples":566,"frequency_pct":20.8,"total_mutation_events":172},{"hugo_gene_symbol":"ZNF536","altered_samples":117,"profiled_samples":566,"frequency_pct":20.7,"total_mutation_events":157},{"hugo_gene_symbol":"ANK2","altered_samples":111,"profiled_samples":566,"frequency_pct":19.6,"total_mutation_events":139},{"hugo_gene_symbol":"MUC17","altered_samples":111,"profiled_samples":566,"frequency_pct":19.6,"total_mutation_events":152}]} ▶ resolve_and_route { "studyIds": [ "luad_tcga_pan_can_atlas_2018" ] } ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566,"studyViewUrl":"https://www.cbioportal.org/study?id=luad_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":["luad_tcga_pan_can_atlas_2018_gistic","luad_tcga_pan_can_atlas_2018_log2CNA","luad_tcga_pan_can_atlas_2018_mutations","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_tcga_pan_can_atlas_2018_rppa","luad_tcga_pan_can_atlas_2018_rppa_Zscores","luad_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["luad_tcga_pan_can_atlas_2018_armlevel_cna","luad_tcga_pan_can_atlas_2018_genetic_ancestry","luad_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","luad_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_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"]}}]}} ▶ navigate_to_results_view { "studyIds": [ "luad_tcga_pan_can_atlas_2018" ], "genes": [ "KRAS: MUT", "TP53", "MUC16", "LRP1B", "ZFHX4", "EGFR", "CSMD1", "NF1" ], "tab": "comparison/alterations", "comparisonSelectedGroups": [ "Unaltered group", "Altered group" ] } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/comparison/alterations?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%0AMUC16%0ALRP1B%0AZFHX4%0AEGFR%0ACSMD1%0ANF1&case_set_id=luad_tcga_pan_can_atlas_2018_all&comparison_selectedGroups=%5B%22Unaltered+group%22%2C%22Altered+group%22%5D","url":"https://www.cbioportal.org/results/comparison/alterations?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%0AMUC16%0ALRP1B%0AZFHX4%0AEGFR%0ACSMD1%0ANF1&case_set_id=luad_tcga_pan_can_atlas_2018_all&comparison_selectedGroups=%5B%22Unaltered+group%22%2C%22Altered+group%22%5D","data":{"studyIds":["luad_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566}],"genes":["KRAS","TP53","MUC16","LRP1B","ZFHX4","EGFR","CSMD1","NF1"],"caseSetId":"luad_tcga_pan_can_atlas_2018_all","pageDescription":"Genomic alteration rates compared across Unaltered group-altered vs Altered group-altered."}} ═ answer (success) [Explore KRAS-mutant vs. KRAS-wild-type alteration rates in TCGA LUAD](https://www.cbioportal.org/results/comparison/alterations?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%0AMUC16%0ALRP1B%0AZFHX4%0AEGFR%0ACSMD1%0ANF1&case_set_id=luad_tcga_pan_can_atlas_2018_all&comparison_selectedGroups=%5B%22Unaltered+group%22%2C%22Altered+group%22%5D) This link opens the Group Comparison "Alterations" tab pre-loaded with KRAS and the top co-mutated genes from the analysis above, letting you toggle between the KRAS-mutant and KRAS-wild-type groups and run the portal's own enrichment statistics (Fisher's exact test) interactively — I have not computed significance myself, only raw frequencies as shown in the table. If you'd like, I can also run this same comparison in a different lung adenocarcinoma cohort (e.g., an MSK-IMPACT clinical cohort) or extend it to copy-number/structural-variant alterations rather than mutations only — just let me know.