Q (sonnet-5.5): what other genes are altered in kras mutant crc or luad ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ▶ read_guide { "uri": "cbioportal://statistical-tests-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) ▶ search_oncotree { "search_term": "colorectal" } ◀ result Statistical Test Selection Guide ================================ Purpose ------- This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults. HARD RULES — NEVER FABRICATE A STATISTIC ---------------------------------------- ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically: 1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."* 2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead. 3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median". 4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do. 5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison. If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number. Ambiguous-Term Clarification (before any analysis) -------------------------------------------------- If the user's question uses an ambiguous term, ask before computing anything. Examples: - **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars) ▶ list_studies { "search": "TCGA PanCancer Atlas colorectal" } ▶ list_studies { "search": "lung adenocarcinoma TCGA" } ◀ result {"result":[{"code":"COADREAD","name":"Colorectal Adenocarcinoma","score":60,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD"},{"code":"CAIS","name":"Colon Adenocarcinoma In Situ","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > CAIS"},{"code":"CMC","name":"Medullary Carcinoma of the Colon","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > CMC"},{"code":"COAD","name":"Colon Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > COAD"},{"code":"MACR","name":"Mucinous Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > MACR"},{"code":"READ","name":"Rectal Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > READ"},{"code":"SRCCR","name":"Signet Ring Cell Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > SRCCR"}]} ◀ result {"result":[{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"coadread","sample_count":594,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018"}]} ◀ result {"result":[{"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_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_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"}]} ▶ clickhouse_run_select_query { "query": "SELECT * FROM co_altered_genes_in_study(study = 'coadread_tcga_pan_can_atlas_2018', gene = 'KRAS', top_n = 15)" } ▶ clickhouse_run_select_query { "query": "SELECT * FROM co_altered_genes_in_study(study = 'luad_tcga_pan_can_atlas_2018', gene = 'KRAS', top_n = 15)" } ▶ resolve_and_route { "studyIds": [ "coadread_tcga_pan_can_atlas_2018", "luad_tcga_pan_can_atlas_2018" ] } ◀ result {"rows":[{"hugo_gene_symbol":"APC","mutant_altered":182,"mutant_profiled":218,"mutant_pct":83.5,"wildtype_altered":205,"wildtype_profiled":316,"wildtype_pct":64.9,"pct_difference":18.6},{"hugo_gene_symbol":"PIK3CA","mutant_altered":83,"mutant_profiled":218,"mutant_pct":38.1,"wildtype_altered":64,"wildtype_profiled":316,"wildtype_pct":20.3,"pct_difference":17.8},{"hugo_gene_symbol":"BRAF","mutant_altered":6,"mutant_profiled":218,"mutant_pct":2.8,"wildtype_altered":56,"wildtype_profiled":316,"wildtype_pct":17.7,"pct_difference":-14.9},{"hugo_gene_symbol":"COL7A1","mutant_altered":7,"mutant_profiled":218,"mutant_pct":3.2,"wildtype_altered":40,"wildtype_profiled":316,"wildtype_pct":12.7,"pct_difference":-9.5},{"hugo_gene_symbol":"PCBP1","mutant_altered":20,"mutant_profiled":218,"mutant_pct":9.2,"wildtype_altered":5,"wildtype_profiled":316,"wildtype_pct":1.6,"pct_difference":7.6},{"hugo_gene_symbol":"ABCA13","mutant_altered":22,"mutant_profiled":218,"mutant_pct":10.1,"wildtype_altered":55,"wildtype_profiled":316,"wildtype_pct":17.4,"pct_difference":-7.3},{"hugo_gene_symbol":"DOCK3","mutant_altered":11,"mutant_profiled":218,"mutant_pct":5,"wildtype_altered":38,"wildtype_profiled":316,"wildtype_pct":12,"pct_difference":-7},{"hugo_gene_symbol":"YLPM1","mutant_altered":6,"mutant_profiled":218,"mutant_pct":2.8,"wildtype_altered":31,"wildtype_profiled":316,"wildtype_pct":9.8,"pct_difference":-7},{"hugo_gene_symbol":"ZNF469","mutant_altered":11,"mutant_profiled":218,"mutant_pct":5,"wildtype_altered":38,"wildtype_profiled":316,"wildtype_pct":12,"pct_difference":-7},{"hugo_gene_symbol":"CHD7","mutant_altered":7,"mutant_profiled":218,"mutant_pct":3.2,"wildtype_altered":32,"wildtype_profiled":316,"wildtype_pct":10.1,"pct_difference":-6.9},{"hugo_gene_symbol":"ARID1A","mutant_altered":15,"mutant_profiled":218,"mutant_pct":6.9,"wildtype_altered":43,"wildtype_profiled":316,"wildtype_pct":13.6,"pct_difference":-6.7},{"hugo_gene_symbol":"CELSR2","mutant_altered":6,"mutant_profiled":218,"mutant_pct":2.8,"wildtype_altered":30,"wildtype_profiled":316,"wildtype_pct":9.5,"pct_difference":-6.7},{"hugo_gene_symbol":"NRAS","mutant_altered":5,"mutant_profiled":218,"mutant_pct":2.3,"wildtype_altered":28,"wildtype_profiled":316,"wildtype_pct":8.9,"pct_difference":-6.6},{"hugo_gene_symbol":"TMPRSS15","mutant_altered":19,"mutant_profiled":218,"mutant_pct":8.7,"wildtype_altered":7,"wildtype_profiled":316,"wildtype_pct":2.2,"pct_difference":6.5},{"hugo_gene_symbol":"DNAH1","mutant_altered":14,"mutant_profiled":218,"mutant_pct":6.4,"wildtype_altered":40,"wildtype_profiled":316,"wildtype_pct":12.7,"pct_difference":-6.3}]} ◀ 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}]} ◀ 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":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594,"studyViewUrl":"https://www.cbioportal.org/study?id=coadread_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":["coadread_tcga_pan_can_atlas_2018_gistic","coadread_tcga_pan_can_atlas_2018_log2CNA","coadread_tcga_pan_can_atlas_2018_mutations","coadread_tcga_pan_can_atlas_2018_protein_quantification","coadread_tcga_pan_can_atlas_2018_protein_quantification_zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","coadread_tcga_pan_can_atlas_2018_rppa","coadread_tcga_pan_can_atlas_2018_rppa_Zscores","coadread_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["coadread_tcga_pan_can_atlas_2018_armlevel_cna","coadread_tcga_pan_can_atlas_2018_genetic_ancestry","coadread_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","coadread_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["coadread_tcga_pan_can_atlas_2018_protein_quantification_zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","coadread_tcga_pan_can_atlas_2018_rp … (2852 more chars) ▶ navigate_to_results_view { "studyIds": [ "coadread_tcga_pan_can_atlas_2018", "luad_tcga_pan_can_atlas_2018" ], "genes": [ "KRAS: MUT" ], "tab": "comparison/alterations", "profileFilter": "mutations" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/comparison/alterations?cancer_study_list=coadread_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT&case_set_id=all&profileFilter=mutations","url":"https://www.cbioportal.org/results/comparison/alterations?cancer_study_list=coadread_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT&case_set_id=all&profileFilter=mutations","data":{"studyIds":["coadread_tcga_pan_can_atlas_2018","luad_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594},{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566}],"genes":["KRAS"],"caseSetId":"all","pageDescription":"Comparison of rate of genomic alterations (mutations, CNA, structural variants) in all genes compared across groups defined by query genes."}} ═ answer (success) I compared mutation rates in KRAS-mutant and KRAS-wild-type tumors in the TCGA PanCancer Atlas colorectal (COADREAD) and lung adenocarcinoma (LUAD) cohorts. Counts are samples profiled for each gene, and mutations only (no copy number or fusions). Genes are ranked by the difference in mutation rate between the two groups. ## Colorectal adenocarcinoma 218 KRAS-mutant and 316 KRAS-wild-type samples. | Gene | KRAS-mutant | KRAS-wild-type | |---|---|---| | APC | 182/218 (83.5%) | 205/316 (64.9%) | | PIK3CA | 83/218 (38.1%) | 64/316 (20.3%) | | BRAF | 6/218 (2.8%) | 56/316 (17.7%) | | NRAS | 5/218 (2.3%) | 28/316 (8.9%) | | ARID1A | 15/218 (6.9%) | 43/316 (13.6%) | | PCBP1 | 20/218 (9.2%) | 5/316 (1.6%) | | TMPRSS15 | 19/218 (8.7%) | 7/316 (2.2%) | Several other large genes (COL7A1, ABCA13, DOCK3, ZNF469, CHD7 and others) are less often mutated in the KRAS-mutant group. Many of these are large, passenger-prone genes. Colorectal tumors with high mutation burden (MSI) are likely to inflate their rates. ## Lung adenocarcinoma 168 KRAS-mutant and 398 KRAS-wild-type samples. | Gene | KRAS-mutant | KRAS-wild-type | |---|---|---| | TP53 | 62/168 (36.9%) | 233/398 (58.5%) | | EGFR | 1/168 (0.6%) | 69/398 (17.3%) | | STK11 | 38/168 (22.6%) | 37/398 (9.3%) | | ATM | 28/168 (16.7%) | 19/398 (4.8%) | | NF1 | 8/168 (4.8%) | 58/398 (14.6%) | | BRAF | 3/168 (1.8%) | 38/398 (9.5%) | Several large genes (MUC16, CSMD1, CNTNAP2, RIMS2, LRP1B, LRRC7, ZFHX4) are somewhat higher in the KRAS-mutant group. These are likely passenger events rather than meaningful associations. ## Caveats - These are descriptive counts only. I can't compute p-values or make mutual-exclusivity or co-occurrence claims from them. The Group Comparison tab in the link below runs Fisher's exact test with FDR correction on these groups. - The link pools both studies into one KRAS-altered vs unaltered comparison. To compare each cancer type separately, open the link with a single study. - The counts above come from TCGA PanCancer Atlas. Other cohorts, such as MSK-IMPACT, will give different numbers. [View KRAS-mutant vs wild-type alteration comparison (CRC + LUAD)](https://www.cbioportal.org/results/comparison/alterations?cancer_study_list=coadread_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT&case_set_id=all&profileFilter=mutations) Study pages: [Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018) · [Lung Adenocarcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018)