Q (sonnet): What percentage of endometrial cancer patients have co-occurring oncogenic mutations in both KRAS and NRAS? ▶ list_guides {} ◀ result {"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]} ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ◀ 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) ▶ read_guide { "uri": "cbioportal://study-resolution-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) ◀ result # Study Resolution Guide Use this guide when the user names a study, cohort, portal, or data source that may not exist in the connected cBioPortal deployment. ## Routing Triggers Read this guide when the user mentions: - PBTA, Pediatric Brain Tumor Atlas, pediatric cBioPortal, Kids First - GENIE, AACR GENIE, MSK private cohorts, institutional cohorts - "download study", "which study", "find cohort", "data from [portal]" - a named cohort that `list_studies(search=...)` does not find ## Core Rules 1. Resolve the requested study before substituting another study. 2. If the requested study is not in this deployment, say so explicitly. 3. Do not silently analyze a substitute cohort. 4. If the user agrees to a substitute, keep a one-line scope caveat when reporting numbers. ## Known External cBioPortal Instances These are not necessarily queryable from this MCP server, but they are useful redirects: | User wording | Likely external instance | Scope | |---|---|---| | PBTA, Pediatric Brain Tumor Atlas, pediatric brain tumors | https://pedcbioportal.kidsfirstdrc.org/ | Pediatric cancer studies, including pediatric brain tumor cohorts | | GENIE | https://genie.cbioportal.org/ | AACR GENIE data access, depending on release and permissions | | MSK private / institutional cohorts | private institutional cBioPortal deployments | Not queryable from public cBioPortal unless exported to the public database | ## Study Resolution Workflow 1. Call `list_studies(search=...)` with the user's exact study/cohort phrase and close variants. 2. If a cancer type is mentioned, call `search_oncotree(search_term)` before disease-level study discovery. 3. If no matching study is found, check known external instances above before declaring the study absent. 4. If proposing a substitute, describe why it is a substitute and how its scope differs. ## Substitute-Cohort Answer Pattern > I cannot query PBTA from this cBioPortal deployment. PBTA is typically accessed through pediatric cBioPortal at https://pedcbioportal.kidsfirstdrc.org/. I can analyze `[substitute_study_id]` here, but its results should not be interpreted as PBTA results. When reporting numbers from a substitute: > Scope note: these counts are from `[substitute_study_id]` in this deployment, not from the requested PBTA cohort. ## Do Not - Do not answer a PBTA question with `brain_cptac_2020` numbers without a scope warning. - Do not let later turns drop the substitute-cohort warning. - Do not claim a study does not exist globally; say it is not available in the connected deployment. ▶ clickhouse_list_table_columns { "table": "genomic_event_derived" } ▶ search_oncotree { "search_term": "endometrial" } ▶ list_studies { "search": "endometrial" } ◀ result {"columns":[{"name":"sample_unique_id","type":"String"},{"name":"hugo_gene_symbol","type":"String","comment":"HUGO gene symbol (e.g., TP53, KRAS, BRAF). Use for gene-specific queries."},{"name":"entrez_gene_id","type":"Int32"},{"name":"gene_panel_stable_id","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"genetic_profile_stable_id","type":"LowCardinality(String)"},{"name":"variant_type","type":"LowCardinality(String)","comment":"Type of genomic event: \"mutation\" for SNVs/indels, \"cna\" for copy number alterations, \"structural_variant\" for SVs. Always filter by this."},{"name":"mutation_variant","type":"String","comment":"Protein change notation (e.g., p.V600E, p.R175H). Use for specific variant queries. \"NA\" for non-mutation events."},{"name":"mutation_type","type":"LowCardinality(String)"},{"name":"mutation_status","type":"LowCardinality(String)","comment":"For mutations: Somatic, Germline, UNKNOWN, or UNCALLED. Filter mutation_status != \"UNCALLED\" to exclude uncertain calls. Include all other statuses."},{"name":"driver_filter","type":"LowCardinality(String)"},{"name":"driver_filter_annotation","type":"String"},{"name":"driver_tiers_filter","type":"LowCardinality(String)"},{"name":"driver_tiers_filter_annotation","type":"String"},{"name":"cna_alteration","type":"Nullable(Int8)","comment":"Copy number alteration: only 2 = amplification (AMP) and -2 = deep deletion (HOMDEL) are stored. Shallow deletion (-1), diploid (0) and gain (1) are not in this table; query genetic_alteration_derived WHERE profile_type = 'gistic' (alteration_value is a String, e.g. '-1'). NULL for non-CNA events."},{"name":"cna_cytoband","type":"String"},{"name":"sv_event_info","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"off_panel","type":"Bool","comment":"Boolean: 1 = mutation outside gene panel coverage (off-panel), 0 = within panel (on-panel). Filter off_panel = 0 for reliable frequency calculations."}]} ◀ result {"result":[{"code":"ESS","name":"Endometrial Stromal Sarcoma","score":60,"mainType":"Uterine Sarcoma","tissue":"Uterus","hierarchy":"UTERUS > USARC > ESS"},{"code":"UCEC","name":"Endometrial Carcinoma","score":60,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC"},{"code":"HGESS","name":"High-Grade Endometrial Stromal Sarcoma","score":50,"mainType":"Uterine Sarcoma","tissue":"Uterus","hierarchy":"UTERUS > USARC > ESS > HGESS"},{"code":"LGESS","name":"Low-Grade Endometrial Stromal Sarcoma","score":50,"mainType":"Uterine Sarcoma","tissue":"Uterus","hierarchy":"UTERUS > USARC > ESS > LGESS"},{"code":"UMEC","name":"Uterine Mixed Endometrial Carcinoma","score":50,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMEC"},{"code":"OUTT","name":"Other Uterine Tumor","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > OUTT"},{"code":"UASC","name":"Uterine Adenosquamous Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UASC"},{"code":"UCCC","name":"Uterine Clear Cell Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UCCC"},{"code":"UCS","name":"Uterine Carcinosarcoma/Uterine Malignant Mixed Mullerian Tumor","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UCS"},{"code":"UDDC","name":"Uterine Dedifferentiated Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UDDC"},{"code":"UEC","name":"Uterine Endometrioid Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UEC"},{"code":"UMC","name":"Uterine Mucinous Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMC"},{"code":"UMNC","name":"Uterine Mesonephric Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UMNC"},{"code":"UNEC","name":"Uterine Neuroendocrine Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UNEC"},{"code":"UPDC","name":"Poorly Differentiated Carcinoma of the Uterus","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UPDC"},{"code":"USC","name":"Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > USC"},{"code":"UUC","name":"Uterine Undifferentiated Carcinoma","score":45,"mainType":"Endometrial Cancer","tissue":"Uterus","hierarchy":"UTERUS > UCEC > UUC"}]} ◀ result {"result":[{"cancer_study_identifier":"ucec_ancestry_cds_msk_2023","name":"Endometrial Cancer (MSK, Cancer Discovery 2023)","type_of_cancer_id":"ucec","sample_count":1882,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_ancestry_cds_msk_2023"},{"cancer_study_identifier":"ucec_tcga","name":"Uterine Corpus Endometrial Carcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"ucec","sample_count":549,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga"},{"cancer_study_identifier":"ucec_tcga_gdc","name":"Endometrial Carcinoma (TCGA GDC, 2025)","type_of_cancer_id":"ucec","sample_count":547,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_gdc"},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"ucec","sample_count":529,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucec_tcga_pub","name":"Uterine Corpus Endometrial Carcinoma (TCGA, Nature 2013)","type_of_cancer_id":"ucec","sample_count":373,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_pub"},{"cancer_study_identifier":"ucec_msk_2018","name":"Endometrial Cancer (MSK, 2018)","type_of_cancer_id":"ucec","sample_count":197,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_msk_2018"},{"cancer_study_identifier":"ucec_ccr_msk_2022","name":"Endometrial Carcinoma MSI (MSK, Clin Cancer Res 2022)","type_of_cancer_id":"ucec","sample_count":181,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_ccr_msk_2022"},{"cancer_study_identifier":"ucec_cptac_2020","name":"Endometrial Carcinoma (CPTAC, Cell 2020)","type_of_cancer_id":"ucec","sample_count":81,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_cptac_2020"},{"cancer_study_identifier":"ucs_msk_2024","name":"Endometrial Carcinoma (MSK, Molecular Oncology 2024)","type_of_cancer_id":"ucec","sample_count":69,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucs_msk_2024"},{"cancer_study_identifier":"ucec_ccr_cfdna_msk_2022","name":"Endometrial Carcinoma cfDNA (MSK, Clin Cancer Res 2022)","type_of_cancer_id":"ucec","sample_count":44,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_ccr_cfdna_msk_2022"},{"cancer_study_identifier":"ucec_msk_2024","name":"Endometrial and Ovarian Cancer (MSK, Nature Medicine 2024)","type_of_cancer_id":"mixed","sample_count":33,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ucec_msk_2024"},{"cancer_study_identifier":"uec_msk_2024","name":"Endometrial cancer HR-positive (MSK, 2024)","type_of_cancer_id":"ucec","sample_count":24,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=uec_msk_2024"},{"cancer_study_identifier":"uccc_nih_2017","name":"Uterine Clear Cell Carcinoma (NIH, Cancer 2017)","type_of_cancer_id … (447 more chars) ▶ clickhouse_run_select_query { "query": "SELECT driver_filter, count() FROM genomic_event_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND variant_type='mutation' AND hugo_gene_symbol IN ('KRAS','NRAS') GROUP BY driver_filter" } ◀ result {"rows":[{"count()":123}]} ▶ clickhouse_run_select_query { "query": "SELECT driver_filter, driver_filter_annotation, driver_tiers_filter, driver_tiers_filter_annotation, count() FROM genomic_event_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND variant_type='mutation' AND hugo_gene_symbol IN ('KRAS','NRAS') GROUP BY driver_filter, driver_filter_annotation, driver_tiers_filter, driver_tiers_filter_annotation" } ◀ result {"rows":[{"count()":123}]} ▶ clickhouse_run_select_query { "query": " WITH profiled AS ( SELECT DISTINCT patient_unique_id FROM sample_to_gene_panel_derived stgp JOIN clinical_data_derived cd USING (sample_unique_id) WHERE stgp.cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND stgp.alteration_type = 'MUTATION_EXTENDED' ), kras_pts AS ( SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND hugo_gene_symbol = 'KRAS' AND off_panel = 0 ), nras_pts AS ( SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier = 'ucec_tcga_pan_can_atlas_2018' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND hugo_gene_symbol = 'NRAS' AND off_panel = 0 ) SELECT (SELECT count() FROM profiled) AS total_profiled_patients, (SELECT count() FROM kras_pts) AS kras_mutant_patients, (SELECT count() FROM nras_pts) AS nras_mutant_patients, (SELECT count() FROM kras_pts WHERE patient_unique_id IN (SELECT patient_unique_id FROM nras_pts)) AS both_mutant_patients " } ◀ result {"rows":[{"total_profiled_patients":517,"kras_mutant_patients":99,"nras_mutant_patients":21,"both_mutant_patients":4}]} ▶ resolve_and_route { "studyIds": [ "ucec_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":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529,"studyViewUrl":"https://www.cbioportal.org/study?id=ucec_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":["ucec_tcga_pan_can_atlas_2018_gistic","ucec_tcga_pan_can_atlas_2018_log2CNA","ucec_tcga_pan_can_atlas_2018_mutations","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_tcga_pan_can_atlas_2018_rppa","ucec_tcga_pan_can_atlas_2018_rppa_Zscores","ucec_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["ucec_tcga_pan_can_atlas_2018_armlevel_cna","ucec_tcga_pan_can_atlas_2018_genetic_ancestry","ucec_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","ucec_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_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": [ "ucec_tcga_pan_can_atlas_2018" ], "genes": [ "KRAS: MUT", "NRAS: MUT" ], "tab": "oncoprint" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ANRAS%3A+MUT&case_set_id=ucec_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ANRAS%3A+MUT&case_set_id=ucec_tcga_pan_can_atlas_2018_all","data":{"studyIds":["ucec_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529}],"genes":["KRAS","NRAS"],"caseSetId":"ucec_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}} ═ answer (success) [View KRAS/NRAS OncoPrint — Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ANRAS%3A+MUT&case_set_id=ucec_tcga_pan_can_atlas_2018_all) Visual summary of alterations in queried genes across samples (529 samples). For the mutual exclusivity / co-occurrence test itself, open the **Mutual Exclusivity** tab from that ResultsView page — it will run Fisher's exact test on KRAS vs. NRAS directly, using the same underlying mutation calls. **Summary of limitations:** - **"Oncogenic" could not be honored** — no OncoKB/driver annotation is loaded for this study in this deployment, so the 0.8% figure reflects all somatic KRAS+NRAS mutations, not oncogenic-only. - **No significance test was run** — the counts above are descriptive; use the Mutual Exclusivity tab or Fisher's exact test externally to assess whether 4/517 co-occurrence is more or less than expected by chance.