Q (sonnet): What are the most commonly co-occurring mutation pairs in breast cancer samples from the MSK-CHORD Study? ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ▶ read_guide { "uri": "cbioportal://sample-filtering-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) ◀ 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 … (7083 more chars) ▶ get_study_guide { "study_id": "msk_chord_2024" } ▶ search_oncotree { "search_term": "breast cancer" } ◀ result # MSK-CHORD (MSK, Nature 2024) **Study ID:** `msk_chord_2024` ## Overview Targeted sequencing via MSK-IMPACT panels. Clinical annotations include some derived from natural language processing (denoted NLP). **Exactly five cancer types** (`CANCER_TYPE`, patients): Non-Small Cell Lung Cancer 7,809, Colorectal Cancer 5,543, Breast Cancer 5,368, Prostate Cancer 3,211, Pancreatic Cancer 3,109. There is **no melanoma** or any other cancer type; say so up front if asked, instead of substituting another type. **No therapy-response variable.** There is no RECIST, objective response, or best-response attribute or event. For treatment-outcome questions (e.g. immunotherapy response), say this first; the only proxies are `OS_MONTHS`/`OS_STATUS`, or NLP radiology progression events (`Diagnosis` events with `SUBTYPE = 'Progression'`, key `PROGRESSION` = Y/N/Indeterminate), in patients with `Treatment` events of the relevant `SUBTYPE` (e.g. `Immuno`: 3,341 patients). Hand off the comparison to cBioPortal group comparison / survival. **Nearly one sample per patient: 24,950 patients / 25,040 samples.** Only 90 patients have more than one sample, and all 90 have samples from two different cancer types (second primaries); only 26 have both a `Primary` and a `Metastasis` sample. There is no meaningful same-patient (paired) primary-vs-metastasis cohort. For "same patient" / paired questions, say this up front, then offer the **unpaired** comparison of all `Primary` vs `Metastasis` samples (`SAMPLE_TYPE`), labelled as unpaired. ```sql SELECT countIf(n > 1) AS multi_sample_patients, -- 90 countIf(has_p AND has_m) AS primary_and_met -- 26 FROM (SELECT patient_unique_id, count() AS n, has(groupArray(attribute_value), 'Primary') AS has_p, has(groupArray(attribute_value), 'Metastasis') AS has_m FROM clinical_data_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'SAMPLE_TYPE' GROUP BY patient_unique_id); ``` ## Gene Panels This study uses multiple MSK-IMPACT panel versions: - **IMPACT341**: Earlier version, 341 genes - **IMPACT410**: 410 genes - **IMPACT468**: 468 genes - **IMPACT505**: Latest version, 505 genes **Important:** Different samples may have different gene coverage. Always use gene-specific denominators when calculating mutation frequencies. ## Clinical Attributes - Semantic Guide ### Cancer Classification | Attribute | Description | Values | |-----------|-------------|--------| | `CANCER_TYPE` | Broad cancer category | e.g., "Non-Small Cell Lung Cancer", "Breast Cancer" | | `CANCER_TYPE_DETAILED` | Specific subtype | e.g., "Lung Adenocarcinoma", "Invasive Ductal Carcinoma" | | `ONCOTREE_CODE` | OncoTree classification code | Standardized cancer type codes | ### Sample Information | Attribute | Description | Values | |-----------|-------------|--------| | `SAMPLE_TYPE` | Sample origin | Primary, Metastasis, Local Recurrence, Unknown | | `SAMPLE_CLASS` | Sample … (5439 more chars) ◀ result {"result":[{"code":"ACBC","name":"Adenoid Cystic Breast Cancer","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > ACBC"},{"code":"BNNOS","name":"Breast Neoplasm, NOS","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BNNOS"},{"code":"BRAME","name":"Adenomyoepithelioma of the Breast","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRAME"},{"code":"BRCA","name":"Invasive Breast Carcinoma","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA"},{"code":"BRCANOS","name":"Breast Invasive Cancer, NOS","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > BRCANOS"},{"code":"BRCNOS","name":"Breast Invasive Carcinoma, NOS","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > BRCNOS"},{"code":"BREAST","name":"Breast","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST"},{"code":"BRSRCC","name":"Breast Carcinoma with Signet Ring","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > BRSRCC"},{"code":"CCHM","name":"Carcinoma with Chondroid Metaplasia","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > MBC > MMBC > CCHM"},{"code":"COM","name":"Carcinoma with Osseous Metaplasia","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > MBC > MMBC > COM"},{"code":"CSNOS","name":"Breast Invasive Carcinosarcoma, NOS","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > CSNOS"},{"code":"DCIS","name":"Breast Ductal Carcinoma In Situ","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > DCIS"},{"code":"EMBC","name":"Epithelial Type Metaplastic Breast Cancer","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > MBC > EMBC"},{"code":"IBC","name":"Inflammatory Breast Cancer","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > IBC"},{"code":"IDC","name":"Breast Invasive Ductal Carcinoma","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > IDC"},{"code":"ILC","name":"Breast Invasive Lobular Carcinoma","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > ILC"},{"code":"IMMC","name":"Breast Invasive Mixed Mucinous Carcinoma","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > IMMC"},{"code":"JSCB","name":"Juvenile Secretory Carcinoma of the Breast","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > JSCB"},{"code":"LCIS","name":"Breast Lobular Carcinoma In Situ","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > LCIS"},{"code":"MASC","name":"Metaplastic Adenosquamous Carcinoma","score":65,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > MBC > EMBC > MASC"},{"code":"MASCC","name":"Metaplastic Adenocarcinoma with Spindle … (712 more chars) ▶ clickhouse_list_table_columns { "table": "genomic_event_derived" } ◀ 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."}]} ▶ clickhouse_run_select_query { "query": " WITH breast_samples AS ( SELECT DISTINCT sample_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'CANCER_TYPE' AND attribute_value = 'Breast Cancer' ) SELECT hugo_gene_symbol, COUNT(DISTINCT g.sample_unique_id) AS mutated_samples FROM genomic_event_derived g INNER JOIN breast_samples b ON g.sample_unique_id = b.sample_unique_id WHERE g.cancer_study_identifier = 'msk_chord_2024' AND g.variant_type = 'mutation' AND g.mutation_status != 'UNCALLED' AND g.off_panel = 0 GROUP BY hugo_gene_symbol ORDER BY mutated_samples DESC LIMIT 15 " } ◀ result {"rows":[{"hugo_gene_symbol":"TP53","mutated_samples":2138},{"hugo_gene_symbol":"PIK3CA","mutated_samples":1908},{"hugo_gene_symbol":"GATA3","mutated_samples":796},{"hugo_gene_symbol":"CDH1","mutated_samples":699},{"hugo_gene_symbol":"KMT2C","mutated_samples":432},{"hugo_gene_symbol":"MAP3K1","mutated_samples":405},{"hugo_gene_symbol":"PTEN","mutated_samples":339},{"hugo_gene_symbol":"ESR1","mutated_samples":323},{"hugo_gene_symbol":"ARID1A","mutated_samples":310},{"hugo_gene_symbol":"TBX3","mutated_samples":275},{"hugo_gene_symbol":"AKT1","mutated_samples":254},{"hugo_gene_symbol":"CBFB","mutated_samples":248},{"hugo_gene_symbol":"FOXA1","mutated_samples":231},{"hugo_gene_symbol":"NF1","mutated_samples":227},{"hugo_gene_symbol":"MAP2K4","mutated_samples":193}]} ▶ clickhouse_run_select_query { "query": " SELECT COUNT(DISTINCT sample_unique_id) AS total_breast_samples FROM clinical_data_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'CANCER_TYPE' AND attribute_value = 'Breast Cancer' " } ◀ result {"rows":[{"total_breast_samples":5368}]} ▶ clickhouse_run_select_query { "query": " WITH breast_samples AS ( SELECT DISTINCT sample_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'CANCER_TYPE' AND attribute_value = 'Breast Cancer' ), top_genes AS ( SELECT arrayJoin(['TP53','PIK3CA','GATA3','CDH1','KMT2C','MAP3K1','PTEN','ESR1','ARID1A','TBX3','AKT1','CBFB','FOXA1','NF1','MAP2K4']) AS hugo_gene_symbol ), muts AS ( SELECT DISTINCT g.sample_unique_id, g.hugo_gene_symbol FROM genomic_event_derived g INNER JOIN breast_samples b ON g.sample_unique_id = b.sample_unique_id WHERE g.cancer_study_identifier = 'msk_chord_2024' AND g.variant_type = 'mutation' AND g.mutation_status != 'UNCALLED' AND g.off_panel = 0 AND g.hugo_gene_symbol IN (SELECT hugo_gene_symbol FROM top_genes) ) SELECT a.hugo_gene_symbol AS gene_a, b.hugo_gene_symbol AS gene_b, COUNT(DISTINCT a.sample_unique_id) AS co_occurring_samples FROM muts a INNER JOIN muts b ON a.sample_unique_id = b.sample_unique_id AND a.hugo_gene_symbol < b.hugo_gene_symbol GROUP BY gene_a, gene_b ORDER BY co_occurring_samples DESC LIMIT 20 " } ◀ result {"rows":[{"gene_a":"PIK3CA","gene_b":"TP53","co_occurring_samples":597},{"gene_a":"CDH1","gene_b":"PIK3CA","co_occurring_samples":346},{"gene_a":"MAP3K1","gene_b":"PIK3CA","co_occurring_samples":256},{"gene_a":"GATA3","gene_b":"PIK3CA","co_occurring_samples":230},{"gene_a":"KMT2C","gene_b":"PIK3CA","co_occurring_samples":218},{"gene_a":"PTEN","gene_b":"TP53","co_occurring_samples":163},{"gene_a":"KMT2C","gene_b":"TP53","co_occurring_samples":148},{"gene_a":"PIK3CA","gene_b":"TBX3","co_occurring_samples":138},{"gene_a":"NF1","gene_b":"TP53","co_occurring_samples":135},{"gene_a":"ESR1","gene_b":"PIK3CA","co_occurring_samples":129},{"gene_a":"CBFB","gene_b":"PIK3CA","co_occurring_samples":129},{"gene_a":"CDH1","gene_b":"TP53","co_occurring_samples":126},{"gene_a":"ARID1A","gene_b":"PIK3CA","co_occurring_samples":126},{"gene_a":"CDH1","gene_b":"TBX3","co_occurring_samples":124},{"gene_a":"FOXA1","gene_b":"PIK3CA","co_occurring_samples":121},{"gene_a":"GATA3","gene_b":"TP53","co_occurring_samples":111},{"gene_a":"NF1","gene_b":"PIK3CA","co_occurring_samples":110},{"gene_a":"MAP3K1","gene_b":"TP53","co_occurring_samples":105},{"gene_a":"PIK3CA","gene_b":"PTEN","co_occurring_samples":100},{"gene_a":"CBFB","gene_b":"GATA3","co_occurring_samples":93}]} ▶ resolve_and_route { "studyIds": [ "msk_chord_2024" ] } ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024","metadata":{"clinicalAttributeIds":["ADRENAL_GLANDS","BONE","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_GROUP","CLINICAL_SUMMARY","CNS_BRAIN","CURRENT_AGE_DEID","DIAGNOSIS_DESCRIPTION","ETHNICITY","FRACTION_GENOME_ALTERED","GENDER","GENE_PANEL","GLEASON_FIRST_REPORTED","GLEASON_HIGHEST_REPORTED","GLEASON_SAMPLE_LEVEL","HER2","HISTORY_OF_PDL1","HR","ICD_O_HISTOLOGY_DESCRIPTION","INTRA_ABDOMINAL","LIVER","LUNG","LYMPH_NODES","METASTATIC_SITE","MSI_COMMENT","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","NUM_ICDO_DX","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER","PATHOLOGICAL_GROUP","PDL1_POSITIVE","PLEURA","PRIMARY_SITE","PRIOR_MED_TO_MSK","RACE","REPRODUCTIVE_ORGANS","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SMOKING_PREDICTIONS_3_CLASSES","SOMATIC_STATUS","STAGE_HIGHEST_RECORDED","TMB_NONSYNONYMOUS","TUMOR_PURITY"],"molecularProfileIds":["msk_chord_2024_cna","msk_chord_2024_mutations","msk_chord_2024_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}}]}} ▶ navigate_to_results_view { "studyIds": [ "msk_chord_2024" ], "genes": [ "TP53", "PIK3CA", "GATA3", "CDH1", "KMT2C", "MAP3K1", "PTEN", "ESR1", "ARID1A", "TBX3", "AKT1", "CBFB", "FOXA1", "NF1", "MAP2K4" ], "tab": "mutualExclusivity", "studyViewFilter": { "clinicalDataFilters": [ { "attributeId": "CANCER_TYPE", "values": [ { "value": "Breast Cancer" } ] } ] } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/mutualExclusivity?session_id=6ab5b8cee497aad53593c249","url":"https://www.cbioportal.org/results/mutualExclusivity?session_id=6ab5b8cee497aad53593c249","data":{"studyIds":["msk_chord_2024"],"studies":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040}],"genes":["TP53","PIK3CA","GATA3","CDH1","KMT2C","MAP3K1","PTEN","ESR1","ARID1A","TBX3","AKT1","CBFB","FOXA1","NF1","MAP2K4"],"filteredSampleCount":5368,"caseSetId":"-1","sessionId":"6ab5b8cee497aad53593c249","studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Breast%20Cancer%22%7D%5D%7D%5D%7D","pageDescription":"Co-occurrence and mutual exclusivity statistics for all queried gene pairs."}} ▶ navigate_to_study_view { "studyIds": [ "msk_chord_2024" ], "filterJson": { "clinicalDataFilters": [ { "attributeId": "CANCER_TYPE", "values": [ { "value": "Breast Cancer" } ] } ] } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Breast%20Cancer%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Breast%20Cancer%22%7D%5D%7D%5D%7D","data":{"studyIds":["msk_chord_2024"],"studies":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}} ═ answer (success) Here are the direct links (both correctly filtered to the 5,368 breast cancer samples): [View Mutual Exclusivity / Co-occurrence Analysis](https://www.cbioportal.org/results/mutualExclusivity?session_id=6ab5b8cee497aad53593c249) Co-occurrence and mutual exclusivity statistics (with log2 odds ratio and p-values) for all 15 queried gene pairs, restricted to breast cancer samples. [View Breast Cancer Cohort Overview (Study View)](https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Breast%20Cancer%22%7D%5D%7D%5D%7D) Overview of all study data displayed as interactive charts summarizing available clinical and genomic data, filtered to Breast Cancer. --- **Summary:** Based on raw counts, the pair most frequently co-mutated in the same sample is **PIK3CA + TP53** (597/5,368 samples, 11.1%), followed by **CDH1 + PIK3CA** (346, 6.4%) and **MAP3K1 + PIK3CA** (256, 4.8%). For the statistically validated co-occurrence (vs. mutual exclusivity) with p-values, use the Mutual Exclusivity tab link above — I have not computed Fisher's exact test results myself.