Q (sonnet-5.5): what are the most common events in her2- breast cancer? ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ▶ list_studies { "search": "breast", "limit": 50 } ◀ 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) ◀ 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":"breast_msk_2026","name":"CCNE1 Amplifications in Breast Cancer (MSK, 2026)","type_of_cancer_id":"breast","sample_count":6318,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=breast_msk_2026"},{"cancer_study_identifier":"breast_msk_2025","name":"Breast Cancer (MSK, Nat Genet 2025)","type_of_cancer_id":"brca","sample_count":3879,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=breast_msk_2025"},{"cancer_study_identifier":"brca_metabric","name":"Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016)","type_of_cancer_id":"brca","sample_count":2509,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brca_metabric"},{"cancer_study_identifier":"breast_msk_2018","name":"Breast Cancer (MSK, Cancer Cell 2018)","type_of_cancer_id":"breast","sample_count":1918,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=breast_msk_2018"},{"cancer_study_identifier":"breast_ink4_msk_2021","name":"Metastatic Breast Cancer (MSK, Cancer Discovery 2022)","type_of_cancer_id":"breast","sample_count":1365,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=breast_ink4_msk_2021"},{"cancer_study_identifier":"brca_tcga","name":"Breast Invasive Carcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"brca","sample_count":1108,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga"},{"cancer_study_identifier":"brca_tcga_gdc","name":"Invasive Breast Carcinoma (TCGA GDC, 2025)","type_of_cancer_id":"brca","sample_count":1102,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga_gdc"},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"brca","sample_count":1084,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"brca_tcga_pub","name":"Breast Invasive Carcinoma (TCGA, Nature 2012)","type_of_cancer_id":"brca","sample_count":825,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga_pub"},{"cancer_study_identifier":"brca_tcga_pub2015","name":"Breast Invasive Carcinoma (TCGA, Cell 2015)","type_of_cancer_id":"brca","sample_count":818,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga_pub2015"},{"cancer_study_identifier":"brca_msk_2025","name":"Breast Carcinoma (MSK, NPJ Precis Oncol 2025)","type_of_cancer_id":"brca","sample_count":527,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brca_msk_2025"},{"cancer_study_identifier":"brca_mbcproject_2022","name":"The Metastatic Breast Cancer Project (Provisional, December 2021)","type_of_cancer_id":"brca","sample_count":379,"has_gu … (6725 more chars) ▶ get_study_guide { "study_id": "brca_tcga_pan_can_atlas_2018" } ▶ read_guide { "uri": "cbioportal://clinical-data-guide" } ◀ result # Breast Invasive Carcinoma (TCGA, PanCancer Atlas) **Study ID:** `brca_tcga_pan_can_atlas_2018` See `_tcga_pancan_template.md` for common TCGA clinical attributes. ## Study-Specific Attributes ### Molecular Subtypes | Attribute | Description | Values (patients) | |-----------|-------------|--------| | `SUBTYPE` | PAM50 molecular subtype | `BRCA_LumA` 499, `BRCA_LumB` 197, `BRCA_Basal` 171, `BRCA_Her2` 78, `BRCA_Normal` 36, blank 103 | ### Histology | Attribute | Description | Values (samples) | |-----------|-------------|--------| | `CANCER_TYPE_DETAILED` | Histological type | Breast Invasive Ductal Carcinoma 780, Breast Invasive Lobular Carcinoma 201, Breast Invasive Carcinoma (NOS) 77, Breast Invasive Mixed Mucinous Carcinoma 17, Metaplastic Breast Cancer 8, Invasive Breast Carcinoma 1 | ### Not available in this study - **ER / PR / HER2 receptor status and triple-negative status**: no clinical attribute; say it is not available here. Do not infer receptor status from `SUBTYPE`: PAM50 is an expression-based classification, not IHC/FISH. If the user accepts an expression-based proxy, `BRCA_Basal` (≈ triple-negative) or `BRCA_Her2` can be offered, labelled as PAM50. ERBB2 amplification is available from CNA data. - `GRADE` is blank for all samples. ## Key Genes - **TP53**: Most frequently mutated (~30%) - **PIK3CA**: Common in ER+ tumors (~35%) - **CDH1**: Enriched in lobular carcinoma - **GATA3**: Luminal marker - **ERBB2**: HER2 amplification target ## Notes - Molecular subtypes (PAM50) correlate strongly with clinical behavior - Lobular vs ductal distinction has different mutation profiles ◀ result # Clinical Data Query Guide ## Overview Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis. ## Data Organization ### Patient-Level vs Sample-Level Data - **Patient-level**: Demographics, overall survival, disease stage (stored once per patient) - **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient) ### Key Tables - `clinical_patient`: Patient-level clinical attributes - `clinical_sample`: Sample-level clinical attributes - `clinical_data_derived`: Pre-joined view combining both levels - `clinical_attribute_meta`: Metadata about available clinical attributes ## Recommended Approach: Use clinical_data_derived The `clinical_data_derived` table is pre-joined and optimized for most queries: ```sql -- Get clinical data for specific attributes SELECT sample_unique_id, patient_unique_id, attribute_name, attribute_value FROM clinical_data_derived WHERE cancer_study_identifier = 'your_study_id' AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS'); ``` ## Clinical Attribute Discovery ### Use clinical_attribute_meta for Discovering Available Attributes **Always start here** to see what clinical attributes are available for a specific study: ```sql -- Discover available clinical attributes for a study SELECT attr_id, description, patient_attribute, cancer_study_id FROM clinical_attribute_meta WHERE cancer_study_id = ( SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'your_study_id' ) ORDER BY patient_attribute, attr_id; ``` **Key Fields:** - **attr_id**: matches attr_id in clinical_sample/clinical_patient tables - **description**: human-readable description of the attribute - **patient_attribute**: true = patient attribute, false = sample attribute - **cancer_study_id**: links to cancer_study table (filter by study) ## Attribute Semantics and Matching ### Case-Insensitive Matching for Attribute Values Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant. When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study: ```sql -- Correct: case-insensitive clinical value filter SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'your_study_id' AND attribute_name = 'MUTATION_STATUS' AND upper(attribute_value) = 'GERMLINE'; ``` Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study. ### Query the Requested Attribute, Not a Proxy Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars) ▶ clickhouse_run_select_query { "query": "SELECT cancer_study_identifier, attribute_name, attribute_value, count(DISTINCT patient_unique_id) AS patients FROM clinical_data_derived WHERE cancer_study_identifier IN ('brca_metabric','breast_msk_2025','breast_msk_2018','brca_mbcproject_2022','breast_ink4_msk_2021') AND (upper(attribute_name) LIKE '%HER2%' OR upper(attribute_name) LIKE '%ERBB2%') GROUP BY 1,2,3 ORDER BY 1,2,4 DESC" } ◀ result {"rows":[{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"NOT_FOUND_IN_RECORD","patients":140},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"N/A_BLOOD_SAMPLE","patients":104},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"NOT_DONE","patients":31},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"TESTING_PERFORMED_ON_DIFFERENT_SAMPLE","patients":17},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"2.2","patients":3},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"2.26","patients":2},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"3.5","patients":2},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","patients":2},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"2.6","patients":2},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"4.4","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"13.63","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"22.75","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"2.78","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"4.2","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"2.3","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"1.1","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"6.8","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"2.09","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"2.68","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"4.25","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"5.45","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"6.2","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"3.99","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2_COPY","attribute_value":"3.6","patients":1},{"cancer_study_identifier":"brca_mbcproject_2022","attribute_name":"BXHER2 … (9510 more chars) ▶ clickhouse_run_select_query { "query": "SELECT cancer_study_identifier, attribute_name, attribute_value, count(DISTINCT patient_unique_id) AS patients FROM clinical_data_derived WHERE cancer_study_identifier IN ('brca_metabric','breast_msk_2025','breast_msk_2018','breast_ink4_msk_2021','brca_msk_2025','breast_msk_2026') AND (upper(attribute_name) LIKE '%HER2%' OR upper(attribute_name) LIKE '%ERBB2%' OR upper(attribute_name) LIKE '%RECEPTOR%' OR upper(attribute_name) LIKE '%SUBTYPE%') GROUP BY 1,2,3 ORDER BY 1,2,4 DESC" } ◀ result {"rows":[{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","attribute_value":"LumA","patients":700},{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","patients":529},{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","attribute_value":"LumB","patients":475},{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","attribute_value":"Her2","patients":224},{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","attribute_value":"claudin-low","patients":218},{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","attribute_value":"Basal","patients":209},{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","attribute_value":"Normal","patients":148},{"cancer_study_identifier":"brca_metabric","attribute_name":"CLAUDIN_SUBTYPE","attribute_value":"NC","patients":6},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_SNP6","attribute_value":"NEUTRAL","patients":1436},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_SNP6","patients":529},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_SNP6","attribute_value":"GAIN","patients":438},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_SNP6","attribute_value":"LOSS","patients":101},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_SNP6","attribute_value":"UNDEF","patients":5},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_STATUS","attribute_value":"Negative","patients":1733},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_STATUS","patients":529},{"cancer_study_identifier":"brca_metabric","attribute_name":"HER2_STATUS","attribute_value":"Positive","patients":247},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Ductal/NST","patients":1810},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Mixed","patients":269},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Lobular","patients":192},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","patients":135},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Medullary","patients":32},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Mucinous","patients":25},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Tubular/ cribriform","patients":23},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Other","patients":21},{"cancer_study_identifier":"brca_metabric","attribute_name":"HISTOLOGICAL_SUBTYPE","attribute_value":"Metaplastic","patients":2},{"cancer_study_identifier":"breast_msk_2018","attribute_na … (9262 more chars) ▶ clickhouse_run_select_query { "query": "WITH cohort AS ( SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier='brca_metabric' AND patient_unique_id IN ( SELECT patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier='brca_metabric' AND attribute_name='HER2_STATUS' AND attribute_value='Negative') ), prof AS ( SELECT sample_unique_id FROM sample_to_gene_panel_derived WHERE cancer_study_identifier='brca_metabric' AND alteration_type='MUTATION_EXTENDED' AND sample_unique_id IN (SELECT sample_unique_id FROM cohort) ), alt AS ( SELECT hugo_gene_symbol g, uniqExact(sample_unique_id) altered, count() events FROM genomic_event_derived WHERE cancer_study_identifier='brca_metabric' AND variant_type='mutation' AND mutation_status!='UNCALLED' AND off_panel=0 AND sample_unique_id IN (SELECT sample_unique_id FROM prof) GROUP BY g ) SELECT g, altered, events, (SELECT count() FROM prof) AS profiled_samples, round(altered*100/profiled_samples,1) pct FROM alt ORDER BY altered DESC LIMIT 15" } ◀ result {"rows":[{"g":"PIK3CA","altered":717,"events":839,"profiled_samples":1668,"pct":43},{"g":"TP53","altered":498,"events":516,"profiled_samples":1668,"pct":29.9},{"g":"MUC16","altered":271,"events":339,"profiled_samples":1668,"pct":16.2},{"g":"AHNAK2","altered":262,"events":356,"profiled_samples":1668,"pct":15.7},{"g":"GATA3","altered":219,"events":227,"profiled_samples":1668,"pct":13.1},{"g":"KMT2C","altered":210,"events":240,"profiled_samples":1668,"pct":12.6},{"g":"SYNE1","altered":200,"events":233,"profiled_samples":1668,"pct":12},{"g":"MAP3K1","altered":191,"events":273,"profiled_samples":1668,"pct":11.5},{"g":"CDH1","altered":160,"events":166,"profiled_samples":1668,"pct":9.6},{"g":"AHNAK","altered":153,"events":173,"profiled_samples":1668,"pct":9.2},{"g":"DNAH11","altered":145,"events":154,"profiled_samples":1668,"pct":8.7},{"g":"DNAH2","altered":134,"events":147,"profiled_samples":1668,"pct":8},{"g":"KMT2D","altered":124,"events":133,"profiled_samples":1668,"pct":7.4},{"g":"USH2A","altered":119,"events":122,"profiled_samples":1668,"pct":7.1},{"g":"DNAH5","altered":117,"events":130,"profiled_samples":1668,"pct":7}]} ▶ clickhouse_run_select_query { "query": "WITH cohort AS ( SELECT DISTINCT sample_unique_id FROM clinical_data_derived WHERE cancer_study_identifier='brca_metabric' AND patient_unique_id IN ( SELECT patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier='brca_metabric' AND attribute_name='HER2_STATUS' AND attribute_value='Negative') ), prof AS ( SELECT sample_unique_id FROM sample_to_gene_panel_derived WHERE cancer_study_identifier='brca_metabric' AND alteration_type='COPY_NUMBER_ALTERATION' AND sample_unique_id IN (SELECT sample_unique_id FROM cohort) ) SELECT hugo_gene_symbol g, if(cna_alteration=2,'AMP','HOMDEL') t, uniqExact(sample_unique_id) altered, (SELECT count() FROM prof) profiled, round(altered*100/profiled,1) pct FROM genomic_event_derived WHERE cancer_study_identifier='brca_metabric' AND variant_type='cna' AND cna_alteration IN (2,-2) AND sample_unique_id IN (SELECT sample_unique_id FROM prof) GROUP BY g,t ORDER BY altered DESC LIMIT 12" } ◀ result {"rows":[{"g":"TRPS1","t":"AMP","altered":436,"profiled":1733,"pct":25.2},{"g":"MIR205HG","t":"AMP","altered":430,"profiled":1733,"pct":24.8},{"g":"SIPA1L2","t":"AMP","altered":426,"profiled":1733,"pct":24.6},{"g":"PLEKHA6","t":"AMP","altered":426,"profiled":1733,"pct":24.6},{"g":"LRRN2","t":"AMP","altered":425,"profiled":1733,"pct":24.5},{"g":"MDM4","t":"AMP","altered":425,"profiled":1733,"pct":24.5},{"g":"CAMK1G","t":"AMP","altered":423,"profiled":1733,"pct":24.4},{"g":"PPP1R15B","t":"AMP","altered":423,"profiled":1733,"pct":24.4},{"g":"LEMD1-AS1","t":"AMP","altered":423,"profiled":1733,"pct":24.4},{"g":"PLXNA2","t":"AMP","altered":423,"profiled":1733,"pct":24.4},{"g":"CDK18","t":"AMP","altered":422,"profiled":1733,"pct":24.4},{"g":"KLHDC8A","t":"AMP","altered":422,"profiled":1733,"pct":24.4}]} ▶ resolve_and_route { "studyIds": [ "brca_metabric" ] } ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"brca_metabric","name":"Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016)","sampleCount":2509,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_metabric","metadata":{"clinicalAttributeIds":["AGE_AT_DIAGNOSIS","BREAST_SURGERY","CANCER_TYPE","CANCER_TYPE_DETAILED","CELLULARITY","CHEMOTHERAPY","CLAUDIN_SUBTYPE","COHORT","ER_IHC","ER_STATUS","GRADE","HER2_SNP6","HER2_STATUS","HISTOLOGICAL_SUBTYPE","HORMONE_THERAPY","INFERRED_MENOPAUSAL_STATE","INTCLUST","LATERALITY","LYMPH_NODES_EXAMINED_POSITIVE","MUTATION_COUNT","NPI","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PR_STATUS","RADIO_THERAPY","RFS_MONTHS","RFS_STATUS","SAMPLE_COUNT","SAMPLE_TYPE","SEX","THREEGENE","TMB_NONSYNONYMOUS","TUMOR_SIZE","TUMOR_STAGE","VITAL_STATUS"],"molecularProfileIds":["brca_metabric_cna","brca_metabric_methylation_promoters_rrbs","brca_metabric_mrna","brca_metabric_mrna_median_all_sample_Zscores","brca_metabric_mutations"],"heatmapProfileIds":["brca_metabric_methylation_promoters_rrbs","brca_metabric_mrna_median_all_sample_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","dna_methylation"]}}]}} ▶ navigate_to_study_view { "studyIds": [ "brca_metabric" ], "filterJson": { "clinicalDataFilters": [ { "attributeId": "HER2_STATUS", "values": [ { "value": "Negative" } ] } ] } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D","data":{"studyIds":["brca_metabric"],"studies":[{"studyId":"brca_metabric","name":"Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016)","sampleCount":2509}],"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."}} ▶ navigate_to_results_view { "studyIds": [ "brca_metabric" ], "genes": [ "PIK3CA", "TP53", "GATA3", "MAP3K1", "CDH1" ], "tab": "oncoprint", "studyViewFilter": { "clinicalDataFilters": [ { "attributeId": "HER2_STATUS", "values": [ { "value": "Negative" } ] } ] } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?session_id=6abb4239e497aad53593ca51","url":"https://www.cbioportal.org/results/oncoprint?session_id=6abb4239e497aad53593ca51","data":{"studyIds":["brca_metabric"],"studies":[{"studyId":"brca_metabric","name":"Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016)","sampleCount":2509}],"genes":["PIK3CA","TP53","GATA3","MAP3K1","CDH1"],"filteredSampleCount":1733,"caseSetId":"-1","sessionId":"6abb4239e497aad53593ca51","studyViewUrl":"https://www.cbioportal.org/study?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D","pageDescription":"Visual summary of alterations in queried genes across samples."}} ═ answer (success) **Cohort:** I used METABRIC (`brca_metabric`) because it records HER2 status directly. The TCGA PanCancer Atlas breast study doesn't, and I didn't infer status from PAM50 subtype. HER2-negative here means `HER2_STATUS = Negative`, which covers 1,733 patients. I read "events" as somatic mutations and copy-number alterations. Mutation percentages are of samples profiled: 1,668 HER2-negative samples in this cohort. Copy-number percentages use 1,733. **Most frequently mutated genes** | Gene | Mutated samples | % | |---|---|---| | PIK3CA | 717 | 43.0% | | TP53 | 498 | 29.9% | | MUC16 | 271 | 16.2% | | AHNAK2 | 262 | 15.7% | | GATA3 | 219 | 13.1% | | KMT2C | 210 | 12.6% | | SYNE1 | 200 | 12.0% | | MAP3K1 | 191 | 11.5% | | CDH1 | 160 | 9.6% | | AHNAK | 153 | 9.2% | - MUC16, AHNAK, AHNAK2, SYNE1 and the DNAH genes are very large genes, so their high counts may be passenger events. I haven't tested whether they are enriched in this group. - The frequently mutated genes that are familiar breast cancer drivers are PIK3CA, TP53, GATA3, KMT2C, MAP3K1 and CDH1. **Most common copy-number events (high-level amplification)** The top amplified genes are a cluster of neighboring genes, including TRPS1, MDM4, PLXNA2 and SIPA1L2. Each is amplified in about 24–25% of HER2-negative patients (for example TRPS1 436/1,733 = 25.2%). These genes sit together in one amplified region, so they are probably one recurrent event rather than many independent ones. I didn't pull the region's chromosomal location. **Caveats** - METABRIC is an older cohort sequenced on a targeted panel, with array-based copy number. Frequencies will differ in other breast cohorts such as MSK or TCGA. - HER2-negative includes both HR+ and triple-negative tumors, so these figures blend subtypes. I can split them by ER status or PAM50 subtype if that would help. - HER2 status is missing for 529 METABRIC patients, and they are excluded. **Links** - [METABRIC study view filtered to HER2-negative](https://www.cbioportal.org/study/summary?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D) (1,733 patients) - [OncoPrint of PIK3CA, TP53, GATA3, MAP3K1, CDH1 in HER2-negative samples](https://www.cbioportal.org/results/oncoprint?session_id=6abb4239e497aad53593ca51)