Q (haiku): In the TCGA Breast Cancer study if a patient has both a PIK3CA mutation and a PTEN deletion what is their probability of having a luminal A subtype? Is this probability different from a patient with only a PIK3CA mutation? ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ▶ read_guide { "uri": "cbioportal://clinical-data-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) ▶ list_studies { "search": "TCGA breast cancer", "limit": 10 } ◀ 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) ◀ result {"result":[{"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":"mixed_msk_tcga_2021","name":"RAD51B Associated Mixed Cancers (MSK, NPJ Breast Cancer 2021)","type_of_cancer_id":"mixed","sample_count":17,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=mixed_msk_tcga_2021"}]} ▶ read_guide { "study_id": "brca_tcga_pan_can_atlas_2018" } ✗ error 2 validation errors for call[read_guide] uri Missing required argument [type=missing_argument, input_value={'study_id': 'brca_tcga_pan_can_atlas_2018'}, input_type=dict] For further information visit https://errors.pydantic.dev/2.11/v/missing_argument study_id Unexpected keyword argument [type=unexpected_keyword_argument, input_value='brca_tcga_pan_can_atlas_2018', input_type=str] For further information visit https://errors.pydantic.dev/2.11/v/unexpected_keyword_argument ▶ mcp__claude_ai_cBioPortal_MCP__clickhouse_list_table_columns { "table": "cancer_study" } ✗ error Error: No such tool available: mcp__claude_ai_cBioPortal_MCP__mcp__claude_ai_cBioPortal_MCP__clickhouse_list_table_columns ▶ mcp__claude_ai_cBioPortal_MCP__clickhouse_list_table_columns { "table": "clinical_attribute_meta" } ✗ error Error: No such tool available: mcp__claude_ai_cBioPortal_MCP__mcp__claude_ai_cBioPortal_MCP__clickhouse_list_table_columns ▶ get_study_guide { "study_id": "brca_tcga_pan_can_atlas_2018" } ▶ clickhouse_list_table_columns { "table": "clinical_attribute_meta" } ▶ clickhouse_list_table_columns { "table": "cancer_study" } ◀ 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 {"columns":[{"name":"attr_id","type":"String"},{"name":"display_name","type":"String"},{"name":"description","type":"String"},{"name":"datatype","type":"String"},{"name":"patient_attribute","type":"Int32"},{"name":"priority","type":"String"},{"name":"cancer_study_id","type":"Int64"}]} ◀ result {"columns":[{"name":"cancer_study_id","type":"Int64","comment":"Internal numeric ID. Prefer cancer_study_identifier for queries as it is more readable and stable."},{"name":"cancer_study_identifier","type":"Nullable(String)","comment":"Stable string identifier for the study (e.g., \"msk_chord_2024\", \"brca_tcga\"). Use this for filtering, not cancer_study_id."},{"name":"type_of_cancer_id","type":"String"},{"name":"name","type":"String","comment":"Full descriptive name of the study (e.g., \"MSK-CHORD (MSK, Nature 2024)\")."},{"name":"description","type":"String"},{"name":"public","type":"Int32"},{"name":"pmid","type":"Nullable(String)"},{"name":"citation","type":"Nullable(String)"},{"name":"groups","type":"Nullable(String)"},{"name":"status","type":"Nullable(Int64)"},{"name":"import_date","type":"Nullable(DateTime64(6))"},{"name":"reference_genome_id","type":"Nullable(Int64)"},{"name":"sample_count","type":"UInt32","comment":"Samples in the study (members of _all), as shown in the portal study list. Precomputed daily at LLM-prep time."},{"name":"mutation_sample_count","type":"UInt32","comment":"Samples profiled for mutations (_sequenced) — portal \"Data type\" filter: \"Mutations\". 0 = no mutation data."},{"name":"cna_sample_count","type":"UInt32","comment":"Samples profiled for copy-number alterations (_cna) — \"CNA\". 0 = no CNA data."},{"name":"structural_variant_sample_count","type":"UInt32","comment":"Distinct samples with at least one structural variant (fusions etc.). 0 = none."},{"name":"rna_seq_sample_count","type":"UInt32","comment":"Samples with RNA-Seq expression (_rna_seq_v2_mrna) — \"RNA-Seq\"."},{"name":"mrna_microarray_sample_count","type":"UInt32","comment":"Samples with microarray mRNA expression (_mrna) — \"RNA (microarray)\"."},{"name":"mirna_sample_count","type":"UInt32","comment":"Samples with microRNA expression (_microrna) — \"miRNA\"."},{"name":"rppa_sample_count","type":"UInt32","comment":"Samples with RPPA protein levels (_rppa) — \"RPPA\"."},{"name":"mass_spectrometry_sample_count","type":"UInt32","comment":"Samples with mass-spectrometry protein quantification (_protein_quantification) — \"Protein Mass-Spectrometry\"."},{"name":"treatment_patient_count","type":"UInt32","comment":"PATIENTS (not samples) with treatment clinical events — \"Treatment\". 0 = no treatment data."},{"name":"resource_sample_counts","type":"Map(String, UInt32)","comment":"Samples with each linked resource, keyed by display name: imaging and pathology such as 'Slide Microscopy', 'Computed Tomography', 'Magnetic Resonance', 'H&E Slide', 'MxIF Image'. Query with mapKeys(resource_sample_counts) or resource_sample_counts['Slide Microscopy'] > 0."},{"name":"mrna_expression_sample_count","type":"UInt32","comment":"Samples in any mRNA expression profile (RNA-Seq, microarray, TPM, z-scores; from sample_profile). Use this for \"does the study have expression data\" — rna_seq_sample_count and mrna_m … (136 more chars) ▶ clickhouse_list_table_columns { "table": "genomic_event_derived" } ▶ clickhouse_list_table_columns { "table": "clinical_data_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."}]} ◀ result {"columns":[{"name":"internal_id","type":"Int32"},{"name":"sample_unique_id","type":"String","comment":"Globally unique sample ID: cancer_study_identifier + \"_\" + sample.stable_id. Empty for patient-level attributes. Use this for sample filtering and joins."},{"name":"patient_unique_id","type":"String","comment":"Globally unique patient ID: cancer_study_identifier + \"_\" + patient.stable_id. Present for both sample and patient-level attributes."},{"name":"attribute_name","type":"LowCardinality(String)","comment":"Clinical attribute name (e.g., SAMPLE_TYPE, CANCER_TYPE, AGE, OS_MONTHS). Use with attribute_value for filtering. AGE may be floored or capped for de-identification (e.g. all children recorded as 18, or everyone 89+ recorded as 89 or 90): before age statistics check for a pile-up at the min/max, and if present compute age from DAYS_TO_BIRTH (-days / 365.25)."},{"name":"attribute_value","type":"String","comment":"Value of the clinical attribute (String). For SAMPLE_TYPE: Primary, Metastasis, Local Recurrence, Unknown. Missing values are empty strings, so use toFloat64OrNull(attribute_value) for numeric comparisons — CAST fails on them."},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"type","type":"LowCardinality(String)","comment":"Data level: \"sample\" for sample-level attributes (e.g., SAMPLE_TYPE), \"patient\" for patient-level attributes (e.g., AGE, OS_MONTHS)."}]} ▶ clickhouse_run_select_query { "query": " -- First, identify the mutation profiles and check for PIK3CA and PTEN data SELECT DISTINCT genetic_profile_stable_id, variant_type FROM genomic_event_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol IN ('PIK3CA', 'PTEN') ORDER BY genetic_profile_stable_id, variant_type ", "max_rows": 50 } ◀ result {"rows":[{"genetic_profile_stable_id":"brca_tcga_pan_can_atlas_2018_gistic","variant_type":"cna"},{"genetic_profile_stable_id":"brca_tcga_pan_can_atlas_2018_mutations","variant_type":"mutation"},{"genetic_profile_stable_id":"brca_tcga_pan_can_atlas_2018_structural_variants","variant_type":"structural_variant"}]} ▶ clickhouse_run_select_query { "query": " -- Build patient-level alteration status WITH patient_alterations AS ( SELECT patient_unique_id, -- PIK3CA mutations MAX(CASE WHEN hugo_gene_symbol = 'PIK3CA' AND variant_type = 'mutation' AND mutation_status != 'GERMLINE' AND mutation_status != 'UNCALLED' AND off_panel = 0 THEN 1 ELSE 0 END) AS pik3ca_mut, -- PTEN deep deletions (HOMDEL = -2) MAX(CASE WHEN hugo_gene_symbol = 'PTEN' AND variant_type = 'cna' AND cna_alteration = -2 THEN 1 ELSE 0 END) AS pten_del, -- TP53 mutations (for context) MAX(CASE WHEN hugo_gene_symbol = 'TP53' AND variant_type = 'mutation' AND mutation_status != 'GERMLINE' AND mutation_status != 'UNCALLED' AND off_panel = 0 THEN 1 ELSE 0 END) AS tp53_mut FROM genomic_event_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' GROUP BY patient_unique_id ), patient_subtype AS ( SELECT patient_unique_id, MAX(CASE WHEN attribute_name = 'SUBTYPE' AND attribute_value = 'BRCA_LumA' THEN 1 ELSE 0 END) AS is_luminal_a FROM clinical_data_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' GROUP BY patient_unique_id ) SELECT -- Group by alteration pattern CASE WHEN pik3ca_mut = 1 AND pten_del = 1 THEN 'PIK3CA_mut + PTEN_del' WHEN pik3ca_mut = 1 AND pten_del = 0 THEN 'PIK3CA_mut only' WHEN pik3ca_mut = 0 AND pten_del = 1 THEN 'PTEN_del only' ELSE 'Neither' END AS alteration_group, -- Luminal A status CASE WHEN is_luminal_a = 1 THEN 'Luminal A' ELSE 'Not Luminal A' END AS subtype_group, COUNT(*) AS patient_count FROM patient_alterations LEFT JOIN patient_subtype USING (patient_unique_id) GROUP BY alteration_group, subtype_group ORDER BY alteration_group, subtype_group ", "max_rows": 100 } ◀ result {"rows":[{"alteration_group":"Neither","subtype_group":"Luminal A","patient_count":255},{"alteration_group":"Neither","subtype_group":"Not Luminal A","patient_count":429},{"alteration_group":"PIK3CA_mut + PTEN_del","subtype_group":"Luminal A","patient_count":3},{"alteration_group":"PIK3CA_mut + PTEN_del","subtype_group":"Not Luminal A","patient_count":3},{"alteration_group":"PIK3CA_mut only","subtype_group":"Luminal A","patient_count":234},{"alteration_group":"PIK3CA_mut only","subtype_group":"Not Luminal A","patient_count":107},{"alteration_group":"PTEN_del only","subtype_group":"Luminal A","patient_count":7},{"alteration_group":"PTEN_del only","subtype_group":"Not Luminal A","patient_count":42}]} ▶ resolve_and_route { "studyIds": [ "brca_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":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_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":["brca_tcga_pan_can_atlas_2018_gistic","brca_tcga_pan_can_atlas_2018_log2CNA","brca_tcga_pan_can_atlas_2018_mutations","brca_tcga_pan_can_atlas_2018_protein_quantification","brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa","brca_tcga_pan_can_atlas_2018_rppa_Zscores","brca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["brca_tcga_pan_can_atlas_2018_armlevel_cna","brca_tcga_pan_can_atlas_2018_genetic_ancestry","brca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","brca_tcga_pan_can_atlas_2018_methylation_hm450","brca_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_anc … (83 more chars) ▶ navigate_to_group_comparison { "studyIds": [ "brca_tcga_pan_can_atlas_2018" ], "groups": [ { "name": "PIK3CA mutation + PTEN deletion", "studyViewFilter": { "geneFilters": [ { "molecularProfileIds": [ "brca_tcga_pan_can_atlas_2018_mutations" ], "geneQueries": [ [ { "hugoGeneSymbol": "PIK3CA" } ] ] }, { "molecularProfileIds": [ "brca_tcga_pan_can_atlas_2018_gistic" ], "geneQueries": [ [ { "hugoGeneSymbol": "PTEN", "alterations": [ "HOMDEL" ] } ] ] } ] } }, { "name": "PIK3CA mutation only", "studyViewFilter": { "geneFilters": [ { "molecularProfileIds": [ "brca_tcga_pan_can_atlas_2018_mutations" ], "geneQueries": [ [ { "hugoGeneSymbol": "PIK3CA" } ] ] } ], "mutationDataFilters": [ { "hugoGeneSymbol": "PTEN", "profileType": "gistic", "categorization": "MUTATED", "values": [ [ { "value": "NOT_MUTATED" } ] ] } ] } } ], "tab": "clinical" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/clinical?comparisonId=6ab7f73ae497aad53593c5aa","url":"https://www.cbioportal.org/comparison/clinical?comparisonId=6ab7f73ae497aad53593c5aa","data":{"description":"Group comparison (2 custom groups)","studies":["brca_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"PIK3CA mutation + PTEN deletion","sampleCount":6},{"name":"PIK3CA mutation only","sampleCount":328}],"studyViewUrl":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"PIK3CA mutation + PTEN deletion","url":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22molecularProfileIds%22%3A%5B%22brca_tcga_pan_can_atlas_2018_mutations%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22PIK3CA%22%7D%5D%5D%7D%2C%7B%22molecularProfileIds%22%3A%5B%22brca_tcga_pan_can_atlas_2018_gistic%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22PTEN%22%2C%22alterations%22%3A%5B%22HOMDEL%22%5D%7D%5D%5D%7D%5D%7D"},{"groupName":"PIK3CA mutation only","url":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22molecularProfileIds%22%3A%5B%22brca_tcga_pan_can_atlas_2018_mutations%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22PIK3CA%22%7D%5D%5D%7D%5D%2C%22mutationDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22PTEN%22%2C%22profileType%22%3A%22gistic%22%2C%22categorization%22%3A%22MUTATED%22%2C%22values%22%3A%5B%5B%7B%22value%22%3A%22NOT_MUTATED%22%7D%5D%5D%7D%5D%7D"}],"pageDescription":"Clinical attribute distributions compared across PIK3CA mutation + PTEN deletion (6) vs PIK3CA mutation only (328)."}} ═ answer (success) Perfect! Here's a direct link to the Group Comparison: [**View Group Comparison: PIK3CA mutation + PTEN deletion vs PIK3CA mutation only**](https://www.cbioportal.org/comparison/clinical?comparisonId=6ab7f73ae497aad53593c5aa) This interactive tool will let you: 1. **Compare clinical attributes** across the two groups (including SUBTYPE) 2. **Click the Overlap tab** to visualize the exact contingency table 3. **Click the Survival tab** to assess OS/DFS/PFS differences (with Kaplan-Meier curves) 4. Use the **Statistics buttons** to run formal tests (Fisher's exact, etc.) --- ## Summary | Question | Answer | |---|---| | **Probability of Luminal A with PIK3CA + PTEN deletion** | 3/6 = **50%** (small sample; n=6) | | **Probability of Luminal A with PIK3CA only** | 234/341 = **68.6%** (larger sample; n=341) | | **Is the difference significant?** | **Unknown without Fisher's exact test.** Use the Group Comparison link above to compute the p-value, or run `fisher.test(matrix(c(3, 3, 234, 107), nrow=2))` in R. | The dual alteration (PIK3CA + PTEN loss) is rare in this cohort (only 6 patients), which limits statistical power. The observed drop in Luminal A prevalence (50% vs 68.6%) suggests a possible trend, but it must be formally tested.