Q (sonnet): 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://clinical-data-guide" } ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ◀ 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) ▶ read_guide { "uri": "cbioportal://sample-filtering-guide" } ▶ list_studies { "search": "breast TCGA pan can atlas" } ◀ 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) ◀ result {"result":[{"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"}]} ▶ get_study_guide { "study_id": "brca_tcga_pan_can_atlas_2018" } ▶ clickhouse_list_table_columns { "table": "genomic_event_derived" } ◀ 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":"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 pik3ca_mut AS ( SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'PIK3CA' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' ), pten_del AS ( SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'PTEN' AND variant_type = 'cna' AND cna_alteration = -2 ), subtype AS ( SELECT patient_unique_id, attribute_value AS subtype FROM clinical_data_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' AND attribute_value != '' ) SELECT if(s.patient_unique_id IN (SELECT patient_unique_id FROM pik3ca_mut), 'PIK3CA_MUT', 'PIK3CA_WT') AS pik3ca_status, if(s.patient_unique_id IN (SELECT patient_unique_id FROM pten_del), 'PTEN_DEL', 'PTEN_INTACT') AS pten_status, s.subtype, count() AS n_patients FROM subtype s GROUP BY pik3ca_status, pten_status, s.subtype ORDER BY pik3ca_status, pten_status, s.subtype " } ◀ result {"rows":[{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_DEL","subtype":"BRCA_Basal","n_patients":1},{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_DEL","subtype":"BRCA_LumA","n_patients":3},{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_DEL","subtype":"BRCA_LumB","n_patients":2},{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_INTACT","subtype":"BRCA_Basal","n_patients":11},{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_INTACT","subtype":"BRCA_Her2","n_patients":26},{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_INTACT","subtype":"BRCA_LumA","n_patients":234},{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_INTACT","subtype":"BRCA_LumB","n_patients":57},{"pik3ca_status":"PIK3CA_MUT","pten_status":"PTEN_INTACT","subtype":"BRCA_Normal","n_patients":8},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_DEL","subtype":"BRCA_Basal","n_patients":27},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_DEL","subtype":"BRCA_Her2","n_patients":1},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_DEL","subtype":"BRCA_LumA","n_patients":7},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_DEL","subtype":"BRCA_LumB","n_patients":8},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_DEL","subtype":"BRCA_Normal","n_patients":1},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_INTACT","subtype":"BRCA_Basal","n_patients":132},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_INTACT","subtype":"BRCA_Her2","n_patients":51},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_INTACT","subtype":"BRCA_LumA","n_patients":255},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_INTACT","subtype":"BRCA_LumB","n_patients":130},{"pik3ca_status":"PIK3CA_WT","pten_status":"PTEN_INTACT","subtype":"BRCA_Normal","n_patients":27}]} ▶ navigate_to_group_comparison { "studyIds": [ "brca_tcga_pan_can_atlas_2018" ], "tab": "clinical", "groups": [ { "name": "PIK3CA mut + PTEN del", "studyViewFilter": { "geneFilters": [ { "molecularProfileIds": [ "brca_tcga_pan_can_atlas_2018_mutations" ], "geneQueries": [ [ { "hugoGeneSymbol": "PIK3CA" } ] ] } ], "genomicDataFilters": [ { "hugoGeneSymbol": "PTEN", "profileType": "gistic", "values": [ { "value": "-2" } ] } ] }, "selectedGene": "PIK3CA" }, { "name": "PIK3CA mut only (PTEN intact)", "studyViewFilter": { "geneFilters": [ { "molecularProfileIds": [ "brca_tcga_pan_can_atlas_2018_mutations" ], "geneQueries": [ [ { "hugoGeneSymbol": "PIK3CA" } ] ] } ], "genomicDataFilters": [ { "hugoGeneSymbol": "PTEN", "profileType": "gistic", "values": [ { "value": "0" }, { "value": "1" }, { "value": "-1" }, { "value": "2" } ] } ] } } ] } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5d10ac2115c492d884e22","url":"https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5d10ac2115c492d884e22","data":{"description":"Group comparison (2 custom groups)","studies":["brca_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"PIK3CA mut + PTEN del","sampleCount":6},{"name":"PIK3CA mut only (PTEN intact)","sampleCount":339}],"studyViewUrl":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"PIK3CA mut + PTEN del","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%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22PTEN%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%22-2%22%7D%5D%7D%5D%7D"},{"groupName":"PIK3CA mut only (PTEN intact)","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%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22PTEN%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%220%22%7D%2C%7B%22value%22%3A%221%22%7D%2C%7B%22value%22%3A%22-1%22%7D%2C%7B%22value%22%3A%222%22%7D%5D%7D%5D%7D"}],"pageDescription":"Clinical attribute distributions compared across PIK3CA mut + PTEN del (6) vs PIK3CA mut only (PTEN intact) (339)."}} ═ answer (success) This confirms my counts (6 vs. 339 samples — very close to my patient-level counts of 6 and 336; the small discrepancy is samples vs. patients). ### [Open Group Comparison — PIK3CA-mutant + PTEN-deleted vs. PIK3CA-mutant only](https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5d10ac2115c492d884e22) This link opens the **Clinical** tab pre-loaded with both groups (6 vs. 339 samples). From there you can switch to the subtype (`SUBTYPE`) bar chart to see the Luminal A proportions plotted side by side with the chi-squared p-value cBioPortal computes automatically — that's the correct way to determine whether the difference I found (50.0% vs. 69.6%) is statistically meaningful, since the double-altered group is very small (n=6). **Bottom line:** In this cohort, the raw Luminal A fraction is lower when a PTEN deep deletion co-occurs with PIK3CA mutation (50%, n=6) versus PIK3CA mutation alone (70%, n=336), but with only 6 patients in the co-altered group, this is not a reliable estimate and I can't tell you whether it's statistically significant — use the link above or a Fisher's exact test (`fisher.test(matrix(c(3,3,234,102), nrow=2))` in R) to test it properly.