Q (haiku): Is BRCA1 promoter methylation associated with decreased BRCA1 mRNA expression in ovarian cancer? ▶ read_guide { "uri": "cbioportal://common-pitfalls" } ▶ list_guides {} ◀ result # Common Query Pitfalls Guide ## Overview This guide highlights frequent mistakes when analyzing cBioPortal data and provides solutions to avoid them. ## Critical Pitfalls ### 1. 🚨 CRITICAL MUTATION FREQUENCY ERRORS #### ❌ WRONG: Using study-wide totals for gene frequencies ```sql -- INCORRECT - This gives wrong frequencies! SELECT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) as altered_samples, (SELECT COUNT(DISTINCT sample_unique_id) FROM genomic_event_derived WHERE cancer_study_identifier = 'your_study_id') as total_samples FROM genomic_event_derived WHERE variant_type = 'mutation' AND cancer_study_identifier = 'your_study_id' GROUP BY hugo_gene_symbol; ``` **Problem**: Different genes have different profiling coverage - you can't use study-wide totals! #### ❌ WRONG: Not using gene-specific profiling denominators ```sql -- INCORRECT - Missing gene-specific denominators SELECT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) as altered_samples FROM genomic_event_derived WHERE variant_type = 'mutation' GROUP BY hugo_gene_symbol; -- Missing: WHERE ARE THE DENOMINATORS FOR EACH GENE? ``` #### ❌ WRONG: Skipping individual gene profiling queries **Problem**: Failing to run separate profiling queries for EACH gene in results. **Each gene has different coverage**: TP53 might be profiled in 25,040 samples, MUC16 in 23,000, etc. #### ✅ CORRECT: Complete gene-specific workflow ```sql -- STEP 1: Get altered counts per gene SELECT hugo_gene_symbol, entrez_gene_id, COUNT(DISTINCT CASE WHEN off_panel = 0 THEN sample_unique_id END) AS numberOfAlteredSamplesOnPanel, COUNT(*) AS totalMutationEvents FROM genomic_event_derived WHERE variant_type = 'mutation' AND mutation_status != 'UNCALLED' GROUP BY entrez_gene_id, hugo_gene_symbol ORDER BY numberOfAlteredSamplesOnPanel DESC; -- STEP 2: FOR EACH GENE, run this profiling query: SELECT COUNT(DISTINCT stgp.sample_unique_id) AS numberOfProfiledSamples FROM sample_to_gene_panel_derived stgp JOIN gene_panel gp ON stgp.gene_panel_id = gp.stable_id JOIN gene_panel_list gpl ON gp.internal_id = gpl.internal_id JOIN gene g ON gpl.gene_id = g.entrez_gene_id WHERE stgp.alteration_type = 'MUTATION_EXTENDED' AND g.hugo_gene_symbol = 'TP53' -- Replace with each gene from Step 1 AND stgp.cancer_study_identifier = 'your_study_id'; -- STEP 3: Calculate frequency = numberOfAlteredSamplesOnPanel / numberOfProfiledSamples * 100 ``` #### 🚨 WORKFLOW REQUIREMENTS VIOLATIONS: - **Missing denominator columns**: Must show gene-specific profiled samples per row - **Wrong table format**: Should be | Gene | # Mutations | # Samples | Profiled Samples | Sample % | - **Not replacing gene symbols**: Must replace 'TP53' with actual gene for each query - **Using study totals**: Never use genomic_event_derived for total sample counts ### 2. 🚨 OFF-PANEL MUTATION INCLUSION #### ❌ Wrong: Including off-panel mutations ```sql -- INCORRECT - Includes mutations outside gene panels SELEC … (37891 more chars) ◀ result {"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]} ▶ read_guide { "uri": "cbioportal://gene-expression-guide" } ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ◀ result # Gene Expression Analysis Guide This guide covers continuous-value genomic data: gene **expression**, **copy number** values, **methylation**, and related profile types. Mutation/CNA/SV *frequency* analysis lives in `cbioportal://mutation-frequency-guide`. ## Where this data lives Continuous per-sample-per-gene values are stored in `genetic_alteration_derived`: | Column | Description | |---|---| | `sample_unique_id` | `_` | | `cancer_study_identifier` | study scope | | `hugo_gene_symbol` | gene | | `profile_type` | which assay/normalization (see below) | | `alteration_value` | the actual value — stored as Nullable(String); cast with `toFloat64OrNull` | `alteration_value` is a string because the same column hosts many different value scales. The `''` and `'NA'` sentinels mean "missing"; always filter them out and use `toFloat64OrNull(alteration_value) IS NOT NULL` for downstream math. ## Discovering profile types for a study Different studies expose different profile types depending on what assays were run and how the data was normalized. Always check what a specific study supports before picking one: ```sql SELECT DISTINCT profile_type FROM genetic_alteration_derived WHERE cancer_study_identifier = 'brca_metabric' ORDER BY profile_type; ``` Common values across the public portal: | Family | Profile types | |---|---| | mRNA expression | `mrna`, `mrna_median_Zscores`, `mrna_seq_v2_rsem`, `mrna_seq_v2_rsem_Zscores`, `mrna_seq_cpm`, `mrna_seq_fpkm`, `mrna_U133`, `mrna_outliers` | | Copy number (continuous) | `cna`, `linear_CNA`, `log2CNA`, `cna_consensus`, `cna_rae`, `gistic` | | Methylation | `methylation_hm27`, `methylation_hm450`, `methylation_epic`, `methylation_promoters_rrbs` | | miRNA | `mirna`, `mirna_median_Zscores` | | Protein | `protein_quantification`, `protein_level`, `RPPA` | **Z-score vs raw choice.** When the user asks "is X correlated with Y", either works for Spearman (rank-based) — Pearson would care. Default to the non-Z-score variant if both exist, and call out which one in the response. ## Canonical recipe — Spearman correlation between two genes ```sql SELECT * FROM gene_pair_coexpression( study = 'brca_metabric', gene_a = 'TP53', gene_b = 'MYC', profile_type = 'mrna' ); ``` Returns one row: `(gene_a, gene_b, profile_type, spearman_correlation, num_samples)`. - `spearman_correlation` in [−1, 1]; `NULL` when fewer than 3 valid paired samples. - Mirrors cbioportal-backend's `ClickhouseCoExpressionMapper.getCoExpressions`, simplified to a pair lookup (the backend computes one ref gene vs ALL other genes for the coexpression page; here the agent asks about a specific pair). ### Verified examples | Study | gene_a | gene_b | profile_type | spearman | n | |---|---|---|---|---|---| | `brca_metabric` | TP53 | MYC | `mrna` | 0.118 | 1980 | | `brca_metabric` | ESR1 | PGR | `mrna` | 0.487 | 1980 | ESR1↔PGR is the textbook breast-cancer estrogen-receptor coregula … (2785 more chars) ◀ 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) ▶ search_oncotree { "search_term": "ovarian" } ▶ resolve_and_route { "studyKeywords": [ "ovarian" ] } ◀ result {"result":[{"code":"OCNOS","name":"Ovarian Choriocarcinoma, NOS","score":60,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OOVC > OCNOS"},{"code":"OCS","name":"Ovarian Carcinosarcoma/Malignant Mixed Mesodermal Tumor","score":60,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > OCS"},{"code":"OGCT","name":"Ovarian Germ Cell Tumor","score":60,"mainType":"Germ Cell Tumor","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OGCT"},{"code":"OOVC","name":"Ovarian Cancer, Other","score":60,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OOVC"},{"code":"OSMAD","name":"Ovarian Seromucinous Adenoma","score":60,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > OSMAD"},{"code":"OSMBT","name":"Ovarian Seromucinous Borderline Tumor","score":60,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > OSMBT"},{"code":"OSMCA","name":"Ovarian Seromucinous Carcinoma","score":60,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > OSMCA"},{"code":"OVT","name":"Ovarian Epithelial Tumor","score":60,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT"},{"code":"CCBOV","name":"Clear Cell Borderline Ovarian Tumor","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > CCBOV"},{"code":"CCOV","name":"Clear Cell Ovarian Cancer","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > CCOV"},{"code":"EBOV","name":"Endometrioid Borderlin Ovarian Tumor","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > EBOV"},{"code":"EOV","name":"Endometrioid Ovarian Cancer","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > EOV"},{"code":"HGSOC","name":"High-Grade Serous Ovarian Cancer","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > SOC > HGSOC"},{"code":"LGSOC","name":"Low-Grade Serous Ovarian Cancer","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > SOC > LGSOC"},{"code":"MBOV","name":"Mucinous Borderline Ovarian Tumor","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > MBOV"},{"code":"MOV","name":"Mucinous Ovarian Cancer","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > MOV"},{"code":"MXOV","name":"Mixed Ovarian Carcinoma","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > MXOV"},{"code":"SBMOV","name":"Serous Borderline Ovarian Tumor, Micropapillary","score":50,"mainType":"Ovarian Cancer","tissue":"Ovary/Fallopian Tube","hierarchy":"OVARY > OVT > SBMOV"},{"code":"SBOV","name":"Serous Borderline Ovarian Tumor","score":50,"mainType":"Ovarian … (1039 more chars) ◀ result {"success":true,"message":"Found 13 matching studies (top 5 with full metadata, rest with basic info). Pick the best match (prefer TCGA → prefer PanCancer Atlas), use its metadata to call the appropriate navigation tool(s) and generate URLs immediately. Present other studies as alternatives.","data":{"totalCount":13,"studiesWithMetadata":[{"studyId":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","sampleCount":25775,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_met_2021","metadata":{"clinicalAttributeIds":["AGE_AT_DEATH","AGE_AT_EVIDENCE_OF_METS","AGE_AT_LAST_CONTACT","AGE_AT_SEQUENCING","AGE_AT_SURGERY","CANCER_TYPE","CANCER_TYPE_DETAILED","DMETS_DX_ADRENAL_GLAND","DMETS_DX_BILIARY_TRACT","DMETS_DX_BLADDER_UT","DMETS_DX_BONE","DMETS_DX_BOWEL","DMETS_DX_BREAST","DMETS_DX_CNS_BRAIN","DMETS_DX_DIST_LN","DMETS_DX_FEMALE_GENITAL","DMETS_DX_HEAD_NECK","DMETS_DX_INTRA_ABDOMINAL","DMETS_DX_KIDNEY","DMETS_DX_LIVER","DMETS_DX_LUNG","DMETS_DX_MALE_GENITAL","DMETS_DX_MEDIASTINUM","DMETS_DX_OVARY","DMETS_DX_PLEURA","DMETS_DX_PNS","DMETS_DX_SKIN","DMETS_DX_UNSPECIFIED","FGA","FRACTION_GENOME_ALTERED","GENE_PANEL","IS_DIST_MET_MAPPED","METASTATIC_SITE","MET_COUNT","MET_SITE_COUNT","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","ONCOTREE_CODE","ORGAN_SYSTEM","OS_MONTHS","OS_STATUS","PRIMARY_SITE","RACE","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SEX","SUBTYPE","SUBTYPE_ABBREVIATION","TMB_NONSYNONYMOUS","TUMOR_PURITY"],"molecularProfileIds":["msk_met_2021_cna","msk_met_2021_mutations","msk_met_2021_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}},{"studyId":"ov_tcga","name":"Ovarian Serous Cystadenocarcinoma (TCGA, Firehose Legacy)","sampleCount":617,"studyViewUrl":"https://www.cbioportal.org/study?id=ov_tcga","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_STAGE","CLIN_M_STAGE","CLIN_N_STAGE","CLIN_T_STAGE","DAYS_TO_COLLECTION","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DAYS_TO_PATIENT_PROGRESSION_FREE","DAYS_TO_SPECIMEN_COLLECTION","DAYS_TO_TUMOR_PROGRESSION","DFS_MONTHS","DFS_STATUS","DISEASE_CODE","ECOG_SCORE","ETHNICITY","EXTRANODAL_INVOLVEMENT","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GRADE","HISTOLOGICAL_DIAGNOSIS","HISTORY_NEOADJUVANT_TRTYN","HISTORY_OTHER_MALIGNANCY","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","INITIAL_PATHOLOGIC_DX_YEAR","IS_FFPE","JEWISH_RELIGION_HERITAGE_INDICATOR","KARNOFSKY_PERFORMANCE_SCORE","LONGEST_DIMENSION","LYMPHOVASCULAR_INVASION_INDICATOR","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT_OTHER","METHOD_OF_SAMPLE_PROCUREMENT","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","OCT_EMBEDDED","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_METHOD_OF_SAMPLE_PROCUREMENT","OTHER_PATIENT_ID","OTHER_SAMPLE_ID","PATHOLOGY_REPORT_FILE_NAME","PATHOLOGY_REPORT_UUID","PATH_M_STAGE","PATH_N_STAGE","PAT … (8944 more chars) ▶ clickhouse_list_table_columns { "table": "genetic_alteration_derived" } ◀ result {"columns":[{"name":"sample_unique_id","type":"String"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"hugo_gene_symbol","type":"String"},{"name":"profile_type","type":"LowCardinality(String)"},{"name":"alteration_value","type":"Nullable(String)"}]} ▶ clickhouse_run_select_query { "query": " -- Step 1: Check what methylation and expression profiles are available for BRCA1 in ovarian studies SELECT DISTINCT profile_type, COUNT(DISTINCT sample_unique_id) as sample_count FROM genetic_alteration_derived WHERE cancer_study_identifier IN ('ov_tcga', 'ov_tcga_pan_can_atlas_2018') AND hugo_gene_symbol = 'BRCA1' AND profile_type IN ('methylation_hm27', 'methylation_hm450', 'mrna', 'mrna_median_Zscores', 'mrna_seq_v2_mrna', 'mrna_seq_v2_mrna_median_Zscores', 'rna_seq_v2_mrna', 'rna_seq_v2_mrna_median_Zscores') GROUP BY profile_type ORDER BY sample_count DESC LIMIT 100 ", "max_rows": 100 } ◀ result {"rows":[{"profile_type":"rna_seq_v2_mrna","sample_count":607},{"profile_type":"rna_seq_v2_mrna_median_Zscores","sample_count":607},{"profile_type":"methylation_hm27","sample_count":592},{"profile_type":"mrna","sample_count":558},{"profile_type":"mrna_median_Zscores","sample_count":558}]} ▶ clickhouse_run_select_query { "query": " -- Check for BRCA1 promoter methylation data -- The methylation data in genetic_alteration_derived has the probe ID in hugo_gene_symbol when profile is methylation SELECT DISTINCT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) as samples FROM genetic_alteration_derived WHERE cancer_study_identifier IN ('ov_tcga', 'ov_tcga_pan_can_atlas_2018') AND profile_type LIKE 'methylation%' AND hugo_gene_symbol LIKE '%BRCA1%' GROUP BY hugo_gene_symbol ORDER BY samples DESC LIMIT 50 ", "max_rows": 100 } ◀ result {"rows":[{"hugo_gene_symbol":"BRCA1","samples":592}]} ▶ clickhouse_run_select_query { "query": " -- Spearman correlation between BRCA1 methylation (hm27) and BRCA1 mRNA expression -- Focus on the PanCancer Atlas study which has both data types WITH methylation AS ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS meth_value FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'methylation_hm450' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ), expression AS ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS expr_value FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'rna_seq_v2_mrna_median_Zscores' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) SELECT rankCorr(m.meth_value, e.expr_value) AS spearman_correlation, count() AS num_paired_samples FROM methylation m INNER JOIN expression e USING (sample_unique_id) ", "max_rows": 10 } ◀ result {"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 241\n Code: 241. DB::Exception: (total) memory limit exceeded: would use 31.27 GiB (attempt to allocate chunk of 0.00 B), current RSS: 31.27 GiB, maximum: 28.80 GiB. (MEMORY_LIMIT_EXCEEDED)\n"} ▶ clickhouse_run_select_query { "query": " -- Try direct correlation via subqueries instead of CTE, to conserve memory SELECT rankCorr(m.v, e.v) AS spearman_correlation, count() AS num_paired_samples FROM ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'methylation_hm450' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) m INNER JOIN ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'rna_seq_v2_mrna_median_Zscores' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) e USING (sample_unique_id) ", "max_rows": 10 } ◀ result {"rows":[{"num_paired_samples":0}]} ▶ clickhouse_run_select_query { "query": " -- Check BRCA1 data availability per study and profile type SELECT cancer_study_identifier, profile_type, COUNT(DISTINCT sample_unique_id) as samples FROM genetic_alteration_derived WHERE hugo_gene_symbol = 'BRCA1' AND cancer_study_identifier IN ('ov_tcga', 'ov_tcga_pan_can_atlas_2018', 'ov_tcga_pub') AND profile_type IN ('methylation_hm27', 'methylation_hm450', 'mrna', 'mrna_median_Zscores', 'rna_seq_v2_mrna', 'rna_seq_v2_mrna_median_Zscores') AND alteration_value NOT IN ('', 'NA') GROUP BY cancer_study_identifier, profile_type ORDER BY cancer_study_identifier, samples DESC ", "max_rows": 100 } ◀ result {"rows":[{"cancer_study_identifier":"ov_tcga","profile_type":"methylation_hm27","samples":592},{"cancer_study_identifier":"ov_tcga","profile_type":"mrna_median_Zscores","samples":558},{"cancer_study_identifier":"ov_tcga","profile_type":"mrna","samples":558},{"cancer_study_identifier":"ov_tcga","profile_type":"rna_seq_v2_mrna_median_Zscores","samples":307},{"cancer_study_identifier":"ov_tcga","profile_type":"rna_seq_v2_mrna","samples":307},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","samples":300},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna","samples":300},{"cancer_study_identifier":"ov_tcga_pub","profile_type":"mrna_median_Zscores","samples":489},{"cancer_study_identifier":"ov_tcga_pub","profile_type":"mrna","samples":489},{"cancer_study_identifier":"ov_tcga_pub","profile_type":"methylation_hm27","samples":489}]} ▶ clickhouse_run_select_query { "query": " -- Spearman correlation between BRCA1 methylation and BRCA1 mRNA expression in TCGA ovarian SELECT rankCorr(m.v, e.v) AS spearman_correlation, count() AS num_paired_samples FROM ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'methylation_hm27' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) m INNER JOIN ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'mrna_median_Zscores' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) e USING (sample_unique_id) ", "max_rows": 10 } ◀ result {"rows":[{"spearman_correlation":-0.40336141757343147,"num_paired_samples":550}]} ▶ clickhouse_run_select_query { "query": " -- BRCA1 methylation vs raw mRNA in ov_tcga, plus ov_tcga_pub for validation SELECT 'ov_tcga' as study, 'mrna (raw)' as expression_type, rankCorr(m.v, e.v) AS spearman_correlation, count() AS num_paired_samples FROM ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'methylation_hm27' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) m INNER JOIN ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'mrna' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) e USING (sample_unique_id) UNION ALL -- Same in ov_tcga_pub SELECT 'ov_tcga_pub' as study, 'mrna (raw)' as expression_type, rankCorr(m.v, e.v) AS spearman_correlation, count() AS num_paired_samples FROM ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pub' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'methylation_hm27' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) m INNER JOIN ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pub' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'mrna' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) e USING (sample_unique_id) ", "max_rows": 10 } ◀ result {"rows":[{"study":"ov_tcga","expression_type":"mrna (raw)","spearman_correlation":-0.40336141757343147,"num_paired_samples":550},{"study":"ov_tcga_pub","expression_type":"mrna (raw)","spearman_correlation":-0.3642538233235566,"num_paired_samples":489}]} ▶ clickhouse_run_select_query { "query": " -- Descriptive statistics: BRCA1 methylation distribution and BRCA1 expression distribution WITH meth_stats AS ( SELECT 'ov_tcga' as study, 'BRCA1 methylation (hm27)' as variable, count() as n, round(avg(toFloat64OrNull(alteration_value)), 3) as mean, round(quantile(0.5)(toFloat64OrNull(alteration_value)), 3) as median, round(min(toFloat64OrNull(alteration_value)), 3) as min_val, round(max(toFloat64OrNull(alteration_value)), 3) as max_val FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'methylation_hm27' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ), expr_stats AS ( SELECT 'ov_tcga' as study, 'BRCA1 mRNA (median Z-score)' as variable, count() as n, round(avg(toFloat64OrNull(alteration_value)), 3) as mean, round(quantile(0.5)(toFloat64OrNull(alteration_value)), 3) as median, round(min(toFloat64OrNull(alteration_value)), 3) as min_val, round(max(toFloat64OrNull(alteration_value)), 3) as max_val FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga' AND hugo_gene_symbol = 'BRCA1' AND profile_type = 'mrna_median_Zscores' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) SELECT * FROM meth_stats UNION ALL SELECT * FROM expr_stats ", "max_rows": 10 } ◀ result {"rows":[{"study":"ov_tcga","variable":"BRCA1 mRNA (median Z-score)","n":558,"mean":-0.383,"median":-0.225,"min_val":-3.762,"max_val":2.227},{"study":"ov_tcga","variable":"BRCA1 methylation (hm27)","n":592,"mean":0.131,"median":0.035,"min_val":0.011,"max_val":0.955}]} ▶ resolve_and_route { "studyIds": [ "ov_tcga" ] } ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"ov_tcga","name":"Ovarian Serous Cystadenocarcinoma (TCGA, Firehose Legacy)","sampleCount":617,"studyViewUrl":"https://www.cbioportal.org/study?id=ov_tcga","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_STAGE","CLIN_M_STAGE","CLIN_N_STAGE","CLIN_T_STAGE","DAYS_TO_COLLECTION","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DAYS_TO_PATIENT_PROGRESSION_FREE","DAYS_TO_SPECIMEN_COLLECTION","DAYS_TO_TUMOR_PROGRESSION","DFS_MONTHS","DFS_STATUS","DISEASE_CODE","ECOG_SCORE","ETHNICITY","EXTRANODAL_INVOLVEMENT","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GRADE","HISTOLOGICAL_DIAGNOSIS","HISTORY_NEOADJUVANT_TRTYN","HISTORY_OTHER_MALIGNANCY","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","INITIAL_PATHOLOGIC_DX_YEAR","IS_FFPE","JEWISH_RELIGION_HERITAGE_INDICATOR","KARNOFSKY_PERFORMANCE_SCORE","LONGEST_DIMENSION","LYMPHOVASCULAR_INVASION_INDICATOR","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT_OTHER","METHOD_OF_SAMPLE_PROCUREMENT","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","OCT_EMBEDDED","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_METHOD_OF_SAMPLE_PROCUREMENT","OTHER_PATIENT_ID","OTHER_SAMPLE_ID","PATHOLOGY_REPORT_FILE_NAME","PATHOLOGY_REPORT_UUID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERFORMANCE_STATUS_TIMING","PHARMACEUTICAL_TX_ADJUVANT","PRIMARY_SITE","PROJECT_CODE","PROSPECTIVE_COLLECTION","RACE","RADIATION_TREATMENT_ADJUVANT","RESIDUAL_TUMOR","RETROSPECTIVE_COLLECTION","SAMPLE_COUNT","SAMPLE_INITIAL_WEIGHT","SAMPLE_TYPE","SAMPLE_TYPE_ID","SEX","SHORTEST_DIMENSION","SOMATIC_STATUS","SPECIMEN_CURRENT_WEIGHT","SPECIMEN_FREEZING_METHOD","SPECIMEN_SECOND_LONGEST_DIMENSION","STAGE_OTHER","TIME_BETWEEN_CLAMPING_AND_FREEZING","TIME_BETWEEN_EXCISION_AND_FREEZING","TISSUE_SOURCE_SITE","TMB_NONSYNONYMOUS","TREATMENT_OUTCOME_FIRST_COURSE","TUMOR_STATUS","TUMOR_TISSUE_SITE","VASCULAR_INVASION_INDICATOR","VIAL_NUMBER"],"molecularProfileIds":["ov_tcga_gistic","ov_tcga_linear_CNA","ov_tcga_methylation_hm27","ov_tcga_mrna","ov_tcga_mrna_U133","ov_tcga_mrna_U133_Zscores","ov_tcga_mrna_U133_all_sample_Zscores","ov_tcga_mrna_median_Zscores","ov_tcga_mrna_median_all_sample_Zscores","ov_tcga_mutations","ov_tcga_protein_quantification","ov_tcga_protein_quantification_zscores","ov_tcga_rna_seq_v2_mrna","ov_tcga_rna_seq_v2_mrna_median_Zscores","ov_tcga_rna_seq_v2_mrna_median_all_sample_Zscores","ov_tcga_rppa","ov_tcga_rppa_Zscores"],"genericAssayProfiles":["ov_tcga_phosphoprotein_quantification"],"heatmapProfileIds":["ov_tcga_methylation_hm27","ov_tcga_mrna_U133_Zscores","ov_tcga_mrna_U133_all_sample_Zscores","ov_tcga_mrna_median_Zscores","ov_tcga_mrna_median_all_sample_Zscores","ov_tcga_protein_quantification_zscores","ov_tcga_rna_seq_v2_mrna_medi … (256 more chars) ▶ navigate_to_results_view { "studyIds": [ "ov_tcga" ], "genes": [ "BRCA1" ], "tab": "oncoprint", "oncoprintHeatmapTracks": [ { "molecularProfileId": "ov_tcga_methylation_hm27", "entities": [ "BRCA1" ] }, { "molecularProfileId": "ov_tcga_mrna_median_Zscores", "entities": [ "BRCA1" ] } ] } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=ov_tcga&gene_list=BRCA1&case_set_id=ov_tcga_all&heatmap_track_groups=ov_tcga_methylation_hm27%2CBRCA1%3Bov_tcga_mrna_median_Zscores%2CBRCA1","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=ov_tcga&gene_list=BRCA1&case_set_id=ov_tcga_all&heatmap_track_groups=ov_tcga_methylation_hm27%2CBRCA1%3Bov_tcga_mrna_median_Zscores%2CBRCA1","data":{"studyIds":["ov_tcga"],"studies":[{"studyId":"ov_tcga","name":"Ovarian Serous Cystadenocarcinoma (TCGA, Firehose Legacy)","sampleCount":617}],"genes":["BRCA1"],"caseSetId":"ov_tcga_all","oncoprintHeatmapTracks":[{"molecularProfileId":"ov_tcga_methylation_hm27","entities":["BRCA1"]},{"molecularProfileId":"ov_tcga_mrna_median_Zscores","entities":["BRCA1"]}],"pageDescription":"Visual summary of alterations in queried genes across samples."}} ═ answer (success) Perfect! Now let me provide a comprehensive answer with the findings: --- ## Answer: Yes, BRCA1 promoter methylation is associated with decreased BRCA1 mRNA expression in ovarian cancer. ### Data Summary I analyzed the **TCGA Ovarian Serous Cystadenocarcinoma (Firehose Legacy)** cohort, which includes both BRCA1 methylation (HumanMethylation27) and BRCA1 mRNA expression data. **Correlation Analysis:** | Study | Metric | Value | |-------|--------|-------| | **ov_tcga** | Spearman correlation | **−0.40** | | | Paired samples with both data types | 550 | | **ov_tcga_pub** (validation) | Spearman correlation | −0.36 | | | Paired samples | 489 | **Interpretation:** The negative Spearman correlation of **−0.40** indicates a **moderate negative association** — higher BRCA1 promoter methylation is associated with lower BRCA1 mRNA expression. This is consistent with the biological principle that DNA methylation in gene promoters typically silences transcription. ### Descriptive Statistics (ov_tcga) | Variable | N | Mean | Median | Min | Max | |----------|---|------|--------|-----|-----| | **BRCA1 methylation (β-value)** | 592 | 0.131 | 0.035 | 0.011 | 0.955 | | **BRCA1 mRNA (median Z-score)** | 558 | −0.383 | −0.225 | −3.762 | 2.227 | The inverse means: samples with higher methylation tend toward lower (more negative) mRNA Z-scores; samples with lower methylation tend toward higher expression. ### Important Caveats 1. **Statistical significance not computed:** The correlation coefficient itself is **not** a p-value. To determine statistical significance, you would run this analysis in R (`cor.test(..., method="spearman")`), Python (`scipy.stats.spearmanr`), or cBioPortal's Group Comparison → DNA Methylation tab. 2. **Probe-level variation:** This analysis uses aggregate BRCA1 methylation scores. BRCA1 has multiple CpG sites, and promoter-specific methylation (e.g., CpG islands near the transcription start site) may show stronger associations than the array-wide average. 3. **Causation:** Correlation does not establish causation. Methylation may silence BRCA1, or both may be consequences of other tumor evolutionary processes. 4. **Validation in other cohorts:** The correlation was confirmed in the **ov_tcga_pub** cohort (r = −0.36, n = 489), supporting reproducibility. ### Visualize the Data [View BRCA1 methylation and mRNA expression heatmaps in TCGA ovarian cancer](https://www.cbioportal.org/results/oncoprint?cancer_study_list=ov_tcga&gene_list=BRCA1&case_set_id=ov_tcga_all&heatmap_track_groups=ov_tcga_methylation_hm27%2CBRCA1%3Bov_tcga_mrna_median_Zscores%2CBRCA1) This link opens the OncoPrint visualization with both methylation (top heatmap) and mRNA expression (bottom heatmap) tracks aligned by sample, making the negative relationship visually apparent.