Q (sonnet): I'm interested in IDH1 in lower grade glioma. What can we look at? In Brain Lower Grade Glioma (TCGA, PanCancer Atlas), IDH1 is mutated in most tumors. A few directions: 1. Compare overall survival of IDH1-mutant vs IDH1-wild-type patients 2. See which genes are co-mutated with IDH1 (e.g. TP53, ATRX, CIC) 3. Look at IDH1 mutation positions (R132H and others) in the mutation diagram Which would you like? Continue this conversation: reply to the user's new message. option 1 please ▶ resolve_and_route { "studyKeywords": [ "TCGA", "lower grade glioma", "pan_can_atlas" ] } ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514,"studyViewUrl":"https://www.cbioportal.org/study?id=lgg_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":["lgg_tcga_pan_can_atlas_2018_gistic","lgg_tcga_pan_can_atlas_2018_log2CNA","lgg_tcga_pan_can_atlas_2018_mutations","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lgg_tcga_pan_can_atlas_2018_rppa","lgg_tcga_pan_can_atlas_2018_rppa_Zscores","lgg_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["lgg_tcga_pan_can_atlas_2018_armlevel_cna","lgg_tcga_pan_can_atlas_2018_genetic_ancestry","lgg_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","lgg_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lgg_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}}]}} ▶ read_guide { "uri": "cbioportal://statistical-tests-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) ▶ navigate_to_results_view { "studyIds": [ "lgg_tcga_pan_can_atlas_2018" ], "genes": [ "IDH1: MUT" ], "tab": "comparison/survival", "profileFilter": "mutations" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/comparison/survival?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+MUT&case_set_id=lgg_tcga_pan_can_atlas_2018_all&profileFilter=mutations","url":"https://www.cbioportal.org/results/comparison/survival?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+MUT&case_set_id=lgg_tcga_pan_can_atlas_2018_all&profileFilter=mutations","data":{"studyIds":["lgg_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514}],"genes":["IDH1"],"caseSetId":"lgg_tcga_pan_can_atlas_2018_all","pageDescription":"Kaplan-Meier survival analysis comparing groups defined by query genes."}} ▶ 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": " WITH idh1_mut_patients AS ( SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'IDH1' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND off_panel = 0 ), os AS ( SELECT patient_unique_id, toFloat64OrNull(argMaxIf(attribute_value, attribute_name, attribute_name='OS_MONTHS')) AS os_months, argMaxIf(attribute_value, attribute_name, attribute_name='OS_STATUS') AS os_status FROM clinical_data_derived WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018' AND type = 'patient' AND attribute_name IN ('OS_MONTHS','OS_STATUS') GROUP BY patient_unique_id ) SELECT CASE WHEN o.patient_unique_id IN (SELECT patient_unique_id FROM idh1_mut_patients) THEN 'IDH1-mutant' ELSE 'IDH1-wildtype' END AS group_name, count() AS n_patients, countIf(os_status = '1:DECEASED') AS n_events, countIf(os_status = '0:LIVING') AS n_censored, min(os_months) AS min_followup, max(os_months) AS max_followup FROM os o WHERE os_months IS NOT NULL GROUP BY group_name " } ◀ result {"rows":[{"group_name":"IDH1-mutant","n_patients":394,"n_events":72,"n_censored":322,"min_followup":0,"max_followup":182.3322484},{"group_name":"IDH1-wildtype","n_patients":119,"n_events":53,"n_censored":66,"min_followup":0.065752704,"max_followup":211.1648092}]} ═ answer (success) Here's the overall survival breakdown for **Brain Lower Grade Glioma (TCGA, PanCancer Atlas)**, at the patient level: | Group | N patients | Events (deceased) | Censored (living) | Follow-up range (months) | |---|---|---|---|---| | IDH1-mutant | 394 | 72 (18%) | 322 | 0 – 182.3 | | IDH1-wildtype | 119 | 53 (45%) | 66 | 0.07 – 211.2 | Since fewer than half of the IDH1-mutant group experienced an event, the Kaplan-Meier median OS for that group is likely **not reached**. The wild-type group is closer to (but still under) 50% events, so its median may also be borderline. Comparing these curves properly requires Kaplan-Meier estimation with a log-rank test, which I can't run directly — cBioPortal's Group Comparison Survival tab does this natively, including the log-rank p-value. [View IDH1 mutant vs. wild-type overall survival comparison](https://www.cbioportal.org/results/comparison/survival?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+MUT&case_set_id=lgg_tcga_pan_can_atlas_2018_all&profileFilter=mutations) This opens the Kaplan-Meier survival analysis comparing IDH1-mutant vs. unaltered patients directly in the portal — you'll see the actual curves, median OS estimates, and log-rank p-value there. Let me know if you'd also like PFS/DFS comparisons, or want to move on to the co-mutation or mutation-position analyses we discussed earlier.