Q (sonnet-5.5): "What are the survival differences between EGFR-mutated, EGFR-amplified, and EGFR wild-type lung adenocarcinoma patients?" ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ▶ list_studies { "search": "lung adenocarcinoma TCGA" } ◀ 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 {"result":[{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"luad","sample_count":566,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"luad","sample_count":586,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga"},{"cancer_study_identifier":"luad_tcga_gdc","name":"Lung Adenocarcinoma (TCGA GDC, 2025)","type_of_cancer_id":"luad","sample_count":571,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_gdc"},{"cancer_study_identifier":"luad_tcga_pub","name":"Lung Adenocarcinoma (TCGA, Nature 2014)","type_of_cancer_id":"luad","sample_count":230,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pub"}]} ▶ get_study_guide { "study_id": "luad_tcga_pan_can_atlas_2018" } ▶ clickhouse_list_table_columns { "table": "genomic_event_derived" } ▶ clickhouse_list_table_columns { "table": "clinical_data_derived" } ◀ result # Lung Adenocarcinoma (TCGA, PanCancer Atlas) **Study ID:** `luad_tcga_pan_can_atlas_2018` See `_tcga_pancan_template.md` for common TCGA clinical attributes. ## Study-Specific Attributes ### Not available in this study - **Smoking history / pack-years**: no smoking attribute. Say smoking status is not available here rather than inferring it. - **EGFR / KRAS / ALK status**: no clinical status attributes. Derive EGFR and KRAS status from mutation data, and ALK fusions from structural-variant data (5 samples with an ALK SV in `genomic_event_derived`). - `SUBTYPE` is `LUAD` (502) or blank (64) and carries no molecular subtype. `GRADE` is blank for all samples. ## Key Genes - Driver Mutations | Gene | Frequency | Notes | |------|-----------|-------| | KRAS | ~30% | G12C targetable with sotorasib/adagrasib | | EGFR | ~15% | L858R, exon 19 del targetable with TKIs | | ALK | ~5% | Fusions targetable with crizotinib, etc. | | BRAF | ~5% | V600E targetable | | ROS1 | ~2% | Fusions targetable | | RET | ~2% | Fusions targetable | | MET | ~3% | Exon 14 skipping, amplification | ## Notes - Driver mutations are largely mutually exclusive - STK11 and KEAP1 mutations associated with poor immunotherapy response ◀ 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 os AS ( SELECT patient_unique_id, anyIf(attribute_value, attribute_name='OS_STATUS') st, anyIf(toFloat64OrNull(attribute_value), attribute_name='OS_MONTHS') mo FROM clinical_data_derived WHERE cancer_study_identifier='luad_tcga_pan_can_atlas_2018' AND attribute_name IN ('OS_STATUS','OS_MONTHS') GROUP BY patient_unique_id), mut AS (SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier='luad_tcga_pan_can_atlas_2018' AND hugo_gene_symbol='EGFR' AND variant_type='mutation' AND mutation_status!='UNCALLED'), amp AS (SELECT DISTINCT patient_unique_id FROM genomic_event_derived WHERE cancer_study_identifier='luad_tcga_pan_can_atlas_2018' AND hugo_gene_symbol='EGFR' AND variant_type='cna' AND cna_alteration=2) SELECT multiIf(p IN (SELECT patient_unique_id FROM mut) AND p IN (SELECT patient_unique_id FROM amp),'Mutated+Amplified', p IN (SELECT patient_unique_id FROM mut),'Mutated only', p IN (SELECT patient_unique_id FROM amp),'Amplified only','Wild-type (neither)') grp, count() n_total, countIf(mo IS NOT NULL AND st!='') n_os, countIf(st LIKE '1:%') events, countIf(st LIKE '0:%') censored, min(mo) mn, max(mo) mx FROM (SELECT patient_unique_id p, st, mo FROM os) GROUP BY grp ORDER BY grp" } ◀ result {"rows":[{"grp":"Amplified only","n_total":11,"n_os":10,"events":6,"censored":5,"mn":2.991748036,"mx":123.5822073},{"grp":"Mutated only","n_total":55,"n_os":50,"events":17,"censored":34,"mn":0,"mx":108.6563435},{"grp":"Mutated+Amplified","n_total":15,"n_os":15,"events":11,"censored":4,"mn":0.723279745,"mx":58.45415393},{"grp":"Wild-type (neither)","n_total":485,"n_os":430,"events":152,"censored":285,"mn":0,"mx":238.2877996}]} ▶ resolve_and_route { "studyIds": [ "luad_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":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566,"studyViewUrl":"https://www.cbioportal.org/study?id=luad_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":["luad_tcga_pan_can_atlas_2018_gistic","luad_tcga_pan_can_atlas_2018_log2CNA","luad_tcga_pan_can_atlas_2018_mutations","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_tcga_pan_can_atlas_2018_rppa","luad_tcga_pan_can_atlas_2018_rppa_Zscores","luad_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["luad_tcga_pan_can_atlas_2018_armlevel_cna","luad_tcga_pan_can_atlas_2018_genetic_ancestry","luad_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","luad_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_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"]}}]}} ▶ navigate_to_group_comparison { "studyIds": [ "luad_tcga_pan_can_atlas_2018" ], "tab": "survival", "groups": [ { "name": "EGFR mutated", "studyViewFilter": { "geneFilters": [ { "molecularProfileIds": [ "luad_tcga_pan_can_atlas_2018_mutations" ], "geneQueries": [ [ { "hugoGeneSymbol": "EGFR" } ] ] } ] } }, { "name": "EGFR amplified", "studyViewFilter": { "geneFilters": [ { "molecularProfileIds": [ "luad_tcga_pan_can_atlas_2018_gistic" ], "geneQueries": [ [ { "hugoGeneSymbol": "EGFR", "alterations": [ "AMP" ] } ] ] } ] } }, { "name": "EGFR wild-type (neither)", "isUnselected": true } ] } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6abb41b7c2115c492d8855c3","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6abb41b7c2115c492d8855c3","data":{"description":"Group comparison (3 custom groups)","studies":["luad_tcga_pan_can_atlas_2018"],"totalGroups":3,"groups":[{"name":"EGFR mutated","sampleCount":70},{"name":"EGFR amplified","sampleCount":26},{"name":"EGFR wild-type (neither)","sampleCount":485}],"studyViewUrl":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"EGFR mutated","url":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22molecularProfileIds%22%3A%5B%22luad_tcga_pan_can_atlas_2018_mutations%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22EGFR%22%7D%5D%5D%7D%5D%7D"},{"groupName":"EGFR amplified","url":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22molecularProfileIds%22%3A%5B%22luad_tcga_pan_can_atlas_2018_gistic%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22EGFR%22%2C%22alterations%22%3A%5B%22AMP%22%5D%7D%5D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing EGFR mutated (70) vs EGFR amplified (26) vs EGFR wild-type (neither) (485)."}} ═ answer (success) I can't give you survival differences directly. Survival comparisons need Kaplan-Meier curves and a log-rank test, and I can't run either one here. What I can give you is the group sizes and event counts, plus a link to the survival comparison in cBioPortal. **Cohort:** Lung Adenocarcinoma (TCGA, PanCancer Atlas), 566 samples. I chose this study because you didn't name one. Three other TCGA LUAD studies exist (Firehose Legacy, GDC 2025 and Nature 2014). Tell me if you'd prefer one of those. **Groups (counted by patient):** - **Mutated** means any EGFR mutation that isn't flagged as uncalled. - **Amplified** means an EGFR high-level amplification. - **Wild-type** means neither of the above. | Group | Patients | Deaths | Alive (censored) | |---|---|---|---| | Mutated only | 55 | 17 | 34 | | Amplified only | 11 | 6 | 5 | | Mutated and amplified | 15 | 11 | 4 | | Wild-type (neither) | 485 | 152 | 285 | - A few patients have a survival status but no follow-up time, so deaths plus censored can differ slightly from the group total. - The comparison link below counts the 15 patients with both alterations in both the mutated group and the amplified group. That makes its group sizes 70, 26 and 485. - Fewer than half of the patients died in the mutated-only and wild-type groups. The Kaplan-Meier median survival is therefore probably not reached in those groups. - The amplified-only group (11 patients) and the mutated-and-amplified group (15 patients) are very small. Any survival estimate for them will be unreliable. I haven't reported median survival, hazard ratios or p-values, and I haven't said which group does better. To get those, open the link below. It shows the Kaplan-Meier curves, medians and log-rank p-values. For hazard ratios, run a Cox model in R (`survival::coxph`) or Python (`lifelines`). [View EGFR mutated vs amplified vs wild-type survival comparison](https://www.cbioportal.org/comparison/survival?comparisonId=6abb41b7c2115c492d8855c3) [Study overview](https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018)