Q (sonnet): "What are the survival differences between EGFR-mutated, EGFR-amplified, and EGFR wild-type lung adenocarcinoma patients?" ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ▶ list_studies { "search": "lung adenocarcinoma" } ◀ 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 # Mutation Frequency Analysis Guide ## IMPORTANT: Reporting Mutation Frequencies - **ALWAYS report frequencies as percentages**, not raw counts: `frequency = (altered_samples / total_profiled_samples) × 100` - For quick frequency lookups, **prefer the TCGA Pan-Cancer Atlas study first**, then offer to expand to other studies - When reporting across multiple studies, show **ranges** (e.g., "TP53 is mutated in 30–60% of samples") rather than a single average - **NEVER** sum mutation events across studies to compute an aggregate frequency — this can exceed 100% due to double-counting - Warn users that samples may overlap across cohorts (e.g., MSK studies may share patients) - **Choose and state the counting unit**: use patient-level frequencies for prevalence/rate questions unless the user explicitly asks for samples; use sample-level frequencies when the user asks about samples. - **For "across cancer types" questions**, jump to the [Cross-Cancer-Type Mutation Frequency](#cross-cancer-type-mutation-frequency) section below — there is one correct recipe and several common wrong ones. ## Counting Unit: Samples vs Patients Before answering any mutation count or frequency question, decide whether the unit is samples or patients and state that choice in the answer. | User wording | Counting unit | |--------------|---------------| | "prevalence", "rate", "fraction of patients", "patients with", "how common is" | Patient-level: `COUNT(DISTINCT patient_unique_id)` | | "samples", "specimens", "biopsies", sample-level cohort composition | Sample-level: `COUNT(DISTINCT sample_unique_id)` | | Ambiguous | Ask, or default to patient-level for prevalence/rate language and say so | ### Cross-study sample-count caveat When an answer touches more than one study and reports a sample count, prepend a one-line caveat: > Sample IDs are unique within cBioPortal study prefixes, not guaranteed biological-sample identifiers across studies; overlapping cohorts can count the same patient/sample more than once. Prefer one of these safer approaches: - Use a shipped `cancer_study_query_preferences` cohort such as `pan_cancer_tcga` or `all_studies_non_redundant`. - Restrict to one named study. - Aggregate by `patient_unique_id` when the biological question is patient prevalence. ## STOP rule: a frequency above 100% means your query is wrong If your query returns a frequency over 100%, **do not try to debug or explain the data inconsistency to the user**. The cause is always one of these query bugs: - Summing mutation events instead of `COUNT(DISTINCT sample_unique_id)` for the numerator - Using a study-wide sample count as the denominator instead of the gene-specific profiled count - Cross-study aggregation where the same biological sample appears under multiple `sample_unique_id` values (e.g., MSK-IMPACT and MSK-CHORD share patients) - **Joining the profiled CTE through `gene_panel` / `gene_panel_list` without a WES branch.** `gene_panel_id = 'WES'` is *not* a row in … (35166 more chars) ◀ result {"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","type_of_cancer_id":"mixed","sample_count":25775,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_met_2021"},{"cancer_study_identifier":"luad_mskcc_2023_met_organotropism","name":"Lung Adenocarcinoma Met Organotropism (MSK, Cancer Cell 2023)","type_of_cancer_id":"luad","sample_count":2653,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2023_met_organotropism"},{"cancer_study_identifier":"lung_msk_2017","name":"Non-Small Cell Cancer (MSK, Cancer Discov 2017)","type_of_cancer_id":"luad","sample_count":915,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_2017"},{"cancer_study_identifier":"luad_mskcc_2020","name":"Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)","type_of_cancer_id":"luad","sample_count":604,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2020"},{"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_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_msk_npjpo_2021","name":"Lung Adenocarcinoma (MSK, NPJ Precision Oncology 2021)","type_of_cancer_id":"luad","sample_count":426,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_msk_npjpo_2021"},{"cancer_study_identifier":"luad_oncosg_2020","name":"Lung Adenocarcinoma (OncoSG, Nat Genet 2020)","type_of_cancer_id":"luad","sample_count":305,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_oncosg_2020"},{"cancer_study_identifier":"lung_msk_mind_2020","name":"Lung Adenocarcinoma (MSK Mind,Nature Cancer 2022)","type_of_cancer_id":"lung","sample_count":247,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_mind_2020"},{"cancer_study_identifier":"luad_cptac_gdc","name":"Lung Adenocarcinoma (CPTAC GDC, 2025)","type_of_cancer_id":"luad","sample_count":241,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_cptac_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"},{"cancer_study_identifier":"luad_mskimpact_2021","name":"Lung Adenocarcinoma (MSK, 2021)","type_of_cancer_id":"luad","sample_count":186,"has_guide":false, … (1174 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": " WITH profiled_patients AS ( SELECT DISTINCT c.patient_unique_id FROM sample_to_gene_panel_derived s JOIN (SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') c USING (sample_unique_id) WHERE s.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND s.alteration_type = 'MUTATION_EXTENDED' ), egfr_mut_patients 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' AND off_panel = 0 ), egfr_amp_patients 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 ), groups AS ( SELECT patient_unique_id, CASE WHEN patient_unique_id IN (SELECT patient_unique_id FROM egfr_mut_patients) THEN 'EGFR-mutated' WHEN patient_unique_id IN (SELECT patient_unique_id FROM egfr_amp_patients) THEN 'EGFR-amplified (no mutation)' ELSE 'EGFR wild-type / non-altered' END AS grp FROM profiled_patients ) SELECT grp, COUNT(*) AS n_patients FROM groups GROUP BY grp ORDER BY n_patients DESC " } ◀ result {"rows":[{"grp":"EGFR wild-type / non-altered","n_patients":485},{"grp":"EGFR-mutated","n_patients":70},{"grp":"EGFR-amplified (no mutation)","n_patients":11}]} ▶ clickhouse_run_select_query { "query": " WITH profiled_patients AS ( SELECT DISTINCT c.patient_unique_id FROM sample_to_gene_panel_derived s JOIN (SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') c USING (sample_unique_id) WHERE s.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND s.alteration_type = 'MUTATION_EXTENDED' ), egfr_mut_patients 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' AND off_panel = 0 ), egfr_amp_patients 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 ), groups AS ( SELECT patient_unique_id, CASE WHEN patient_unique_id IN (SELECT patient_unique_id FROM egfr_mut_patients) THEN 'EGFR-mutated' WHEN patient_unique_id IN (SELECT patient_unique_id FROM egfr_amp_patients) THEN 'EGFR-amplified (no mutation)' ELSE 'EGFR wild-type / non-altered' END AS grp FROM profiled_patients ), 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 = 'luad_tcga_pan_can_atlas_2018' AND attribute_name IN ('OS_MONTHS','OS_STATUS') AND type = 'patient' GROUP BY patient_unique_id ) SELECT g.grp, COUNT(*) AS n_patients, countIf(o.os_status = '1:DECEASED') AS n_events, countIf(o.os_status = '0:LIVING') AS n_censored, countIf(o.os_months IS NULL OR o.os_status = '') AS n_missing_os, round(min(o.os_months),1) AS min_followup, round(max(o.os_months),1) AS max_followup FROM groups g LEFT JOIN os o USING (patient_unique_id) GROUP BY g.grp ORDER BY n_patients DESC " } ◀ result {"rows":[{"grp":"EGFR wild-type / non-altered","n_patients":485,"n_events":152,"n_censored":285,"n_missing_os":55,"min_followup":0,"max_followup":238.3},{"grp":"EGFR-mutated","n_patients":70,"n_events":28,"n_censored":38,"n_missing_os":5,"min_followup":0,"max_followup":108.7},{"grp":"EGFR-amplified (no mutation)","n_patients":11,"n_events":6,"n_censored":5,"n_missing_os":1,"min_followup":3,"max_followup":123.6}]} ▶ 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 / non-altered", "isUnselected": true } ] } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6ab5cff6e497aad53593c285","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6ab5cff6e497aad53593c285","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 / non-altered","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 / non-altered (485)."}} ═ answer (success) Note: the navigation tool's "EGFR-amplified" group (26 patients) uses the portal's default definition (all EGFR-amplified, including those that also carry an EGFR mutation), which differs slightly from the mutually-exclusive breakdown I used for the SQL summary below (11 amplified-but-not-mutated). I'll flag that distinction. ## Summary I can't compute Kaplan-Meier survival curves or a log-rank p-value directly — that requires proper handling of censoring, which a database query alone cannot do correctly. Here's what the data show, and a direct link to run the actual analysis. **Cohort:** [Lung Adenocarcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018) — 566 samples, patient-level counts below, restricted to the 566 patients profiled for mutations. Groups defined as mutually exclusive (mutation takes priority over amplification when both occur): | Group | N patients | OS events (deceased) | Censored (living) | Missing OS | Follow-up range (months) | |---|---|---|---|---|---| | EGFR-mutated | 70 | 28 | 38 | 5 (excl. below) | 0 – 108.7 | | EGFR-amplified (no mutation) | 11 | 6 | 5 | 1 | 3.0 – 123.6 | | EGFR wild-type / non-altered | 485 | 152 | 285 | 55 | 0 – 238.3 | Roughly 40% of the EGFR-mutated and EGFR-amplified groups had an event, and about 31% of the wild-type group — so a KM median may be estimable in the mutated/amplified groups, but interpret with caution given the small amplified-only group (N=11). This is a 3-group time-to-event comparison, so the appropriate method is **Kaplan-Meier estimation with a log-rank test** (not a t-test or Wilcoxon on raw months, which would ignore censoring). I cannot generate that curve or p-value here. **Run the actual KM/log-rank analysis:** [EGFR-mutated vs EGFR-amplified vs wild-type — Survival comparison](https://www.cbioportal.org/comparison/survival?comparisonId=6ab5cff6e497aad53593c285) Note: that link's "EGFR-amplified" group (N=26) uses cBioPortal's standard definition — all EGFR-amplified patients regardless of mutation status — which is not mutation-exclusive like the 11-patient group in my table above. If you want a strictly non-overlapping 3-way split matched to my table, let me know and I can adjust the comparison groups. You can also reproduce this yourself in R (`survival::survfit(Surv(OS_MONTHS, OS_STATUS=="1:DECEASED") ~ group)` + `survdiff(...)`) or Python (`lifelines.KaplanMeierFitter` + `lifelines.statistics.multivariate_logrank_test`) using the raw OS_MONTHS/OS_STATUS pairs per group.