Q (sonnet-5.5): Which genes are enriched for mutations between NSCLC vs squamous cell carcinoma? ▶ 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) ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ▶ search_oncotree { "search_term": "non-small cell lung cancer" } ◀ 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 … (36467 more chars) ◀ result {"result":[{"code":"NSCLC","name":"Non-Small Cell Lung Cancer","score":80,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC"},{"code":"ALUCA","name":"Atypical Lung Carcinoid","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET > ALUCA"},{"code":"BLCLC","name":"Basaloid Large Cell Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > BLCLC"},{"code":"CCLC","name":"Clear Cell Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > CCLC"},{"code":"CMPT","name":"Ciliated Muconodular Papillary Tumor of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > CMPT"},{"code":"GCLC","name":"Giant Cell Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > GCLC"},{"code":"IMTL","name":"Inflammatory Myofibroblastic Lung Tumor","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > IMTL"},{"code":"LAIS","name":"Lung Adenocarcinoma In Situ","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LAIS"},{"code":"LAM","name":"Pulmonary Lymphangiomyomatosis","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LAM"},{"code":"LCLC","name":"Large Cell Lung Carcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC"},{"code":"LECLC","name":"Lymphoepithelioma-like Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > LECLC"},{"code":"LNET","name":"Lung Neuroendocrine Tumor","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET"},{"code":"LUACC","name":"Adenoid Cystic Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > SGTTL > LUACC"},{"code":"LUAD","name":"Lung Adenocarcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAD"},{"code":"LUAS","name":"Lung Adenosquamous Carcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAS"},{"code":"LUCA","name":"Lung Carcinoid","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET > LUCA"},{"code":"LUMEC","name":"Mucoepidermoid Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > SGTTL > LUMEC"},{"code":"LUNE","name":"Large Cell Neuroendocrine Carcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET > LUNE"},{"code":"LUPC","name":"Pleomorphic Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierar … (999 more chars) ▶ clickhouse_run_select_query { "query": "SELECT a.hugo_gene_symbol AS gene, a.altered_samples AS luad_mut, a.profiled_samples AS luad_n, a.frequency_pct AS luad_pct, b.altered_samples AS lusc_mut, b.profiled_samples AS lusc_n, b.frequency_pct AS lusc_pct, round(b.frequency_pct - a.frequency_pct,1) AS diff_lusc_minus_luad FROM top_mutated_genes_in_study(study='luad_tcga_pan_can_atlas_2018', top_n=2000) a JOIN top_mutated_genes_in_study(study='lusc_tcga_pan_can_atlas_2018', top_n=2000) b USING hugo_gene_symbol WHERE a.altered_samples + b.altered_samples >= 40 ORDER BY abs(diff_lusc_minus_luad) DESC LIMIT 30" } ▶ resolve_and_route { "studyIds": [ "luad_tcga_pan_can_atlas_2018", "lusc_tcga_pan_can_atlas_2018" ] } ◀ result {"rows":[{"gene":"TP53","luad_mut":295,"luad_n":566,"luad_pct":52.1,"lusc_mut":404,"lusc_n":484,"lusc_pct":83.5,"diff_lusc_minus_luad":31.4},{"gene":"TTN","luad_mut":272,"luad_n":566,"luad_pct":48.1,"lusc_mut":345,"lusc_n":484,"lusc_pct":71.3,"diff_lusc_minus_luad":23.2},{"gene":"SYNE1","luad_mut":81,"luad_n":566,"luad_pct":14.3,"lusc_mut":151,"lusc_n":484,"lusc_pct":31.2,"diff_lusc_minus_luad":16.9},{"gene":"KMT2D","luad_mut":41,"luad_n":566,"luad_pct":7.2,"lusc_mut":111,"lusc_n":484,"lusc_pct":22.9,"diff_lusc_minus_luad":15.7},{"gene":"NFE2L2","luad_mut":18,"luad_n":566,"luad_pct":3.2,"lusc_mut":72,"lusc_n":484,"lusc_pct":14.9,"diff_lusc_minus_luad":11.7},{"gene":"CDKN2A","luad_mut":25,"luad_n":566,"luad_pct":4.4,"lusc_mut":73,"lusc_n":484,"lusc_pct":15.1,"diff_lusc_minus_luad":10.7},{"gene":"PTPRD","luad_mut":92,"luad_n":566,"luad_pct":16.3,"lusc_mut":32,"lusc_n":484,"lusc_pct":6.6,"diff_lusc_minus_luad":-9.7},{"gene":"EGFR","luad_mut":70,"luad_n":566,"luad_pct":12.4,"lusc_mut":14,"lusc_n":484,"lusc_pct":2.9,"diff_lusc_minus_luad":-9.5},{"gene":"FAM135B","luad_mut":86,"luad_n":566,"luad_pct":15.2,"lusc_mut":118,"lusc_n":484,"lusc_pct":24.4,"diff_lusc_minus_luad":9.2},{"gene":"RP1L1","luad_mut":97,"luad_n":566,"luad_pct":17.1,"lusc_mut":39,"lusc_n":484,"lusc_pct":8.1,"diff_lusc_minus_luad":-9},{"gene":"FLG","luad_mut":145,"luad_n":566,"luad_pct":25.6,"lusc_mut":84,"lusc_n":484,"lusc_pct":17.4,"diff_lusc_minus_luad":-8.2},{"gene":"LRRK2","luad_mut":27,"luad_n":566,"luad_pct":4.8,"lusc_mut":62,"lusc_n":484,"lusc_pct":12.8,"diff_lusc_minus_luad":8},{"gene":"SNTG1","luad_mut":69,"luad_n":566,"luad_pct":12.2,"lusc_mut":21,"lusc_n":484,"lusc_pct":4.3,"diff_lusc_minus_luad":-7.9},{"gene":"KEAP1","luad_mut":102,"luad_n":566,"luad_pct":18,"lusc_mut":49,"lusc_n":484,"lusc_pct":10.1,"diff_lusc_minus_luad":-7.9},{"gene":"TSHZ3","luad_mut":73,"luad_n":566,"luad_pct":12.9,"lusc_mut":26,"lusc_n":484,"lusc_pct":5.4,"diff_lusc_minus_luad":-7.5},{"gene":"ZNF536","luad_mut":117,"luad_n":566,"luad_pct":20.7,"lusc_mut":64,"lusc_n":484,"lusc_pct":13.2,"diff_lusc_minus_luad":-7.5},{"gene":"FBN2","luad_mut":94,"luad_n":566,"luad_pct":16.6,"lusc_mut":45,"lusc_n":484,"lusc_pct":9.3,"diff_lusc_minus_luad":-7.3},{"gene":"XIRP2","luad_mut":150,"luad_n":566,"luad_pct":26.5,"lusc_mut":93,"lusc_n":484,"lusc_pct":19.2,"diff_lusc_minus_luad":-7.3},{"gene":"PLPPR4","luad_mut":59,"luad_n":566,"luad_pct":10.4,"lusc_mut":16,"lusc_n":484,"lusc_pct":3.3,"diff_lusc_minus_luad":-7.1},{"gene":"MXRA5","luad_mut":81,"luad_n":566,"luad_pct":14.3,"lusc_mut":35,"lusc_n":484,"lusc_pct":7.2,"diff_lusc_minus_luad":-7.1},{"gene":"FLG2","luad_mut":78,"luad_n":566,"luad_pct":13.8,"lusc_mut":33,"lusc_n":484,"lusc_pct":6.8,"diff_lusc_minus_luad":-7},{"gene":"TENM2","luad_mut":59,"luad_n":566,"luad_pct":10.4,"lusc_mut":17,"lusc_n":484,"lusc_pct":3.5,"diff_lusc_minus_luad":-6.9},{"gene":"ADGRV1","luad_mut":55,"luad_n":566,"luad_pct":9.7,"lusc_mut":80,"lusc_n":484,"lusc_pct":16.5,"diff_lusc_minus_luad" … (918 more chars) ◀ result {"success":true,"message":"Found 2 matching studies. 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":2,"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"]}},{"studyId":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell … (2585 more chars) ▶ navigate_to_group_comparison { "studyIds": [ "luad_tcga_pan_can_atlas_2018", "lusc_tcga_pan_can_atlas_2018" ], "groups": [ { "name": "Lung Adenocarcinoma (LUAD)", "studyViewFilter": { "studyIds": [ "luad_tcga_pan_can_atlas_2018" ] } }, { "name": "Lung Squamous Cell Carcinoma (LUSC)", "studyViewFilter": { "studyIds": [ "lusc_tcga_pan_can_atlas_2018" ] } } ], "tab": "alterations" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/alterations?comparisonId=6ab44069e497aad53593be3f","url":"https://www.cbioportal.org/comparison/alterations?comparisonId=6ab44069e497aad53593be3f","data":{"description":"Group comparison (2 custom groups)","studies":["luad_tcga_pan_can_atlas_2018","lusc_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"Lung Adenocarcinoma (LUAD)","sampleCount":566},{"name":"Lung Squamous Cell Carcinoma (LUSC)","sampleCount":487}],"studyViewUrl":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"Lung Adenocarcinoma (LUAD)","url":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018"},{"groupName":"Lung Squamous Cell Carcinoma (LUSC)","url":"https://www.cbioportal.org/study?id=lusc_tcga_pan_can_atlas_2018"}],"pageDescription":"Comparison of genomic alteration rates across Lung Adenocarcinoma (LUAD) (566) vs Lung Squamous Cell Carcinoma (LUSC) (487)."}} ═ answer (success) Squamous cell carcinoma is itself a subtype of NSCLC, so I took your question to mean the two main NSCLC histologies: lung adenocarcinoma (LUAD) against lung squamous cell carcinoma (LUSC). I used the TCGA PanCancer Atlas cohorts, which are whole-exome sequenced, so every gene is profiled in every sample. Counts are samples: 566 LUAD and 484 LUSC with mutation profiling. I can't compute p-values or q-values here. The table shows the genes with the largest differences in mutation frequency, not statistically tested enrichment. The appropriate test is a two-tailed Fisher's exact test with Benjamini-Hochberg correction across genes. The comparison link below runs it. **Genes more frequently mutated in squamous (LUSC)** | Gene | LUAD | LUSC | |---|---|---| | TP53 | 295/566 (52.1%) | 404/484 (83.5%) | | TTN* | 272/566 (48.1%) | 345/484 (71.3%) | | SYNE1* | 81/566 (14.3%) | 151/484 (31.2%) | | KMT2D | 41/566 (7.2%) | 111/484 (22.9%) | | NFE2L2 | 18/566 (3.2%) | 72/484 (14.9%) | | CDKN2A | 25/566 (4.4%) | 73/484 (15.1%) | | FAM135B | 86/566 (15.2%) | 118/484 (24.4%) | | LRRK2* | 27/566 (4.8%) | 62/484 (12.8%) | **Genes more frequently mutated in adenocarcinoma (LUAD)** | Gene | LUAD | LUSC | |---|---|---| | EGFR | 70/566 (12.4%) | 14/484 (2.9%) | | KEAP1 | 102/566 (18.0%) | 49/484 (10.1%) | | PTPRD | 92/566 (16.3%) | 32/484 (6.6%) | | RP1L1 | 97/566 (17.1%) | 39/484 (8.1%) | | SNTG1 | 69/566 (12.2%) | 21/484 (4.3%) | | TSHZ3 | 73/566 (12.9%) | 26/484 (5.4%) | | SETBP1 | 61/566 (10.8%) | 21/484 (4.3%) | The ranking covers genes mutated in at least 40 samples across the two cohorts. TTN, SYNE1 and LRRK2 (marked *) are very large genes. Their high mutation rates are common in tumors with heavy mutation loads and probably don't reflect a biological difference between the histologies. Several other genes in the ranking (for example FLG, XIRP2 and ZNF536) are also large. I would treat them with the same caution. The database has no driver annotations for these studies, so none of these genes can be called drivers from this data. If you meant something different, tell me and I'll rerun it. For example, I could compare all NSCLC against squamous only. Another option is a larger targeted-panel cohort such as MSK-IMPACT, which would need per-gene panel denominators. **Links** - [Group Comparison: LUAD vs LUSC, alteration enrichment with statistics](https://www.cbioportal.org/comparison/alterations?comparisonId=6ab44069e497aad53593be3f) - [Study View: both cohorts](https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018)