Q (sonnet): In the TCGA Breast Cancer study, do TP53 mutations and high MYC expression co-occur or are they mutually exclusive? ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ▶ list_studies { "search": "Breast TCGA PanCancer Atlas" } ◀ 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":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"brca","sample_count":1084,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018"}]} ▶ get_study_guide { "study_id": "brca_tcga_pan_can_atlas_2018" } ▶ clickhouse_list_table_columns { "table": "genomic_event_derived" } ▶ clickhouse_list_table_columns { "table": "genetic_alteration_derived" } ◀ result # Breast Invasive Carcinoma (TCGA, PanCancer Atlas) **Study ID:** `brca_tcga_pan_can_atlas_2018` See `_tcga_pancan_template.md` for common TCGA clinical attributes. ## Study-Specific Attributes ### Molecular Subtypes | Attribute | Description | Values (patients) | |-----------|-------------|--------| | `SUBTYPE` | PAM50 molecular subtype | `BRCA_LumA` 499, `BRCA_LumB` 197, `BRCA_Basal` 171, `BRCA_Her2` 78, `BRCA_Normal` 36, blank 103 | ### Histology | Attribute | Description | Values (samples) | |-----------|-------------|--------| | `CANCER_TYPE_DETAILED` | Histological type | Breast Invasive Ductal Carcinoma 780, Breast Invasive Lobular Carcinoma 201, Breast Invasive Carcinoma (NOS) 77, Breast Invasive Mixed Mucinous Carcinoma 17, Metaplastic Breast Cancer 8, Invasive Breast Carcinoma 1 | ### Not available in this study - **ER / PR / HER2 receptor status and triple-negative status**: no clinical attribute; say it is not available here. Do not infer receptor status from `SUBTYPE`: PAM50 is an expression-based classification, not IHC/FISH. If the user accepts an expression-based proxy, `BRCA_Basal` (≈ triple-negative) or `BRCA_Her2` can be offered, labelled as PAM50. ERBB2 amplification is available from CNA data. - `GRADE` is blank for all samples. ## Key Genes - **TP53**: Most frequently mutated (~30%) - **PIK3CA**: Common in ER+ tumors (~35%) - **CDH1**: Enriched in lobular carcinoma - **GATA3**: Luminal marker - **ERBB2**: HER2 amplification target ## Notes - Molecular subtypes (PAM50) correlate strongly with clinical behavior - Lobular vs ductal distinction has different mutation profiles ◀ 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":"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)"}]} ▶ read_guide { "uri": "cbioportal://gene-expression-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) ▶ clickhouse_run_select_query { "query": "SELECT DISTINCT profile_type FROM genetic_alteration_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'" } ◀ result {"rows":[{"profile_type":"gistic"},{"profile_type":"log2CNA"},{"profile_type":"protein_quantification"},{"profile_type":"protein_quantification_zscores"},{"profile_type":"rna_seq_v2_mrna"},{"profile_type":"rna_seq_v2_mrna_median_Zscores"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_Zscores"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores"},{"profile_type":"rppa"},{"profile_type":"rppa_Zscores"}]} ▶ clickhouse_run_select_query { "query": " WITH tp53_mut AS ( SELECT DISTINCT sample_unique_id FROM genomic_event_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'TP53' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND off_panel = FALSE ), myc_expr AS ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS z FROM genetic_alteration_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND profile_type = 'rna_seq_v2_mrna_median_Zscores' AND hugo_gene_symbol = 'MYC' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) SELECT CASE WHEN sample_unique_id IN (SELECT sample_unique_id FROM tp53_mut) THEN 'TP53_MUT' ELSE 'TP53_WT' END AS tp53_status, CASE WHEN z >= 2 THEN 'MYC_HIGH' ELSE 'MYC_NOT_HIGH' END AS myc_status, COUNT(*) AS n FROM myc_expr GROUP BY tp53_status, myc_status ORDER BY tp53_status, myc_status " } ◀ result {"rows":[{"tp53_status":"TP53_MUT","myc_status":"MYC_HIGH","n":37},{"tp53_status":"TP53_MUT","myc_status":"MYC_NOT_HIGH","n":310},{"tp53_status":"TP53_WT","myc_status":"MYC_HIGH","n":39},{"tp53_status":"TP53_WT","myc_status":"MYC_NOT_HIGH","n":696}]} ▶ resolve_and_route { "studyIds": [ "brca_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":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_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":["brca_tcga_pan_can_atlas_2018_gistic","brca_tcga_pan_can_atlas_2018_log2CNA","brca_tcga_pan_can_atlas_2018_mutations","brca_tcga_pan_can_atlas_2018_protein_quantification","brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa","brca_tcga_pan_can_atlas_2018_rppa_Zscores","brca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["brca_tcga_pan_can_atlas_2018_armlevel_cna","brca_tcga_pan_can_atlas_2018_genetic_ancestry","brca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","brca_tcga_pan_can_atlas_2018_methylation_hm450","brca_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_anc … (83 more chars) ▶ navigate_to_results_view { "studyIds": [ "brca_tcga_pan_can_atlas_2018" ], "genes": [ "TP53: MUT", "MYC" ], "tab": "plots", "plotsHorzSelection": { "dataType": "MRNA_EXPRESSION", "selectedGeneOption": "MYC", "selectedDataSourceOption": "rna_seq_v2_mrna_median_Zscores" }, "plotsVertSelection": { "dataType": "GENOMIC_TYPE", "selectedGeneOption": "TP53" } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT%0AMYC&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna_median_Zscores%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%227157%22%2C%22dataType%22%3A%22GENOMIC_TYPE%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT%0AMYC&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna_median_Zscores%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%227157%22%2C%22dataType%22%3A%22GENOMIC_TYPE%22%7D","data":{"studyIds":["brca_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084}],"genes":["TP53","MYC"],"caseSetId":"brca_tcga_pan_can_atlas_2018_all","pageDescription":"Scatter/box plots: TP53 genomic_type (y-axis) by MYC mRNA expression (x-axis)."}} ▶ navigate_to_group_comparison { "studyIds": [ "brca_tcga_pan_can_atlas_2018" ], "tab": "mrna", "selectedGene": "MYC", "groups": [ { "name": "TP53 mutant", "studyViewFilter": { "geneFilters": [ { "molecularProfileIds": [ "brca_tcga_pan_can_atlas_2018_mutations" ], "geneQueries": [ [ { "hugoGeneSymbol": "TP53" } ] ] } ] } }, { "name": "TP53 wildtype", "isUnselected": true } ] } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/mrna?comparisonId=6ab4a3c8e497aad53593bf0f&selectedGene=MYC","url":"https://www.cbioportal.org/comparison/mrna?comparisonId=6ab4a3c8e497aad53593bf0f&selectedGene=MYC","data":{"description":"Group comparison (2 custom groups)","studies":["brca_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"TP53 mutant","sampleCount":347},{"name":"TP53 wildtype","sampleCount":737}],"studyViewUrl":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"TP53 mutant","url":"https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22molecularProfileIds%22%3A%5B%22brca_tcga_pan_can_atlas_2018_mutations%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22TP53%22%7D%5D%5D%7D%5D%7D"}],"pageDescription":"mRNA expression for all genes compared across TP53 mutant (347) vs TP53 wildtype (737)."}} ═ answer (success) ## TCGA Breast Cancer (PanCancer Atlas): TP53 mutation vs. high MYC expression **Cohort:** `brca_tcga_pan_can_atlas_2018` (1,084 samples). Analysis restricted to the 1,082 samples with both mutation and RNA-seq data (samples, not patients). **Definition used:** "High MYC expression" = mRNA z-score ≥ 2 relative to diploid samples (standard cBioPortal RNA-Seq V2 z-score, `rna_seq_v2_mrna_median_Zscores`). If you intended a different threshold or a different expression profile, let me know and I can rerun. **2×2 contingency table (samples):** | | MYC high (z≥2) | MYC not high | Total | |---|---|---|---| | **TP53 mutant** | 37 | 310 | 347 | | **TP53 wild-type** | 39 | 696 | 735 | | **Total** | 76 | 1006 | 1082 | Descriptively, MYC-high samples are somewhat more frequent among TP53-mutant tumors (37/347 ≈ 10.7%) than TP53 wild-type tumors (39/735 ≈ 5.3%), but **I can't determine from this table alone whether that's a statistically significant co-occurrence or mutual exclusivity** — ClickHouse doesn't run statistical tests. This needs Fisher's exact test: - **R:** `fisher.test(matrix(c(37, 310, 39, 696), nrow=2))` - **Python:** `from scipy.stats import fisher_exact; fisher_exact([[37, 310], [39, 696]])` - Or run it directly in cBioPortal's Group Comparison / Mutual Exclusivity tools (links below), which apply the same defaults. ### Explore directly in cBioPortal - [MYC expression vs. TP53 mutation status — scatter/box plot](https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT%0AMYC&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna_median_Zscores%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%227157%22%2C%22dataType%22%3A%22GENOMIC_TYPE%22%7D) — TP53 genomic status (y-axis) against MYC mRNA z-score (x-axis) for all 1,084 samples. - [Group Comparison: TP53-mutant (347 samples) vs. TP53 wild-type (737 samples) — mRNA tab](https://www.cbioportal.org/comparison/mrna?comparisonId=6ab4a3c8e497aad53593bf0f&selectedGene=MYC) — pre-loaded on MYC; this tab runs the Student's t-test comparing MYC expression between the two groups and reports the p-value directly. Note: the group-comparison sample counts (347/737) differ slightly from the table above because that split uses all TP53-mutated samples regardless of expression-data availability, while the contingency table above is limited to samples with valid MYC expression calls.