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_guides
{}

◀ 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":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

▶ read_guide
{
 "uri": "cbioportal://gene-expression-guide"
}

▶ resolve_and_route
{
 "studyKeywords": [
  "TCGA",
  "breast",
  "pan_can_atlas"
 ]
}

◀ 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` | `<study>_<sample_stable_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)

◀ 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)

▶ clickhouse_run_select_query
{
 "query": "
WITH tp53 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 != 'GERMLINE'
      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
),
profiled AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
)
SELECT
    CASE WHEN tp53.sample_unique_id IS NOT NULL THEN 'TP53_MUT' ELSE 'TP53_WT' END AS tp53_status,
    CASE WHEN myc_expr.z > 2 THEN 'MYC_HIGH' ELSE 'MYC_NOT_HIGH' END AS myc_status,
    COUNT(*) AS n_samples
FROM myc_expr
LEFT JOIN tp53 ON myc_expr.sample_unique_id = tp53.sample_unique_id
GROUP BY tp53_status, myc_status
ORDER BY tp53_status, myc_status
"
}

◀ result
{"rows":[{"tp53_status":"TP53_MUT","myc_status":"MYC_HIGH","n_samples":76},{"tp53_status":"TP53_MUT","myc_status":"MYC_NOT_HIGH","n_samples":1006}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH tp53 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 != 'GERMLINE'
      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
    (SELECT count() FROM tp53) AS n_tp53_mut,
    (SELECT count() FROM myc_expr) AS n_myc_profiled,
    (SELECT count() FROM myc_expr m INNER JOIN tp53 t ON m.sample_unique_id = t.sample_unique_id) AS n_overlap_mut_and_profiled
"
}

◀ result
{"rows":[{"n_tp53_mut":347,"n_myc_profiled":1082,"n_overlap_mut_and_profiled":347}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH tp53 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 != 'GERMLINE'
      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
),
combined AS (
    SELECT
        m.sample_unique_id,
        m.z,
        (t.sample_unique_id IS NOT NULL) AS is_tp53_mut
    FROM myc_expr m
    LEFT JOIN tp53 t ON m.sample_unique_id = t.sample_unique_id
)
SELECT
    if(is_tp53_mut, 'TP53_MUT', 'TP53_WT') AS tp53_status,
    if(z > 2, 'MYC_HIGH', 'MYC_NOT_HIGH') AS myc_status,
    count() AS n_samples
FROM combined
GROUP BY tp53_status, myc_status
ORDER BY tp53_status, myc_status
"
}

◀ result
{"rows":[{"tp53_status":"TP53_MUT","myc_status":"MYC_HIGH","n_samples":76},{"tp53_status":"TP53_MUT","myc_status":"MYC_NOT_HIGH","n_samples":1006}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH tp53 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 != 'GERMLINE'
      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
    countIf(t.sample_unique_id IS NOT NULL) AS n_mut_rows,
    countIf(t.sample_unique_id IS NULL) AS n_wt_rows,
    countIf(t.sample_unique_id IS NOT NULL AND m.z > 2) AS mut_and_high,
    countIf(t.sample_unique_id IS NOT NULL AND m.z <= 2) AS mut_and_nothigh,
    countIf(t.sample_unique_id IS NULL AND m.z > 2) AS wt_and_high,
    countIf(t.sample_unique_id IS NULL AND m.z <= 2) AS wt_and_nothigh
FROM myc_expr m
LEFT JOIN tp53 t ON m.sample_unique_id = t.sample_unique_id
"
}

◀ result
{"rows":[{"n_mut_rows":1082,"n_wt_rows":0,"mut_and_high":76,"mut_and_nothigh":1006,"wt_and_high":0,"wt_and_nothigh":0}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 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'
LIMIT 5
"
}

◀ result
{"rows":[{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-3C-AALI-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-A1-A0SI-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-A1-A0SK-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-A1-A0SO-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-A1-A0SP-01"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT sample_unique_id FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'MYC' AND profile_type='rna_seq_v2_mrna_median_Zscores'
LIMIT 5
"
}

◀ result
{"rows":[{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-3C-AAAU-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-3C-AALI-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-3C-AALJ-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-3C-AALK-01"},{"sample_unique_id":"brca_tcga_pan_can_atlas_2018_TCGA-4H-AAAK-01"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH tp53 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 != 'GERMLINE'
      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
    countIf(t.sample_unique_id != '') AS n_mut_rows,
    countIf(t.sample_unique_id = '') AS n_wt_rows,
    countIf(t.sample_unique_id != '' AND m.z > 2) AS mut_and_high,
    countIf(t.sample_unique_id != '' AND m.z <= 2) AS mut_and_nothigh,
    countIf(t.sample_unique_id = '' AND m.z > 2) AS wt_and_high,
    countIf(t.sample_unique_id = '' AND m.z <= 2) AS wt_and_nothigh
FROM myc_expr m
LEFT JOIN tp53 t ON m.sample_unique_id = t.sample_unique_id
"
}

◀ result
{"rows":[{"n_mut_rows":347,"n_wt_rows":735,"mut_and_high":37,"mut_and_nothigh":310,"wt_and_high":39,"wt_and_nothigh":696}]}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "groups": [
  {
   "name": "TP53 Mutant",
   "studyViewFilter": {
    "geneFilters": [
     {
      "molecularProfileIds": [
       "brca_tcga_pan_can_atlas_2018_mutations"
      ],
      "geneQueries": [
       [
        {
         "hugoGeneSymbol": "TP53"
        }
       ]
      ]
     }
    ]
   }
  },
  {
   "name": "TP53 Wild-type",
   "isUnselected": true
  }
 ],
 "tab": "mrna",
 "selectedGene": "MYC"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/mrna?comparisonId=6ab5b276c2115c492d884dce&selectedGene=MYC","url":"https://www.cbioportal.org/comparison/mrna?comparisonId=6ab5b276c2115c492d884dce&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 Wild-type","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 Wild-type (737)."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "TP53",
  "MYC"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "MRNA_EXPRESSION",
  "selectedGeneOption": "MYC"
 },
 "plotsVertSelection": {
  "dataType": "MRNA_EXPRESSION",
  "selectedGeneOption": "MYC"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%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%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%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%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%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: MYC mRNA expression (y-axis) by MYC mRNA expression (x-axis)."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "TP53",
  "MYC"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "MUTATION_EXTENDED",
  "mutationCountBy": "MutatedGenes",
  "selectedGeneOption": "TP53"
 },
 "plotsVertSelection": {
  "dataType": "MRNA_EXPRESSION",
  "selectedGeneOption": "MYC"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%0AMYC&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%227157%22%2C%22dataType%22%3A%22MUTATION_EXTENDED%22%2C%22mutationCountBy%22%3A%22MutatedGenes%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%0AMYC&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%227157%22%2C%22dataType%22%3A%22MUTATION_EXTENDED%22%2C%22mutationCountBy%22%3A%22MutatedGenes%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%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: MYC mRNA expression (y-axis) by TP53 mutation (x-axis)."}}

═ answer (success)
## Links

**[Group Comparison — MYC expression, TP53 Mutant (347) vs Wild-type (737)](https://www.cbioportal.org/comparison/mrna?comparisonId=6ab5b276c2115c492d884dce&selectedGene=MYC)**
mRNA expression for all genes compared across TP53 Mutant vs TP53 Wild-type groups — includes a box plot with a t-test for MYC.

**[Plots Tab — MYC mRNA expression by TP53 mutation status](https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%0AMYC&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%227157%22%2C%22dataType%22%3A%22MUTATION_EXTENDED%22%2C%22mutationCountBy%22%3A%22MutatedGenes%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%224609%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%7D)**
Scatter/box plot: MYC mRNA expression (y-axis) by TP53 mutation (x-axis).

---

**Summary:** I built the 2×2 contingency table from raw data (37/310 vs. 39/696), but cannot state whether TP53 mutation and high MYC expression co-occur or are mutually exclusive without running Fisher's exact test — ClickHouse doesn't compute p-values or odds ratios. Use the linked Group Comparison tab (or R/Python `fisher_exact`) to get the definitive answer.