Q (sonnet): In the Breast Invasive Carcinoma TCGA study what are the top 5 down-regulated genes in TP53 mutated samples compared to non-mutated ones?

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

▶ list_studies
{
 "search": "Breast Invasive Carcinoma TCGA PanCancer 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
{"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"
}

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

◀ 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
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)

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT profile_type FROM genetic_alteration_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' ORDER BY profile_type"
}

◀ 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": "
SELECT count(DISTINCT sample_unique_id) AS n
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'
"
}

◀ result
{"rows":[{"n":347}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT count(DISTINCT sample_unique_id) AS n
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
  AND profile_type = 'rna_seq_v2_mrna'
"
}

◀ result
{"rows":[{"n":1082}]}

▶ 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 != 'GERMLINE'
),
expr AS (
    SELECT sample_unique_id, hugo_gene_symbol,
           log2(toFloat64OrNull(alteration_value) + 1) AS logval
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND profile_type = 'rna_seq_v2_mrna'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    hugo_gene_symbol,
    countIf(sample_unique_id IN (SELECT sample_unique_id FROM tp53_mut)) AS n_mut,
    countIf(sample_unique_id NOT IN (SELECT sample_unique_id FROM tp53_mut)) AS n_wt,
    avgIf(logval, sample_unique_id IN (SELECT sample_unique_id FROM tp53_mut)) AS mean_mut,
    avgIf(logval, sample_unique_id NOT IN (SELECT sample_unique_id FROM tp53_mut)) AS mean_wt,
    mean_mut - mean_wt AS log2fc
FROM expr
GROUP BY hugo_gene_symbol
HAVING n_mut >= 20 AND n_wt >= 20
ORDER BY log2fc ASC
LIMIT 5
"
}

◀ result
{"rows":[{"hugo_gene_symbol":"AGR3","n_mut":347,"n_wt":735,"mean_mut":5.686748202756944,"mean_wt":10.413633643732332,"log2fc":-4.726885440975388},{"hugo_gene_symbol":"TFF1","n_mut":347,"n_wt":735,"mean_mut":5.307444261966188,"mean_wt":9.988200996710296,"log2fc":-4.680756734744108},{"hugo_gene_symbol":"SRARP","n_mut":347,"n_wt":735,"mean_mut":3.8719242049893636,"mean_wt":8.065452022116588,"log2fc":-4.193527817127224},{"hugo_gene_symbol":"CYP2B7P","n_mut":347,"n_wt":735,"mean_mut":5.170856152508416,"mean_wt":9.00102101897816,"log2fc":-3.830164866469743},{"hugo_gene_symbol":"CPB1","n_mut":347,"n_wt":735,"mean_mut":3.925547058531706,"mean_wt":7.748578895156615,"log2fc":-3.8230318366249088}]}

▶ 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"
 ],
 "tab": "comparison/mrna",
 "profileFilter": "mutations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/comparison/mrna?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=mutations","url":"https://www.cbioportal.org/results/comparison/mrna?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=mutations","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"],"caseSetId":"brca_tcga_pan_can_atlas_2018_all","pageDescription":"mRNA expression for all genes compared across groups defined by query genes."}}

═ answer (success)
## Top 5 down-regulated genes in TP53-mutated vs. TP53-wildtype samples

**Study:** [Breast Invasive Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018) — `brca_tcga_pan_can_atlas_2018`
**Groups:** 347 TP53-mutated samples vs. 735 TP53-wildtype samples (of 1082 samples with RNA-seq data; denominator = samples with both mutation and expression profiling)
**Data:** RSEM mRNA expression (`rna_seq_v2_mrna`), log2(RSEM+1) transformed, mean difference (mutant − wildtype) as the ranking metric

| Rank | Gene | Mean log2(RSEM+1), mutant | Mean log2(RSEM+1), wildtype | log2 fold-change |
|---|---|---|---|---|
| 1 | AGR3 | 5.69 | 10.41 | −4.73 |
| 2 | TFF1 | 5.31 | 9.99 | −4.68 |
| 3 | SRARP | 3.87 | 8.07 | −4.19 |
| 4 | CYP2B7P | 5.17 | 9.00 | −3.83 |
| 5 | CPB1 | 3.93 | 7.75 | −3.82 |

These genes (AGR3, TFF1, SRARP, CPB1) are all classic luminal/hormone-responsive markers — this pattern is consistent with TP53-mutant tumors being enriched for the basal-like PAM50 subtype rather than a direct causal effect of TP53 loss, though I have not tested that association here.

**Important caveat:** this ranking is by raw mean difference (effect size), not statistical significance. I have not run — and cannot run from this database — a t-test or FDR correction across all genes, so I cannot state which of these differences are statistically significant after multiple-testing correction. For a rigorous differential expression result (t-test/moderated t-test per gene with BH-FDR q-values), use cBioPortal's built-in comparison view:

[View TP53-mutant vs. wildtype mRNA comparison](https://www.cbioportal.org/results/comparison/mrna?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=mutations)

This page runs the group comparison natively and reports q-values per gene, sortable by log2 fold-change or significance.