Q (haiku): In the TCGA PanCancer Atlas glioblastoma study, is CDK4 mRNA expression significantly higher in samples with CDK4 amplification compared to diploid samples?

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

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-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` | `<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 | `rna_seq_v2_mrna`, `rna_seq_v2_mrna_median_Zscores`, `rna_seq_v2_mrna_median_all_sample_Zscores` (TCGA PanCancer Atlas), `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 |
|  … (4931 more chars)

◀ 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)

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

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592,"studyViewUrl":"https://www.cbioportal.org/study?id=gbm_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":["gbm_tcga_pan_can_atlas_2018_gistic","gbm_tcga_pan_can_atlas_2018_log2CNA","gbm_tcga_pan_can_atlas_2018_mutations","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_tcga_pan_can_atlas_2018_rppa","gbm_tcga_pan_can_atlas_2018_rppa_Zscores","gbm_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["gbm_tcga_pan_can_atlas_2018_armlevel_cna","gbm_tcga_pan_can_atlas_2018_genetic_ancestry","gbm_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","gbm_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_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"]}}]}}

▶ clickhouse_run_select_query
{
 "query": "
-- Query CDK4 mRNA expression and CNA status in GBM TCGA PanCancer Atlas
-- Profile types available: rna_seq_v2_mrna (raw), log2CNA (continuous CNA)
-- gistic (discrete CNA with AMP=2, HOMDEL=-2, DIPLOID=0)

WITH mrna_data AS (
  SELECT 
    sample_unique_id,
    toFloat64OrNull(alteration_value) AS cdk4_mrna
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'rna_seq_v2_mrna'
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
),
cna_data AS (
  SELECT 
    sample_unique_id,
    toFloat64OrNull(alteration_value) AS cdk4_cna
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'log2CNA'
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
),
gistic_data AS (
  SELECT 
    sample_unique_id,
    toFloat64OrNull(alteration_value) AS cdk4_gistic
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'gistic'
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
),
combined AS (
  SELECT 
    m.sample_unique_id,
    m.cdk4_mrna,
    c.cdk4_cna,
    g.cdk4_gistic,
    CASE 
      WHEN g.cdk4_gistic = 2 THEN 'AMP'
      WHEN g.cdk4_gistic = 0 THEN 'DIPLOID'
      WHEN g.cdk4_gistic = -1 THEN 'HETLOSS'
      WHEN g.cdk4_gistic = -2 THEN 'HOMDEL'
      ELSE 'OTHER'
    END AS cna_status
  FROM mrna_data m
  LEFT JOIN cna_data c USING (sample_unique_id)
  LEFT JOIN gistic_data g USING (sample_unique_id)
  WHERE g.cdk4_gistic IS NOT NULL
)
SELECT 
  cna_status,
  COUNT(DISTINCT sample_unique_id) AS num_samples,
  ROUND(AVG(cdk4_mrna), 2) AS mean_mrna,
  ROUND(quantile(0.5)(cdk4_mrna), 2) AS median_mrna,
  ROUND(MIN(cdk4_mrna), 2) AS min_mrna,
  ROUND(MAX(cdk4_mrna), 2) AS max_mrna,
  ROUND(stddevPop(cdk4_mrna), 2) AS stddev_mrna
FROM combined
GROUP BY cna_status
ORDER BY 
  CASE cna_status 
    WHEN 'AMP' THEN 1
    WHEN 'DIPLOID' THEN 2
    WHEN 'HETLOSS' THEN 3
    WHEN 'HOMDEL' THEN 4
  END;
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 47\n Code: 47. DB::Exception: Unknown expression or function identifier `sample_unique_id` in scope WITH mrna_data AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS cdk4_mrna FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'CDK4') AND (profile_type = 'rna_seq_v2_mrna') AND (alteration_value NOT IN ('', 'NA')) AND (toFloat64OrNull(alteration_value) IS NOT NULL)), cna_data AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS cdk4_cna FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'CDK4') AND (profile_type = 'log2CNA') AND (alteration_value NOT IN ('', 'NA')) AND (toFloat64OrNull(alteration_value) IS NOT NULL)), gistic_data AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS cdk4_gistic FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'CDK4') AND (profile_type = 'gistic') AND (alteration_value NOT IN ('', 'NA')) AND (toFloat64OrNull(alteration_value) IS NOT NULL)), combined AS (SELECT m.sample_unique_id, m.cdk4_mrna, c.cdk4_cna, g.cdk4_gistic, multiIf(g.cdk4_gistic = 2, 'AMP', g.cdk4_gistic = 0, 'DIPLOID', g.cdk4_gistic = -1, 'HETLOSS', g.cdk4_gistic = -2, 'HOMDEL', 'OTHER') AS cna_status FROM mrna_data AS m LEFT JOIN cna_data AS c USING (sample_unique_id) LEFT JOIN gistic_data AS g USING (sample_unique_id) WHERE g.cdk4_gistic IS NOT NULL) SELECT cna_status, COUNTDistinct(sample_unique_id) AS num_samples, ROUND(AVG(cdk4_mrna), 2) AS mean_mrna, ROUND(quantile(0.5)(cdk4_mrna), 2) AS median_mrna, ROUND(MIN(cdk4_mrna), 2) AS min_mrna, ROUND(MAX(cdk4_mrna), 2) AS max_mrna, ROUND(stddevPop(cdk4_mrna), 2) AS stddev_mrna FROM combined GROUP BY cna_status ORDER BY caseWithExpression(cna_status, 'AMP', 1, 'DIPLOID', 2, 'HETLOSS', 3, 'HOMDEL', 4, NULL) ASC. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_list_table_columns
{
 "table": "genetic_alteration_derived"
}

◀ 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)"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get CDK4 mRNA and gistic CNA data, then join
SELECT 
  MULTIIF(
    g.gistic = 2, 'AMP',
    g.gistic = 0, 'DIPLOID', 
    g.gistic = -1, 'HETLOSS',
    g.gistic = -2, 'HOMDEL',
    'OTHER'
  ) AS cna_status,
  COUNT(DISTINCT g.sample_unique_id) AS num_samples,
  ROUND(AVG(m.mrna), 2) AS mean_mrna,
  ROUND(quantile(0.5)(m.mrna), 2) AS median_mrna,
  ROUND(MIN(m.mrna), 2) AS min_mrna,
  ROUND(MAX(m.mrna), 2) AS max_mrna,
  ROUND(stddevPop(m.mrna), 2) AS stddev_mrna
FROM (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS gistic
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'gistic'
    AND alteration_value NOT IN ('', 'NA')
) g
LEFT JOIN (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'rna_seq_v2_mrna'
    AND alteration_value NOT IN ('', 'NA')
) m USING (sample_unique_id)
WHERE m.mrna IS NOT NULL
GROUP BY cna_status
ORDER BY 
  CASE cna_status 
    WHEN 'AMP' THEN 1
    WHEN 'DIPLOID' THEN 2
    WHEN 'HETLOSS' THEN 3
    WHEN 'HOMDEL' THEN 4
    ELSE 5
  END;
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 46\n Code: 46. DB::Exception: Function with name `MULTIIF` does not exist. In scope SELECT MULTIIF(g.gistic = 2, 'AMP', g.gistic = 0, 'DIPLOID', g.gistic = -1, 'HETLOSS', g.gistic = -2, 'HOMDEL', 'OTHER') AS cna_status, COUNTDistinct(g.sample_unique_id) AS num_samples, ROUND(AVG(m.mrna), 2) AS mean_mrna, ROUND(quantile(0.5)(m.mrna), 2) AS median_mrna, ROUND(MIN(m.mrna), 2) AS min_mrna, ROUND(MAX(m.mrna), 2) AS max_mrna, ROUND(stddevPop(m.mrna), 2) AS stddev_mrna FROM (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS gistic FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'CDK4') AND (profile_type = 'gistic') AND (alteration_value NOT IN ('', 'NA'))) AS g LEFT JOIN (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'CDK4') AND (profile_type = 'rna_seq_v2_mrna') AND (alteration_value NOT IN ('', 'NA'))) AS m USING (sample_unique_id) WHERE m.mrna IS NOT NULL GROUP BY cna_status ORDER BY caseWithExpression(cna_status, 'AMP', 1, 'DIPLOID', 2, 'HETLOSS', 3, 'HOMDEL', 4, 5) ASC. Maybe you meant: ['multiIf','multiply']. (UNKNOWN_FUNCTION) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
-- Get CDK4 mRNA and gistic CNA data
SELECT 
  multiIf(
    g.gistic = 2, 'AMP',
    g.gistic = 0, 'DIPLOID', 
    g.gistic = -1, 'HETLOSS',
    g.gistic = -2, 'HOMDEL',
    'OTHER'
  ) AS cna_status,
  COUNT(DISTINCT g.sample_unique_id) AS num_samples,
  ROUND(AVG(m.mrna), 2) AS mean_mrna,
  ROUND(quantile(0.5)(m.mrna), 2) AS median_mrna,
  ROUND(MIN(m.mrna), 2) AS min_mrna,
  ROUND(MAX(m.mrna), 2) AS max_mrna,
  ROUND(stddevPop(m.mrna), 2) AS stddev_mrna
FROM (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS gistic
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'gistic'
    AND alteration_value NOT IN ('', 'NA')
) g
LEFT JOIN (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'rna_seq_v2_mrna'
    AND alteration_value NOT IN ('', 'NA')
) m USING (sample_unique_id)
WHERE m.mrna IS NOT NULL
GROUP BY cna_status;
",
 "max_rows": 100
}

◀ result
{"rows":[{"cna_status":"HETLOSS","num_samples":11,"mean_mrna":3006.62,"median_mrna":2634.87,"min_mrna":1969.9,"max_mrna":6068.32,"stddev_mrna":1199.15},{"cna_status":"AMP","num_samples":27,"mean_mrna":81097.63,"median_mrna":57836.2,"min_mrna":3224.87,"max_mrna":458978,"stddev_mrna":89979.06},{"cna_status":"OTHER","num_samples":15,"mean_mrna":4772.56,"median_mrna":4387.2,"min_mrna":2713.61,"max_mrna":10565.7,"stddev_mrna":1868.98},{"cna_status":"DIPLOID","num_samples":95,"mean_mrna":4491.64,"median_mrna":3118.18,"min_mrna":1370.75,"max_mrna":96332.1,"stddev_mrna":9557.15}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Summary stats for AMP vs DIPLOID comparison
SELECT 
  multiIf(
    g.gistic = 2, 'AMP',
    g.gistic = 0, 'DIPLOID'
  ) AS cna_status,
  COUNT(DISTINCT g.sample_unique_id) AS n,
  ROUND(AVG(m.mrna), 1) AS mean_rsem,
  ROUND(quantile(0.25)(m.mrna), 1) AS q1_rsem,
  ROUND(quantile(0.5)(m.mrna), 1) AS median_rsem,
  ROUND(quantile(0.75)(m.mrna), 1) AS q3_rsem,
  ROUND(stddevPop(m.mrna), 1) AS stddev_rsem
FROM (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS gistic
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'gistic'
    AND alteration_value NOT IN ('', 'NA')
) g
LEFT JOIN (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'rna_seq_v2_mrna'
    AND alteration_value NOT IN ('', 'NA')
) m USING (sample_unique_id)
WHERE m.mrna IS NOT NULL AND (g.gistic = 2 OR g.gistic = 0)
GROUP BY cna_status;
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 42\n Code: 42. DB::Exception: Invalid number of arguments for function multiIf: In scope SELECT multiIf(g.gistic = 2, 'AMP', g.gistic = 0, 'DIPLOID') AS cna_status, COUNTDistinct(g.sample_unique_id) AS n, ROUND(AVG(m.mrna), 1) AS mean_rsem, ROUND(quantile(0.25)(m.mrna), 1) AS q1_rsem, ROUND(quantile(0.5)(m.mrna), 1) AS median_rsem, ROUND(quantile(0.75)(m.mrna), 1) AS q3_rsem, ROUND(stddevPop(m.mrna), 1) AS stddev_rsem FROM (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS gistic FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'CDK4') AND (profile_type = 'gistic') AND (alteration_value NOT IN ('', 'NA'))) AS g LEFT JOIN (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'CDK4') AND (profile_type = 'rna_seq_v2_mrna') AND (alteration_value NOT IN ('', 'NA'))) AS m USING (sample_unique_id) WHERE (m.mrna IS NOT NULL) AND ((g.gistic = 2) OR (g.gistic = 0)) GROUP BY cna_status. (NUMBER_OF_ARGUMENTS_DOESNT_MATCH) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
-- Summary stats for AMP vs DIPLOID comparison
SELECT 
  multiIf(
    g.gistic = 2, 'AMP',
    g.gistic = 0, 'DIPLOID',
    'OTHER'
  ) AS cna_status,
  COUNT(DISTINCT g.sample_unique_id) AS n,
  ROUND(AVG(m.mrna), 1) AS mean_rsem,
  ROUND(quantile(0.25)(m.mrna), 1) AS q1_rsem,
  ROUND(quantile(0.5)(m.mrna), 1) AS median_rsem,
  ROUND(quantile(0.75)(m.mrna), 1) AS q3_rsem,
  ROUND(stddevPop(m.mrna), 1) AS stddev_rsem
FROM (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS gistic
  FROM genetic_alteration_derived
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    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'gistic'
    AND alteration_value NOT IN ('', 'NA')
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LEFT JOIN (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'CDK4'
    AND profile_type = 'rna_seq_v2_mrna'
    AND alteration_value NOT IN ('', 'NA')
) m USING (sample_unique_id)
WHERE m.mrna IS NOT NULL AND (g.gistic = 2 OR g.gistic = 0)
GROUP BY cna_status;
",
 "max_rows": 100
}

◀ result
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▶ navigate_to_group_comparison
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 ],
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 ],
 "tab": "mrna"
}

◀ result
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▶ navigate_to_results_view
{
 "studyIds": [
  "gbm_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "CDK4"
 ],
 "tab": "oncoprint"
}

◀ result
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═ answer (success)
## Analysis: CDK4 mRNA Expression in GBM TCGA PanCancer Atlas

Based on query results from the **Glioblastoma Multiforme (TCGA, PanCancer Atlas)** study with 592 samples:

### Summary Findings

**CDK4 mRNA expression is substantially elevated in amplified samples:**

| Group | N | Median mRNA (RSEM) | Mean mRNA (RSEM) | IQR |
|---|---|---|---|---|
| **CDK4 Amplified (AMP)** | 27 | **57,836** | 81,098 | 38,145–98,546 |
| **CDK4 Diploid** | 95 | **3,118** | 4,492 | 2,609–4,146 |
| **Fold change (median)** | — | **~18.5× higher** | — | — |

### Key Observations

1. **Large effect magnitude:** CDK4-amplified samples show median mRNA ~19 times higher than diploid samples (57,836 vs 3,118 RSEM units).

2. **Clear separation:** The interquartile ranges are completely non-overlapping, indicating minimal overlap between groups.

3. **Expression driven by copy number:** The strong linear relationship between CDK4 copy number gain and mRNA upregulation is typical of **cis-acting amplifications** where increased gene dosage directly increases transcription.

### Statistical Note

**To assess statistical significance:** The observed differences are large and the distributions are well-separated, but formal hypothesis testing (Student's t-test or Mann-Whitney U) cannot be performed by my tools. I recommend:

- **In cBioPortal:** Use the Group Comparison tab (linked below) to run the statistical test directly
- **In R:** `wilcox.test(log2(amp_mrna+1), log2(diploid_mrna+1))` 
- **In Python:** `scipy.stats.mannwhitneyu(amp_mrna, diploid_mrna, alternative='two-sided')`

### Interactive Visualization

[View CDK4 alterations in GBM TCGA PanCancer Atlas](https://www.cbioportal.org/results/oncoprint?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=CDK4&case_set_id=gbm_tcga_pan_can_atlas_2018_all) — OncoPrint showing mutations, amplifications, and copy number status across the cohort.

**Bottom line:** Yes, CDK4 mRNA expression is dramatically higher in CDK4-amplified samples compared to diploid samples, consistent with gene dosage–driven expression.