Q (haiku): How does ERBB2 mRNA expression vary across different cancer types in TCGA Pan-Cancer Atlas studies?

▶ 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` | `<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)

▶ clickhouse_run_select_query
{
 "query": "
-- ERBB2 mRNA expression across TCGA Pan-Cancer Atlas studies
WITH tcga_studies AS (
    SELECT cancer_study_identifier, name, sample_count
    FROM cancer_study
    WHERE cancer_study_identifier IN (
        SELECT cancer_study_identifier
        FROM cancer_study_query_preferences
        WHERE preference_name = 'pan_cancer_tcga'
    )
),
erbb2_data AS (
    SELECT 
        cs.cancer_study_identifier,
        cs.name AS study_name,
        COUNT(DISTINCT gad.sample_unique_id) AS num_samples_with_expr,
        ROUND(AVG(toFloat64OrNull(gad.alteration_value)), 3) AS mean_expr,
        ROUND(median(toFloat64OrNull(gad.alteration_value)), 3) AS median_expr,
        ROUND(min(toFloat64OrNull(gad.alteration_value)), 3) AS min_expr,
        ROUND(max(toFloat64OrNull(gad.alteration_value)), 3) AS max_expr,
        ROUND(stddevPop(toFloat64OrNull(gad.alteration_value)), 3) AS stdev_expr
    FROM genetic_alteration_derived gad
    INNER JOIN tcga_studies cs ON gad.cancer_study_identifier = cs.cancer_study_identifier
    WHERE gad.hugo_gene_symbol = 'ERBB2'
        AND gad.profile_type IN ('mrna_seq_v2_rsem_Zscores', 'mrna_median_Zscores')
        AND gad.alteration_value NOT IN ('', 'NA')
        AND toFloat64OrNull(gad.alteration_value) IS NOT NULL
    GROUP BY cs.cancer_study_identifier, cs.name
    ORDER BY median_expr DESC NULLS LAST
)
SELECT * FROM erbb2_data;
",
 "max_rows": 100
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check available profile types for ERBB2 in TCGA studies
SELECT DISTINCT 
    gad.cancer_study_identifier,
    gad.profile_type,
    COUNT(*) AS sample_count
FROM genetic_alteration_derived gad
WHERE gad.hugo_gene_symbol = 'ERBB2'
    AND gad.cancer_study_identifier LIKE '%tcga%'
    AND gad.alteration_value NOT IN ('', 'NA')
GROUP BY gad.cancer_study_identifier, gad.profile_type
ORDER BY gad.cancer_study_identifier, gad.profile_type;
",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_study_identifier":"acc_tcga","profile_type":"gistic","sample_count":90},{"cancer_study_identifier":"acc_tcga","profile_type":"linear_CNA","sample_count":90},{"cancer_study_identifier":"acc_tcga","profile_type":"methylation_hm450","sample_count":80},{"cancer_study_identifier":"acc_tcga","profile_type":"rna_seq_v2_mrna","sample_count":79},{"cancer_study_identifier":"acc_tcga","profile_type":"rna_seq_v2_mrna_median_Zscores","sample_count":79},{"cancer_study_identifier":"acc_tcga","profile_type":"rna_seq_v2_mrna_median_all_sample_Zscores","sample_count":79},{"cancer_study_identifier":"acc_tcga","profile_type":"rppa","sample_count":46},{"cancer_study_identifier":"acc_tcga","profile_type":"rppa_Zscores","sample_count":46},{"cancer_study_identifier":"acc_tcga_gdc","profile_type":"cna","sample_count":90},{"cancer_study_identifier":"acc_tcga_gdc","profile_type":"mrna_seq_fpkm","sample_count":79},{"cancer_study_identifier":"acc_tcga_gdc","profile_type":"mrna_seq_fpkm_Zscores","sample_count":79},{"cancer_study_identifier":"acc_tcga_gdc","profile_type":"mrna_seq_read_counts_Zscores","sample_count":79},{"cancer_study_identifier":"acc_tcga_gdc","profile_type":"mrna_seq_tpm","sample_count":79},{"cancer_study_identifier":"acc_tcga_gdc","profile_type":"mrna_seq_tpm_Zscores","sample_count":79},{"cancer_study_identifier":"acc_tcga_gdc","profile_type":"rna_seq_mrna","sample_count":79},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"gistic","sample_count":89},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"log2CNA","sample_count":89},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna","sample_count":78},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_Zscores","sample_count":78},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"rna_seq_v2_mrna_median_all_sample_Zscores","sample_count":78},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"rppa","sample_count":45},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","profile_type":"rppa_Zscores","sample_count":45},{"cancer_study_identifier":"aml_tcga_gdc","profile_type":"cna","sample_count":190},{"cancer_study_identifier":"aml_tcga_gdc","profile_type":"mrna_seq_fpkm","sample_count":137},{"cancer_study_identifier":"aml_tcga_gdc","profile_type":"mrna_seq_fpkm_Zscores","sample_count":137},{"cancer_study_identifier":"aml_tcga_gdc","profile_type":"mrna_seq_read_counts_Zscores","sample_count":137},{"cancer_study_identifier":"aml_tcga_gdc","profile_type":"mrna_seq_tpm","sample_count":137},{"cancer_study_identifier":"aml_tcga_gdc","profile_type":"mrna_seq_tpm_Zscores","sample_count":137},{"cancer_study_identifier":"aml_tcga_gdc","profile_type":"rna_seq_mrna","sample_count":137},{"cancer_study_identifier":"blca_msk_tcga_2020","profile_type":"gistic","sample_count":442},{"cancer_study_identifier":"blca_msk_tcga_2020","profile_typ … (7314 more chars)

▶ clickhouse_run_select_query
{
 "query": "
-- ERBB2 mRNA expression across TCGA Pan-Cancer Atlas studies
WITH erbb2_data AS (
    SELECT 
        gad.cancer_study_identifier,
        cs.name AS study_name,
        COUNT(DISTINCT gad.sample_unique_id) AS num_samples_with_expr,
        ROUND(AVG(toFloat64OrNull(gad.alteration_value)), 2) AS mean_expr_zscore,
        ROUND(median(toFloat64OrNull(gad.alteration_value)), 2) AS median_expr_zscore,
        ROUND(min(toFloat64OrNull(gad.alteration_value)), 2) AS min_expr_zscore,
        ROUND(max(toFloat64OrNull(gad.alteration_value)), 2) AS max_expr_zscore,
        ROUND(stddevPop(toFloat64OrNull(gad.alteration_value)), 2) AS stdev_expr_zscore
    FROM genetic_alteration_derived gad
    INNER JOIN cancer_study cs ON gad.cancer_study_identifier = cs.cancer_study_identifier
    WHERE gad.hugo_gene_symbol = 'ERBB2'
        AND gad.cancer_study_identifier LIKE '%tcga_pan_can_atlas_2018'
        AND gad.profile_type = 'rna_seq_v2_mrna_median_Zscores'
        AND gad.alteration_value NOT IN ('', 'NA')
        AND toFloat64OrNull(gad.alteration_value) IS NOT NULL
    GROUP BY gad.cancer_study_identifier, cs.name
    ORDER BY median_expr_zscore DESC NULLS LAST
)
SELECT * FROM erbb2_data;
",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018","study_name":"Kidney Renal Papillary Cell Carcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":283,"mean_expr_zscore":0.47,"median_expr_zscore":0.31,"min_expr_zscore":-1.57,"max_expr_zscore":6.3,"stdev_expr_zscore":1.03},{"cancer_study_identifier":"ucs_tcga_pan_can_atlas_2018","study_name":"Uterine Carcinosarcoma (TCGA, PanCancer Atlas)","num_samples_with_expr":57,"mean_expr_zscore":1.58,"median_expr_zscore":0.26,"min_expr_zscore":-1.68,"max_expr_zscore":37.55,"stdev_expr_zscore":5.31},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018","study_name":"Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":181,"mean_expr_zscore":5.45,"median_expr_zscore":0.24,"min_expr_zscore":-1.58,"max_expr_zscore":208.56,"stdev_expr_zscore":22.82},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","study_name":"Cholangiocarcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":36,"mean_expr_zscore":0.11,"median_expr_zscore":0.22,"min_expr_zscore":-1.87,"max_expr_zscore":1.99,"stdev_expr_zscore":0.93},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018","study_name":"Stomach Adenocarcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":412,"mean_expr_zscore":6.29,"median_expr_zscore":0.19,"min_expr_zscore":-2.11,"max_expr_zscore":230.51,"stdev_expr_zscore":25.75},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","study_name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":510,"mean_expr_zscore":0.74,"median_expr_zscore":0.13,"min_expr_zscore":-1.98,"max_expr_zscore":94.14,"stdev_expr_zscore":5.27},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","study_name":"Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":407,"mean_expr_zscore":1.15,"median_expr_zscore":0.03,"min_expr_zscore":-1.27,"max_expr_zscore":54.94,"stdev_expr_zscore":5.61},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018","study_name":"Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":177,"mean_expr_zscore":0.7,"median_expr_zscore":0.01,"min_expr_zscore":-2.19,"max_expr_zscore":106.72,"stdev_expr_zscore":8.09},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018","study_name":"Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":294,"mean_expr_zscore":3.13,"median_expr_zscore":-0.02,"min_expr_zscore":-1.81,"max_expr_zscore":197.36,"stdev_expr_zscore":17.81},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","study_name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":592,"mean_expr_zscore":1.76,"median_expr_zscore":-0.03,"min_expr_zscore":-2.55,"max_expr_zscore":258.54,"stdev_expr_zscore":14.41},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","study_name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","num_samples_with_expr":527,"mean_expr_zscore":1.78,"median_expr_zscore":-0.06,"min_expr_zscore" … (5795 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018",
  "brca_tcga_pan_can_atlas_2018",
  "stad_tcga_pan_can_atlas_2018",
  "cesc_tcga_pan_can_atlas_2018",
  "esca_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 6 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":6,"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":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Ca … (13427 more chars)

▶ navigate_to_results_view
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018",
  "brca_tcga_pan_can_atlas_2018",
  "stad_tcga_pan_can_atlas_2018",
  "cesc_tcga_pan_can_atlas_2018",
  "esca_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018",
  "ucec_tcga_pan_can_atlas_2018",
  "blca_tcga_pan_can_atlas_2018",
  "lusc_tcga_pan_can_atlas_2018",
  "paad_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "ERBB2"
 ],
 "tab": "plots",
 "plotsVertSelection": {
  "selectedGeneOption": "ERBB2",
  "dataType": "mrna_seq_v2_mrna_median_Zscores"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=luad_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=all&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna_seq_v2_mrna_median_Zscores%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=luad_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=all&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna_seq_v2_mrna_median_Zscores%22%7D","data":{"studyIds":["luad_tcga_pan_can_atlas_2018","brca_tcga_pan_can_atlas_2018","stad_tcga_pan_can_atlas_2018","cesc_tcga_pan_can_atlas_2018","esca_tcga_pan_can_atlas_2018","coadread_tcga_pan_can_atlas_2018","ucec_tcga_pan_can_atlas_2018","blca_tcga_pan_can_atlas_2018","lusc_tcga_pan_can_atlas_2018","paad_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566},{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084},{"studyId":"stad_tcga_pan_can_atlas_2018","name":"Stomach Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":440},{"studyId":"cesc_tcga_pan_can_atlas_2018","name":"Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","sampleCount":297},{"studyId":"esca_tcga_pan_can_atlas_2018","name":"Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":182},{"studyId":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594},{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529},{"studyId":"blca_tcga_pan_can_atlas_2018","name":"Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":411},{"studyId":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","sampleCount":487},{"studyId":"paad_tcga_pan_can_atlas_2018","name":"Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":184}],"genes":["ERBB2"],"caseSetId":"all","pageDescription":"Scatter/box plots comparing any two data elements."}}

═ answer (success)
## ERBB2 mRNA Expression Variation Across TCGA Cancer Types

I've analyzed ERBB2 mRNA expression (Z-scores) across 32 TCGA Pan-Cancer Atlas studies. Here are the key findings:

### **Highest ERBB2 Expression (by median Z-score):**

| Cancer Type | Median Z-score | Mean Z-score | N Samples |
|---|---|---|---|
| **Kidney Renal Papillary** | +0.31 | +0.47 | 283 |
| **Esophageal Adenocarcinoma** | +0.24 | +5.45 | 181 |
| **Cholangiocarcinoma** | +0.22 | +0.11 | 36 |
| **Stomach Adenocarcinoma** | +0.19 | +6.29 | 412 |
| **Lung Adenocarcinoma** | +0.13 | +0.74 | 510 |

### **Lowest ERBB2 Expression (by median Z-score):**

| Cancer Type | Median Z-score | Mean Z-score | N Samples |
|---|---|---|---|
| **Ovarian Serous Cystadenoma** | −0.76 | +0.45 | 300 |
| **Kidney Chromophobe** | −0.55 | −0.42 | 65 |
| **Adrenocortical Carcinoma** | −0.44 | −0.09 | 78 |
| **Pheochromocytoma/Paraganglioma** | −0.42 | −0.15 | 178 |
| **Acute Myeloid Leukemia** | −0.33 | −0.05 | 173 |

### **Key Observations:**

1. **GI malignancies show elevated ERBB2**: Esophageal, stomach, and colorectal adenocarcinomas trend toward higher expression (consistent with HER2-driven gastric/GE junction cancers in clinical practice).

2. **Breast cancer shows high variance**: Despite a median near neutral (−0.13 Z-score across 1,082 samples), breast cancer has a large range (−2.22 to +113.26), reflecting known HER2 heterogeneity.

3. **Most cancers cluster near baseline**: The majority of cancer types show median Z-scores between −0.3 and +0.3, indicating ERBB2 is not universally overexpressed.

4. **Outliers are common**: Multiple cancer types show extreme positive outliers (Z-scores >50–200), suggesting occasional samples with marked ERBB2 amplification/overexpression regardless of cancer type.

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**[Explore these studies interactively](https://www.cbioportal.org/results/plots?cancer_study_list=luad_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=all&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna_seq_v2_mrna_median_Zscores%22%7D)** — Opens cBioPortal's Plots tab with ERBB2 expression pre-selected across the top cancer types (Lung, Breast, Stomach, Cervical, Esophageal, Colorectal, Endometrial, Bladder, Lung Squamous, Pancreatic).