Q (sonnet-5.5): In the TCGA PanCancer Atlas breast cancer study, what is the concordance between ERBB2 copy number amplification, mRNA overexpression, and protein overexpression?

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

▶ list_studies
{
 "search": "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 | `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
{"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"
}

◀ 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


▶ clickhouse_run_select_query
{
 "query": "SELECT profile_type, count() n, uniqExact(sample_unique_id) s, min(alteration_value), max(alteration_value) FROM genetic_alteration_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol='ERBB2' GROUP BY profile_type"
}

◀ result
{"rows":[{"profile_type":"rppa","n":876,"s":876,"min(alteration_value)":"-0.00292987","max(alteration_value)":"5.01322121"},{"profile_type":"rppa_Zscores","n":876,"s":876,"min(alteration_value)":"-0.0008","max(alteration_value)":"4.2354"},{"profile_type":"rna_seq_v2_mrna","n":1082,"s":1082,"min(alteration_value)":"10017.9","max(alteration_value)":"9997.87"},{"profile_type":"protein_quantification","n":105,"s":105,"min(alteration_value)":"-0.0817","max(alteration_value)":"2.5154"},{"profile_type":"log2CNA","n":1070,"s":1070,"min(alteration_value)":"-0.001","max(alteration_value)":"3.66"},{"profile_type":"rna_seq_v2_mrna_median_Zscores","n":1082,"s":1082,"min(alteration_value)":"-0.0039","max(alteration_value)":"9.9972"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","n":1082,"s":1082,"min(alteration_value)":"-0.0039","max(alteration_value)":"5.5961"},{"profile_type":"protein_quantification_zscores","n":105,"s":105,"min(alteration_value)":"-0.0039","max(alteration_value)":"3.4945"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_Zscores","n":1082,"s":1082,"min(alteration_value)":"-0.0009","max(alteration_value)":"3.7214"},{"profile_type":"gistic","n":1070,"s":1070,"min(alteration_value)":"-1","max(alteration_value)":"2"}]}

▶ clickhouse_run_select_query
{
 "query": "WITH c AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) v FROM genetic_alteration_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol='ERBB2' AND profile_type='gistic'),
m AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) v FROM genetic_alteration_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol='ERBB2' AND profile_type='rna_seq_v2_mrna_median_Zscores' AND toFloat64OrNull(alteration_value) IS NOT NULL),
p AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) v FROM genetic_alteration_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol='ERBB2' AND profile_type='rppa_Zscores' AND toFloat64OrNull(alteration_value) IS NOT NULL)
SELECT c.v>=2 AS amp, m.v>2 AS mrna_hi, p.v>2 AS prot_hi, count() n
FROM c JOIN m USING (sample_unique_id) JOIN p USING (sample_unique_id)
GROUP BY amp, mrna_hi, prot_hi ORDER BY amp DESC, mrna_hi DESC, prot_hi DESC"
}

▶ clickhouse_run_select_query
{
 "query": "SELECT hugo_gene_symbol, profile_type, count() FROM genetic_alteration_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND profile_type IN ('rppa_Zscores') AND hugo_gene_symbol LIKE 'ERBB2%' GROUP BY 1,2"
}

◀ result
{"rows":[{"amp":1,"mrna_hi":1,"prot_hi":1,"n":50},{"amp":1,"mrna_hi":1,"prot_hi":0,"n":44},{"amp":1,"mrna_hi":0,"prot_hi":0,"n":7},{"amp":0,"mrna_hi":1,"prot_hi":1,"n":2},{"amp":0,"mrna_hi":1,"prot_hi":0,"n":41},{"amp":0,"mrna_hi":0,"prot_hi":0,"n":719}]}

◀ result
{"rows":[{"hugo_gene_symbol":"ERBB2","profile_type":"rppa_Zscores","count()":876},{"hugo_gene_symbol":"ERBB2_PY1248","profile_type":"rppa_Zscores","count()":876}]}

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "ERBB2: AMP EXP>2 PROT>2"
 ],
 "profileFilter": "gistic,rna_seq_v2_mrna_median_Zscores,rppa_Zscores",
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=ERBB2%3A+AMP+EXP%3E2+PROT%3E2&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=gistic%2Crna_seq_v2_mrna_median_Zscores%2Crppa_Zscores","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=ERBB2%3A+AMP+EXP%3E2+PROT%3E2&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=gistic%2Crna_seq_v2_mrna_median_Zscores%2Crppa_Zscores","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":["ERBB2"],"caseSetId":"brca_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
ERBB2 copy number amplification and mRNA overexpression agree closely in the TCGA PanCancer Atlas breast study. Protein overexpression agrees less well, mainly because it is called in fewer samples.

**Cohort and thresholds:** [Breast Invasive Carcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018). I used the 863 samples that have all three data types.
- **Amplification:** GISTIC call of 2 (high-level amplification).
- **mRNA overexpression:** RNA-seq z-score above 2.
- **Protein overexpression:** RPPA (reverse-phase protein array) ERBB2 z-score above 2.

The z-score cutoff of 2 is my choice. Different cutoffs will change the numbers.

**Sample counts by combination (n = 863)**

| Amplified | mRNA high | Protein high | Samples |
|---|---|---|---|
| Yes | Yes | Yes | 50 |
| Yes | Yes | No | 44 |
| Yes | No | No | 7 |
| No | Yes | Yes | 2 |
| No | Yes | No | 41 |
| No | No | No | 719 |

No sample was protein-high without also being mRNA-high.

**Key concordance figures**
- **Amplified samples (101):**
  - 94 (93%) also have high mRNA.
  - 50 (50%) also have high protein.
  - 50 (50%) have all three.
- **mRNA-high samples (137):**
  - 94 (69%) are amplified.
  - 52 (38%) are protein-high.
  - The remaining 41 have high mRNA without amplification or high protein.
- **Protein-high samples (52):**
  - 50 (96%) are amplified.
  - All 52 (100%) are mRNA-high.
- **Non-amplified, non-overexpressing samples:** 719 have none of the three events.

**Interpretation of the numbers**
- Amplification almost always drives mRNA overexpression, but about 30% of mRNA-high tumors are not amplified.
- Protein-high calls are rare and specific. Almost every one is amplified and mRNA-high. Only about half of amplified tumors reach the protein threshold at z > 2.
- The protein assay is a limitation. RPPA is a semi-quantitative antibody measurement, and a z-score cutoff is a blunt call. It is not a clinical HER2 test (IHC or FISH), which this study does not include. I haven't tested whether a lower protein cutoff would raise the agreement.

**Sample availability:** GISTIC covers 1,070 samples, mRNA covers 1,082 and RPPA covers 876. The counts above use only samples with all three.

[View the ERBB2 OncoPrint (AMP, mRNA > 2, protein > 2)](https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=ERBB2%3A+AMP+EXP%3E2+PROT%3E2&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=gistic%2Crna_seq_v2_mrna_median_Zscores%2Crppa_Zscores)