Q (haiku): Is there a correlation between ERBB2 gene amplification and ERBB2 protein expression levels in breast cancer samples from the MSK-CHORD Study?

▶ 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 | `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)

▶ list_studies
{
 "search": "MSK CHORD breast",
 "verbose": true
}

▶ get_study_guide
{
 "study_id": "msk_chord_2024"
}

◀ result
{"result":[]}

◀ result
# MSK-CHORD (MSK, Nature 2024)

**Study ID:** `msk_chord_2024`

## Overview
Targeted sequencing via MSK-IMPACT panels. Clinical annotations include some derived from natural language processing (denoted NLP).

**Exactly five cancer types** (`CANCER_TYPE`, patients): Non-Small Cell Lung Cancer 7,809, Colorectal Cancer 5,543, Breast Cancer 5,368, Prostate Cancer 3,211, Pancreatic Cancer 3,109. There is **no melanoma** or any other cancer type; say so up front if asked, instead of substituting another type.

**No therapy-response variable.** There is no RECIST, objective response, or best-response attribute or event. For treatment-outcome questions (e.g. immunotherapy response), say this first; the only proxies are `OS_MONTHS`/`OS_STATUS`, or NLP radiology progression events (`Diagnosis` events with `SUBTYPE = 'Progression'`, key `PROGRESSION` = Y/N/Indeterminate), in patients with `Treatment` events of the relevant `SUBTYPE` (e.g. `Immuno`: 3,341 patients). Hand off the comparison to cBioPortal group comparison / survival.

**Nearly one sample per patient: 24,950 patients / 25,040 samples.** Only 90 patients have more than one sample, and all 90 have samples from two different cancer types (second primaries); only 26 have both a `Primary` and a `Metastasis` sample. There is no meaningful same-patient (paired) primary-vs-metastasis cohort. For "same patient" / paired questions, say this up front, then offer the **unpaired** comparison of all `Primary` vs `Metastasis` samples (`SAMPLE_TYPE`), labelled as unpaired.
```sql
SELECT countIf(n > 1) AS multi_sample_patients,         -- 90
       countIf(has_p AND has_m) AS primary_and_met      -- 26
FROM (SELECT patient_unique_id, count() AS n,
             has(groupArray(attribute_value), 'Primary') AS has_p,
             has(groupArray(attribute_value), 'Metastasis') AS has_m
      FROM clinical_data_derived
      WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'SAMPLE_TYPE'
      GROUP BY patient_unique_id);
```

## Gene Panels
This study uses multiple MSK-IMPACT panel versions:
- **IMPACT341**: Earlier version, 341 genes
- **IMPACT410**: 410 genes
- **IMPACT468**: 468 genes  
- **IMPACT505**: Latest version, 505 genes

**Important:** Different samples may have different gene coverage. Always use gene-specific denominators when calculating mutation frequencies.

## Clinical Attributes - Semantic Guide

### Cancer Classification
| Attribute | Description | Values |
|-----------|-------------|--------|
| `CANCER_TYPE` | Broad cancer category | e.g., "Non-Small Cell Lung Cancer", "Breast Cancer" |
| `CANCER_TYPE_DETAILED` | Specific subtype | e.g., "Lung Adenocarcinoma", "Invasive Ductal Carcinoma" |
| `ONCOTREE_CODE` | OncoTree classification code | Standardized cancer type codes |

### Sample Information
| Attribute | Description | Values |
|-----------|-------------|--------|
| `SAMPLE_TYPE` | Sample origin | Primary, Metastasis, Local Recurrence, Unknown |
| `SAMPLE_CLASS` | Sample … (5439 more chars)

▶ 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": "
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'msk_chord_2024'
  AND hugo_gene_symbol = 'ERBB2'
ORDER BY profile_type;
"
}

◀ result
{"rows":[{"profile_type":"cna"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'msk_chord_2024'
ORDER BY profile_type;
",
 "max_rows": 50
}

◀ result
{"rows":[{"profile_type":"cna"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check for protein/expression data in MSK-CHORD
SELECT DISTINCT hugo_gene_symbol, profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'msk_chord_2024'
GROUP BY hugo_gene_symbol, profile_type
LIMIT 20;
",
 "max_rows": 50
}

◀ result
{"rows":[{"hugo_gene_symbol":"EED","profile_type":"cna"},{"hugo_gene_symbol":"GPS2","profile_type":"cna"},{"hugo_gene_symbol":"ACVR1","profile_type":"cna"},{"hugo_gene_symbol":"PTEN","profile_type":"cna"},{"hugo_gene_symbol":"EPCAM","profile_type":"cna"},{"hugo_gene_symbol":"RAD51","profile_type":"cna"},{"hugo_gene_symbol":"SDHA","profile_type":"cna"},{"hugo_gene_symbol":"RECQL4","profile_type":"cna"},{"hugo_gene_symbol":"PIK3C3","profile_type":"cna"},{"hugo_gene_symbol":"EIF4E","profile_type":"cna"},{"hugo_gene_symbol":"DDR2","profile_type":"cna"},{"hugo_gene_symbol":"RAF1","profile_type":"cna"},{"hugo_gene_symbol":"DROSHA","profile_type":"cna"},{"hugo_gene_symbol":"DTX1","profile_type":"cna"},{"hugo_gene_symbol":"H3C7","profile_type":"cna"},{"hugo_gene_symbol":"TSC2","profile_type":"cna"},{"hugo_gene_symbol":"TNFRSF1B","profile_type":"cna"},{"hugo_gene_symbol":"FBXO11","profile_type":"cna"},{"hugo_gene_symbol":"RXRA","profile_type":"cna"},{"hugo_gene_symbol":"GREM1","profile_type":"cna"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get ERBB2 CNA data for breast cancer samples in MSK-CHORD
WITH breast_samples AS (
  SELECT DISTINCT s.sample_unique_id
  FROM clinical_data_derived cd
  JOIN sample s ON cd.sample_unique_id = s.stable_id || '_' || cs.cancer_study_identifier
  WHERE cd.cancer_study_identifier = 'msk_chord_2024'
    AND cd.attribute_name = 'CANCER_TYPE'
    AND cd.attribute_value = 'Breast Cancer'
),
erbb2_cna AS (
  SELECT 
    sample_unique_id,
    toFloat64OrNull(alteration_value) AS cna_value
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'msk_chord_2024'
    AND hugo_gene_symbol = 'ERBB2'
    AND profile_type = 'cna'
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT 
  COUNT(*) AS total_samples,
  countIf(cna_value > 0.5) AS amplified_samples,
  countIf(cna_value <= 0.5) AS non_amplified_samples,
  round(100.0 * countIf(cna_value > 0.5) / count(), 2) AS amplification_pct
FROM erbb2_cna;
"
}

◀ result
{"rows":[{"total_samples":25034,"amplified_samples":960,"non_amplified_samples":24074,"amplification_pct":3.83}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check molecular profile table for protein/expression data types
SELECT molecular_profile_id, profile_type, datatype 
FROM molecular_profile 
WHERE cancer_study_id IN (
  SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'msk_chord_2024'
)
ORDER BY profile_type;
",
 "max_rows": 50
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 60\n Code: 60. DB::Exception: Unknown table expression identifier 'molecular_profile' in scope SELECT molecular_profile_id, profile_type, datatype FROM molecular_profile WHERE cancer_study_id IN (SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'msk_chord_2024') ORDER BY profile_type ASC. (UNKNOWN_TABLE) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_list_tables
{}

◀ result
{"tables":[{"name":"allele_specific_copy_number"},{"name":"alteration_driver_annotation"},{"name":"cancer_study"},{"name":"cancer_study_query_preferences"},{"name":"cancer_study_tags"},{"name":"cbioportal_sequence_state"},{"name":"clinical_attribute_counts"},{"name":"clinical_attribute_meta"},{"name":"clinical_data_derived"},{"name":"clinical_event"},{"name":"clinical_event_data"},{"name":"clinical_event_data_derived"},{"name":"clinical_event_derived"},{"name":"clinical_patient"},{"name":"clinical_sample"},{"name":"cna_event"},{"name":"cna_panel_gene_coverage"},{"name":"cna_wes_coverage"},{"name":"co_altered_genes_in_study"},{"name":"copy_number_seg"},{"name":"copy_number_seg_file"},{"name":"gene"},{"name":"gene_alias"},{"name":"gene_alteration_frequency_by_cancer_type"},{"name":"gene_cna_distribution_in_study"},{"name":"gene_mutation_frequency_by_cancer_type"},{"name":"gene_mutation_frequency_in_studies"},{"name":"gene_mutation_frequency_in_study"},{"name":"gene_mutation_variants_in_study"},{"name":"gene_pair_coexpression"},{"name":"gene_panel"},{"name":"gene_panel_list"},{"name":"gene_panel_to_gene_derived"},{"name":"generic_assay_data_derived"},{"name":"generic_assay_meta_derived"},{"name":"generic_assay_profile_entity_derived"},{"name":"generic_entity_properties"},{"name":"geneset"},{"name":"geneset_gene"},{"name":"geneset_hierarchy_leaf"},{"name":"geneset_hierarchy_node"},{"name":"genetic_alteration"},{"name":"genetic_alteration_derived"},{"name":"genetic_entity"},{"name":"genetic_profile"},{"name":"genetic_profile_link"},{"name":"genetic_profile_samples"},{"name":"genomic_event_derived"},{"name":"gistic"},{"name":"gistic_to_gene"},{"name":"info"},{"name":"mut_sig"},{"name":"mutation"},{"name":"mutation_count_by_keyword"},{"name":"mutation_derived"},{"name":"mutation_event"},{"name":"mutation_panel_gene_coverage"},{"name":"mutation_wes_coverage"},{"name":"patient"},{"name":"reference_genome"},{"name":"reference_genome_gene"},{"name":"resource_definition"},{"name":"resource_patient"},{"name":"resource_sample"},{"name":"resource_study"},{"name":"sample"},{"name":"sample_cna_event"},{"name":"sample_derived"},{"name":"sample_list"},{"name":"sample_list_list"},{"name":"sample_profile"},{"name":"sample_to_gene_panel_derived"},{"name":"structural_variant"},{"name":"sv_panel_gene_coverage"},{"name":"sv_wes_coverage"},{"name":"top_cna_genes_in_study"},{"name":"top_mutated_genes_in_cohort"},{"name":"top_mutated_genes_in_study"},{"name":"top_sv_genes_in_study"},{"name":"treatment_counts_in_study"},{"name":"treatment_regimens_in_study"},{"name":"type_of_cancer"}]}

▶ clickhouse_list_table_columns
{
 "table": "genetic_profile"
}

◀ result
{"columns":[{"name":"genetic_profile_id","type":"Int64"},{"name":"stable_id","type":"String"},{"name":"cancer_study_id","type":"Int64"},{"name":"genetic_alteration_type","type":"String"},{"name":"generic_assay_type","type":"Nullable(String)"},{"name":"datatype","type":"String"},{"name":"name","type":"String"},{"name":"description","type":"Nullable(String)"},{"name":"show_profile_in_analysis_tab","type":"Int32"},{"name":"pivot_threshold","type":"Nullable(Float64)"},{"name":"sort_order","type":"Nullable(String)"},{"name":"patient_level","type":"Nullable(Int32)"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check available profile types in MSK-CHORD
SELECT 
  gp.stable_id,
  gp.genetic_alteration_type,
  gp.datatype,
  gp.name
FROM genetic_profile gp
JOIN cancer_study cs ON gp.cancer_study_id = cs.cancer_study_id
WHERE cs.cancer_study_identifier = 'msk_chord_2024'
ORDER BY gp.genetic_alteration_type, gp.datatype;
",
 "max_rows": 50
}

◀ result
{"rows":[{"stable_id":"msk_chord_2024_cna","genetic_alteration_type":"COPY_NUMBER_ALTERATION","datatype":"DISCRETE","name":"Putative copy-number alterations from GISTIC"},{"stable_id":"msk_chord_2024_mutations","genetic_alteration_type":"MUTATION_EXTENDED","datatype":"MAF","name":"Mutations"},{"stable_id":"msk_chord_2024_structural_variants","genetic_alteration_type":"STRUCTURAL_VARIANT","datatype":"SV","name":"Structural Variants"}]}

═ answer (success)
Perfect. Now I have the complete picture. Let me provide a comprehensive answer:

---

## Answer

**Unfortunately, MSK-CHORD does not have ERBB2 protein expression data available**, so I cannot compute a direct correlation between gene amplification and protein levels.

### What Data is Available

MSK-CHORD contains **only copy-number alteration (CNA) data**, with three molecular profiles:
- Mutations (MAF format)
- Copy-number alterations (GISTIC-derived, discrete)
- Structural variants

There are **no protein expression, mRNA expression, or immunoassay profiles** in this study.

### ERBB2 Amplification in MSK-CHORD

For context, here's what we can report about ERBB2 amplification alone:

| Metric | Value |
|--------|-------|
| **Total samples with CNA data** | 25,034 |
| **ERBB2-amplified samples** | 960 |
| **Amplification frequency** | **3.83%** |

### Why This Study Lacks Protein Data

MSK-CHORD is a **targeted sequencing study** (MSK-IMPACT panels) focused on genomic alterations. It does not include:
- Protein quantification (RPPA, mass spec, immunoassay)
- mRNA expression (RNA-seq, microarray)
- Immunohistochemistry (IHC) scores

### Alternative Approaches

If you need to correlate ERBB2 amplification with protein expression, consider:

1. **Use a different breast cancer cohort with both data types:**
   - [METABRIC](https://www.cbioportal.org/study/summary?id=brca_metabric) (aCGH + RNA-seq)
   - [TCGA BRCA](https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018) (SNP6 + RNA-seq)

2. **Use MSK-CHORD CNA data with external protein reference data** (not in cBioPortal) — your institution may have HER2 IHC/FISH scores linked to MSK-CHORD samples; contact MSK's data team.

3. **Query MSK-CHORD mutation data instead** — ERBB2 mutations are rare but present in some samples.

Would you like me to query ERBB2 amplification in breast cancer samples from METABRIC or TCGA, or explore ERBB2 mutations in MSK-CHORD?