Q (sonnet): 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"
}

▶ list_studies
{
 "search": "MSK CHORD"
}

◀ 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":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","type_of_cancer_id":"mixed","sample_count":25040,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024"}]}

▶ get_study_guide
{
 "study_id": "msk_chord_2024"
}

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

◀ 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)

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

▶ clickhouse_run_select_query
{
 "query": "SELECT table FROM (SELECT 'resource_study' AS table) LIMIT 1"
}

◀ result
{"rows":[{"table":"resource_study"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_value, count(DISTINCT sample_unique_id) AS n
FROM clinical_data_derived
WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'HER2'
GROUP BY attribute_value ORDER BY n DESC"
}

◀ result
{"rows":[{"n":1},{"attribute_value":"Yes","n":1},{"attribute_value":"No","n":1}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(DISTINCT sample_unique_id) AS n
FROM clinical_data_derived
WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'HER2'
GROUP BY attribute_value ORDER BY n DESC"
}

▶ clickhouse_run_select_query
{
 "query": "SELECT count(*) FROM resource_study WHERE cancer_study_identifier = 'msk_chord_2024'"
}

◀ result
{"rows":[{"n":1},{"attribute_value":"Yes","n":1},{"attribute_value":"No","n":1}]}

◀ 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 `cancer_study_identifier` in scope SELECT count(*) FROM resource_study WHERE cancer_study_identifier = 'msk_chord_2024'. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_list_table_columns
{
 "table": "resource_study"
}

◀ result
{"columns":[{"name":"internal_id","type":"Int64"},{"name":"resource_id","type":"String"},{"name":"url","type":"String"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count() AS n FROM clinical_data_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'HER2' GROUP BY attribute_value"
}

◀ result
{"rows":[{"n":19582},{"attribute_value":"Yes","n":866},{"attribute_value":"No","n":4502}]}

▶ resolve_and_route
{
 "studyIds": [
  "msk_chord_2024"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024","metadata":{"clinicalAttributeIds":["ADRENAL_GLANDS","BONE","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_GROUP","CLINICAL_SUMMARY","CNS_BRAIN","CURRENT_AGE_DEID","DIAGNOSIS_DESCRIPTION","ETHNICITY","FRACTION_GENOME_ALTERED","GENDER","GENE_PANEL","GLEASON_FIRST_REPORTED","GLEASON_HIGHEST_REPORTED","GLEASON_SAMPLE_LEVEL","HER2","HISTORY_OF_PDL1","HR","ICD_O_HISTOLOGY_DESCRIPTION","INTRA_ABDOMINAL","LIVER","LUNG","LYMPH_NODES","METASTATIC_SITE","MSI_COMMENT","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","NUM_ICDO_DX","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER","PATHOLOGICAL_GROUP","PDL1_POSITIVE","PLEURA","PRIMARY_SITE","PRIOR_MED_TO_MSK","RACE","REPRODUCTIVE_ORGANS","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SMOKING_PREDICTIONS_3_CLASSES","SOMATIC_STATUS","STAGE_HIGHEST_RECORDED","TMB_NONSYNONYMOUS","TUMOR_PURITY"],"molecularProfileIds":["msk_chord_2024_cna","msk_chord_2024_mutations","msk_chord_2024_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}}]}}

▶ get_studyviewfilter_options
{
 "studyId": "msk_chord_2024",
 "geneSpecificQueries": [
  {
   "hugoGeneSymbol": "ERBB2",
   "profileType": "cna"
  }
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"msk_chord_2024","geneSpecificCounts":[{"hugoGeneSymbol":"ERBB2","profileType":"cna","counts":[{"value":"NA","label":"NA","count":6},{"value":"0","label":"Diploid","count":24070},{"value":"-2","label":"Homozygously deleted","count":4},{"value":"2","label":"Amplified","count":960}]}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "msk_chord_2024"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "attributeId": "CANCER_TYPE",
    "values": [
     {
      "value": "Breast Cancer"
     }
    ]
   }
  ],
  "genomicDataFilters": [
   {
    "hugoGeneSymbol": "ERBB2",
    "profileType": "cna",
    "values": [
     {
      "value": "2"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Breast%20Cancer%22%7D%5D%7D%5D%2C%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22ERBB2%22%2C%22profileType%22%3A%22cna%22%2C%22values%22%3A%5B%7B%22value%22%3A%222%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Breast%20Cancer%22%7D%5D%7D%5D%2C%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22ERBB2%22%2C%22profileType%22%3A%22cna%22%2C%22values%22%3A%5B%7B%22value%22%3A%222%22%7D%5D%7D%5D%7D","data":{"studyIds":["msk_chord_2024"],"studies":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}}

═ answer (success)
## Summary

**A gene-amplification–vs–protein-expression correlation is not computable in MSK-CHORD** because the study contains no protein expression data (no RPPA, no proteomics) and no mRNA expression profile — it's a targeted MSK-IMPACT panel study whose only molecular readouts are copy number, mutations, and structural variants.

What the data does show, across MSK-CHORD's 25,040 samples:
- **ERBB2 copy-number status** (all cancer types): 960 samples amplified, 24,070 diploid, 4 homozygously deleted, 6 not profiled.
- A separate categorical clinical field, **`HER2`** (Yes/No — likely an abstracted IHC/FISH call, not a numeric expression value), recorded for 866 "Yes" and 4,502 "No" samples (mostly in the breast cancer cohort; other cancer types largely leave this blank).

You could compare ERBB2 amplification status against this categorical `HER2` field as a rough concordance check, but that is not the same as a quantitative amplification-vs-expression correlation.

[View ERBB2-amplified breast cancer samples in MSK-CHORD](https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Breast%20Cancer%22%7D%5D%7D%5D%2C%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22ERBB2%22%2C%22profileType%22%3A%22cna%22%2C%22values%22%3A%5B%7B%22value%22%3A%222%22%7D%5D%7D%5D%7D) — from here you can cross-tabulate against the HER2 clinical attribute in the chart panel.

If you'd like the actual amplification-vs-protein-expression correlation, that requires a cohort with proteomic data. Good candidates in cBioPortal are **Breast Cancer (TCGA, PanCancer Atlas)** or **METABRIC**, both of which include RPPA/protein data alongside CNA calls for ERBB2/HER2. Let me know if you'd like me to run that analysis in either of those studies instead.