Q (sonnet): "What is the concordance between ERBB2 copy number amplification, mRNA overexpression, and protein overexpression in breast cancer?"

▶ list_guides
{}

▶ list_studies
{
 "search": "breast TCGA pan can atlas"
}

◀ result
{"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

◀ 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"}]}

▶ 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 | `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)

▶ get_study_guide
{
 "study_id": "brca_tcga_pan_can_atlas_2018"
}

◀ 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)

◀ 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 DISTINCT profile_type FROM genetic_alteration_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' ORDER BY profile_type"
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"rows":[{"profile_type":"gistic"},{"profile_type":"log2CNA"},{"profile_type":"protein_quantification"},{"profile_type":"protein_quantification_zscores"},{"profile_type":"rna_seq_v2_mrna"},{"profile_type":"rna_seq_v2_mrna_median_Zscores"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_Zscores"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores"},{"profile_type":"rppa"},{"profile_type":"rppa_Zscores"}]}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"hugo_gene_symbol","type":"String","comment":"HUGO gene symbol (e.g., TP53, KRAS, BRAF). Use for gene-specific queries."},{"name":"entrez_gene_id","type":"Int32"},{"name":"gene_panel_stable_id","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"genetic_profile_stable_id","type":"LowCardinality(String)"},{"name":"variant_type","type":"LowCardinality(String)","comment":"Type of genomic event: \"mutation\" for SNVs/indels, \"cna\" for copy number alterations, \"structural_variant\" for SVs. Always filter by this."},{"name":"mutation_variant","type":"String","comment":"Protein change notation (e.g., p.V600E, p.R175H). Use for specific variant queries. \"NA\" for non-mutation events."},{"name":"mutation_type","type":"LowCardinality(String)"},{"name":"mutation_status","type":"LowCardinality(String)","comment":"For mutations: Somatic, Germline, UNKNOWN, or UNCALLED. Filter mutation_status != \"UNCALLED\" to exclude uncertain calls. Include all other statuses."},{"name":"driver_filter","type":"LowCardinality(String)"},{"name":"driver_filter_annotation","type":"String"},{"name":"driver_tiers_filter","type":"LowCardinality(String)"},{"name":"driver_tiers_filter_annotation","type":"String"},{"name":"cna_alteration","type":"Nullable(Int8)","comment":"Copy number alteration: only 2 = amplification (AMP) and -2 = deep deletion (HOMDEL) are stored. Shallow deletion (-1), diploid (0) and gain (1) are not in this table; query genetic_alteration_derived WHERE profile_type = 'gistic' (alteration_value is a String, e.g. '-1'). NULL for non-CNA events."},{"name":"cna_cytoband","type":"String"},{"name":"sv_event_info","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"off_panel","type":"Bool","comment":"Boolean: 1 = mutation outside gene panel coverage (off-panel), 0 = within panel (on-panel). Filter off_panel = 0 for reliable frequency calculations."}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH cna AS (
    SELECT sample_unique_id, alteration_value AS gistic_val
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND profile_type = 'gistic'
      AND hugo_gene_symbol = 'ERBB2'
      AND alteration_value NOT IN ('', 'NA')
),
mrna AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna_z
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND profile_type = 'rna_seq_v2_mrna_median_Zscores'
      AND hugo_gene_symbol = 'ERBB2'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
),
prot AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS prot_z
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa_Zscores'
      AND hugo_gene_symbol = 'ERBB2'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    count() AS n_samples_all_three,
    sum(gistic_val = '2') AS n_cna_amp,
    sum(mrna_z > 2) AS n_mrna_overexpr,
    sum(prot_z > 2) AS n_prot_overexpr,
    sum(gistic_val = '2' AND mrna_z > 2) AS n_cna_and_mrna,
    sum(gistic_val = '2' AND prot_z > 2) AS n_cna_and_prot,
    sum(mrna_z > 2 AND prot_z > 2) AS n_mrna_and_prot,
    sum(gistic_val = '2' AND mrna_z > 2 AND prot_z > 2) AS n_all_three_concordant,
    sum(gistic_val != '2' AND mrna_z <= 2 AND prot_z <= 2) AS n_all_three_negative
FROM cna
INNER JOIN mrna USING (sample_unique_id)
INNER JOIN prot USING (sample_unique_id)
"
}

◀ result
{"rows":[{"n_samples_all_three":863,"n_cna_amp":101,"n_mrna_overexpr":137,"n_prot_overexpr":52,"n_cna_and_mrna":94,"n_cna_and_prot":50,"n_mrna_and_prot":52,"n_all_three_concordant":50,"n_all_three_negative":719}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH cna AS (
    SELECT sample_unique_id, (alteration_value = '2') AS amp
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND profile_type = 'gistic'
      AND hugo_gene_symbol = 'ERBB2'
      AND alteration_value NOT IN ('', 'NA')
),
mrna AS (
    SELECT sample_unique_id, (toFloat64OrNull(alteration_value) > 2) AS overexpr
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND profile_type = 'rna_seq_v2_mrna_median_Zscores'
      AND hugo_gene_symbol = 'ERBB2'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
),
prot AS (
    SELECT sample_unique_id, (toFloat64OrNull(alteration_value) > 2) AS overexpr
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa_Zscores'
      AND hugo_gene_symbol = 'ERBB2'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT 'CNA_vs_mRNA' AS pair,
       sum(cna.amp AND mrna.overexpr) AS both_pos,
       sum(cna.amp AND NOT mrna.overexpr) AS cna_only,
       sum(NOT cna.amp AND mrna.overexpr) AS mrna_only,
       sum(NOT cna.amp AND NOT mrna.overexpr) AS both_neg,
       count() AS n
FROM cna INNER JOIN mrna USING (sample_unique_id)
UNION ALL
SELECT 'CNA_vs_protein',
       sum(cna.amp AND prot.overexpr),
       sum(cna.amp AND NOT prot.overexpr),
       sum(NOT cna.amp AND prot.overexpr),
       sum(NOT cna.amp AND NOT prot.overexpr),
       count()
FROM cna INNER JOIN prot USING (sample_unique_id)
UNION ALL
SELECT 'mRNA_vs_protein',
       sum(mrna.overexpr AND prot.overexpr),
       sum(mrna.overexpr AND NOT prot.overexpr),
       sum(NOT mrna.overexpr AND prot.overexpr),
       sum(NOT mrna.overexpr AND NOT prot.overexpr),
       count()
FROM mrna INNER JOIN prot USING (sample_unique_id)
"
}

◀ result
{"rows":[{"pair":"CNA_vs_protein","both_pos":50,"cna_only":52,"mrna_only":2,"both_neg":761,"n":865},{"pair":"CNA_vs_mRNA","both_pos":112,"cna_only":10,"mrna_only":53,"both_neg":893,"n":1068},{"pair":"mRNA_vs_protein","both_pos":53,"cna_only":87,"mrna_only":0,"both_neg":734,"n":874}]}

▶ resolve_and_route
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_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":["brca_tcga_pan_can_atlas_2018_gistic","brca_tcga_pan_can_atlas_2018_log2CNA","brca_tcga_pan_can_atlas_2018_mutations","brca_tcga_pan_can_atlas_2018_protein_quantification","brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa","brca_tcga_pan_can_atlas_2018_rppa_Zscores","brca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["brca_tcga_pan_can_atlas_2018_armlevel_cna","brca_tcga_pan_can_atlas_2018_genetic_ancestry","brca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","brca_tcga_pan_can_atlas_2018_methylation_hm450","brca_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_anc … (83 more chars)

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "ERBB2"
 ],
 "tab": "plots",
 "profileFilter": "gistic,rna_seq_v2_mrna_median_Zscores,rppa_Zscores",
 "plotsHorzSelection": {
  "dataType": "COPY_NUMBER_ALTERATION",
  "selectedGeneOption": "ERBB2",
  "selectedDataSourceOption": "brca_tcga_pan_can_atlas_2018_gistic"
 },
 "plotsVertSelection": {
  "dataType": "MRNA_EXPRESSION",
  "selectedGeneOption": "ERBB2",
  "selectedDataSourceOption": "brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=gistic%2Crna_seq_v2_mrna_median_Zscores%2Crppa_Zscores&plots_horz_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22COPY_NUMBER_ALTERATION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_gistic%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=gistic%2Crna_seq_v2_mrna_median_Zscores%2Crppa_Zscores&plots_horz_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22COPY_NUMBER_ALTERATION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_gistic%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores%22%7D","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":"Scatter/box plots: ERBB2 mRNA expression (y-axis) by ERBB2 CNA (x-axis)."}}

▶ navigate_to_study_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "filterJson": {
  "geneFilters": [
   {
    "molecularProfileIds": [
     "brca_tcga_pan_can_atlas_2018_gistic"
    ],
    "geneQueries": [
     [
      {
       "hugoGeneSymbol": "ERBB2",
       "alterations": [
        "AMP"
       ]
      }
     ]
    ]
   }
  ]
 }
}

◀ result
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═ answer (success)
## ERBB2 (HER2) Concordance in Breast Cancer — TCGA PanCancer Atlas (`brca_tcga_pan_can_atlas_2018`, n=1,084 samples)

**Definitions used** (standard cBioPortal thresholds):
- CNA amplification = GISTIC discrete call of `2` (amplification)
- mRNA overexpression = `rna_seq_v2_mrna_median_Zscores` > 2
- Protein overexpression = `rppa_Zscores` > 2

### Pairwise 2×2 contingency tables

**CNA amplified × mRNA overexpressed** (n=1,068 samples with both assays)
| | mRNA overexpr | mRNA not overexpr |
|---|---|---|
| **CNA amp** | 112 | 10 |
| **CNA not amp** | 53 | 893 |

**CNA amplified × Protein overexpressed** (n=865)
| | Protein overexpr | Protein not overexpr |
|---|---|---|
| **CNA amp** | 50 | 52 |
| **CNA not amp** | 2 | 761 |

**mRNA overexpressed × Protein overexpressed** (n=874)
| | Protein overexpr | Protein not overexpr |
|---|---|---|
| **mRNA overexpr** | 53 | 0 |
| **mRNA not overexpr** | 87 | 734 |

**All three assays jointly** (n=863 samples with CNA + mRNA + protein data): 101 CNA-amplified, 137 mRNA-overexpressed, 52 protein-overexpressed; 50 samples positive across all three; 719 negative across all three.

### Interpretation notes
- CNA↔mRNA shows the tightest correspondence: of 122 amplified samples, 112 (92%) also show mRNA overexpression; only 10 amplified samples lack an mRNA overexpression signal.
- CNA↔protein shows a wider gap: only 50/102 amplified samples (49%) cross the protein z>2 threshold, and roughly half of amplified samples (52) do not show strong RPPA protein overexpression — consistent with known post-transcriptional/proteomic attenuation and RPPA's narrower dynamic range/antibody-based limits.
- mRNA↔protein: essentially all protein-overexpressed samples (53/53) also show mRNA overexpression, but the reverse is not true — 87 mRNA-overexpressed samples do not reach the protein z>2 threshold.
- These are raw contingency counts, not statistical tests. Per the concordance question framing, a formal association test (e.g., Fisher's exact per pair) or a correlation coefficient (Spearman/Pearson on the continuous z-scores) would be needed to quantify agreement — I have not computed p-values or a kappa/correlation statistic here. If you want that, I can run `rankCorr` (Spearman) between the continuous CNA (log2CNA), mRNA, and protein z-score profiles, or you can run Fisher's exact test on the tables above in R/Python.

### Navigate

[ERBB2 CNA vs. mRNA Expression Scatter Plot](https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=brca_tcga_pan_can_atlas_2018_all&profileFilter=gistic%2Crna_seq_v2_mrna_median_Zscores%2Crppa_Zscores&plots_horz_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22COPY_NUMBER_ALTERATION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_gistic%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores%22%7D) — box/scatter plot of ERBB2 mRNA expression by CNA status; switch the y-axis dropdown to Protein (RPPA) to view mRNA↔protein or CNA↔protein directly.

[ERBB2-Amplified Samples — Study View](https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22alterations%22%3A%5B%22AMP%22%5D%2C%22hugoGeneSymbol%22%3A%22ERBB2%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22brca_tcga_pan_can_atlas_2018_gistic%22%5D%7D%5D%7D) — cohort filtered to ERBB2-amplified samples for further clinical/molecular breakdown.