Q (sonnet-5.5): In the TCGA PanCancer Atlas breast cancer study, what is the correlation coefficient between EGFR expression levels and PIK3CA mutation status considering only patients who also have a homozygous deletion of PTEN?

▶ 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)

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

▶ list_studies
{
 "search": "breast pan_can_atlas"
}

◀ 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
{"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"}]}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

▶ get_study_guide
{
 "study_id": "brca_tcga_pan_can_atlas_2018"
}

◀ 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."}]}

◀ 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": "WITH pten AS (SELECT DISTINCT sample_unique_id FROM genomic_event_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND variant_type='cna' AND hugo_gene_symbol='PTEN' AND cna_alteration=-2),
mut AS (SELECT DISTINCT sample_unique_id FROM genomic_event_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND variant_type='mutation' AND hugo_gene_symbol='PIK3CA' AND mutation_status!='UNCALLED' AND off_panel=0),
e AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND profile_type='rna_seq_v2_mrna' AND hugo_gene_symbol='EGFR' AND alteration_value NOT IN ('','NA') AND toFloat64OrNull(alteration_value) IS NOT NULL)
SELECT count() n, countIf(sample_unique_id IN (SELECT * FROM mut)) mutated, rankCorr(v, if(sample_unique_id IN (SELECT * FROM mut),1,0)) spearman, corr(log2(v+1), if(sample_unique_id IN (SELECT * FROM mut),1,0)) pearson_log2, avgIf(log2(v+1), sample_unique_id IN (SELECT * FROM mut)) mean_mut, avgIf(log2(v+1), sample_unique_id NOT IN (SELECT * FROM mut)) mean_wt FROM e WHERE sample_unique_id IN (SELECT * FROM pten)"
}

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

◀ result
{"rows":[{"n":55,"mutated":6,"spearman":0.27083333333333337,"pearson_log2":-0.1689393744935911,"mean_mut":7.275759844838014,"mean_wt":8.232828321874019}]}

✗ error
<tool_use_error>Error: No such tool available: mcp__claude_ai_cBioPortal_MCP__resolve_and_route</tool_use_error>

▶ 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": [
  "PTEN: HOMDEL",
  "PIK3CA: MUT",
  "EGFR"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "MUTATION_EXTENDED",
  "selectedGeneOption": "PIK3CA"
 },
 "plotsVertSelection": {
  "dataType": "MRNA_EXPRESSION",
  "selectedGeneOption": "EGFR",
  "selectedDataSourceOption": "brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+HOMDEL%0APIK3CA%3A+MUT%0AEGFR&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%225290%22%2C%22dataType%22%3A%22MUTATION_EXTENDED%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+HOMDEL%0APIK3CA%3A+MUT%0AEGFR&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%225290%22%2C%22dataType%22%3A%22MUTATION_EXTENDED%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna%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":["PTEN","PIK3CA","EGFR"],"caseSetId":"brca_tcga_pan_can_atlas_2018_all","pageDescription":"Scatter/box plots: EGFR mRNA expression (y-axis) by PIK3CA mutation (x-axis)."}}

═ answer (success)
The Spearman correlation is about **0.27**, but it rests on only 6 mutated samples, so I wouldn't read much into it.

**Cohort:** Breast Invasive Carcinoma (TCGA, PanCancer Atlas). I counted samples with a homozygous (deep) deletion of PTEN that also have EGFR RNA-seq data.
- 55 samples have a PTEN homozygous deletion.
- 6 of those have a PIK3CA mutation and 49 do not.

**Correlation between EGFR mRNA and PIK3CA mutation status (mutated = 1, not mutated = 0):**
- **Spearman: 0.27.** This uses the raw RSEM expression values (`rna_seq_v2_mrna`).
- **Pearson: −0.17.** This uses log2(RSEM + 1) expression.

The two coefficients have opposite signs. That, together with the 6 mutated samples, means there's no reliable correlation here.

**Mean log2(RSEM + 1) EGFR expression:**

| Group | Samples | Mean EGFR expression |
|---|---|---|
| PIK3CA-mutated | 6 | 7.28 |
| PIK3CA not mutated | 49 | 8.23 |

The group means are lower in the mutated samples, which matches the negative Pearson value. I can't compute a p-value or significance test for this correlation. To test it, you could run the comparison in the cBioPortal Plots view linked below, or export the data to R or Python.

[View EGFR expression by PIK3CA mutation status](https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+HOMDEL%0APIK3CA%3A+MUT%0AEGFR&case_set_id=brca_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%225290%22%2C%22dataType%22%3A%22MUTATION_EXTENDED%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna%22%7D). This plot covers the whole study. To limit it to PTEN-deleted samples, you'd need to filter on PTEN homozygous deletion in the portal.

[Open the study in StudyView](https://www.cbioportal.org/study?id=brca_tcga_pan_can_atlas_2018)