Q (haiku): Are there differences in chromosome arm 7p copy number between lower grade glioma molecular subtypes?

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

▶ search_oncotree
{
 "search_term": "lower grade glioma"
}

◀ 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":[]}

▶ list_studies
{
 "search": "glioma",
 "limit": 20
}

▶ search_oncotree
{
 "search_term": "glioma"
}

◀ result
{"result":[{"cancer_study_identifier":"glioma_mskcc_2019","name":"Glioma (MSK, Clin Cancer Res 2019)","type_of_cancer_id":"difg","sample_count":1004,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=glioma_mskcc_2019"},{"cancer_study_identifier":"difg_glass","name":"Diffuse Glioma (GLASS Consortium)","type_of_cancer_id":"difg","sample_count":693,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=difg_glass"},{"cancer_study_identifier":"difg_tcga_gdc","name":"Diffuse Glioma (TCGA GDC, 2025)","type_of_cancer_id":"difg","sample_count":530,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=difg_tcga_gdc"},{"cancer_study_identifier":"lgg_tcga","name":"Brain Lower Grade Glioma (TCGA, Firehose Legacy)","type_of_cancer_id":"difg","sample_count":530,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_tcga"},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","type_of_cancer_id":"difg","sample_count":514,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"difg_glass_2019","name":"Diffuse Glioma (GLASS Consortium, Nature 2019)","type_of_cancer_id":"difg","sample_count":444,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=difg_glass_2019"},{"cancer_study_identifier":"brain_cptac_2020","name":"Pediatric Brain Cancer (CPTAC/CHOP, Cell 2020)","type_of_cancer_id":"brain","sample_count":218,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=brain_cptac_2020"},{"cancer_study_identifier":"pcpg_tcga","name":"Pheochromocytoma and Paraganglioma (TCGA, Firehose Legacy)","type_of_cancer_id":"mnet","sample_count":184,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pcpg_tcga"},{"cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018","name":"Pheochromocytoma and Paraganglioma (TCGA, PanCancer Atlas)","type_of_cancer_id":"soft_tissue","sample_count":178,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pcpg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"pcpg_tcga_pub","name":"Pheochromocytoma and Paraganglioma (TCGA, Cell 2017)","type_of_cancer_id":"soft_tissue","sample_count":178,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pcpg_tcga_pub"},{"cancer_study_identifier":"hnsc_a5consortium_2025","name":"Hereditary SDHB-Mutant Pheochromocytomas and Paragangliomas (A5 Consortium, Nature Comm 2025)","type_of_cancer_id":"hnsc","sample_count":94,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=hnsc_a5consortium_2025"},{"cancer_study_identifier":"glioma_msk_2018","name":"Glioma (MSK, Nature 2019)","type_of_cancer_id":"difg","sample_count":91,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=glioma_msk_2018"},{"cancer_study_identifier":"difg_msk_2023","name":"IDH-mutated Diffuse Glioma (MSK, Clin Cancer Res 2024)","type_of_cance … (855 more chars)

◀ result
{"result":[{"code":"AGNG","name":"Anaplastic Ganglioglioma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > AGNG"},{"code":"APXA","name":"Anaplastic Pleomorphic Xanthoastrocytoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > APXA"},{"code":"ASTR","name":"Astrocytoma, IDH-Mutant","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > ADIFG > ASTR","replacedCodes":["AASTR"]},{"code":"DASTR","name":"Diffuse Astrocytoma, MYB- or MYBL1-Altered","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > PDIFLG > DASTR"},{"code":"DIFG","name":"Diffuse Glioma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG"},{"code":"DNT","name":"Dysembryoplastic Neuroepithelial Tumor","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > DNT"},{"code":"ENCG","name":"Encapsulated Glioma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG"},{"code":"EPM","name":"Ependymoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > EPMT > EPM"},{"code":"GB","name":"Glioblastoma, IDH-Wildtype","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > ADIFG > GB","replacedCodes":["GBM"]},{"code":"GNC","name":"Gangliocytoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > GNC"},{"code":"GNG","name":"Ganglioglioma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > GNG"},{"code":"GNOS","name":"Glioma, NOS","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > GNOS","replacedCodes":["AOAST","OAST"]},{"code":"GSARC","name":"Gliosarcoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > ADIFG > GB > GSARC"},{"code":"HGGNOS","name":"High-Grade Glioma, NOS","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > HGGNOS"},{"code":"LGGNOS","name":"Low-Grade Glioma, NOS","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > LGGNOS"},{"code":"ODG","name":"Oligodendroglioma, IDH-mutant, and 1p/19q-Codeleted","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > ADIFG > ODG","replacedCodes":["AODG"]},{"code":"PAST","name":"Pilocytic Astrocytoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > PAST"},{"code":"PMA","name":"Pilomyxoid Astrocytoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > PMA"},{"code":"PXA","name":"Pleomorphic Xanthoastrocytoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > ENCG > PXA"},{"code":"SCGBM","name":"Small Cell Glioblastoma","score":65,"mainType":"Glioma","tissue":"CNS/Brain","hierarchy":"BRAIN > DIFG > ADIFG > GB > SCGBM"},{"code":"ADIFG","name":"Adult-Type Diffuse Glioma","score":50,"mainType":"Gliomas, Glioneuronal Tumors, and Neuronal Tumors","t … (782 more chars)

▶ clickhouse_run_select_query
{
 "query": "
-- Check available CNA profiles and molecular subtypes in LGG TCGA
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND hugo_gene_symbol = '7p'
ORDER BY profile_type;
",
 "max_rows": 20
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
{"rows":[]}

◀ result
{"columns":[{"name":"internal_id","type":"Int32"},{"name":"sample_unique_id","type":"String","comment":"Globally unique sample ID: cancer_study_identifier + \"_\" + sample.stable_id. Empty for patient-level attributes. Use this for sample filtering and joins."},{"name":"patient_unique_id","type":"String","comment":"Globally unique patient ID: cancer_study_identifier + \"_\" + patient.stable_id. Present for both sample and patient-level attributes."},{"name":"attribute_name","type":"LowCardinality(String)","comment":"Clinical attribute name (e.g., SAMPLE_TYPE, CANCER_TYPE, AGE, OS_MONTHS). Use with attribute_value for filtering. AGE may be floored or capped for de-identification (e.g. all children recorded as 18, or everyone 89+ recorded as 89 or 90): before age statistics check for a pile-up at the min/max, and if present compute age from DAYS_TO_BIRTH (-days / 365.25)."},{"name":"attribute_value","type":"String","comment":"Value of the clinical attribute (String). For SAMPLE_TYPE: Primary, Metastasis, Local Recurrence, Unknown. Missing values are empty strings, so use toFloat64OrNull(attribute_value) for numeric comparisons — CAST fails on them."},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"type","type":"LowCardinality(String)","comment":"Data level: \"sample\" for sample-level attributes (e.g., SAMPLE_TYPE), \"patient\" for patient-level attributes (e.g., AGE, OS_MONTHS)."}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check available CNA profiles in LGG TCGA
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
ORDER BY profile_type;
",
 "max_rows": 20
}

◀ result
{"rows":[{"profile_type":"gistic"},{"profile_type":"log2CNA"},{"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":"rppa"},{"profile_type":"rppa_Zscores"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check what clinical attributes contain subtype information
SELECT DISTINCT attribute_name
FROM clinical_data_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND attribute_name LIKE '%SUBTYPE%' OR attribute_name LIKE '%IDH%' OR attribute_name LIKE '%1P19Q%'
ORDER BY attribute_name;
",
 "max_rows": 30
}

◀ result
{"rows":[{"attribute_name":"1P19Q_STATUS"},{"attribute_name":"IDH1_AA_ALTERATION"},{"attribute_name":"IDH1_MUTATION"},{"attribute_name":"IDH1_MUTATION_TEST_INDICATOR"},{"attribute_name":"IDH1_MUTATION_TEST_METHOD"},{"attribute_name":"IDH2_NEGATIVE"},{"attribute_name":"IDH_1P19Q_SUBTYPE"},{"attribute_name":"IDH_CODEL_STATUS"},{"attribute_name":"IDH_CODEL_SUBTYPE"},{"attribute_name":"IDH_COHORT"},{"attribute_name":"IDH_LEVEL"},{"attribute_name":"IDH_SPECIFIC_DNA_METHYLATION_CLUSTER"},{"attribute_name":"IDH_SPECIFIC_RNA_EXPRESSION_CLUSTER"},{"attribute_name":"IDH_STATUS"},{"attribute_name":"IMPACT_1P19Q"},{"attribute_name":"SUBTYPE"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check available subtype values
SELECT attribute_name, attribute_value, count(*) as sample_count
FROM clinical_data_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND attribute_name IN ('SUBTYPE', 'IDH_1P19Q_SUBTYPE', 'IDH_CODEL_SUBTYPE')
  AND attribute_value != ''
GROUP BY attribute_name, attribute_value
ORDER BY attribute_name, sample_count DESC;
",
 "max_rows": 30
}

◀ result
{"rows":[{"attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-non-codel","sample_count":248},{"attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-codel","sample_count":167},{"attribute_name":"SUBTYPE","attribute_value":"LGG_IDHwt","sample_count":92}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check what genes/chromosome arms are available for CNA
SELECT DISTINCT hugo_gene_symbol
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND profile_type = 'gistic'
  AND hugo_gene_symbol LIKE '7%'
ORDER BY hugo_gene_symbol
LIMIT 10;
",
 "max_rows": 20
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
-- Sample CNA data structure
SELECT sample_unique_id, hugo_gene_symbol, profile_type, alteration_value
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND profile_type IN ('gistic', 'log2CNA')
LIMIT 20;
",
 "max_rows": 25
}

◀ result
{"rows":[{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7620-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.023"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7676-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.008"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7677-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"0.015"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7680-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.004"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7681-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.027"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7684-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"0"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7686-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.004"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7687-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.011"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7688-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"0.117"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7689-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.672"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7690-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.731"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7691-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.001"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7692-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.445"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7693-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7694-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.115"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7695-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.009"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7854-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.002"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7855-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"0.01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7856-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.268"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7857-01","hugo_gene_symbol":"CDC16","profile_type":"log2CNA","alteration_value":"-0.161"}]}

▶ clickhouse_list_table_columns
{
 "table": "gene"
}

◀ result
{"columns":[{"name":"entrez_gene_id","type":"Int64"},{"name":"hugo_gene_symbol","type":"String"},{"name":"genetic_entity_id","type":"Int64"},{"name":"type","type":"Nullable(String)"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get genes on chromosome 7p from cBioPortal's gene table
SELECT hugo_gene_symbol, entrez_gene_id
FROM gene
WHERE entrez_gene_id IN (
  6469,  -- EGF
  2065,  -- EGFR
  2263,  -- FGFR1
  2264,  -- FGFR2
  7849,  -- PAX3
  2944,  -- GRIN2A
  4478,  -- MOSPD2
  1111,  -- CHKB
  7494,  -- XPB (ERCC3)
  1123,  -- CHM
  1129,  -- CHMP2A
  1130,  -- CHMP3
  1138,  -- CHMP5
  1139,  -- CHMP6
  1145,  -- CHPT1
  8436,  -- CITED2
  51343, -- CPSF6
  1155,  -- CCAR1
  1158,  -- CCND3
  1278,  -- COL1A2
  1284,  -- COL4A1
  1286,  -- COL4A2
  3688,  -- ITGA5
  3717,  -- JAK1
  3725,  -- JUN
  3958,  -- LGALS1
  5155,  -- PDGFA
  5159,  -- PDGFRB
  5728,  -- PTEN
  7481,  -- MYC
  6741,  -- SSB
  841,   -- CASP1
  8379,  -- MAD1L1
  5704,  -- PSMD10
  5706,  -- PSMD7
  5707,  -- PSMD8
  5721,  -- PSME1
  8243,  -- SMC3
  7399,  -- USH1C
  7412,  -- VRK1
  58486  -- SEZ6L
)
ORDER BY hugo_gene_symbol
LIMIT 50;
",
 "max_rows": 50
}

◀ result
{"rows":[{"hugo_gene_symbol":"CASP8","entrez_gene_id":841},{"hugo_gene_symbol":"CAVIN2","entrez_gene_id":8436},{"hugo_gene_symbol":"CHEK1","entrez_gene_id":1111},{"hugo_gene_symbol":"CHN1","entrez_gene_id":1123},{"hugo_gene_symbol":"CHRM2","entrez_gene_id":1129},{"hugo_gene_symbol":"CHRNA5","entrez_gene_id":1138},{"hugo_gene_symbol":"CHRNA7","entrez_gene_id":1139},{"hugo_gene_symbol":"CHRNE","entrez_gene_id":1145},{"hugo_gene_symbol":"CKM","entrez_gene_id":1158},{"hugo_gene_symbol":"COL1A2","entrez_gene_id":1278},{"hugo_gene_symbol":"COL4A2","entrez_gene_id":1284},{"hugo_gene_symbol":"COL4A4","entrez_gene_id":1286},{"hugo_gene_symbol":"ERBB3","entrez_gene_id":2065},{"hugo_gene_symbol":"FGFR2","entrez_gene_id":2263},{"hugo_gene_symbol":"FGFR4","entrez_gene_id":2264},{"hugo_gene_symbol":"FZR1","entrez_gene_id":51343},{"hugo_gene_symbol":"GSTM1","entrez_gene_id":2944},{"hugo_gene_symbol":"ITGB1","entrez_gene_id":3688},{"hugo_gene_symbol":"JAK2","entrez_gene_id":3717},{"hugo_gene_symbol":"JUN","entrez_gene_id":3725},{"hugo_gene_symbol":"LGALS3","entrez_gene_id":3958},{"hugo_gene_symbol":"LYST","entrez_gene_id":1130},{"hugo_gene_symbol":"MAD1L1","entrez_gene_id":8379},{"hugo_gene_symbol":"MSN","entrez_gene_id":4478},{"hugo_gene_symbol":"PAX8","entrez_gene_id":7849},{"hugo_gene_symbol":"PDGFB","entrez_gene_id":5155},{"hugo_gene_symbol":"PDGFRB","entrez_gene_id":5159},{"hugo_gene_symbol":"PSMC4","entrez_gene_id":5704},{"hugo_gene_symbol":"PSMC6","entrez_gene_id":5706},{"hugo_gene_symbol":"PSMD1","entrez_gene_id":5707},{"hugo_gene_symbol":"PSME2","entrez_gene_id":5721},{"hugo_gene_symbol":"PTEN","entrez_gene_id":5728},{"hugo_gene_symbol":"SHH","entrez_gene_id":6469},{"hugo_gene_symbol":"SMC1A","entrez_gene_id":8243},{"hugo_gene_symbol":"SSB","entrez_gene_id":6741},{"hugo_gene_symbol":"TBCB","entrez_gene_id":1155},{"hugo_gene_symbol":"USH2A","entrez_gene_id":7399},{"hugo_gene_symbol":"VCAM1","entrez_gene_id":7412},{"hugo_gene_symbol":"WNT11","entrez_gene_id":7481},{"hugo_gene_symbol":"XBP1","entrez_gene_id":7494},{"hugo_gene_symbol":"ZBED5","entrez_gene_id":58486}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Calculate mean log2 CNA for 7p genes by molecular subtype in LGG
-- Key 7p genes: EGFR, FGFR2, PAX3, PDGFRB, WNT11, and others
WITH cna_data AS (
  SELECT 
    s.sample_unique_id,
    c.attribute_value AS subtype,
    g.hugo_gene_symbol,
    toFloat64OrNull(g.alteration_value) AS cna_value
  FROM genetic_alteration_derived g
  JOIN clinical_data_derived c USING (sample_unique_id)
  WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND c.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND c.attribute_name = 'SUBTYPE'
    AND c.attribute_value != ''
    AND g.profile_type = 'log2CNA'
    AND g.hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PAX3', 'PDGFRB', 'WNT11', 'CASP8', 'COL1A2')
    AND g.alteration_value NOT IN ('', 'NA')
    AND cna_value IS NOT NULL
)
SELECT 
  subtype,
  count(DISTINCT sample_unique_id) AS num_samples,
  count(cna_value) AS total_values,
  round(avg(cna_value), 3) AS mean_log2_cna,
  round(median(cna_value), 3) AS median_log2_cna,
  round(quantile(0.25)(cna_value), 3) AS q25_log2_cna,
  round(quantile(0.75)(cna_value), 3) AS q75_log2_cna,
  round(min(cna_value), 3) AS min_log2_cna,
  round(max(cna_value), 3) AS max_log2_cna
FROM cna_data
GROUP BY subtype
ORDER BY subtype;
",
 "max_rows": 10
}

◀ 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 identifier `s.sample_unique_id` in scope  cna_data. Maybe you meant: ['c.sample_unique_id']. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
-- Calculate mean log2 CNA for 7p genes by molecular subtype in LGG
WITH cna_data AS (
  SELECT 
    g.sample_unique_id,
    c.attribute_value AS subtype,
    g.hugo_gene_symbol,
    toFloat64OrNull(g.alteration_value) AS cna_value
  FROM genetic_alteration_derived g
  JOIN clinical_data_derived c USING (sample_unique_id)
  WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND c.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND c.attribute_name = 'SUBTYPE'
    AND c.attribute_value != ''
    AND g.profile_type = 'log2CNA'
    AND g.hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PAX3', 'PDGFRB', 'WNT11', 'CASP8', 'COL1A2')
    AND g.alteration_value NOT IN ('', 'NA')
    AND cna_value IS NOT NULL
)
SELECT 
  subtype,
  count(DISTINCT sample_unique_id) AS num_samples,
  count(cna_value) AS total_cna_values,
  round(avg(cna_value), 3) AS mean_log2_cna,
  round(median(cna_value), 3) AS median_log2_cna,
  round(quantile(0.25)(cna_value), 3) AS q25_log2_cna,
  round(quantile(0.75)(cna_value), 3) AS q75_log2_cna,
  round(min(cna_value), 3) AS min_log2_cna,
  round(max(cna_value), 3) AS max_log2_cna
FROM cna_data
GROUP BY subtype
ORDER BY subtype;
",
 "max_rows": 10
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check available genes with CNA data in LGG
SELECT DISTINCT hugo_gene_symbol
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND profile_type = 'log2CNA'
ORDER BY hugo_gene_symbol
LIMIT 50;
",
 "max_rows": 60
}

◀ result
{"rows":[{"hugo_gene_symbol":"A1BG"},{"hugo_gene_symbol":"A1BG-AS1"},{"hugo_gene_symbol":"A1CF"},{"hugo_gene_symbol":"A2M"},{"hugo_gene_symbol":"A2M-AS1"},{"hugo_gene_symbol":"A2ML1"},{"hugo_gene_symbol":"A3GALT2"},{"hugo_gene_symbol":"A4GALT"},{"hugo_gene_symbol":"A4GNT"},{"hugo_gene_symbol":"AAAS"},{"hugo_gene_symbol":"AACS"},{"hugo_gene_symbol":"AACSP1"},{"hugo_gene_symbol":"AADAC"},{"hugo_gene_symbol":"AADACL2"},{"hugo_gene_symbol":"AADACL2-AS1"},{"hugo_gene_symbol":"AADACL3"},{"hugo_gene_symbol":"AADACL4"},{"hugo_gene_symbol":"AADACP1"},{"hugo_gene_symbol":"AADAT"},{"hugo_gene_symbol":"AAGAB"},{"hugo_gene_symbol":"AAK1"},{"hugo_gene_symbol":"AAMDC"},{"hugo_gene_symbol":"AAMP"},{"hugo_gene_symbol":"AANAT"},{"hugo_gene_symbol":"AAR2"},{"hugo_gene_symbol":"AARD"},{"hugo_gene_symbol":"AARS1"},{"hugo_gene_symbol":"AARS2"},{"hugo_gene_symbol":"AARSD1"},{"hugo_gene_symbol":"AASDH"},{"hugo_gene_symbol":"AASDHPPT"},{"hugo_gene_symbol":"AASS"},{"hugo_gene_symbol":"AATBC"},{"hugo_gene_symbol":"AATF"},{"hugo_gene_symbol":"AATK"},{"hugo_gene_symbol":"ABAT"},{"hugo_gene_symbol":"ABCA1"},{"hugo_gene_symbol":"ABCA10"},{"hugo_gene_symbol":"ABCA11P"},{"hugo_gene_symbol":"ABCA12"},{"hugo_gene_symbol":"ABCA13"},{"hugo_gene_symbol":"ABCA15P"},{"hugo_gene_symbol":"ABCA17P"},{"hugo_gene_symbol":"ABCA2"},{"hugo_gene_symbol":"ABCA3"},{"hugo_gene_symbol":"ABCA4"},{"hugo_gene_symbol":"ABCA5"},{"hugo_gene_symbol":"ABCA6"},{"hugo_gene_symbol":"ABCA7"},{"hugo_gene_symbol":"ABCA8"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Find 7p genes in LGG CNA data (search for common 7p genes)
SELECT DISTINCT hugo_gene_symbol
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND profile_type = 'log2CNA'
  AND hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PDGFRB', 'MYC', 'SHH', 'PTEN', 'CASP8', 'PAX8')
ORDER BY hugo_gene_symbol;
",
 "max_rows": 20
}

◀ result
{"rows":[{"hugo_gene_symbol":"CASP8"},{"hugo_gene_symbol":"EGFR"},{"hugo_gene_symbol":"FGFR2"},{"hugo_gene_symbol":"MYC"},{"hugo_gene_symbol":"PAX8"},{"hugo_gene_symbol":"PDGFRB"},{"hugo_gene_symbol":"PTEN"},{"hugo_gene_symbol":"SHH"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Analyze 7p CNA by molecular subtype in LGG TCGA PanCancer
WITH cna_data AS (
  SELECT 
    g.sample_unique_id,
    c.attribute_value AS subtype,
    g.hugo_gene_symbol,
    toFloat64OrNull(g.alteration_value) AS cna_value
  FROM genetic_alteration_derived g
  JOIN clinical_data_derived c USING (sample_unique_id)
  WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND c.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND c.attribute_name = 'SUBTYPE'
    AND c.attribute_value != ''
    AND g.profile_type = 'log2CNA'
    AND g.hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PDGFRB', 'MYC', 'SHH', 'PTEN', 'CASP8', 'PAX8')
    AND g.alteration_value NOT IN ('', 'NA')
)
SELECT 
  subtype,
  count(DISTINCT sample_unique_id) AS num_samples,
  count(cna_value) AS total_gene_cna_values,
  round(avg(cna_value), 3) AS mean_log2_cna,
  round(median(cna_value), 3) AS median_log2_cna,
  round(stddevSamp(cna_value), 3) AS stdev_log2_cna,
  round(quantile(0.25)(cna_value), 3) AS q25_log2_cna,
  round(quantile(0.75)(cna_value), 3) AS q75_log2_cna,
  round(min(cna_value), 3) AS min_log2_cna,
  round(max(cna_value), 3) AS max_log2_cna
FROM cna_data
GROUP BY subtype
ORDER BY subtype;
",
 "max_rows": 10
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
-- Debug: check if JOIN works and data exists
SELECT COUNT(*) as cna_count
FROM genetic_alteration_derived g
WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND g.profile_type = 'log2CNA'
  AND g.hugo_gene_symbol = 'EGFR';
",
 "max_rows": 5
}

◀ result
{"rows":[{"cna_count":511}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Debug: check clinical data
SELECT COUNT(*) as subtype_count
FROM clinical_data_derived c
WHERE c.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND c.attribute_name = 'SUBTYPE'
  AND c.attribute_value != '';
",
 "max_rows": 5
}

◀ result
{"rows":[{"subtype_count":507}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Debug: check JOIN
SELECT COUNT(*) as joined_count
FROM genetic_alteration_derived g
JOIN clinical_data_derived c ON g.sample_unique_id = c.sample_unique_id
WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND c.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND g.profile_type = 'log2CNA'
  AND g.hugo_gene_symbol = 'EGFR'
  AND c.attribute_name = 'SUBTYPE'
  AND c.attribute_value != '';
",
 "max_rows": 5
}

◀ result
{"rows":[{"joined_count":0}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check sample_unique_id format
SELECT DISTINCT g.sample_unique_id 
FROM genetic_alteration_derived g
WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND g.profile_type = 'log2CNA'
  AND g.hugo_gene_symbol = 'EGFR'
LIMIT 5;
",
 "max_rows": 10
}

◀ result
{"rows":[{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4938-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4941-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4942-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4943-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4944-01"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check clinical data sample_unique_id format
SELECT DISTINCT c.sample_unique_id 
FROM clinical_data_derived c
WHERE c.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND c.attribute_name = 'SUBTYPE'
  AND c.attribute_value != ''
LIMIT 5;
",
 "max_rows": 10
}

◀ result
{"rows":[{}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check patient data level
SELECT c.patient_unique_id, c.type, c.attribute_name, c.attribute_value, COUNT(*) 
FROM clinical_data_derived c
WHERE c.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND c.attribute_name = 'SUBTYPE'
  AND c.attribute_value != ''
GROUP BY c.patient_unique_id, c.type, c.attribute_name, c.attribute_value
LIMIT 10;
",
 "max_rows": 15
}

◀ result
{"rows":[{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6399","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-non-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-FG-A70Z","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHwt","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DB-5280","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-non-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-A74L","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A5TU","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-non-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-TM-A84S","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-FG-6691","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-non-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-HT-7873","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-non-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DB-A64R","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-codel","COUNT()":1},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-S9-A7QX","type":"patient","attribute_name":"SUBTYPE","attribute_value":"LGG_IDHmut-non-codel","COUNT()":1}]}

▶ clickhouse_list_table_columns
{
 "table": "sample"
}

◀ result
{"columns":[{"name":"internal_id","type":"Int64","comment":"Primary key. Unique internal identifier for the sample."},{"name":"stable_id","type":"String","comment":"Stable identifier for the sample within the study. Combined with cancer_study_identifier forms sample_unique_id."},{"name":"patient_id","type":"Int64","comment":"Foreign key to patient.internal_id. Links sample to its patient."}]}

▶ clickhouse_list_table_columns
{
 "table": "patient"
}

◀ result
{"columns":[{"name":"internal_id","type":"Int64","comment":"Primary key. Unique internal identifier for the patient."},{"name":"stable_id","type":"String","comment":"Stable patient identifier within the study. Combined with cancer_study_identifier forms patient_unique_id."},{"name":"cancer_study_id","type":"Int64","comment":"Foreign key to cancer_study.cancer_study_id. Links patient to their study."}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Analyze 7p CNA by molecular subtype in LGG TCGA (join through patient)
WITH patient_subtypes AS (
  SELECT 
    patient_unique_id,
    attribute_value AS subtype
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND attribute_name = 'SUBTYPE'
    AND attribute_value != ''
),
cna_data AS (
  SELECT 
    g.sample_unique_id,
    ps.subtype,
    g.hugo_gene_symbol,
    toFloat64OrNull(g.alteration_value) AS cna_value
  FROM genetic_alteration_derived g
  -- Extract patient from sample_unique_id: prefix before last underscore
  JOIN patient_subtypes ps 
    ON substring(g.sample_unique_id, 1, position(g.sample_unique_id, '_' || reverse(substring(reverse(g.sample_unique_id), 3))) - 1) = ps.patient_unique_id
  WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND g.profile_type = 'log2CNA'
    AND g.hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PDGFRB', 'MYC', 'SHH', 'PTEN', 'CASP8', 'PAX8')
    AND g.alteration_value NOT IN ('', 'NA')
    AND cna_value IS NOT NULL
)
SELECT 
  subtype,
  count(DISTINCT sample_unique_id) AS num_samples,
  count(cna_value) AS total_gene_cna_values,
  round(avg(cna_value), 3) AS mean_log2_cna,
  round(median(cna_value), 3) AS median_log2_cna,
  round(stddevSamp(cna_value), 3) AS stdev_log2_cna
FROM cna_data
GROUP BY subtype
ORDER BY subtype;
",
 "max_rows": 10
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
-- Extract patient ID from sample_unique_id to verify the pattern
SELECT DISTINCT 
  g.sample_unique_id,
  substring(g.sample_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1, position(substring(g.sample_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1), '-01') - 1 + 2) AS patient_code
FROM genetic_alteration_derived g
WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND g.profile_type = 'log2CNA'
  AND g.hugo_gene_symbol = 'EGFR'
LIMIT 5;
",
 "max_rows": 10
}

◀ result
{"rows":[{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4938-01","patient_code":"TCGA-CS-4938-0"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4941-01","patient_code":"TCGA-CS-4941-0"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4942-01","patient_code":"TCGA-CS-4942-0"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4943-01","patient_code":"TCGA-CS-4943-0"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4944-01","patient_code":"TCGA-CS-4944-0"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Analyze 7p CNA by molecular subtype using patient ID extracted from sample
WITH patient_subtypes AS (
  SELECT 
    substring(patient_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1) AS patient_code,
    attribute_value AS subtype
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND attribute_name = 'SUBTYPE'
    AND attribute_value != ''
),
cna_data AS (
  SELECT 
    g.sample_unique_id,
    ps.subtype,
    g.hugo_gene_symbol,
    toFloat64OrNull(g.alteration_value) AS cna_value
  FROM genetic_alteration_derived g
  JOIN patient_subtypes ps 
    ON substring(g.sample_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1, length('TCGA-CS-4938')) = ps.patient_code
  WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND g.profile_type = 'log2CNA'
    AND g.hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PDGFRB', 'MYC', 'SHH', 'PTEN', 'CASP8', 'PAX8')
    AND g.alteration_value NOT IN ('', 'NA')
)
SELECT 
  subtype,
  count(DISTINCT sample_unique_id) AS num_samples,
  count(cna_value) AS total_gene_cna_values,
  round(avg(cna_value), 3) AS mean_log2_cna,
  round(median(cna_value), 3) AS median_log2_cna,
  round(stddevSamp(cna_value), 3) AS stdev_log2_cna,
  round(quantile(0.25)(cna_value), 3) AS q25_log2_cna,
  round(quantile(0.75)(cna_value), 3) AS q75_log2_cna
FROM cna_data
GROUP BY subtype
ORDER BY subtype;
",
 "max_rows": 10
}

◀ result
{"rows":[{"subtype":"LGG_IDHmut-codel","num_samples":167,"total_gene_cna_values":1336,"mean_log2_cna":0.016,"median_log2_cna":0,"stdev_log2_cna":0.138,"q25_log2_cna":-0.005,"q75_log2_cna":0.008},{"subtype":"LGG_IDHmut-non-codel","num_samples":248,"total_gene_cna_values":1984,"mean_log2_cna":0.037,"median_log2_cna":0.001,"stdev_log2_cna":0.32,"q25_log2_cna":-0.008,"q75_log2_cna":0.017},{"subtype":"LGG_IDHwt","num_samples":92,"total_gene_cna_values":736,"mean_log2_cna":0.143,"median_log2_cna":0,"stdev_log2_cna":0.902,"q25_log2_cna":-0.018,"q75_log2_cna":0.026}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Analyze 7p CNA by molecular subtype AND gene
WITH patient_subtypes AS (
  SELECT 
    substring(patient_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1) AS patient_code,
    attribute_value AS subtype
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND attribute_name = 'SUBTYPE'
    AND attribute_value != ''
),
cna_data AS (
  SELECT 
    ps.subtype,
    g.hugo_gene_symbol,
    toFloat64OrNull(g.alteration_value) AS cna_value
  FROM genetic_alteration_derived g
  JOIN patient_subtypes ps 
    ON substring(g.sample_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1, length('TCGA-CS-4938')) = ps.patient_code
  WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND g.profile_type = 'log2CNA'
    AND g.hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PDGFRB', 'MYC', 'SHH', 'PTEN', 'CASP8', 'PAX8')
    AND g.alteration_value NOT IN ('', 'NA')
)
SELECT 
  hugo_gene_symbol,
  subtype,
  count() AS sample_values,
  round(avg(cna_value), 3) AS mean_log2_cna,
  round(median(cna_value), 3) AS median_log2_cna,
  round(stddevSamp(cna_value), 3) AS stdev_log2_cna,
  round(quantile(0.25)(cna_value), 3) AS q25_log2_cna,
  round(quantile(0.75)(cna_value), 3) AS q75_log2_cna,
  round(min(cna_value), 3) AS min_log2_cna,
  round(max(cna_value), 3) AS max_log2_cna
FROM cna_data
GROUP BY hugo_gene_symbol, subtype
ORDER BY hugo_gene_symbol, subtype;
",
 "max_rows": 40
}

◀ result
{"rows":[{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-codel","sample_values":167,"mean_log2_cna":-0.01,"median_log2_cna":-0.006,"stdev_log2_cna":0.044,"q25_log2_cna":-0.011,"q75_log2_cna":-0.002,"min_log2_cna":-0.387,"max_log2_cna":0.254},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-non-codel","sample_values":248,"mean_log2_cna":-0.015,"median_log2_cna":0,"stdev_log2_cna":0.106,"q25_log2_cna":-0.008,"q75_log2_cna":0.005,"min_log2_cna":-0.619,"max_log2_cna":0.825},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHwt","sample_values":92,"mean_log2_cna":0.016,"median_log2_cna":-0.001,"stdev_log2_cna":0.139,"q25_log2_cna":-0.005,"q75_log2_cna":0.004,"min_log2_cna":-0.873,"max_log2_cna":0.658},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-codel","sample_values":167,"mean_log2_cna":0.044,"median_log2_cna":0.002,"stdev_log2_cna":0.156,"q25_log2_cna":0,"q75_log2_cna":0.01,"min_log2_cna":-0.147,"max_log2_cna":0.901},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-non-codel","sample_values":248,"mean_log2_cna":0.102,"median_log2_cna":0.012,"stdev_log2_cna":0.256,"q25_log2_cna":0.001,"q75_log2_cna":0.046,"min_log2_cna":-0.162,"max_log2_cna":1.77},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHwt","sample_values":92,"mean_log2_cna":1.653,"median_log2_cna":0.736,"stdev_log2_cna":1.648,"q25_log2_cna":0.095,"q75_log2_cna":3.66,"min_log2_cna":-0.916,"max_log2_cna":3.66},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-codel","sample_values":167,"mean_log2_cna":-0.016,"median_log2_cna":0.008,"stdev_log2_cna":0.108,"q25_log2_cna":0,"q75_log2_cna":0.014,"min_log2_cna":-0.799,"max_log2_cna":0.084},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-non-codel","sample_values":248,"mean_log2_cna":-0.146,"median_log2_cna":-0.008,"stdev_log2_cna":0.299,"q25_log2_cna":-0.116,"q75_log2_cna":0,"min_log2_cna":-0.989,"max_log2_cna":1.56},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHwt","sample_values":92,"mean_log2_cna":-0.493,"median_log2_cna":-0.547,"stdev_log2_cna":0.314,"q25_log2_cna":-0.748,"q75_log2_cna":-0.249,"min_log2_cna":-0.973,"max_log2_cna":0.018},{"hugo_gene_symbol":"MYC","subtype":"LGG_IDHmut-codel","sample_values":167,"mean_log2_cna":0.021,"median_log2_cna":-0.004,"stdev_log2_cna":0.116,"q25_log2_cna":-0.007,"q75_log2_cna":0,"min_log2_cna":-0.066,"max_log2_cna":0.744},{"hugo_gene_symbol":"MYC","subtype":"LGG_IDHmut-non-codel","sample_values":248,"mean_log2_cna":0.291,"median_log2_cna":0.027,"stdev_log2_cna":0.522,"q25_log2_cna":0.003,"q75_log2_cna":0.452,"min_log2_cna":-0.05,"max_log2_cna":3.11},{"hugo_gene_symbol":"MYC","subtype":"LGG_IDHwt","sample_values":92,"mean_log2_cna":-0.037,"median_log2_cna":-0.002,"stdev_log2_cna":0.202,"q25_log2_cna":-0.007,"q75_log2_cna":0.002,"min_log2_cna":-0.916,"max_log2_cna":0.781},{"hugo_gene_symbol":"PAX8","subtype":"LGG_IDHmut-codel","sample_values":167,"mean_log2_cna":-0.007,"median_log2_cna":-0.005,"stdev_log2_cna":0.049,"q25_log2_cna":-0.009,"q75_log2_cna":-0.002,"min_log2_cna":-0.387,"max_log2_cna":0.276},{"hu … (2524 more chars)

▶ clickhouse_run_select_query
{
 "query": "
-- Check GISTIC discrete CNA calls (gains/losses) for 7p genes by subtype
WITH patient_subtypes AS (
  SELECT 
    substring(patient_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1) AS patient_code,
    attribute_value AS subtype
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND attribute_name = 'SUBTYPE'
    AND attribute_value != ''
)
SELECT 
  hugo_gene_symbol,
  subtype,
  alteration_value AS cna_status,
  count(*) AS sample_count,
  round(100.0 * count(*) / MAX(count(*)) OVER (PARTITION BY subtype), 1) AS pct_of_subtype
FROM genetic_alteration_derived g
JOIN patient_subtypes ps 
  ON substring(g.sample_unique_id, length('lgg_tcga_pan_can_atlas_2018_') + 1, length('TCGA-CS-4938')) = ps.patient_code
WHERE g.cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND g.profile_type = 'gistic'
  AND g.hugo_gene_symbol IN ('EGFR', 'FGFR2', 'PDGFRB', 'MYC', 'SHH', 'PTEN', 'CASP8', 'PAX8')
  AND g.alteration_value != ''
GROUP BY hugo_gene_symbol, subtype, alteration_value
ORDER BY hugo_gene_symbol, subtype, CAST(alteration_value AS INT) DESC;
",
 "max_rows": 50
}

◀ result
{"rows":[{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-codel","cna_status":"1","sample_count":1,"pct_of_subtype":0.6},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-codel","cna_status":"0","sample_count":163,"pct_of_subtype":99.4},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-codel","cna_status":"-1","sample_count":3,"pct_of_subtype":1.8},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-non-codel","cna_status":"1","sample_count":4,"pct_of_subtype":1.7},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-non-codel","cna_status":"0","sample_count":225,"pct_of_subtype":96.6},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHmut-non-codel","cna_status":"-1","sample_count":19,"pct_of_subtype":8.2},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHwt","cna_status":"1","sample_count":7,"pct_of_subtype":8.2},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHwt","cna_status":"0","sample_count":84,"pct_of_subtype":98.8},{"hugo_gene_symbol":"CASP8","subtype":"LGG_IDHwt","cna_status":"-1","sample_count":1,"pct_of_subtype":1.2},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-codel","cna_status":"1","sample_count":14,"pct_of_subtype":8.5},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-codel","cna_status":"0","sample_count":152,"pct_of_subtype":92.7},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-codel","cna_status":"-1","sample_count":1,"pct_of_subtype":0.6},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-non-codel","cna_status":"2","sample_count":2,"pct_of_subtype":0.9},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-non-codel","cna_status":"1","sample_count":40,"pct_of_subtype":17.2},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-non-codel","cna_status":"0","sample_count":204,"pct_of_subtype":87.6},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHmut-non-codel","cna_status":"-1","sample_count":2,"pct_of_subtype":0.9},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHwt","cna_status":"2","sample_count":37,"pct_of_subtype":43.5},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHwt","cna_status":"1","sample_count":32,"pct_of_subtype":37.6},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHwt","cna_status":"0","sample_count":22,"pct_of_subtype":25.9},{"hugo_gene_symbol":"EGFR","subtype":"LGG_IDHwt","cna_status":"-1","sample_count":1,"pct_of_subtype":1.2},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-codel","cna_status":"0","sample_count":159,"pct_of_subtype":97},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-codel","cna_status":"-1","sample_count":8,"pct_of_subtype":4.9},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-non-codel","cna_status":"2","sample_count":1,"pct_of_subtype":0.4},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-non-codel","cna_status":"0","sample_count":182,"pct_of_subtype":78.1},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-non-codel","cna_status":"-1","sample_count":55,"pct_of_subtype":23.6},{"hugo_gene_symbol":"FGFR2","subtype":"LGG_IDHmut-non-codel","cna_status":"-2","sample_count":10,"pct_of_subtype":4.3},{"hugo_gene_symbol":"FGFR2"," … (2957 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "lgg_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":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514,"studyViewUrl":"https://www.cbioportal.org/study?id=lgg_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":["lgg_tcga_pan_can_atlas_2018_gistic","lgg_tcga_pan_can_atlas_2018_log2CNA","lgg_tcga_pan_can_atlas_2018_mutations","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lgg_tcga_pan_can_atlas_2018_rppa","lgg_tcga_pan_can_atlas_2018_rppa_Zscores","lgg_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["lgg_tcga_pan_can_atlas_2018_armlevel_cna","lgg_tcga_pan_can_atlas_2018_genetic_ancestry","lgg_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","lgg_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lgg_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}}]}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ],
 "groups": [
  {
   "name": "IDH-mutant, 1p/19q-codeleted",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "LGG_IDHmut-codel"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "IDH-mutant, non-codeleted",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "LGG_IDHmut-non-codel"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "IDH-wildtype",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "LGG_IDHwt"
       }
      ]
     }
    ]
   }
  }
 ],
 "tab": "overlap"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/overlap?comparisonId=6a87209dc2115c492d87eea5","url":"https://www.cbioportal.org/comparison/overlap?comparisonId=6a87209dc2115c492d87eea5","data":{"description":"Group comparison (3 custom groups)","studies":["lgg_tcga_pan_can_atlas_2018"],"totalGroups":3,"groups":[{"name":"IDH-mutant, 1p/19q-codeleted","sampleCount":167},{"name":"IDH-mutant, non-codeleted","sampleCount":248},{"name":"IDH-wildtype","sampleCount":92}],"studyViewUrl":"https://www.cbioportal.org/study?id=lgg_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"IDH-mutant, 1p/19q-codeleted","url":"https://www.cbioportal.org/study?id=lgg_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22LGG_IDHmut-codel%22%7D%5D%7D%5D%7D"},{"groupName":"IDH-mutant, non-codeleted","url":"https://www.cbioportal.org/study?id=lgg_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22LGG_IDHmut-non-codel%22%7D%5D%7D%5D%7D"},{"groupName":"IDH-wildtype","url":"https://www.cbioportal.org/study?id=lgg_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22LGG_IDHwt%22%7D%5D%7D%5D%7D"}],"pageDescription":"Diagram showing sample and patient overlap across IDH-mutant, 1p/19q-codeleted (167) vs IDH-mutant, non-codeleted (248) vs IDH-wildtype (92)."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "EGFR",
  "FGFR2",
  "PDGFRB",
  "MYC",
  "SHH",
  "PTEN",
  "CASP8",
  "PAX8"
 ],
 "tab": "oncoprint",
 "oncoprintClinicalTracks": [
  "SUBTYPE"
 ]
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=EGFR%0AFGFR2%0APDGFRB%0AMYC%0ASHH%0APTEN%0ACASP8%0APAX8&case_set_id=lgg_tcga_pan_can_atlas_2018_all&clinicallist=SUBTYPE","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=EGFR%0AFGFR2%0APDGFRB%0AMYC%0ASHH%0APTEN%0ACASP8%0APAX8&case_set_id=lgg_tcga_pan_can_atlas_2018_all&clinicallist=SUBTYPE","data":{"studyIds":["lgg_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514}],"genes":["EGFR","FGFR2","PDGFRB","MYC","SHH","PTEN","CASP8","PAX8"],"caseSetId":"lgg_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
Excellent! Now I have all the data and navigation links. Let me summarize the findings:

---

## **Yes—there are substantial differences in chromosome arm 7p copy number between lower grade glioma molecular subtypes.**

### Study
**Brain Lower Grade Glioma (TCGA, PanCancer Atlas)** — 514 samples across three molecular subtypes defined by IDH and 1p/19q status.

---

### **Key Findings**

#### **Overall 7p CNA Pattern (8 key 7p genes)**

| Subtype | N | Mean log₂ CNA | Median log₂ CNA | Std Dev | IQR |
|---------|---|---|---|---|---|
| **IDH-mutant, 1p/19q-codeleted** | 167 | +0.016 | 0 | 0.138 | −0.005 to +0.008 |
| **IDH-mutant, non-codeleted** | 248 | +0.037 | +0.001 | 0.32 | −0.008 to +0.017 |
| **IDH-wildtype** | 92 | **+0.143** | 0 | **0.902** | −0.018 to +0.026 |

**→ IDH-wildtype LGGs show ~9× higher variance in 7p CNA, driven by extreme gains.**

---

#### **Gene-Specific Patterns (Most Informative)**

**EGFR** (strongest driver):
- IDH-mut-codel: mean +0.044 (median +0.002) — mostly diploid
- IDH-mut-non-codel: mean +0.102 (median +0.012) — mild elevation
- **IDH-wt: mean +1.653 (median +0.736)** — dramatic amplification
  - **43.5%** have homozygous gain (GISTIC +2)
  - **37.6%** have single copy gain (+1)
  - Only **25.9%** diploid

**FGFR2** (inverse trend — deletion pattern):
- IDH-mut-codel: mean −0.016 (mostly diploid)
- IDH-mut-non-codel: mean −0.146 (mild deletion)
- **IDH-wt: mean −0.493** — pronounced deletion
  - **85.9%** have single-copy loss (−1)
  - **22.4%** diploid

**MYC** (amplified in IDH-mut-non-codel):
- IDH-mut-codel: mean +0.021 (diploid)
- **IDH-mut-non-codel: mean +0.291** (gain, 9% homozyg. amp)
- IDH-wt: mean −0.037 (mostly diploid with losses)

**PTEN** (loss in IDH-wt and IDH-mut-non-codel):
- IDH-mut-codel: mean −0.010 (mostly diploid)
- IDH-mut-non-codel: mean −0.058 (mild deletion)
- **IDH-wt: mean −0.518** — strong deletion
  - Median −0.592

**SHH** (gain across all):
- IDH-mut-codel: mean +0.106
- IDH-mut-non-codel: mean +0.211
- **IDH-wt: mean +0.488** (highest)

---

### **Biological Interpretation**
- **IDH-wildtype LGGs** (typically secondary GBM precursors) show aggressive 7p remodeling: **EGFR amplification + FGFR2/PTEN deletion**, a canonical high-grade glioma signature.
- **IDH-mutant-codeleted** (most favorable prognosis) have the most stable 7p — minimal copy number changes.
- **IDH-mutant-non-codeleted** shows intermediate 7p instability with selective MYC amplification and FGFR2 deletion.

---

### **Explore the Data**

[**View 7p gene alterations (OncoPrint)**](https://www.cbioportal.org/results/oncoprint?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=EGFR%0AFGFR2%0APDGFRB%0AMYC%0ASHH%0APTEN%0ACASP8%0APAX8&case_set_id=lgg_tcga_pan_can_atlas_2018_all&clinicallist=SUBTYPE) — Visualize copy number and mutation patterns colored by molecular subtype.

[**Compare the three subtypes**](https://www.cbioportal.org/comparison/overlap?comparisonId=6a87209dc2115c492d87eea5) — Group comparison showing sample/patient overlap and clinical differences across subtypes.