Q (sonnet): In lower grade glioma, are there genes which are overexpressed in any of the molecular subtypes?

▶ 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 | `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)

▶ read_guide
{
 "uri": "cbioportal://clinical-data-guide"
}

▶ list_studies
{
 "search": "lower grade glioma"
}

◀ result
# Clinical Data Query Guide

## Overview
Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis.

## Data Organization

### Patient-Level vs Sample-Level Data
- **Patient-level**: Demographics, overall survival, disease stage (stored once per patient)
- **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient)

### Key Tables
- `clinical_patient`: Patient-level clinical attributes
- `clinical_sample`: Sample-level clinical attributes
- `clinical_data_derived`: Pre-joined view combining both levels
- `clinical_attribute_meta`: Metadata about available clinical attributes

## Recommended Approach: Use clinical_data_derived

The `clinical_data_derived` table is pre-joined and optimized for most queries:

```sql
-- Get clinical data for specific attributes
SELECT
    sample_unique_id,
    patient_unique_id,
    attribute_name,
    attribute_value
FROM clinical_data_derived
WHERE
    cancer_study_identifier = 'your_study_id'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS');
```

## Clinical Attribute Discovery

### Use clinical_attribute_meta for Discovering Available Attributes
**Always start here** to see what clinical attributes are available for a specific study:

```sql
-- Discover available clinical attributes for a study
SELECT
    attr_id,
    description,
    patient_attribute,
    cancer_study_id
FROM clinical_attribute_meta
WHERE
    cancer_study_id = (
        SELECT cancer_study_id
        FROM cancer_study
        WHERE cancer_study_identifier = 'your_study_id'
    )
ORDER BY patient_attribute, attr_id;
```

**Key Fields:**
- **attr_id**: matches attr_id in clinical_sample/clinical_patient tables
- **description**: human-readable description of the attribute
- **patient_attribute**: true = patient attribute, false = sample attribute
- **cancer_study_id**: links to cancer_study table (filter by study)

## Attribute Semantics and Matching

### Case-Insensitive Matching for Attribute Values

Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant.

When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study:

```sql
-- Correct: case-insensitive clinical value filter
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'your_study_id'
  AND attribute_name = 'MUTATION_STATUS'
  AND upper(attribute_value) = 'GERMLINE';
```

Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study.

### Query the Requested Attribute, Not a Proxy

Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars)

◀ result
{"result":[{"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"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attr_id, description, patient_attribute
FROM clinical_attribute_meta
WHERE cancer_study_id = (SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018')
AND (upper(attr_id) LIKE '%SUBTYPE%' OR upper(attr_id) LIKE '%CODEL%' OR upper(attr_id) LIKE '%IDH%')
ORDER BY attr_id"
}

◀ result
{"rows":[{"attr_id":"SUBTYPE","description":"Subtype","patient_attribute":1}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT profile_type FROM genetic_alteration_derived WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018' ORDER BY profile_type"
}

◀ 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": "SELECT attribute_value AS subtype, count(DISTINCT patient_unique_id) AS n_patients
FROM clinical_data_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
AND attribute_name = 'SUBTYPE'
GROUP BY subtype
ORDER BY n_patients DESC"
}

◀ result
{"rows":[{"subtype":"LGG_IDHmut-non-codel","n_patients":248},{"subtype":"LGG_IDHmut-codel","n_patients":167},{"subtype":"LGG_IDHwt","n_patients":92},{"n_patients":7}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype 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 != ''
),
expr AS (
    SELECT sample_unique_id, hugo_gene_symbol, toFloat64OrNull(alteration_value) AS z
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND profile_type = 'rna_seq_v2_mrna_median_Zscores'
    AND hugo_gene_symbol IN ('IDH1','IDH2','EGFR','PDGFRA','CDKN2A','ATRX','TERT','OLIG2','CIC','FUBP1','GFAP','VIM','SOX2','MKI67','VEGFA','CHI3L1','TNC')
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
),
joined AS (
    SELECT s.subtype, e.hugo_gene_symbol, e.z
    FROM expr e
    INNER JOIN subtype s ON substring(e.sample_unique_id, 1, length(e.sample_unique_id)-3) = 'lgg_tcga_pan_can_atlas_2018_' || s.patient_unique_id
    -- fallback join below if this pattern fails
)
SELECT subtype, hugo_gene_symbol, round(avg(z),3) AS mean_z, count() AS n
FROM joined
GROUP BY subtype, hugo_gene_symbol
ORDER BY hugo_gene_symbol, subtype
"
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "SELECT sample_unique_id FROM genetic_alteration_derived WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018' AND profile_type = 'rna_seq_v2_mrna_median_Zscores' LIMIT 3"
}

◀ 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"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT patient_unique_id, sample_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' LIMIT 3"
}

◀ result
{"rows":[{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4938"},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4941"},{"patient_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4942"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH subtype 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 != ''
),
expr AS (
    SELECT sample_unique_id, substring(sample_unique_id, 1, length(sample_unique_id)-3) AS patient_unique_id,
           hugo_gene_symbol, toFloat64OrNull(alteration_value) AS z
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND profile_type = 'rna_seq_v2_mrna_median_Zscores'
    AND hugo_gene_symbol IN ('IDH1','IDH2','EGFR','PDGFRA','CDKN2A','ATRX','OLIG2','CIC','FUBP1','GFAP','VIM','SOX2','MKI67','VEGFA','CHI3L1','TNC','DLL3','SOX10')
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT s.subtype, e.hugo_gene_symbol, round(avg(e.z),3) AS mean_z, count() AS n
FROM expr e
INNER JOIN subtype s USING (patient_unique_id)
GROUP BY s.subtype, e.hugo_gene_symbol
ORDER BY e.hugo_gene_symbol, s.subtype
"
}

◀ result
{"rows":[{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"ATRX","mean_z":0.869,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"ATRX","mean_z":-0.809,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"ATRX","mean_z":0.12,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"CDKN2A","mean_z":-0.212,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"CDKN2A","mean_z":0.071,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"CDKN2A","mean_z":0.11,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"CHI3L1","mean_z":-0.206,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"CHI3L1","mean_z":-0.206,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"CHI3L1","mean_z":0.988,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"CIC","mean_z":-1.665,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"CIC","mean_z":-0.178,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"CIC","mean_z":-0.164,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"DLL3","mean_z":0.052,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"DLL3","mean_z":0.138,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"DLL3","mean_z":-0.858,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"EGFR","mean_z":0.285,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"EGFR","mean_z":-0.067,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"EGFR","mean_z":5.941,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"FUBP1","mean_z":-0.901,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"FUBP1","mean_z":0.062,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"FUBP1","mean_z":-0.177,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"GFAP","mean_z":-0.348,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"GFAP","mean_z":0.176,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"GFAP","mean_z":0.101,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"IDH1","mean_z":-0.092,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"IDH1","mean_z":-0.076,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"IDH1","mean_z":0.48,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"IDH2","mean_z":-0.158,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"IDH2","mean_z":-0.151,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"IDH2","mean_z":0.467,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"MKI67","mean_z":0.037,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"MKI67","mean_z":0.15,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"MKI67","mean_z":0.599,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"OLIG2","mean_z":0.14,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_symbol":"OLIG2","mean_z":0.259,"n":248},{"subtype":"LGG_IDHwt","hugo_gene_symbol":"OLIG2","mean_z":-1.026,"n":92},{"subtype":"LGG_IDHmut-codel","hugo_gene_symbol":"PDGFRA","mean_z":0.244,"n":167},{"subtype":"LGG_IDHmut-non-codel","hugo_gene_sy … (1302 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"]}}]}}

▶ get_studyviewfilter_options
{
 "studyId": "lgg_tcga_pan_can_atlas_2018",
 "attributeIds": [
  "SUBTYPE"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"lgg_tcga_pan_can_atlas_2018","attributes":[{"attributeId":"SUBTYPE","displayName":"Subtype","description":"Subtype","datatype":"STRING","values":["NA","LGG_IDHwt","LGG_IDHmut-non-codel","LGG_IDHmut-codel"]}]}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ],
 "groups": [
  {
   "name": "IDH-wildtype",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "LGG_IDHwt"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "IDH-mut codel",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "LGG_IDHmut-codel"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "IDH-mut non-codel",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "LGG_IDHmut-non-codel"
       }
      ]
     }
    ]
   }
  }
 ],
 "tab": "mrna"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/mrna?comparisonId=6ab5d277e497aad53593c295","url":"https://www.cbioportal.org/comparison/mrna?comparisonId=6ab5d277e497aad53593c295","data":{"description":"Group comparison (3 custom groups)","studies":["lgg_tcga_pan_can_atlas_2018"],"totalGroups":3,"groups":[{"name":"IDH-wildtype","sampleCount":92},{"name":"IDH-mut codel","sampleCount":167},{"name":"IDH-mut non-codel","sampleCount":248}],"studyViewUrl":"https://www.cbioportal.org/study?id=lgg_tcga_pan_can_atlas_2018","groupUrls":[{"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"},{"groupName":"IDH-mut codel","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-mut non-codel","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"}],"pageDescription":"mRNA expression for all genes compared across IDH-wildtype (92) vs IDH-mut codel (167) vs IDH-mut non-codel (248)."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "EGFR",
  "CHI3L1",
  "VIM",
  "TNC",
  "OLIG2",
  "CIC",
  "ATRX"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "clinical_attribute",
  "selectedDataSourceOption": "SUBTYPE"
 },
 "plotsVertSelection": {
  "dataType": "mrna",
  "selectedGeneOption": "EGFR"
 }
}

◀ result
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═ answer (success)
## Yes — expression differs substantially across the three molecular subtypes of lower grade glioma

Using the [Brain Lower Grade Glioma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018) cohort (514 samples, patients split into three transcriptomic/molecular subtypes: IDH-wildtype n=92, IDH-mutant codel n=167, IDH-mutant non-codel n=248), I compared mean mRNA Z-scores (RNA-Seq V2, gene-level, tumor-cohort-normalized) for a panel of glioma-relevant genes across subtypes. This is a descriptive comparison of average expression, not a formal differential-expression test — no p-values are reported here.

**Most striking pattern — EGFR:**
- IDH-wildtype: mean Z-score **+5.94** (n=92)
- IDH-mutant codel: +0.29 (n=167)
- IDH-mutant non-codel: −0.07 (n=248)

This is a dramatic, subtype-restricted elevation, consistent with the known biology that a subset of IDH-wildtype lower-grade gliomas carry EGFR amplification (a hallmark also seen in glioblastoma).

**Other genes with notable subtype-skewed elevation (mean Z-score):**

| Gene | IDH-wildtype | IDH-mut codel | IDH-mut non-codel | Pattern |
|---|---|---|---|---|
| EGFR | **+5.94** | +0.29 | −0.07 | High in IDHwt |
| VIM | **+1.07** | −0.48 | +0.40 | Highest in IDHwt |
| CHI3L1 | **+0.99** | −0.21 | −0.21 | Highest in IDHwt |
| TNC | **+0.91** | −0.41 | −0.04 | Highest in IDHwt |
| ATRX | +0.12 | **+0.87** | −0.81 | Highest in codel |
| MKI67 | **+0.60** | +0.04 | +0.15 | Highest in IDHwt (proliferation marker) |

Genes like OLIG2 and CIC trend *lower* in IDH-wildtype rather than higher elsewhere, and IDH1/IDH2 mRNA itself shows only modest differences (subtype is defined by IDH1/2 mutation status, not mRNA level).

**Caveat:** these are mean Z-scores over a curated glioma-marker gene panel, not a genome-wide screen, and no multiple-testing-corrected statistical test was run. For a rigorous, genome-wide differential expression analysis with significance testing, use the links below.

### Explore further
- **[Group Comparison — mRNA tab, all 3 subtypes](https://www.cbioportal.org/comparison/mrna?comparisonId=6ab5d277e497aad53593c295)**: runs the portal's built-in enrichment test across all genes for IDH-wildtype vs. IDH-mut-codel vs. IDH-mut-non-codel and will give you a ranked, statistically-tested list of subtype-enriched genes.
- **[EGFR expression by subtype (Plots tab)](https://www.cbioportal.org/results/plots?cancer_study_list=lgg_tcga_pan_can_atlas_2018&gene_list=EGFR%0ACHI3L1%0AVIM%0ATNC%0AOLIG2%0ACIC%0AATRX&case_set_id=lgg_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22SUBTYPE%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22mrna%22%7D)**: box/scatter plot of EGFR mRNA by subtype; switch the gene dropdown to view CHI3L1, VIM, TNC, ATRX, OLIG2, or CIC.