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

▶ list_guides
{}

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

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

◀ result
{"result":[{"cancer_study_identifier":"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"}]}

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

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

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

◀ 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
# Gene Expression Analysis Guide

This guide covers continuous-value genomic data: gene **expression**, **copy number** values, **methylation**, and related profile types. Mutation/CNA/SV *frequency* analysis lives in `cbioportal://mutation-frequency-guide`.

## Where this data lives

Continuous per-sample-per-gene values are stored in `genetic_alteration_derived`:

| Column | Description |
|---|---|
| `sample_unique_id` | `<study>_<sample_stable_id>` |
| `cancer_study_identifier` | study scope |
| `hugo_gene_symbol` | gene |
| `profile_type` | which assay/normalization (see below) |
| `alteration_value` | the actual value — stored as Nullable(String); cast with `toFloat64OrNull` |

`alteration_value` is a string because the same column hosts many different value scales. The `''` and `'NA'` sentinels mean "missing"; always filter them out and use `toFloat64OrNull(alteration_value) IS NOT NULL` for downstream math.

## Discovering profile types for a study

Different studies expose different profile types depending on what assays were run and how the data was normalized. Always check what a specific study supports before picking one:

```sql
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'brca_metabric'
ORDER BY profile_type;
```

Common values across the public portal:

| Family | Profile types |
|---|---|
| mRNA expression | `mrna`, `mrna_median_Zscores`, `mrna_seq_v2_rsem`, `mrna_seq_v2_rsem_Zscores`, `mrna_seq_cpm`, `mrna_seq_fpkm`, `mrna_U133`, `mrna_outliers` |
| Copy number (continuous) | `cna`, `linear_CNA`, `log2CNA`, `cna_consensus`, `cna_rae`, `gistic` |
| Methylation | `methylation_hm27`, `methylation_hm450`, `methylation_epic`, `methylation_promoters_rrbs` |
| miRNA | `mirna`, `mirna_median_Zscores` |
| Protein | `protein_quantification`, `protein_level`, `RPPA` |

**Z-score vs raw choice.** When the user asks "is X correlated with Y", either works for Spearman (rank-based) — Pearson would care. Default to the non-Z-score variant if both exist, and call out which one in the response.

## Canonical recipe — Spearman correlation between two genes

```sql
SELECT *
FROM gene_pair_coexpression(
    study        = 'brca_metabric',
    gene_a       = 'TP53',
    gene_b       = 'MYC',
    profile_type = 'mrna'
);
```

Returns one row: `(gene_a, gene_b, profile_type, spearman_correlation, num_samples)`.

- `spearman_correlation` in [−1, 1]; `NULL` when fewer than 3 valid paired samples.
- Mirrors cbioportal-backend's `ClickhouseCoExpressionMapper.getCoExpressions`, simplified to a pair lookup (the backend computes one ref gene vs ALL other genes for the coexpression page; here the agent asks about a specific pair).

### Verified examples

| Study | gene_a | gene_b | profile_type | spearman | n |
|---|---|---|---|---|---|
| `brca_metabric` | TP53 | MYC | `mrna` | 0.118 | 1980 |
| `brca_metabric` | ESR1 | PGR | `mrna` | 0.487 | 1980 |

ESR1↔PGR is the textbook breast-cancer estrogen-receptor coregula … (2785 more chars)

◀ result
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)

▶ get_study_guide
{
 "study_id": "lgg_tcga_pan_can_atlas_2018"
}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_name FROM clinical_data_derived WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018' AND (upper(attribute_name) LIKE '%SUBTYPE%' OR upper(attribute_name) LIKE '%IDH%' OR upper(attribute_name) LIKE '%CODEL%' OR upper(attribute_name) LIKE '%ARM%' OR upper(attribute_name) LIKE '%7P%')"
}

◀ result
# Study Guide: Brain Lower Grade Glioma (TCGA, PanCancer Atlas)

**Study ID:** `lgg_tcga_pan_can_atlas_2018`
**Cancer Type:** difg
**Description:** Brain Lower Grade Glioma TCGA PanCancer data. The original data is <a href="https://gdc.cancer.gov/about-data/publications/pancanatlas">here</a>. The publications are <a href="https://www.cell.com/pb-assets/consortium/pancanceratlas/pancani3/index.html">here</a>.

## Cohort Statistics
- **Patients:** 514
- **Samples:** 515

## Available Data Types

- **PROTEIN_LEVEL**: Protein expression (RPPA)
- **PROTEIN_LEVEL**: Protein expression z-scores (RPPA)
- **COPY_NUMBER_ALTERATION**: Putative copy-number alterations from GISTIC
- **COPY_NUMBER_ALTERATION**: Log2 copy-number values
- **GENERIC_ASSAY**: Putative arm-level copy-number from GISTIC
- **MUTATION_EXTENDED**: Mutations
- **STRUCTURAL_VARIANT**: Structural variants
- **GENERIC_ASSAY**: Methylation (HM450)
- **GENERIC_ASSAY**: Methylation (HM27 and HM450 merge)
- **MRNA_EXPRESSION**: mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_RNASeqV2)
- **MRNA_EXPRESSION**: mRNA expression z-scores relative to diploid samples (RNA Seq V2 RSEM)
- **MRNA_EXPRESSION**: mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM)
- **GENERIC_ASSAY**: Genetic Ancestry

## Gene Panels

- **WES** (Whole Exome): 514 samples — all genes profiled

## Available Clinical Attributes

| Attribute | Samples with Data |
|-----------|------------------|
| MUTATION_COUNT | 514 |
| TBL_SCORE | 514 |
| CANCER_TYPE | 514 |
| TMB_NONSYNONYMOUS | 514 |
| MSI_SENSOR_SCORE | 514 |
| TISSUE_SOURCE_SITE | 514 |
| TISSUE_SOURCE_SITE_CODE | 514 |
| SAMPLE_TYPE | 514 |
| MSI_SCORE_MANTIS | 514 |
| ONCOTREE_CODE | 514 |
| TISSUE_PROSPECTIVE_COLLECTION_INDICATOR | 514 |
| SOMATIC_STATUS | 514 |
| TUMOR_TYPE | 514 |
| ANEUPLOIDY_SCORE | 514 |
| CANCER_TYPE_DETAILED | 514 |
| TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR | 514 |
| TUMOR_TISSUE_SITE | 514 |
| FRACTION_GENOME_ALTERED | 514 |
| GRADE | 514 |
| ETHNICITY | 1 |

## Top Mutated Genes

| Gene | Altered Samples |
|------|----------------|
| IDH1 | 395 |
| TP53 | 249 |
| ATRX | 194 |
| CIC | 108 |
| TTN | 62 |
| FUBP1 | 48 |
| PIK3CA | 42 |
| NOTCH1 | 38 |
| MUC16 | 36 |
| EGFR | 35 |

## Sample Types

- **Primary**: 514 samples

## Query Tips for lgg_tcga_pan_can_atlas_2018

```sql
-- Get all samples in this study
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018';

-- Get mutations for a specific gene
SELECT sample_unique_id, hugo_gene_symbol, mutation_variant, mutation_type
FROM genomic_event_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND hugo_gene_symbol = 'TP53'
    AND variant_type = 'mutation';

-- Get clinical data for specific attributes
SELECT sample_unique_id, attribute_name, attribute_value
FROM clinical_data_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
    AND a … (66 more chars)

◀ result
{"rows":[{"attribute_name":"SUBTYPE"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(DISTINCT patient_unique_id) as n FROM clinical_data_derived WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' GROUP BY attribute_value ORDER BY n DESC"
}

▶ clickhouse_list_tables
{}

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

◀ result
{"tables":[{"name":"allele_specific_copy_number"},{"name":"alteration_driver_annotation"},{"name":"cancer_study"},{"name":"cancer_study_query_preferences"},{"name":"cancer_study_tags"},{"name":"cbioportal_sequence_state"},{"name":"clinical_attribute_counts"},{"name":"clinical_attribute_meta"},{"name":"clinical_data_derived"},{"name":"clinical_event"},{"name":"clinical_event_data"},{"name":"clinical_event_data_derived"},{"name":"clinical_event_derived"},{"name":"clinical_patient"},{"name":"clinical_sample"},{"name":"cna_event"},{"name":"cna_panel_gene_coverage"},{"name":"cna_wes_coverage"},{"name":"co_altered_genes_in_study"},{"name":"copy_number_seg"},{"name":"copy_number_seg_file"},{"name":"gene"},{"name":"gene_alias"},{"name":"gene_alteration_frequency_by_cancer_type"},{"name":"gene_cna_distribution_in_study"},{"name":"gene_mutation_frequency_by_cancer_type"},{"name":"gene_mutation_frequency_in_studies"},{"name":"gene_mutation_frequency_in_study"},{"name":"gene_mutation_variants_in_study"},{"name":"gene_pair_coexpression"},{"name":"gene_panel"},{"name":"gene_panel_list"},{"name":"gene_panel_to_gene_derived"},{"name":"generic_assay_data_derived"},{"name":"generic_assay_meta_derived"},{"name":"generic_assay_profile_entity_derived"},{"name":"generic_entity_properties"},{"name":"geneset"},{"name":"geneset_gene"},{"name":"geneset_hierarchy_leaf"},{"name":"geneset_hierarchy_node"},{"name":"genetic_alteration"},{"name":"genetic_alteration_derived"},{"name":"genetic_entity"},{"name":"genetic_profile"},{"name":"genetic_profile_link"},{"name":"genetic_profile_samples"},{"name":"genomic_event_derived"},{"name":"gistic"},{"name":"gistic_to_gene"},{"name":"info"},{"name":"mut_sig"},{"name":"mutation"},{"name":"mutation_count_by_keyword"},{"name":"mutation_derived"},{"name":"mutation_event"},{"name":"mutation_panel_gene_coverage"},{"name":"mutation_wes_coverage"},{"name":"patient"},{"name":"reference_genome"},{"name":"reference_genome_gene"},{"name":"resource_definition"},{"name":"resource_patient"},{"name":"resource_sample"},{"name":"resource_study"},{"name":"sample"},{"name":"sample_cna_event"},{"name":"sample_derived"},{"name":"sample_list"},{"name":"sample_list_list"},{"name":"sample_profile"},{"name":"sample_to_gene_panel_derived"},{"name":"structural_variant"},{"name":"sv_panel_gene_coverage"},{"name":"sv_wes_coverage"},{"name":"top_cna_genes_in_study"},{"name":"top_mutated_genes_in_cohort"},{"name":"top_mutated_genes_in_study"},{"name":"top_sv_genes_in_study"},{"name":"treatment_counts_in_study"},{"name":"treatment_regimens_in_study"},{"name":"type_of_cancer"}]}

▶ clickhouse_list_table_columns
{
 "table": "generic_assay_meta_derived"
}

▶ clickhouse_list_table_columns
{
 "table": "generic_assay_data_derived"
}

◀ result
{"columns":[{"name":"entity_stable_id","type":"String"},{"name":"entity_type","type":"LowCardinality(String)"},{"name":"properties","type":"Map(String, String)"}]}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"genetic_entity_id","type":"String"},{"name":"value","type":"String"},{"name":"generic_assay_type","type":"String"},{"name":"profile_stable_id","type":"String"},{"name":"entity_stable_id","type":"String"},{"name":"datatype","type":"String"},{"name":"patient_level","type":"Decimal(10, 0)"},{"name":"profile_type","type":"String"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT profile_type, generic_assay_type FROM generic_assay_data_derived WHERE profile_type LIKE '%arm%' OR generic_assay_type LIKE '%ARM%' LIMIT 20"
}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT entity_stable_id FROM generic_assay_data_derived WHERE profile_stable_id LIKE '%lgg_tcga_pan_can_atlas_2018%' AND generic_assay_type LIKE '%ARM%' LIMIT 50"
}

◀ result
{"rows":[{"profile_type":"armlevel_cna","generic_assay_type":"ARMLEVEL_CNA"}]}

◀ result
{"error_message":"Query timed out after 30 seconds"}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT entity_stable_id FROM generic_assay_data_derived WHERE profile_type = 'armlevel_cna' AND entity_stable_id LIKE '%7p%' LIMIT 20"
}

◀ result
{"rows":[{"entity_stable_id":"17p_status"},{"entity_stable_id":"7p_status"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT value FROM generic_assay_data_derived WHERE profile_type = 'armlevel_cna' AND entity_stable_id = '7p_status' AND profile_stable_id LIKE 'lgg_tcga_pan_can_atlas_2018%'"
}

▶ clickhouse_list_table_columns
{
 "table": "sample_derived"
}

◀ result
{"rows":[{"value":"Unchanged"},{"value":"Gain"},{"value":"NA"}]}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"sample_unique_id_base64","type":"String"},{"name":"sample_stable_id","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"patient_unique_id_base64","type":"String"},{"name":"patient_stable_id","type":"String"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"internal_id","type":"Int32"},{"name":"patient_internal_id","type":"Int32"},{"name":"sample_type","type":"String"},{"name":"sequenced","type":"Int32"},{"name":"copy_number_segment_present","type":"Int32"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH arm AS (
    SELECT patient_unique_id, value AS arm7p
    FROM generic_assay_data_derived
    WHERE profile_type = 'armlevel_cna'
      AND entity_stable_id = '7p_status'
      AND profile_stable_id LIKE 'lgg_tcga_pan_can_atlas_2018%'
),
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 != ''
)
SELECT s.subtype, a.arm7p, count(*) AS n_patients
FROM arm a
INNER JOIN subtype s USING (patient_unique_id)
GROUP BY s.subtype, a.arm7p
ORDER BY s.subtype, a.arm7p
"
}

◀ result
{"rows":[{"subtype":"LGG_IDHmut-codel","arm7p":"Gain","n_patients":8},{"subtype":"LGG_IDHmut-codel","arm7p":"NA","n_patients":3},{"subtype":"LGG_IDHmut-codel","arm7p":"Unchanged","n_patients":156},{"subtype":"LGG_IDHmut-non-codel","arm7p":"Gain","n_patients":29},{"subtype":"LGG_IDHmut-non-codel","arm7p":"NA","n_patients":5},{"subtype":"LGG_IDHmut-non-codel","arm7p":"Unchanged","n_patients":214},{"subtype":"LGG_IDHwt","arm7p":"Gain","n_patients":59},{"subtype":"LGG_IDHwt","arm7p":"NA","n_patients":3},{"subtype":"LGG_IDHwt","arm7p":"Unchanged","n_patients":30}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH arm AS (
    SELECT patient_unique_id, value AS arm7p
    FROM generic_assay_data_derived
    WHERE profile_type = 'armlevel_cna'
      AND entity_stable_id = '7p_status'
      AND profile_stable_id LIKE 'lgg_tcga_pan_can_atlas_2018%'
      AND value != 'NA'
),
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 != ''
)
SELECT s.subtype,
       count(*) AS n_called,
       countIf(a.arm7p = 'Gain') AS n_gain,
       round(100.0 * countIf(a.arm7p = 'Gain') / count(*), 1) AS pct_gain
FROM arm a
INNER JOIN subtype s USING (patient_unique_id)
GROUP BY s.subtype
ORDER BY pct_gain DESC
"
}

◀ result
{"rows":[{"subtype":"LGG_IDHwt","n_called":89,"n_gain":59,"pct_gain":66.3},{"subtype":"LGG_IDHmut-non-codel","n_called":243,"n_gain":29,"pct_gain":11.9},{"subtype":"LGG_IDHmut-codel","n_called":164,"n_gain":8,"pct_gain":4.9}]}

▶ resolve_and_route
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ]
}

◀ result
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▶ get_studyviewfilter_options
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◀ result
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▶ navigate_to_group_comparison
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◀ result
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▶ navigate_to_study_view
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◀ result
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═ answer (success)
## Findings: 7p Copy Number Across LGG Molecular Subtypes

Using the **[Brain Lower Grade Glioma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018)** cohort (514 patients), GISTIC arm-level calls for chromosome **7p** were cross-tabulated against the three TCGA-defined molecular subtypes (`SUBTYPE`: IDH-mutant/1p19q-codeleted, IDH-mutant/non-codeleted, IDH-wildtype). GISTIC only reported two 7p arm states in this cohort — **Gain** or **Unchanged** (no arm-level loss calls were made for 7p).

| Subtype | Patients with a 7p call | 7p Gain | % Gain |
|---|---|---|---|
| **IDH-wildtype** | 89 | 59 | **66.3%** |
| **IDH-mutant, non-codel** | 243 | 29 | **11.9%** |
| **IDH-mutant, codel** | 164 | 8 | **4.9%** |

There is a clear gradient: 7p gain is roughly 5–6x more common in the IDH-wildtype subgroup than in either IDH-mutant subgroup, and somewhat more common in IDH-mutant/non-codel than in IDH-mutant/codel tumors. This tracks with the known enrichment of chr7 gain (often co-occurring with EGFR amplification/chr10 loss) in IDH-wildtype gliomas.

This is a categorical comparison across 3 groups, so a **chi-squared test** (or pairwise Fisher's exact with multiple-testing correction) would be the appropriate way to formally test this difference — I have not computed a p-value here; you can run the test directly in the Group Comparison view below, or in R (`chisq.test`) / Python (`scipy.stats.chi2_contingency`) using the counts above.

**Links:**
- [Group Comparison — Arm-level CNA across LGG subtypes](https://www.cbioportal.org/comparison/generic_assay_armlevel_cna?comparisonId=6ab22c8ae497aad53593b7bb) — chromosome arm-level CNA enrichment, ranked by differential gain/loss frequency across the three subtype groups (includes 7p and all other arms).
- [Study View — LGG cohort filtered to 7p Gain](https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018#filterJson=%7B%22genericAssayDataFilters%22%3A%5B%7B%22profileType%22%3A%22armlevel_cna%22%2C%22stableId%22%3A%227p_status%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Gain%22%7D%5D%7D%5D%7D) — explore clinical/genomic characteristics of the 7p-gain subset interactively.