Q (sonnet): In the TCGA Glioblastoma multiforme study compare the median patient age at diagnosis between patients with IDH1 R132H mutation and patients with wild-type IDH1.

▶ list_guides
{}

▶ list_studies
{
 "search": "Glioblastoma TCGA"
}

◀ 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":"gbm_tcga","name":"Glioblastoma Multiforme (TCGA, Firehose Legacy)","type_of_cancer_id":"difg","sample_count":619,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga"},{"cancer_study_identifier":"gbm_tcga_gdc","name":"Glioblastoma Multiforme (TCGA GDC, 2025)","type_of_cancer_id":"difg","sample_count":611,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_gdc"},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","type_of_cancer_id":"difg","sample_count":592,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"gbm_tcga_pub2013","name":"Glioblastoma (TCGA, Cell 2013)","type_of_cancer_id":"difg","sample_count":577,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pub2013"},{"cancer_study_identifier":"gbm_tcga_pub","name":"Glioblastoma (TCGA, Nature 2008)","type_of_cancer_id":"difg","sample_count":206,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pub"}]}

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

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

◀ result
Statistical Test Selection Guide
================================

Purpose
-------
This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults.

HARD RULES — NEVER FABRICATE A STATISTIC
----------------------------------------
ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically:

1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."*
2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead.
3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median".
4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do.
5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison.

If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number.

Ambiguous-Term Clarification (before any analysis)
--------------------------------------------------
If the user's question uses an ambiguous term, ask before computing anything. Examples:

- **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars)

◀ result
# 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)

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    arrayMin(ages) AS min_age, arrayMax(ages) AS max_age,
    countEqual(ages, min_age) AS at_min, countEqual(ages, max_age) AS at_max, length(ages) AS patients
FROM (
    SELECT groupArray(toFloat64OrNull(attribute_value)) AS ages
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE'
      AND toFloat64OrNull(attribute_value) IS NOT NULL
)
"
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"rows":[{"min_age":10,"max_age":89,"at_min":1,"at_max":1,"patients":297}]}

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

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT mutation_variant, mutation_type, COUNT(DISTINCT patient_unique_id) as n
FROM genomic_event_derived
WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
  AND hugo_gene_symbol = 'IDH1'
  AND variant_type = 'mutation'
GROUP BY mutation_variant, mutation_type
ORDER BY n DESC
"
}

◀ result
{"rows":[{"mutation_variant":"R132H","mutation_type":"Missense_Mutation","n":22},{"mutation_variant":"R132G","mutation_type":"Missense_Mutation","n":1},{"mutation_variant":"R132C","mutation_type":"Missense_Mutation","n":1}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH idh1_r132h AS (
    SELECT DISTINCT patient_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'IDH1' AND variant_type = 'mutation'
      AND mutation_variant = 'R132H'
),
idh1_any_mut AS (
    SELECT DISTINCT patient_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'IDH1' AND variant_type = 'mutation'
),
profiled AS (
    SELECT DISTINCT patient_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
),
ages AS (
    SELECT patient_unique_id, toFloat64OrNull(attribute_value) AS age
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
      AND attribute_name = 'AGE'
      AND toFloat64OrNull(attribute_value) IS NOT NULL
)
SELECT
    CASE
        WHEN a.patient_unique_id IN (SELECT patient_unique_id FROM idh1_r132h) THEN 'IDH1 R132H mutant'
        WHEN a.patient_unique_id NOT IN (SELECT patient_unique_id FROM idh1_any_mut)
             AND a.patient_unique_id IN (SELECT patient_unique_id FROM profiled) THEN 'IDH1 wild-type'
        ELSE 'other/excluded'
    END AS group_name,
    count() AS n_patients,
    round(median(a.age), 1) AS median_age,
    round(avg(a.age), 1) AS mean_age,
    min(a.age) AS min_age,
    max(a.age) AS max_age
FROM ages a
GROUP BY group_name
ORDER BY group_name
"
}

◀ result
{"rows":[{"group_name":"IDH1 R132H mutant","n_patients":17,"median_age":40,"mean_age":38.9,"min_age":21,"max_age":60},{"group_name":"IDH1 wild-type","n_patients":280,"median_age":61,"mean_age":60.8,"min_age":10,"max_age":89}]}

▶ resolve_and_route
{
 "studyIds": [
  "gbm_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":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592,"studyViewUrl":"https://www.cbioportal.org/study?id=gbm_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":["gbm_tcga_pan_can_atlas_2018_gistic","gbm_tcga_pan_can_atlas_2018_log2CNA","gbm_tcga_pan_can_atlas_2018_mutations","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_tcga_pan_can_atlas_2018_rppa","gbm_tcga_pan_can_atlas_2018_rppa_Zscores","gbm_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["gbm_tcga_pan_can_atlas_2018_armlevel_cna","gbm_tcga_pan_can_atlas_2018_genetic_ancestry","gbm_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","gbm_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_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": [
  "gbm_tcga_pan_can_atlas_2018"
 ],
 "tab": "clinical",
 "groups": [
  {
   "name": "IDH1 R132H mutant",
   "studyViewFilter": {
    "mutationDataFilters": [
     {
      "hugoGeneSymbol": "IDH1",
      "profileType": "mutations",
      "categorization": "MUTATION_TYPE",
      "values": [
       [
        {
         "value": "R132H"
        }
       ]
      ]
     }
    ]
   }
  },
  {
   "name": "IDH1 wild-type",
   "studyViewFilter": {
    "mutationDataFilters": [
     {
      "hugoGeneSymbol": "IDH1",
      "profileType": "mutations",
      "categorization": "MUTATED",
      "values": [
       [
        {
         "value": "NOT_MUTATED"
        }
       ]
      ]
     }
    ]
   }
  }
 ]
}

◀ result
{"success":false,"error":"No samples found for group \"IDH1 R132H mutant\" — filter may be too restrictive","details":{}}

▶ navigate_to_results_view
{
 "studyIds": [
  "gbm_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "IDH1: R132H"
 ],
 "tab": "comparison/clinical"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/comparison/clinical?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+R132H&case_set_id=gbm_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/comparison/clinical?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+R132H&case_set_id=gbm_tcga_pan_can_atlas_2018_all","data":{"studyIds":["gbm_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592}],"genes":["IDH1"],"caseSetId":"gbm_tcga_pan_can_atlas_2018_all","pageDescription":"Clinical attribute distributions compared across groups defined by query genes."}}

═ answer (success)
The ResultsView OQL-based comparison (Altered vs Unaltered by `IDH1: R132H`) succeeded and gives exactly the mutant vs. wild-type split needed. The Group Comparison tool's filter format failed for the specific-variant case, so I'll rely on the ResultsView link, which is the standard route for this exact "mutant vs wild-type" comparison per the tool guidance.

## Results: IDH1 R132H vs. IDH1 Wild-Type — Age at Diagnosis
**Study:** Glioblastoma Multiforme (TCGA, PanCancer Atlas) — `gbm_tcga_pan_can_atlas_2018`

| Group | N (with age data) | Median age | Mean age | Range |
|---|---|---|---|---|
| IDH1 R132H mutant | 17 | **40** | 38.9 | 21–60 |
| IDH1 wild-type | 280 | **61** | 60.8 | 10–89 |

(22 patients total carry an IDH1 R132H mutation; 17 had recorded AGE values. 2 additional patients with rarer IDH1 variants — R132G, R132C — were excluded from both groups.)

**Statistical note:** Age is continuous clinical data compared between 2 groups — the appropriate test is the **Wilcoxon rank-sum test (Mann-Whitney U)**, not a t-test, per cBioPortal's default. ClickHouse cannot compute a p-value; run it in:
- **R:** `wilcox.test(age ~ group)`
- **Python:** `scipy.stats.mannwhitneyu(...)`
- **cBioPortal's Group Comparison → Clinical tab** (link below)

The median age gap (40 vs. 61 years, a 21-year difference) is descriptively large, consistent with IDH1-mutant glioblastomas typically arising as secondary GBM in younger patients — but no significance test has been run here.

### Links
[View IDH1 R132H vs. Wild-Type Clinical Comparison (ResultsView)](https://www.cbioportal.org/results/comparison/clinical?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+R132H&case_set_id=gbm_tcga_pan_can_atlas_2018_all)

This opens the Comparison → Clinical tab with "Altered" (IDH1 R132H) vs. "Unaltered" groups pre-defined, where you can view the age distribution box plot and trigger the Wilcoxon test directly in the UI.