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.

▶ read_guide
{
 "uri": "cbioportal://clinical-data-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)

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-guide"
}

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

◀ result
# Mutation Frequency Analysis Guide

## IMPORTANT: Reporting Mutation Frequencies
- **ALWAYS report frequencies as percentages**, not raw counts: `frequency = (altered_samples / total_profiled_samples) × 100`
- For quick frequency lookups, **prefer the TCGA Pan-Cancer Atlas study first**, then offer to expand to other studies
- When reporting across multiple studies, show **ranges** (e.g., "TP53 is mutated in 30–60% of samples") rather than a single average
- **NEVER** sum mutation events across studies to compute an aggregate frequency — this can exceed 100% due to double-counting
- Warn users that samples may overlap across cohorts (e.g., MSK studies may share patients)
- **Choose and state the counting unit**: use patient-level frequencies for prevalence/rate questions unless the user explicitly asks for samples; use sample-level frequencies when the user asks about samples.
- **For "across cancer types" questions**, jump to the [Cross-Cancer-Type Mutation Frequency](#cross-cancer-type-mutation-frequency) section below — there is one correct recipe and several common wrong ones.

## Counting Unit: Samples vs Patients

Before answering any mutation count or frequency question, decide whether the unit is samples or patients and state that choice in the answer.

| User wording | Counting unit |
|--------------|---------------|
| "prevalence", "rate", "fraction of patients", "patients with", "how common is" | Patient-level: `COUNT(DISTINCT patient_unique_id)` |
| "samples", "specimens", "biopsies", sample-level cohort composition | Sample-level: `COUNT(DISTINCT sample_unique_id)` |
| Ambiguous | Ask, or default to patient-level for prevalence/rate language and say so |

### Cross-study sample-count caveat

When an answer touches more than one study and reports a sample count, prepend a one-line caveat:

> Sample IDs are unique within cBioPortal study prefixes, not guaranteed biological-sample identifiers across studies; overlapping cohorts can count the same patient/sample more than once.

Prefer one of these safer approaches:

- Use a shipped `cancer_study_query_preferences` cohort such as `pan_cancer_tcga` or `all_studies_non_redundant`.
- Restrict to one named study.
- Aggregate by `patient_unique_id` when the biological question is patient prevalence.

## STOP rule: a frequency above 100% means your query is wrong

If your query returns a frequency over 100%, **do not try to debug or explain the data inconsistency to the user**. The cause is always one of these query bugs:

- Summing mutation events instead of `COUNT(DISTINCT sample_unique_id)` for the numerator
- Using a study-wide sample count as the denominator instead of the gene-specific profiled count
- Cross-study aggregation where the same biological sample appears under multiple `sample_unique_id` values (e.g., MSK-IMPACT and MSK-CHORD share patients)
- **Joining the profiled CTE through `gene_panel` / `gene_panel_list` without a WES branch.** `gene_panel_id = 'WES'` is *not* a row in … (36467 more chars)

◀ 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"}]}

▶ get_study_guide
{
 "study_id": "gbm_tcga_pan_can_atlas_2018"
}

◀ result
# Glioblastoma Multiforme (TCGA, PanCancer Atlas)

**Study ID:** `gbm_tcga_pan_can_atlas_2018`

See `_tcga_pancan_template.md` for common TCGA clinical attributes.

## Study-Specific Attributes

### Molecular Classification
| Attribute | Description | Values (patients) |
|-----------|-------------|--------|
| `SUBTYPE` | TCGA glioma IDH classification | `GBM_IDHwt` 114, `GBM_IDHmut-non-codel` 7, `GBM` 5, blank 459 |

`SUBTYPE` is blank for most patients and does not hold the transcriptional subtypes (Classical/Mesenchymal/Proneural/Neural).

### Not available in this study
- **IDH status**: no `IDH_STATUS` attribute. Use IDH1/IDH2 mutations from mutation data (covers all sequenced samples), or `SUBTYPE` for the 121 classified patients.
- **MGMT promoter methylation status**: not available (the methylation profiles are CpG-probe level, with no MGMT status call).
- **Karnofsky performance status and extent of resection**: not available.

## Key Genes & Pathways
- **TP53**: Frequently mutated
- **PTEN**: Common deletions
- **EGFR**: Amplified in ~40%, often with EGFRvIII variant
- **IDH1**: R132H mutation defines IDH-mutant subtype (better prognosis)
- **RB pathway**: CDKN2A deletion, CDK4 amplification, RB1 mutation (often mutually exclusive)

## Notes
- IDH-mutant GBMs have significantly better prognosis
- MGMT methylation predicts temozolomide response
- EGFR amplification and EGFRvIII are GBM-specific
- Molecular subtypes have distinct transcriptional programs


▶ clickhouse_run_select_query
{
 "query": "SELECT * FROM gene_mutation_variants_in_study(study = 'gbm_tcga_pan_can_atlas_2018', gene = 'IDH1')"
}

◀ result
{"rows":[{"mutation_variant":"R132H","mutation_type":"Missense_Mutation","altered_samples":22,"profiled_samples":397,"frequency_pct":5.5,"total_mutation_events":22},{"mutation_variant":"R132G","mutation_type":"Missense_Mutation","altered_samples":2,"profiled_samples":397,"frequency_pct":0.5,"total_mutation_events":2},{"mutation_variant":"R132C","mutation_type":"Missense_Mutation","altered_samples":1,"profiled_samples":397,"frequency_pct":0.3,"total_mutation_events":1}]}

▶ 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
)
"
}

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

▶ clickhouse_run_select_query
{
 "query": "
WITH idh1_profiled_patients AS (
    SELECT DISTINCT c.patient_unique_id
    FROM sample_to_gene_panel_derived s
    JOIN (SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived
          WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018') c USING (sample_unique_id)
    WHERE s.cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018' AND s.alteration_type = 'MUTATION_EXTENDED'
),
r132h_patients 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' AND mutation_status != 'UNCALLED'
),
idh1_any_mut_patients 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_status != 'UNCALLED'
),
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'
)
SELECT
    grp,
    count() AS n_patients,
    round(avg(age),1) AS mean_age,
    round(quantile(0.5)(age),1) AS median_age,
    round(quantile(0.25)(age),1) AS q1_age,
    round(quantile(0.75)(age),1) AS q3_age,
    min(age) AS min_age,
    max(age) AS max_age
FROM (
    SELECT p.patient_unique_id AS patient_unique_id,
        if(p.patient_unique_id IN (SELECT patient_unique_id FROM r132h_patients), 'R132H',
           if(p.patient_unique_id IN (SELECT patient_unique_id FROM idh1_any_mut_patients), 'OTHER_IDH1_MUT', 'WILD_TYPE')) AS grp,
        a.age AS age
    FROM idh1_profiled_patients p
    JOIN ages a ON a.patient_unique_id = p.patient_unique_id
    WHERE a.age IS NOT NULL
)
WHERE grp != 'OTHER_IDH1_MUT'
GROUP BY grp
"
}

◀ result
{"rows":[{"grp":"R132H","n_patients":17,"mean_age":38.9,"median_age":40,"q1_age":30,"q3_age":45,"min_age":21,"max_age":60},{"grp":"WILD_TYPE","n_patients":248,"mean_age":61.6,"median_age":61.5,"q1_age":54,"q3_age":71,"min_age":21,"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_results_view
{
 "studyIds": [
  "gbm_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "IDH1: MUT=R132H"
 ],
 "tab": "comparison/clinical",
 "comparisonSelectedGroups": [
  "IDH1"
 ]
}

◀ 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+MUT%3DR132H&case_set_id=gbm_tcga_pan_can_atlas_2018_all&comparison_selectedGroups=%5B%22IDH1%22%2C%22IDH1%3A+MUT%3DR132H%22%5D","url":"https://www.cbioportal.org/results/comparison/clinical?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+MUT%3DR132H&case_set_id=gbm_tcga_pan_can_atlas_2018_all&comparison_selectedGroups=%5B%22IDH1%22%2C%22IDH1%3A+MUT%3DR132H%22%5D","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 IDH1-altered."}}

═ answer (success)
Here is the live comparison view, pre-configured for IDH1 R132H (Altered) vs. Wild-type, on the Clinical tab where you can inspect the AGE distribution and any statistical test cBioPortal computes:

**[Compare IDH1 R132H-mutant vs. Wild-type — Clinical attributes (AGE)](https://www.cbioportal.org/results/comparison/clinical?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=IDH1%3A+MUT%3DR132H&case_set_id=gbm_tcga_pan_can_atlas_2018_all&comparison_selectedGroups=%5B%22IDH1%22%2C%22IDH1%3A+MUT%3DR132H%22%5D)**

Clinical attribute distributions compared across IDH1-altered (R132H) vs. unaltered patients in the Glioblastoma Multiforme (TCGA, PanCancer Atlas) study (592 samples). Select **AGE** in the chart list to see the boxplot/violin comparison and the built-in significance test.

### Summary of what the underlying data show
- **IDH1 R132H-mutant** (n=17 with age data): median age **40.0** years (mean 38.9, IQR 30–45)
- **IDH1 wild-type** (n=248 with age data): median age **61.5** years (mean 61.6, IQR 54–71)

R132H-mutant GBM patients are diagnosed at a markedly younger age than wild-type patients in this cohort — consistent with the well-established association between IDH1 mutation and secondary/younger-onset glioblastoma. I have not run a formal significance test (e.g., Mann-Whitney U) on this age difference; use the Clinical comparison link above, which computes it directly in the portal.