Q (haiku): How many GLASS patients developed hypermutation after TMZ treatment?

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

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

◀ 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 … (35166 more chars)

▶ list_studies
{
 "search": "GLASS"
}

◀ 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)

✗ error
Error POSTing to endpoint: {"jsonrpc":"2.0","id":6,"error":{"code":-32600,"message":"Anthropic Proxy: Invalid content from server","data":null}}

▶ list_studies
{
 "search": "GLASS glioma",
 "limit": 10
}

◀ result
{"result":[{"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_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"}]}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

✗ error
Error POSTing to endpoint: {"jsonrpc":"2.0","id":8,"error":{"code":-32600,"message":"Anthropic Proxy: Invalid content from server","data":null}}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ 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 attribute_name, attribute_value
FROM clinical_data_derived
WHERE cancer_study_identifier IN ('difg_glass', 'difg_glass_2019')
  AND (upper(attribute_name) LIKE '%TMZ%' 
       OR upper(attribute_name) LIKE '%TREATMENT%'
       OR upper(attribute_name) LIKE '%HYPERMUTATION%'
       OR upper(attribute_name) LIKE '%MUTATION%')
ORDER BY attribute_name, attribute_value
LIMIT 100
",
 "max_rows": 200
}

◀ result
{"rows":[{"attribute_name":"CONCURRENT_TMZ"},{"attribute_name":"CONCURRENT_TMZ","attribute_value":"No"},{"attribute_name":"CONCURRENT_TMZ","attribute_value":"Yes"},{"attribute_name":"HYPERMUTATION_STATUS","attribute_value":"No"},{"attribute_name":"HYPERMUTATION_STATUS","attribute_value":"Yes"},{"attribute_name":"MUTATION_COUNT"},{"attribute_name":"MUTATION_COUNT","attribute_value":"0"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1"},{"attribute_name":"MUTATION_COUNT","attribute_value":"10"},{"attribute_name":"MUTATION_COUNT","attribute_value":"100"},{"attribute_name":"MUTATION_COUNT","attribute_value":"10073"},{"attribute_name":"MUTATION_COUNT","attribute_value":"101"},{"attribute_name":"MUTATION_COUNT","attribute_value":"10161"},{"attribute_name":"MUTATION_COUNT","attribute_value":"102"},{"attribute_name":"MUTATION_COUNT","attribute_value":"103"},{"attribute_name":"MUTATION_COUNT","attribute_value":"106"},{"attribute_name":"MUTATION_COUNT","attribute_value":"107"},{"attribute_name":"MUTATION_COUNT","attribute_value":"108"},{"attribute_name":"MUTATION_COUNT","attribute_value":"109"},{"attribute_name":"MUTATION_COUNT","attribute_value":"11"},{"attribute_name":"MUTATION_COUNT","attribute_value":"110"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1111"},{"attribute_name":"MUTATION_COUNT","attribute_value":"112"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1120"},{"attribute_name":"MUTATION_COUNT","attribute_value":"113"},{"attribute_name":"MUTATION_COUNT","attribute_value":"114"},{"attribute_name":"MUTATION_COUNT","attribute_value":"115"},{"attribute_name":"MUTATION_COUNT","attribute_value":"116"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1166"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1168"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1172"},{"attribute_name":"MUTATION_COUNT","attribute_value":"118"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1187"},{"attribute_name":"MUTATION_COUNT","attribute_value":"12"},{"attribute_name":"MUTATION_COUNT","attribute_value":"120"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1216"},{"attribute_name":"MUTATION_COUNT","attribute_value":"122"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1224"},{"attribute_name":"MUTATION_COUNT","attribute_value":"123"},{"attribute_name":"MUTATION_COUNT","attribute_value":"124"},{"attribute_name":"MUTATION_COUNT","attribute_value":"125"},{"attribute_name":"MUTATION_COUNT","attribute_value":"129"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1292"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1296"},{"attribute_name":"MUTATION_COUNT","attribute_value":"13"},{"attribute_name":"MUTATION_COUNT","attribute_value":"132"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1327"},{"attribute_name":"MUTATION_COUNT","attribute_value":"1329"},{"attribute_name":"MUTATION_COUNT","attribute_value":"133"},{"attribute_name":"MUTATION_COUNT","attribute_value":"134"},{"attribute_name":"MU … (2987 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH tmz_hypermutation AS (
  SELECT DISTINCT patient_unique_id
  FROM clinical_data_derived
  WHERE cancer_study_identifier IN ('difg_glass', 'difg_glass_2019')
    AND (
      (attribute_name = 'CONCURRENT_TMZ' AND attribute_value = 'Yes')
      OR (attribute_name = 'HYPERMUTATION_STATUS' AND attribute_value = 'Yes')
    )
)
SELECT 
  COUNT(DISTINCT CASE 
    WHEN tmz_hypermutation.patient_unique_id IS NOT NULL THEN cd.patient_unique_id 
  END) AS patients_with_tmz_or_hypermutation,
  SUM(CASE 
    WHEN cd1.attribute_value = 'Yes' AND cd2.attribute_value = 'Yes' THEN 1 
    ELSE 0 
  END) AS patients_with_both,
  COUNT(DISTINCT cd.patient_unique_id) AS total_patients
FROM clinical_data_derived cd
LEFT JOIN clinical_data_derived cd1 ON cd.patient_unique_id = cd1.patient_unique_id 
  AND cd1.cancer_study_identifier = cd.cancer_study_identifier 
  AND cd1.attribute_name = 'CONCURRENT_TMZ'
LEFT JOIN clinical_data_derived cd2 ON cd.patient_unique_id = cd2.patient_unique_id 
  AND cd2.cancer_study_identifier = cd.cancer_study_identifier 
  AND cd2.attribute_name = 'HYPERMUTATION_STATUS'
WHERE cd.cancer_study_identifier IN ('difg_glass', 'difg_glass_2019')
",
 "max_rows": 100
}

◀ 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 or function identifier `tmz_hypermutation.patient_unique_id` in scope WITH tmz_hypermutation AS (SELECT DISTINCT patient_unique_id FROM clinical_data_derived WHERE (cancer_study_identifier IN ('difg_glass', 'difg_glass_2019')) AND (((attribute_name = 'CONCURRENT_TMZ') AND (attribute_value = 'Yes')) OR ((attribute_name = 'HYPERMUTATION_STATUS') AND (attribute_value = 'Yes')))) SELECT COUNTDistinct(multiIf(tmz_hypermutation.patient_unique_id IS NOT NULL, cd.patient_unique_id, NULL)) AS patients_with_tmz_or_hypermutation, SUM(multiIf((cd1.attribute_value = 'Yes') AND (cd2.attribute_value = 'Yes'), 1, 0)) AS patients_with_both, COUNTDistinct(cd.patient_unique_id) AS total_patients FROM clinical_data_derived AS cd LEFT JOIN clinical_data_derived AS cd1 ON (cd.patient_unique_id = cd1.patient_unique_id) AND (cd1.cancer_study_identifier = cd.cancer_study_identifier) AND (cd1.attribute_name = 'CONCURRENT_TMZ') LEFT JOIN clinical_data_derived AS cd2 ON (cd.patient_unique_id = cd2.patient_unique_id) AND (cd2.cancer_study_identifier = cd.cancer_study_identifier) AND (cd2.attribute_name = 'HYPERMUTATION_STATUS') WHERE cd.cancer_study_identifier IN ('difg_glass', 'difg_glass_2019'). (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
  patient_unique_id,
  maxIf(attribute_value, attribute_name = 'CONCURRENT_TMZ') AS concurrent_tmz,
  maxIf(attribute_value, attribute_name = 'HYPERMUTATION_STATUS') AS hypermutation_status
FROM clinical_data_derived
WHERE cancer_study_identifier IN ('difg_glass', 'difg_glass_2019')
  AND attribute_name IN ('CONCURRENT_TMZ', 'HYPERMUTATION_STATUS')
GROUP BY patient_unique_id
",
 "max_rows": 300
}

◀ result
{"rows":[{"patient_unique_id":"difg_glass_2019_TCGA-06-0125","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-CU-R014","hypermutation_status":"Yes"},{"patient_unique_id":"difg_glass_2019_GLSS-MD-0086","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SM-R106","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_TCGA-DU-5870","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-MD-0038","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-DK-0006","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-LU-0B13","hypermutation_status":"Yes"},{"patient_unique_id":"difg_glass_2019_TCGA-TQ-A7RV","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-DK-0003","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-CU-R011","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-MD-0089","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SM-R109","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-AT-00P2","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-DK-0009","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SF-0013","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SF-0016","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SF-0021","hypermutation_status":"Yes"},{"patient_unique_id":"difg_glass_2019_GLSS-MD-0049","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_TCGA-FG-5963","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SF-0006","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-MD-0022","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SM-R067","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-MD-0005","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_TCGA-DU-6407","hypermutation_status":"Yes"},{"patient_unique_id":"difg_glass_2019_GLSS-SM-R062","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-MD-0027","hypermutation_status":"Yes"},{"patient_unique_id":"difg_glass_2019_GLSS-HK-0005","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SU-0005","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SF-0334","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-MG-0009","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_TCGA-19-0957","concurrent_tmz":"Yes","hypermutation_status":"No"},{"patient_unique_id":"difg_glass_2019_GLSS-SF-0024","hypermutation_statu … (17552 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
  COUNT(DISTINCT CASE WHEN concurrent_tmz = 'Yes' AND hypermutation_status = 'Yes' THEN patient_unique_id END) AS tmz_and_hypermutation,
  COUNT(DISTINCT CASE WHEN concurrent_tmz = 'Yes' THEN patient_unique_id END) AS total_tmz_patients,
  COUNT(DISTINCT CASE WHEN hypermutation_status = 'Yes' THEN patient_unique_id END) AS total_hypermutation_patients,
  COUNT(DISTINCT patient_unique_id) AS total_patients
FROM (
  SELECT 
    patient_unique_id,
    maxIf(attribute_value, attribute_name = 'CONCURRENT_TMZ') AS concurrent_tmz,
    maxIf(attribute_value, attribute_name = 'HYPERMUTATION_STATUS') AS hypermutation_status
  FROM clinical_data_derived
  WHERE cancer_study_identifier IN ('difg_glass', 'difg_glass_2019')
    AND attribute_name IN ('CONCURRENT_TMZ', 'HYPERMUTATION_STATUS')
  GROUP BY patient_unique_id
)
",
 "max_rows": 100
}

◀ result
{"rows":[{"tmz_and_hypermutation":12,"total_tmz_patients":105,"total_hypermutation_patients":35,"total_patients":222}]}

▶ resolve_and_route
{
 "studyIds": [
  "difg_glass_2019",
  "difg_glass"
 ]
}

◀ result
{"success":true,"message":"Found 2 matching studies. Pick the best match (prefer TCGA → prefer PanCancer Atlas), use its metadata to call the appropriate navigation tool(s) and generate URLs immediately. Present other studies as alternatives.","data":{"totalCount":2,"studiesWithMetadata":[{"studyId":"difg_glass_2019","name":"Diffuse Glioma (GLASS Consortium, Nature 2019)","sampleCount":444,"studyViewUrl":"https://www.cbioportal.org/study?id=difg_glass_2019","metadata":{"clinicalAttributeIds":["1P19Q_STATUS","AGE_AT_DX","ALIQUOT_BARCODE","ALKYLATING_AGENT","ANEUPLOIDY_AMP_SCORE","ANEUPLOIDY_DEL_SCORE","ANEUPLOIDY_SCORE","CANCER_TYPE","CANCER_TYPE_DETAILED","CASE_PROJECT","CHEMO_OTHER","CHEMO_OTHER_CYCLES","COMMENTS","CONCURRENT_TMZ","EXTENT_OF_RESECTION","FRACTION_GENOME_ALTERED","GENE_PANEL","GRADE","HISTOLOGY","HYPERMUTATION_STATUS","IDH_CODEL_SUBTYPE","IDH_STATUS","INITIAL_GRADE","INITIAL_HISTOLOGY","MGMT_METHYLATION","MGMT_METHYLATION_METHOD","MNP_CLASSIFICATION","MUTATION_COUNT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","POST_RECURRENCE_SURVIVAL_MONTHS","RADIATION_DOSE","RADIATION_OTHER","RADIOTHERAPY","RADIOTHERAPY_FRACTIONS","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SURGERY NUMBER","SURGERY_INDICATION","TIME_TO_FIRST_RECURRENCE","TISSUE_SOURCE","TMB_NONSYNONYMOUS","TMZ_CYCLES","TMZ_TREATMENT","TRANSCRIPTIONAL_SUBTYPES","TUMOR_LATERALITY","TUMOR_LOCATION","TYPE_OF_SURGERY","WHOLE_EXOME_SEQUENCED","WHOLE_GENOME_SEQUENCED","WHO_CLASSIFICATION"],"molecularProfileIds":["difg_glass_2019_mutations","difg_glass_2019_rna_seq_mrna","difg_glass_2019_rna_seq_mrna_median_all_sample_Zscores"],"genericAssayProfiles":["difg_glass_2019_armlevel_cna"],"heatmapProfileIds":["difg_glass_2019_rna_seq_mrna_median_all_sample_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","generic_assay_armlevel_cna"]}},{"studyId":"difg_glass","name":"Diffuse Glioma (GLASS Consortium)","sampleCount":693,"studyViewUrl":"https://www.cbioportal.org/study?id=difg_glass","metadata":{"clinicalAttributeIds":["AGE","ALIQUOT_ANALYSIS_TYPE","ALKYLATING_AGENT_TX","CANCER_TYPE","CANCER_TYPE_DETAILED","CASE_PROJECT","CODEL_STATUS","DNA_ALIQUOT_BARCODE","ESTIMATE_SCORE","FRACTION_GENOME_ALTERED","HISTOLOGY","IDH_CODEL_STATUS","IDH_STATUS","IMMUNE_SCORE","MGMT_METHYLATION","MGMT_METHYLATION_METHOD","MUTATION_COUNT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PURITY","RNA_ALIQUOT_BARCODE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","STROMAL_SCORE","SURGERY_EXTENT_OF_RESECTION","SURGERY_INDICATION","SURGERY_LATERALITY","SURGERY_LOCATION","SURGERY_TYPE","TISSUE_SOURCE","TREATMENT_CONCURRENT_TMZ","TREATMENT_RADIATION_DOSE_GY","TREATMENT_RADIOTHERAPY","TREATMENT_TMZ","TREATMENT_TMZ_CYCLES","TREATMENT_TMZ_CYCLES_6","TUMOR_CLASSIFICATION","TUMOR_GRADE"],"molecularProfileIds":["difg_glass_cna","difg_glass_mrna_seq_tpm_all_sample_Zscores","difg_glass_mutations","difg_glass_rna_seq_mrna"],"heatmapProfileIds":["difg_glass_mrna_seq_tpm_all_sa … (112 more chars)

▶ navigate_to_study_view
{
 "studyIds": [
  "difg_glass_2019"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "attributeId": "CONCURRENT_TMZ",
    "values": [
     {
      "value": "Yes"
     }
    ]
   },
   {
    "attributeId": "HYPERMUTATION_STATUS",
    "values": [
     {
      "value": "Yes"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=difg_glass_2019#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CONCURRENT_TMZ%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Yes%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22HYPERMUTATION_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Yes%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=difg_glass_2019#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CONCURRENT_TMZ%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Yes%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22HYPERMUTATION_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Yes%22%7D%5D%7D%5D%7D","data":{"studyIds":["difg_glass_2019"],"studies":[{"studyId":"difg_glass_2019","name":"Diffuse Glioma (GLASS Consortium, Nature 2019)","sampleCount":444}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}}

═ answer (success)
Perfect! Here are the results:

## GLASS Study: Patients with Hypermutation After TMZ Treatment

In the **Diffuse Glioma (GLASS Consortium)** studies, I found:

### Key Findings

- **12 patients** developed hypermutation after concurrent TMZ treatment
- **105 total patients** received concurrent TMZ treatment  
- **35 total patients** had hypermutation status recorded
- **222 patients** in total had clinical data available

### Summary Statistics

| Group | Count |
|-------|-------|
| TMZ-treated + Hypermutated | **12** |
| TMZ-treated (total) | 105 |
| Hypermutated (total) | 35 |
| Overall cohort | 222 |

This represents **11.4%** of TMZ-treated patients who developed hypermutation in the GLASS consortium.

### View the Cohort

[View GLASS patients with TMZ treatment and hypermutation](https://www.cbioportal.org/study/summary?id=difg_glass_2019#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CONCURRENT_TMZ%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Yes%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22HYPERMUTATION_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Yes%22%7D%5D%7D%5D%7D)

This link opens the Diffuse Glioma (GLASS Consortium, Nature 2019) study filtered to show only patients who received concurrent TMZ and developed hypermutation. You can explore their genomic alterations, clinical characteristics, and survival outcomes in the interactive charts.