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

▶ list_guides
{}

▶ list_studies
{
 "search": "GLASS"
}

◀ 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":"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"}]}

▶ 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://treatment-guide"
}

▶ get_study_guide
{
 "study_id": "difg_glass"
}

◀ result
# Treatment Data Query Guide

## Overview

Treatment data in cBioPortal is stored in **clinical event tables**, separate from clinical attributes. This allows for timeline-based treatment records with start/stop dates.

## Start Here: Treatment Views

For "most common treatments / agents / regimens in study X", use the parameterized views (documented in `cbioportal://clinical-data-guide`, Study-View Chart Counts):

```sql
-- Patients per agent (the portal's Treatment chart), with type/subtype arrays
SELECT * FROM treatment_counts_in_study(study='msk_chord_2024') ORDER BY patients DESC LIMIT 20;

-- Same-day agent combinations (investigational, prior-medication and radiation events excluded)
SELECT * FROM treatment_regimens_in_study(study='msk_chord_2024') ORDER BY patients DESC LIMIT 20;
```

Write raw event queries (below) only for subgroups, timelines or keys the views do not expose.

## Key Tables

| Table | Description |
|-------|-------------|
| `clinical_event` | Event records with patient_id, event_type, start_date, stop_date |
| `clinical_event_data` | Key-value pairs linked to each clinical_event_id |

## Schema

```
clinical_event
├── clinical_event_id (PK)
├── patient_id (FK → patient.internal_id)
├── event_type (Treatment, TREATMENT, Diagnosis, SURGERY, etc.)
├── start_date (days from diagnosis)
└── stop_date (days from diagnosis)

clinical_event_data
├── clinical_event_id (FK)
├── key (AGENT, SUBTYPE, etc.)
└── value
```

## Event Types

Not all studies have all event types. Common ones include:

| Event Type | Description |
|------------|-------------|
| `Treatment` / `TREATMENT` | Drug/therapy administration |
| `SURGERY` | Surgical procedures |
| `Diagnosis` | Diagnosis events |
| `LAB_TEST` | Laboratory results |
| `Sequencing` | Sequencing events |
| `Sample acquisition` | Sample collection |
| `PATHOLOGY` / `Pathology` | Pathology reports |

## Basic Treatment Queries

### List Available Event Types in a Study

```sql
SELECT DISTINCT ce.event_type, COUNT(*) as event_count
FROM clinical_event ce
JOIN patient p ON ce.patient_id = p.internal_id
JOIN cancer_study cs ON p.cancer_study_id = cs.cancer_study_id
WHERE cs.cancer_study_identifier = 'your_study_id'
GROUP BY ce.event_type
ORDER BY event_count DESC;
```

### List Treatment Data Keys in a Study

```sql
SELECT DISTINCT ced.key, COUNT(*) as cnt
FROM clinical_event ce
JOIN clinical_event_data ced ON ce.clinical_event_id = ced.clinical_event_id
JOIN patient p ON ce.patient_id = p.internal_id
JOIN cancer_study cs ON p.cancer_study_id = cs.cancer_study_id
WHERE cs.cancer_study_identifier = 'your_study_id'
  AND ce.event_type IN ('Treatment', 'TREATMENT')
GROUP BY ced.key
ORDER BY cnt DESC;
```

### Get Most Common Treatment Agents

```sql
SELECT 
    ced.value as agent,
    COUNT(DISTINCT ce.patient_id) as patient_count
FROM clinical_event ce
JOIN clinical_event_data ced ON ce.clinical_event_id = ced.clinical_event_id
JOIN patient p ON ce.patient_id = p.internal_id
JOIN cancer_stud … (6220 more chars)

◀ result
# Study Guide: Diffuse Glioma (GLASS Consortium)

**Study ID:** `difg_glass`
**Cancer Type:** difg
**Description:** Whole genome or whole exome sequencing analysis of temporally separated diffuse glioma tumor/normal pairs from 329 adult patients. The data provided is based on the May 31, 2022 version and can be accessed at <a href="https://www.synapse.org/#!Synapse:syn17038081/wiki/585622">Synapse</a>.

## Cohort Statistics
- **Patients:** 329
- **Samples:** 694

## Available Data Types

- **COPY_NUMBER_ALTERATION**: Copy Number Alterations
- **MUTATION_EXTENDED**: Mutations
- **MRNA_EXPRESSION**: mRNA expression (RNA Seq TPM)
- **MRNA_EXPRESSION**: mRNA expression z-scores relative to all samples (log RNA Seq TPM)

## Gene Panels

- **WES** (Whole Exome): 693 samples — all genes profiled

## Available Clinical Attributes

| Attribute | Samples with Data |
|-----------|------------------|
| CANCER_TYPE_DETAILED | 693 |
| TREATMENT_CONCURRENT_TMZ | 693 |
| ESTIMATE_SCORE | 693 |
| MGMT_METHYLATION | 693 |
| MUTATION_COUNT | 693 |
| ALIQUOT_ANALYSIS_TYPE | 693 |
| FRACTION_GENOME_ALTERED | 693 |
| SURGERY_TYPE | 693 |
| CODEL_STATUS | 693 |
| SURGERY_EXTENT_OF_RESECTION | 693 |
| TREATMENT_RADIOTHERAPY | 693 |
| IDH_STATUS | 693 |
| RNA_ALIQUOT_BARCODE | 693 |
| TUMOR_GRADE | 693 |
| CANCER_TYPE | 693 |
| SAMPLE_TYPE | 693 |
| IDH_CODEL_STATUS | 693 |
| SURGERY_LATERALITY | 693 |
| ONCOTREE_CODE | 693 |
| ALKYLATING_AGENT_TX | 693 |

## Top Mutated Genes

| Gene | Altered Samples |
|------|----------------|
| TP53 | 273 |
| IDH1 | 227 |
| TTN | 147 |
| PTEN | 143 |
| ATRX | 139 |
| EGFR | 112 |
| MUC16 | 106 |
| NF1 | 97 |
| RYR2 | 81 |
| OBSCN | 70 |

## Sample Types

- **First Recurrence**: 318 samples
- **Tumor Primary**: 309 samples
- **Second Recurrence**: 49 samples
- **Third Recurrence**: 14 samples
- **Fourth recurrence**: 2 samples
- **First Metastasis**: 1 samples

## Query Tips for difg_glass

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

-- 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 = 'difg_glass'
    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 = 'difg_glass'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'OS_MONTHS');
```


▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_name FROM clinical_data_derived WHERE cancer_study_identifier IN ('difg_glass','difg_glass_2019') AND (upper(attribute_name) LIKE '%HYPERMUT%' OR upper(attribute_name) LIKE '%MUTATOR%' OR upper(attribute_name) LIKE '%TMZ%' OR upper(attribute_name) LIKE '%ALKYL%')"
}

▶ get_study_guide
{
 "study_id": "difg_glass_2019"
}

◀ result
{"rows":[{"attribute_name":"ALKYLATING_AGENT_TX"},{"attribute_name":"TREATMENT_CONCURRENT_TMZ"},{"attribute_name":"TREATMENT_TMZ"},{"attribute_name":"TREATMENT_TMZ_CYCLES"},{"attribute_name":"TREATMENT_TMZ_CYCLES_6"},{"attribute_name":"HYPERMUTATION_STATUS"},{"attribute_name":"ALKYLATING_AGENT"},{"attribute_name":"CONCURRENT_TMZ"},{"attribute_name":"TMZ_CYCLES"},{"attribute_name":"TMZ_TREATMENT"}]}

◀ result
# Study Guide: Diffuse Glioma (GLASS Consortium, Nature 2019)

**Study ID:** `difg_glass_2019`
**Cancer Type:** difg
**Description:** Whole genome or whole exome sequencing analysis of temporally separated diffuse glioma tumor/normal pairs from 222 adult patients.

## Cohort Statistics
- **Patients:** 222
- **Samples:** 445

## Available Data Types

- **GENERIC_ASSAY**: Arm-level copy number changes from GISTIC
- **MUTATION_EXTENDED**: Mutations
- **MRNA_EXPRESSION**: mRNA expression (log RNA Seq RPKM)
- **MRNA_EXPRESSION**: mRNA expression z-scores relative to all samples (log RNA Seq RPKM)

## Gene Panels

- **WES** (Whole Exome): 444 samples — all genes profiled

## Available Clinical Attributes

| Attribute | Samples with Data |
|-----------|------------------|
| TMZ_CYCLES | 444 |
| ONCOTREE_CODE | 444 |
| RADIOTHERAPY | 444 |
| SAMPLE_TYPE | 444 |
| CANCER_TYPE | 444 |
| TUMOR_LATERALITY | 444 |
| RADIOTHERAPY_FRACTIONS | 444 |
| COMMENTS | 444 |
| MGMT_METHYLATION | 444 |
| ANEUPLOIDY_SCORE | 444 |
| CHEMO_OTHER_CYCLES | 444 |
| SURGERY NUMBER | 444 |
| SOMATIC_STATUS | 444 |
| ALIQUOT_BARCODE | 444 |
| CHEMO_OTHER | 444 |
| FRACTION_GENOME_ALTERED | 444 |
| SAMPLE_CLASS | 444 |
| WHO_CLASSIFICATION | 444 |
| TMB_NONSYNONYMOUS | 444 |
| MUTATION_COUNT | 444 |

## Top Mutated Genes

| Gene | Altered Samples |
|------|----------------|
| TP53 | 206 |
| IDH1 | 174 |
| TTN | 109 |
| ATRX | 103 |
| PTEN | 101 |
| EGFR | 83 |
| MUC16 | 80 |
| NF1 | 71 |
| RYR2 | 59 |
| OBSCN | 57 |

## Sample Types

- **Tumor Primary**: 215 samples
- **First Recurrence**: 204 samples
- **Second Recurrence**: 17 samples
- **Third Recurrence**: 6 samples
- **Fourth recurrence**: 2 samples

## Query Tips for difg_glass_2019

```sql
-- Get all samples in this study
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'difg_glass_2019';

-- 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 = 'difg_glass_2019'
    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 = 'difg_glass_2019'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'OS_MONTHS');
```


▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier, attribute_name, count(*) FROM clinical_data_derived WHERE attribute_name IN ('HYPERMUTATION_STATUS','TREATMENT_TMZ','CONCURRENT_TMZ','TMZ_TREATMENT','ALKYLATING_AGENT_TX','ALKYLATING_AGENT','TREATMENT_CONCURRENT_TMZ') GROUP BY cancer_study_identifier, attribute_name ORDER BY cancer_study_identifier, attribute_name"
}

◀ result
{"rows":[{"cancer_study_identifier":"difg_glass","attribute_name":"ALKYLATING_AGENT_TX","count()":693},{"cancer_study_identifier":"difg_glass","attribute_name":"TREATMENT_CONCURRENT_TMZ","count()":693},{"cancer_study_identifier":"difg_glass","attribute_name":"TREATMENT_TMZ","count()":693},{"cancer_study_identifier":"difg_glass_2019","attribute_name":"ALKYLATING_AGENT","count()":444},{"cancer_study_identifier":"difg_glass_2019","attribute_name":"CONCURRENT_TMZ","count()":444},{"cancer_study_identifier":"difg_glass_2019","attribute_name":"HYPERMUTATION_STATUS","count()":222},{"cancer_study_identifier":"difg_glass_2019","attribute_name":"TMZ_TREATMENT","count()":444}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(*) FROM clinical_data_derived WHERE cancer_study_identifier = 'difg_glass' AND attribute_name = 'HYPERMUTATION_STATUS' GROUP BY attribute_value"
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(DISTINCT patient_unique_id) FROM clinical_data_derived WHERE cancer_study_identifier = 'difg_glass_2019' AND attribute_name = 'HYPERMUTATION_STATUS' GROUP BY attribute_value"
}

◀ result
{"rows":[{"attribute_value":"Yes","countDistinct(patient_unique_id)":35},{"attribute_value":"No","countDistinct(patient_unique_id)":187}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(DISTINCT patient_unique_id) FROM clinical_data_derived WHERE cancer_study_identifier = 'difg_glass_2019' AND attribute_name IN ('TMZ_TREATMENT','CONCURRENT_TMZ','ALKYLATING_AGENT') GROUP BY attribute_value"
}

◀ result
{"rows":[{"countDistinct(patient_unique_id)":201},{"attribute_value":"Yes","countDistinct(patient_unique_id)":168},{"attribute_value":"No","countDistinct(patient_unique_id)":88}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_name, attribute_value, count(DISTINCT patient_unique_id) as n_pat, count(DISTINCT sample_unique_id) as n_samp
FROM clinical_data_derived
WHERE cancer_study_identifier = 'difg_glass_2019' AND attribute_name IN ('TMZ_TREATMENT','CONCURRENT_TMZ','ALKYLATING_AGENT')
GROUP BY attribute_name, attribute_value
ORDER BY attribute_name, attribute_value"
}

◀ result
{"rows":[{"attribute_name":"ALKYLATING_AGENT","n_pat":135,"n_samp":160},{"attribute_name":"ALKYLATING_AGENT","attribute_value":"No","n_pat":76,"n_samp":94},{"attribute_name":"ALKYLATING_AGENT","attribute_value":"Yes","n_pat":155,"n_samp":190},{"attribute_name":"CONCURRENT_TMZ","n_pat":199,"n_samp":311},{"attribute_name":"CONCURRENT_TMZ","attribute_value":"No","n_pat":23,"n_samp":25},{"attribute_name":"CONCURRENT_TMZ","attribute_value":"Yes","n_pat":105,"n_samp":108},{"attribute_name":"TMZ_TREATMENT","n_pat":135,"n_samp":161},{"attribute_name":"TMZ_TREATMENT","attribute_value":"No","n_pat":85,"n_samp":104},{"attribute_name":"TMZ_TREATMENT","attribute_value":"Yes","n_pat":151,"n_samp":179}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH hyper AS (
    SELECT patient_unique_id, attribute_value AS hyper_status
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'difg_glass_2019' AND attribute_name = 'HYPERMUTATION_STATUS'
),
tmz AS (
    SELECT patient_unique_id, max(attribute_value = 'Yes') AS tmz_treated
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'difg_glass_2019' AND attribute_name = 'TMZ_TREATMENT' AND attribute_value != ''
    GROUP BY patient_unique_id
)
SELECT h.hyper_status, t.tmz_treated, count(DISTINCT h.patient_unique_id) as n_patients
FROM hyper h
LEFT JOIN tmz t ON h.patient_unique_id = t.patient_unique_id
GROUP BY h.hyper_status, t.tmz_treated
ORDER BY h.hyper_status, t.tmz_treated
"
}

◀ result
{"rows":[{"hyper_status":"No","tmz_treated":0,"n_patients":66},{"hyper_status":"No","tmz_treated":1,"n_patients":121},{"hyper_status":"Yes","tmz_treated":0,"n_patients":5},{"hyper_status":"Yes","tmz_treated":1,"n_patients":30}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_value FROM clinical_data_derived WHERE cancer_study_identifier = 'difg_glass_2019' AND attribute_name = 'COMMENTS' AND upper(attribute_value) LIKE '%HYPERMUT%' LIMIT 20"
}

◀ result
{"rows":[]}

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

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"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"]}}]}}

▶ get_studyviewfilter_options
{
 "studyId": "difg_glass_2019",
 "attributeIds": [
  "HYPERMUTATION_STATUS",
  "TMZ_TREATMENT"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"difg_glass_2019","attributes":[{"attributeId":"HYPERMUTATION_STATUS","displayName":"Recurrent Tumor Hypermutation Status","description":"Recurrent tumor in the tumor pairs have a mutation burden (Mutations/Mb) > 10?","datatype":"STRING","values":["No","Yes"]},{"attributeId":"TMZ_TREATMENT","displayName":"Temozolomide Treatment","description":"Indicates whether a subject received temozolomide for each surgical sample","datatype":"STRING","values":["No","NA","Yes"]}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "difg_glass_2019"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "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%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%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)
## Answer

In the [Diffuse Glioma (GLASS Consortium, Nature 2019)](https://www.cbioportal.org/study/summary?id=difg_glass_2019) study — the GLASS cohort with a dedicated hypermutation annotation — **35 of 222 patients (15.8%)** are flagged with `HYPERMUTATION_STATUS = Yes`, defined by the study as recurrent-tumor mutation burden > 10 mutations/Mb in the longitudinal tumor pair.

Cross-referencing hypermutation status with recorded temozolomide (TMZ) exposure (per-sample `TMZ_TREATMENT` field):

| Hypermutated at recurrence | Received TMZ (any sample) | Patients |
|---|---|---|
| Yes | Yes | **30** |
| Yes | No / not recorded | 5 |
| No | Yes | 121 |
| No | No / not recorded | 66 |

**30 patients developed hypermutation with a documented history of TMZ treatment.** The remaining 5 hypermutated patients had no "Yes" TMZ record in this dataset (data may be incomplete for them, since treatment fields can be under-annotated rather than confirmed-negative — see the portal's own caveat on treatment data completeness).

Note: the newer, larger [Diffuse Glioma (GLASS Consortium)](https://www.cbioportal.org/study/summary?id=difg_glass) study (329 patients) does not carry a `HYPERMUTATION_STATUS` field, so this analysis is restricted to the original 2019 GLASS release (222 patients).

You can explore the filtered cohort of hypermutated patients directly here:

[View Hypermutated GLASS Patients (StudyView)](https://www.cbioportal.org/study/summary?id=difg_glass_2019#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HYPERMUTATION_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Yes%22%7D%5D%7D%5D%7D)

From there you can add a TMZ_TREATMENT filter via the Charts menu to see the overlap interactively.