Q (sonnet): In the TCGA glioblastoma study (Cell 2013), how does methylation of the MGMT gene promoter affect the prognosis and treatment response in patients with glioblastoma?

▶ list_studies
{
 "search": "glioblastoma Cell 2013"
}

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

◀ result
{"result":[{"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"}]}

◀ 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 attr_id, description, patient_attribute
FROM clinical_attribute_meta
WHERE cancer_study_id = (SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'gbm_tcga_pub2013')
  AND (upper(attr_id) LIKE '%MGMT%' OR upper(description) LIKE '%MGMT%' OR upper(attr_id) LIKE '%METHYL%')
ORDER BY attr_id
"
}

◀ result
{"rows":[{"attr_id":"G_CIMP_METHYLATION","description":"G-CIMP  Methylation","patient_attribute":0},{"attr_id":"METHYLATION_STATUS","description":"Yes/No indicator for methylation data","patient_attribute":0},{"attr_id":"MGMT_STATUS","description":"MGMT Status","patient_attribute":0}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT attribute_value, COUNT(DISTINCT sample_unique_id) AS n_samples
FROM clinical_data_derived
WHERE cancer_study_identifier = 'gbm_tcga_pub2013'
  AND attribute_name = 'MGMT_STATUS'
GROUP BY attribute_value
ORDER BY n_samples DESC
"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
{"rows":[{"n_samples":227},{"attribute_value":"UNMETHYLATED","n_samples":180},{"attribute_value":"METHYLATED","n_samples":170}]}

◀ result
{"columns":[{"name":"internal_id","type":"Int32"},{"name":"sample_unique_id","type":"String","comment":"Globally unique sample ID: cancer_study_identifier + \"_\" + sample.stable_id. Empty for patient-level attributes. Use this for sample filtering and joins."},{"name":"patient_unique_id","type":"String","comment":"Globally unique patient ID: cancer_study_identifier + \"_\" + patient.stable_id. Present for both sample and patient-level attributes."},{"name":"attribute_name","type":"LowCardinality(String)","comment":"Clinical attribute name (e.g., SAMPLE_TYPE, CANCER_TYPE, AGE, OS_MONTHS). Use with attribute_value for filtering. AGE may be floored or capped for de-identification (e.g. all children recorded as 18, or everyone 89+ recorded as 89 or 90): before age statistics check for a pile-up at the min/max, and if present compute age from DAYS_TO_BIRTH (-days / 365.25)."},{"name":"attribute_value","type":"String","comment":"Value of the clinical attribute (String). For SAMPLE_TYPE: Primary, Metastasis, Local Recurrence, Unknown. Missing values are empty strings, so use toFloat64OrNull(attribute_value) for numeric comparisons — CAST fails on them."},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"type","type":"LowCardinality(String)","comment":"Data level: \"sample\" for sample-level attributes (e.g., SAMPLE_TYPE), \"patient\" for patient-level attributes (e.g., AGE, OS_MONTHS)."}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH mgmt AS (
    SELECT patient_unique_id,
           anyIf(attribute_value, attribute_name = 'MGMT_STATUS') AS mgmt_status
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'gbm_tcga_pub2013'
      AND attribute_name = 'MGMT_STATUS'
    GROUP BY patient_unique_id
),
os AS (
    SELECT patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'OS_MONTHS') AS os_months,
        maxIf(attribute_value, attribute_name = 'OS_STATUS') AS os_status
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'gbm_tcga_pub2013' AND attribute_name IN ('OS_MONTHS', 'OS_STATUS')
    GROUP BY patient_unique_id
)
SELECT
    mgmt.mgmt_status AS group_name,
    count() AS n_patients,
    countIf(startsWith(os.os_status, '1')) AS n_events_death,
    countIf(startsWith(os.os_status, '0')) AS n_censored,
    round(min(os.os_months),1) AS min_followup_months,
    round(max(os.os_months),1) AS max_followup_months,
    round(avg(os.os_months),1) AS mean_os_months_censored_caveat
FROM mgmt
JOIN os ON mgmt.patient_unique_id = os.patient_unique_id
WHERE mgmt.mgmt_status IN ('METHYLATED','UNMETHYLATED')
  AND os.os_months IS NOT NULL AND os.os_status != ''
GROUP BY group_name
"
}

◀ result
{"rows":[{"group_name":"UNMETHYLATED","n_patients":179,"n_events_death":131,"n_censored":48,"min_followup_months":0.1,"max_followup_months":127.6,"mean_os_months_censored_caveat":11.9},{"group_name":"METHYLATED","n_patients":169,"n_events_death":115,"n_censored":54,"min_followup_months":0.1,"max_followup_months":100,"mean_os_months_censored_caveat":16}]}

▶ resolve_and_route
{
 "studyIds": [
  "gbm_tcga_pub2013"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"gbm_tcga_pub2013","name":"Glioblastoma (TCGA, Cell 2013)","sampleCount":577,"studyViewUrl":"https://www.cbioportal.org/study?id=gbm_tcga_pub2013","metadata":{"clinicalAttributeIds":["AGE","CANCER_TYPE","CANCER_TYPE_DETAILED","DFS_MONTHS","DFS_STATUS","EXPRESSION_SUBTYPE","FRACTION_GENOME_ALTERED","G_CIMP_METHYLATION","IDH1_MUTATION","METHYLATION_STATUS","MGMT_STATUS","MUTATION_COUNT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","THERAPY","TMB_NONSYNONYMOUS"],"molecularProfileIds":["gbm_tcga_pub2013_gistic","gbm_tcga_pub2013_linear_CNA","gbm_tcga_pub2013_mutations","gbm_tcga_pub2013_protein_quantification","gbm_tcga_pub2013_protein_quantification_zscores","gbm_tcga_pub2013_rna_seq_v2_mrna","gbm_tcga_pub2013_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pub2013_rna_seq_v2_mrna_median_all_sample_Zscores"],"heatmapProfileIds":["gbm_tcga_pub2013_protein_quantification_zscores","gbm_tcga_pub2013_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pub2013_rna_seq_v2_mrna_median_all_sample_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein"]}}]}}

▶ get_studyviewfilter_options
{
 "studyId": "gbm_tcga_pub2013",
 "attributeIds": [
  "MGMT_STATUS"
 ]
}

◀ result
{"success":false,"error":"Internal Server Error","details":{"status":500,"response":{"req":{"method":"POST","url":"https://www.cbioportal.org/api/clinical-data-counts/fetch","data":{"attributes":[{"attributeId":"MGMT_STATUS","values":[]}],"studyViewFilter":{"studyIds":["gbm_tcga_pub2013"]}},"headers":{"user-agent":"node-superagent/3.8.3","accept":"application/json","content-type":"application/json"}},"header":{"cache-control":"no-cache, no-store, max-age=0, must-revalidate","content-encoding":"gzip","content-type":"application/json","date":"Sat, 26 Sep 2026 16:45:29 GMT","elapsed-time":"1","expires":"0","pragma":"no-cache","strict-transport-security":"max-age=31536000 ; includeSubDomains","vary":"origin,access-control-request-method,access-control-request-headers,accept-encoding","x-content-type-options":"nosniff","x-frame-options":"DENY","x-xss-protection":"0","connection":"close","transfer-encoding":"chunked"},"status":500,"text":"{\"timestamp\":1790441129892,\"status\":500,\"error\":\"Internal Server Error\",\"path\":\"/api/clinical-data-counts/fetch\"}"}}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "gbm_tcga_pub2013"
 ],
 "tab": "survival",
 "groups": [
  {
   "name": "MGMT Methylated",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MGMT_STATUS",
      "values": [
       {
        "value": "METHYLATED"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "MGMT Unmethylated",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MGMT_STATUS",
      "values": [
       {
        "value": "UNMETHYLATED"
       }
      ]
     }
    ]
   }
  }
 ]
}

▶ navigate_to_study_view
{
 "studyIds": [
  "gbm_tcga_pub2013"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "attributeId": "MGMT_STATUS",
    "values": [
     {
      "value": "METHYLATED"
     },
     {
      "value": "UNMETHYLATED"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":false,"error":"Internal Server Error","details":{"status":500,"response":{"req":{"method":"POST","url":"https://www.cbioportal.org/api/filtered-samples/fetch","data":{"clinicalDataFilters":[{"attributeId":"MGMT_STATUS","values":[{"value":"UNMETHYLATED"}]}],"studyIds":["gbm_tcga_pub2013"]},"headers":{"user-agent":"node-superagent/3.8.3","accept":"application/json","content-type":"application/json"}},"header":{"cache-control":"no-cache, no-store, max-age=0, must-revalidate","content-encoding":"gzip","content-type":"application/json","date":"Sat, 26 Sep 2026 16:46:03 GMT","elapsed-time":"1","expires":"0","pragma":"no-cache","strict-transport-security":"max-age=31536000 ; includeSubDomains","vary":"origin,access-control-request-method,access-control-request-headers,accept-encoding","x-content-type-options":"nosniff","x-frame-options":"DENY","x-xss-protection":"0","connection":"close","transfer-encoding":"chunked"},"status":500,"text":"{\"timestamp\":1790441163733,\"status\":500,\"error\":\"Internal Server Error\",\"path\":\"/api/filtered-samples/fetch\"}"}}}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=gbm_tcga_pub2013#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22MGMT_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22METHYLATED%22%7D%2C%7B%22value%22%3A%22UNMETHYLATED%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pub2013#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22MGMT_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22METHYLATED%22%7D%2C%7B%22value%22%3A%22UNMETHYLATED%22%7D%5D%7D%5D%7D","data":{"studyIds":["gbm_tcga_pub2013"],"studies":[{"studyId":"gbm_tcga_pub2013","name":"Glioblastoma (TCGA, Cell 2013)","sampleCount":577}],"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."}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "gbm_tcga_pub2013"
 ],
 "tab": "survival",
 "groups": [
  {
   "name": "MGMT Methylated",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MGMT_STATUS",
      "values": [
       {
        "value": "METHYLATED"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "MGMT Unmethylated",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MGMT_STATUS",
      "values": [
       {
        "value": "UNMETHYLATED"
       }
      ]
     }
    ]
   }
  }
 ]
}

◀ result
{"success":false,"error":"Internal Server Error","details":{"status":500,"response":{"req":{"method":"POST","url":"https://www.cbioportal.org/api/filtered-samples/fetch","data":{"clinicalDataFilters":[{"attributeId":"MGMT_STATUS","values":[{"value":"METHYLATED"}]}],"studyIds":["gbm_tcga_pub2013"]},"headers":{"user-agent":"node-superagent/3.8.3","accept":"application/json","content-type":"application/json"}},"header":{"cache-control":"no-cache, no-store, max-age=0, must-revalidate","content-encoding":"gzip","content-type":"application/json","date":"Sat, 26 Sep 2026 16:46:37 GMT","elapsed-time":"0","expires":"0","pragma":"no-cache","strict-transport-security":"max-age=31536000 ; includeSubDomains","vary":"origin,access-control-request-method,access-control-request-headers,accept-encoding","x-content-type-options":"nosniff","x-frame-options":"DENY","x-xss-protection":"0","connection":"close","transfer-encoding":"chunked"},"status":500,"text":"{\"timestamp\":1790441197852,\"status\":500,\"error\":\"Internal Server Error\",\"path\":\"/api/filtered-samples/fetch\"}"}}}

═ answer (success)
The comparison API is currently erroring on the portal's side — this is not something I can resolve by retrying differently. Here's the full picture I can give you.

## Important scope note

cBioPortal's database stores clinical/genomic *data*, not clinical *conclusions*. It cannot tell you "how methylation affects treatment response" as a causal or mechanistic statement — that is a finding from the clinical trial/radiation-oncology literature (the seminal reference is Hegi et al., *NEJM* 2005, showing MGMT promoter methylation predicts benefit from temozolomide + radiotherapy). I have not consulted that literature here and won't present it as verified by this database. What I *can* show you is the actual MGMT methylation and survival data recorded in this specific TCGA cohort.

## What's in the Glioblastoma (TCGA, Cell 2013) study

577 samples total. MGMT promoter methylation status (`MGMT_STATUS`) is available for 350 samples:

| MGMT status | Samples | Patients with OS data | Deaths | Censored (alive/lost to follow-up) |
|---|---|---|---|---|
| Methylated | 170 | 169 | 115 | 54 |
| Unmethylated | 180 | 179 | 131 | 48 |
| Not recorded | 227 | — | — | — |

Descriptive follow-up (not a survival estimate): the unmethylated group has a higher proportion of observed deaths (131/179, 73%) than the methylated group (115/169, 68%), and mean observed OS is numerically lower in the unmethylated group (11.9 vs. 16.0 months) — but **this is a raw, censored-data average, not a Kaplan-Meier median, and I have not run a log-rank test or Cox model**, so I cannot state whether this difference is statistically significant or report a hazard ratio. This dataset also does not distinguish temozolomide-treated vs. untreated patients in a way I queried here, so I cannot speak to treatment-response interaction from this data alone.

To get a proper statistically-tested comparison (Kaplan-Meier curves, log-rank p-value), use the portal's own Group Comparison Survival tool. I attempted to generate a direct link for you, but the cBioPortal comparison API is currently returning a server error — you can reach the same view manually:

1. Open the [Glioblastoma (TCGA, Cell 2013) study](https://www.cbioportal.org/study/summary?id=gbm_tcga_pub2013) — this link is filtered to samples with definitive METHYLATED/UNMETHYLATED MGMT status.
2. Click the "MGMT Status" chart, select the two groups, and choose "Compare" to open Group Comparison, then go to the Survival tab.

Let me know if you'd like me to retry the direct comparison link later, or if you'd like the raw per-patient MGMT/OS table for your own analysis.