Q (sonnet): How does methylation of the MGMT gene promoter affect the prognosis and treatment response in patients with glioblastoma?

▶ resolve_and_route
{
 "studyKeywords": [
  "glioblastoma",
  "TCGA"
 ]
}

▶ list_guides
{}

◀ result
{"success":true,"message":"Found 5 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":5,"studiesWithMetadata":[{"studyId":"gbm_tcga","name":"Glioblastoma Multiforme (TCGA, Firehose Legacy)","sampleCount":619,"studyViewUrl":"https://www.cbioportal.org/study?id=gbm_tcga","metadata":{"clinicalAttributeIds":["AGE","CANCER_TYPE","CANCER_TYPE_DETAILED","DAYS_TO_COLLECTION","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DAYS_TO_SPECIMEN_COLLECTION","DFS_MONTHS","DFS_STATUS","DISEASE_CODE","ECOG_SCORE","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","HISTOLOGICAL_DIAGNOSIS","HISTORY_LGG_DX_OF_BRAIN_TISSUE","HISTORY_NEOADJUVANT_TRTYN","HISTORY_OTHER_MALIGNANCY","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","INITIAL_PATHOLOGIC_DX_YEAR","IS_FFPE","KARNOFSKY_PERFORMANCE_SCORE","LONGEST_DIMENSION","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT_OTHER","METHOD_OF_SAMPLE_PROCUREMENT","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","OCT_EMBEDDED","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_METHOD_OF_SAMPLE_PROCUREMENT","OTHER_PATIENT_ID","OTHER_SAMPLE_ID","PATHOLOGY_REPORT_FILE_NAME","PATHOLOGY_REPORT_UUID","PERFORMANCE_STATUS_TIMING","PHARMACEUTICAL_TX_ADJUVANT","PRIMARY_SITE_PATIENT","PROJECT_CODE","PROSPECTIVE_COLLECTION","RACE","RADIATION_TREATMENT_ADJUVANT","RETROSPECTIVE_COLLECTION","SAMPLE_COUNT","SAMPLE_INITIAL_WEIGHT","SAMPLE_TYPE","SAMPLE_TYPE_ID","SEX","SHORTEST_DIMENSION","SITE_OF_TUMOR_TISSUE","SOMATIC_STATUS","SPECIMEN_CURRENT_WEIGHT","SPECIMEN_FREEZING_METHOD","SPECIMEN_SECOND_LONGEST_DIMENSION","TIME_BETWEEN_CLAMPING_AND_FREEZING","TIME_BETWEEN_EXCISION_AND_FREEZING","TISSUE_SOURCE_SITE","TMB_NONSYNONYMOUS","TREATMENT_OUTCOME_FIRST_COURSE","TUMOR_STATUS","VIAL_NUMBER"],"molecularProfileIds":["gbm_tcga_gistic","gbm_tcga_linear_CNA","gbm_tcga_methylation_hm27","gbm_tcga_methylation_hm450","gbm_tcga_mrna","gbm_tcga_mrna_U133","gbm_tcga_mrna_U133_Zscores","gbm_tcga_mrna_U133_all_sample_Zscores","gbm_tcga_mrna_median_Zscores","gbm_tcga_mrna_median_all_sample_Zscores","gbm_tcga_mutations","gbm_tcga_rna_seq_v2_mrna","gbm_tcga_rna_seq_v2_mrna_median_Zscores","gbm_tcga_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_tcga_rppa","gbm_tcga_rppa_Zscores"],"heatmapProfileIds":["gbm_tcga_methylation_hm27","gbm_tcga_methylation_hm450","gbm_tcga_mrna_U133_Zscores","gbm_tcga_mrna_U133_all_sample_Zscores","gbm_tcga_mrna_median_Zscores","gbm_tcga_mrna_median_all_sample_Zscores","gbm_tcga_rna_seq_v2_mrna_median_Zscores","gbm_tcga_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_tcga_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","dna_methylation"]}},{"studyId":"gbm_tcga_gdc","name":"Glioblastoma Multiforme (TCGA GDC, 2025)","sampleCount":611,"studyVi … (5985 more chars)

◀ 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"}]}

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-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)

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

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"gbm_tcga_pub2013","attributes":[{"attributeId":"G_CIMP_METHYLATION","displayName":"G-CIMP  Methylation","description":"G-CIMP  Methylation","datatype":"STRING","values":["NA","non-G-CIMP","G-CIMP"]},{"attributeId":"METHYLATION_STATUS","displayName":"Methylation Status","description":"Yes/No indicator for methylation data","datatype":"STRING","values":["CL_4","CL_3","NA","CL_2","CL_1","G-CIMP","CL_6"]},{"attributeId":"MGMT_STATUS","displayName":"MGMT Status","description":"MGMT Status","datatype":"STRING","values":["NA","METHYLATED","UNMETHYLATED"]},{"attributeId":"THERAPY","displayName":"therapy","description":"therapy","datatype":"STRING","values":["Unspecified Radiation","Standard Radiation, TMZ Chemo","Nonstandard Radiation, TMZ Chemo","Unspecified Therapy","Alkylating Chemo","Nonstandard Radiation","NA","Unspecified Radiation, Alkylating Chemo","Alkylating Chemoradiation, Alkylating Chemo","Nonstandard Radiation, Alkylating Chemo","TMZ Chemo","Standard Radiation, Alkylating Chemo","TMZ Chemoradiation, TMZ Chemo","Alkylating Chemoradiation, TMZ Chemo","Standard Radiation","Unspecified Radiation, TMZ Chemo"]}]}}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
  attr.attribute_value AS mgmt_status,
  COUNT(DISTINCT attr.sample_unique_id) AS n_samples,
  COUNT(DISTINCT CASE WHEN os.attribute_value = '1:DECEASED' THEN attr.sample_unique_id END) AS n_events,
  COUNT(DISTINCT CASE WHEN os.attribute_value = '0:LIVING' THEN attr.sample_unique_id END) AS n_censored
FROM clinical_data_derived attr
LEFT JOIN clinical_data_derived os
  ON attr.sample_unique_id = os.sample_unique_id
  AND os.attribute_name = 'OS_STATUS'
  AND os.cancer_study_identifier = 'gbm_tcga_pub2013'
WHERE attr.attribute_name = 'MGMT_STATUS'
  AND attr.cancer_study_identifier = 'gbm_tcga_pub2013'
  AND attr.attribute_value IN ('METHYLATED','UNMETHYLATED')
GROUP BY mgmt_status
"
}

◀ result
{"rows":[{"mgmt_status":"UNMETHYLATED","n_samples":180,"n_events":0,"n_censored":0},{"mgmt_status":"METHYLATED","n_samples":170,"n_events":0,"n_censored":0}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT attribute_name, attribute_value, COUNT(*) 
FROM clinical_data_derived
WHERE cancer_study_identifier = 'gbm_tcga_pub2013'
  AND attribute_name IN ('OS_STATUS','OS_MONTHS')
GROUP BY attribute_name, attribute_value
ORDER BY attribute_name
LIMIT 20
"
}

◀ result
{"rows":[{"attribute_name":"OS_MONTHS","attribute_value":"27.8","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"11.6","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"7.6","COUNT()":9},{"attribute_name":"OS_MONTHS","attribute_value":"37","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"22.2","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"6.8","COUNT()":3},{"attribute_name":"OS_MONTHS","attribute_value":"15.2","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"16.4","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"9.3","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"30.2","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"72.4","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"0.4","COUNT()":3},{"attribute_name":"OS_MONTHS","attribute_value":"10.8","COUNT()":5},{"attribute_name":"OS_MONTHS","attribute_value":"3.2","COUNT()":5},{"attribute_name":"OS_MONTHS","attribute_value":"0.5","COUNT()":2},{"attribute_name":"OS_MONTHS","attribute_value":"26.7","COUNT()":2},{"attribute_name":"OS_MONTHS","attribute_value":"3.3","COUNT()":3},{"attribute_name":"OS_MONTHS","attribute_value":"10.9","COUNT()":1},{"attribute_name":"OS_MONTHS","attribute_value":"25.1","COUNT()":2},{"attribute_name":"OS_MONTHS","attribute_value":"15.3","COUNT()":2}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT attribute_value, COUNT(*) 
FROM clinical_data_derived
WHERE cancer_study_identifier = 'gbm_tcga_pub2013'
  AND attribute_name = 'OS_STATUS'
GROUP BY attribute_value
"
}

◀ result
{"rows":[{"COUNT()":36},{"attribute_value":"0:LIVING","COUNT()":124},{"attribute_value":"1:DECEASED","COUNT()":417}]}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ 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": "
SELECT
  mgmt.attribute_value AS mgmt_status,
  COUNT(DISTINCT mgmt.patient_unique_id) AS n_patients,
  COUNT(DISTINCT CASE WHEN os.attribute_value = '1:DECEASED' THEN mgmt.patient_unique_id END) AS n_events,
  COUNT(DISTINCT CASE WHEN os.attribute_value = '0:LIVING' THEN mgmt.patient_unique_id END) AS n_censored,
  MIN(toFloat64OrNull(osm.attribute_value)) AS min_followup_months,
  MAX(toFloat64OrNull(osm.attribute_value)) AS max_followup_months
FROM clinical_data_derived mgmt
LEFT JOIN clinical_data_derived os
  ON mgmt.patient_unique_id = os.patient_unique_id
  AND os.attribute_name = 'OS_STATUS'
LEFT JOIN clinical_data_derived osm
  ON mgmt.patient_unique_id = osm.patient_unique_id
  AND osm.attribute_name = 'OS_MONTHS'
WHERE mgmt.attribute_name = 'MGMT_STATUS'
  AND mgmt.cancer_study_identifier = 'gbm_tcga_pub2013'
  AND mgmt.attribute_value IN ('METHYLATED','UNMETHYLATED')
GROUP BY mgmt_status
"
}

◀ result
{"rows":[{"mgmt_status":"UNMETHYLATED","n_patients":180,"n_events":131,"n_censored":48,"min_followup_months":0,"max_followup_months":127.6},{"mgmt_status":"METHYLATED","n_patients":170,"n_events":115,"n_censored":54,"min_followup_months":0,"max_followup_months":100}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
  mgmt.attribute_value AS mgmt_status,
  th.attribute_value AS therapy,
  COUNT(DISTINCT mgmt.patient_unique_id) AS n_patients
FROM clinical_data_derived mgmt
LEFT JOIN clinical_data_derived th
  ON mgmt.patient_unique_id = th.patient_unique_id
  AND th.attribute_name = 'THERAPY'
WHERE mgmt.attribute_name = 'MGMT_STATUS'
  AND mgmt.cancer_study_identifier = 'gbm_tcga_pub2013'
  AND mgmt.attribute_value IN ('METHYLATED','UNMETHYLATED')
  AND th.attribute_value != ''
GROUP BY mgmt_status, therapy
ORDER BY mgmt_status, n_patients DESC
"
}

◀ result
{"rows":[{"mgmt_status":"METHYLATED","therapy":"TMZ Chemoradiation, TMZ Chemo","n_patients":83},{"mgmt_status":"METHYLATED","therapy":"Standard Radiation, TMZ Chemo","n_patients":30},{"mgmt_status":"METHYLATED","therapy":"Unspecified Radiation","n_patients":20},{"mgmt_status":"METHYLATED","therapy":"Standard Radiation","n_patients":8},{"mgmt_status":"METHYLATED","therapy":"Nonstandard Radiation, TMZ Chemo","n_patients":7},{"mgmt_status":"METHYLATED","therapy":"Unspecified Therapy","n_patients":6},{"mgmt_status":"METHYLATED","therapy":"Alkylating Chemo","n_patients":5},{"mgmt_status":"METHYLATED","therapy":"Standard Radiation, Alkylating Chemo","n_patients":5},{"mgmt_status":"METHYLATED","therapy":"Nonstandard Radiation","n_patients":3},{"mgmt_status":"METHYLATED","therapy":"Alkylating Chemoradiation, Alkylating Chemo","n_patients":2},{"mgmt_status":"METHYLATED","therapy":"Unspecified Radiation, TMZ Chemo","n_patients":1},{"mgmt_status":"UNMETHYLATED","therapy":"TMZ Chemoradiation, TMZ Chemo","n_patients":80},{"mgmt_status":"UNMETHYLATED","therapy":"Standard Radiation, TMZ Chemo","n_patients":30},{"mgmt_status":"UNMETHYLATED","therapy":"Unspecified Radiation","n_patients":21},{"mgmt_status":"UNMETHYLATED","therapy":"Standard Radiation","n_patients":16},{"mgmt_status":"UNMETHYLATED","therapy":"Nonstandard Radiation, TMZ Chemo","n_patients":8},{"mgmt_status":"UNMETHYLATED","therapy":"Unspecified Therapy","n_patients":7},{"mgmt_status":"UNMETHYLATED","therapy":"Nonstandard Radiation","n_patients":7},{"mgmt_status":"UNMETHYLATED","therapy":"Standard Radiation, Alkylating Chemo","n_patients":6},{"mgmt_status":"UNMETHYLATED","therapy":"TMZ Chemo","n_patients":3},{"mgmt_status":"UNMETHYLATED","therapy":"Alkylating Chemoradiation, TMZ Chemo","n_patients":2}]}

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

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6ab4301ac2115c492d884961","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6ab4301ac2115c492d884961","data":{"description":"Group comparison (2 custom groups)","studies":["gbm_tcga_pub2013"],"totalGroups":2,"groups":[{"name":"MGMT Methylated","sampleCount":170},{"name":"MGMT Unmethylated","sampleCount":180}],"studyViewUrl":"https://www.cbioportal.org/study?id=gbm_tcga_pub2013","groupUrls":[{"groupName":"MGMT Methylated","url":"https://www.cbioportal.org/study?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%5D%7D%5D%7D"},{"groupName":"MGMT Unmethylated","url":"https://www.cbioportal.org/study?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%22UNMETHYLATED%22%7D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing MGMT Methylated (170) vs MGMT Unmethylated (180)."}}

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

◀ 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."}}

═ answer (success)
## Scoping note

This is fundamentally a clinical/biological interpretation question, and I can't answer it from general knowledge or make prognostic claims. What I *can* do is show you the actual cohort data in cBioPortal and route you to tools that compute (rather than fabricate) the relevant statistics.

## What's available: TCGA GBM (Cell 2013) cohort

This is the only glioblastoma study in this cBioPortal instance with an explicit `MGMT_STATUS` clinical attribute (METHYLATED / UNMETHYLATED), derived from promoter methylation array data.

**Cohort composition (n=350 patients with defined MGMT status):**

| MGMT Status | N patients | N deaths (events) | N censored (alive) | Follow-up range (months) |
|---|---|---|---|---|
| Methylated | 170 | 115 | 54 | 0 – 100 |
| Unmethylated | 180 | 131 | 48 | 0 – 127.6 |

I can't report a median OS or hazard ratio here — that requires Kaplan-Meier estimation (to properly handle the censored/still-living patients), which ClickHouse cannot compute. The link below opens the actual KM curves with log-rank test in cBioPortal.

**Treatment context** (from the `THERAPY` attribute — not treatment *response*, only therapy received): the most common regimen in both groups is "TMZ Chemoradiation + TMZ Chemo" (temozolomide-based), given to 83/170 methylated and 80/180 unmethylated patients. This dataset does not contain a treatment-response/outcome variable (e.g., RECIST response) — only OS/DFS and therapy category — so I cannot construct a response-rate comparison here.

## Links

[Kaplan-Meier Survival Comparison: MGMT Methylated vs Unmethylated](https://www.cbioportal.org/comparison/survival?comparisonId=6ab4301ac2115c492d884961) — Kaplan-Meier survival analysis comparing MGMT Methylated (170) vs MGMT Unmethylated (180). Includes the log-rank test p-value computed by cBioPortal itself.

[Study View — GBM (Cell 2013) filtered to MGMT-annotated patients](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) — 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.

**On the underlying biology/prognosis question** (does methylation silence MGMT and improve alkylating-agent/temozolomide response?): that is a well-established finding in the clinical literature, but I'm not able to assert it as fact from this database query alone — the KM/log-rank result in the survival link above is the actual evidence this specific cohort provides. Please consult primary literature or a clinician for treatment-relevant interpretation.