Q (sonnet-5.5): 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? ▶ read_guide { "uri": "cbioportal://statistical-tests-guide" } ▶ read_guide { "uri": "cbioportal://clinical-data-guide" } ▶ list_studies { "search": "glioblastoma" } ◀ 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) ◀ 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) ◀ result {"result":[{"cancer_study_identifier":"gbm_tcga","name":"Glioblastoma Multiforme (TCGA, Firehose Legacy)","type_of_cancer_id":"difg","sample_count":619,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga"},{"cancer_study_identifier":"gbm_tcga_gdc","name":"Glioblastoma Multiforme (TCGA GDC, 2025)","type_of_cancer_id":"difg","sample_count":611,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_gdc"},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","type_of_cancer_id":"difg","sample_count":592,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"gbm_tcga_pub2013","name":"Glioblastoma (TCGA, Cell 2013)","type_of_cancer_id":"difg","sample_count":577,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pub2013"},{"cancer_study_identifier":"gbm_tcga_pub","name":"Glioblastoma (TCGA, Nature 2008)","type_of_cancer_id":"difg","sample_count":206,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pub"},{"cancer_study_identifier":"gbm_cptac_2021","name":"Glioblastoma (CPTAC, Cell 2021)","type_of_cancer_id":"difg","sample_count":99,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_cptac_2021"},{"cancer_study_identifier":"gbm_columbia_2019","name":"Glioblastoma (Columbia, Nat Med. 2019)","type_of_cancer_id":"difg","sample_count":42,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_columbia_2019"},{"cancer_study_identifier":"gbm_iatlas_prins_2019","name":"Glioblastoma (Randomized Clinical Trial, Nat Med. 2019) - iAtlas Harmonized","type_of_cancer_id":"adifg","sample_count":30,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_iatlas_prins_2019"}]} ▶ 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') ORDER BY attr_id" } ▶ get_study_guide { "study_id": "gbm_tcga_pub2013" } ◀ result {"rows":[{"attr_id":"AGE","description":"Age at which a condition or disease was first diagnosed.","patient_attribute":1},{"attr_id":"CANCER_TYPE","description":"Cancer Type","patient_attribute":0},{"attr_id":"CANCER_TYPE_DETAILED","description":"Cancer Type Detailed","patient_attribute":0},{"attr_id":"DFS_MONTHS","description":"Disease free (months) since initial treatment.","patient_attribute":1},{"attr_id":"DFS_STATUS","description":"Disease free status since initial treatment.","patient_attribute":1},{"attr_id":"EXPRESSION_SUBTYPE","description":"Gene Expression Subtype","patient_attribute":0},{"attr_id":"FRACTION_GENOME_ALTERED","description":"Fraction Genome Altered","patient_attribute":0},{"attr_id":"G_CIMP_METHYLATION","description":"G-CIMP Methylation","patient_attribute":0},{"attr_id":"IDH1_MUTATION","description":"IDH1 mutation is present","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},{"attr_id":"MUTATION_COUNT","description":"Mutation Count","patient_attribute":0},{"attr_id":"ONCOTREE_CODE","description":"Oncotree Code","patient_attribute":0},{"attr_id":"OS_MONTHS","description":"Overall survival in months since initial diagonosis.","patient_attribute":1},{"attr_id":"OS_STATUS","description":"Overall patient survival status.","patient_attribute":1},{"attr_id":"SAMPLE_COUNT","description":"Number of Samples Per Patient","patient_attribute":1},{"attr_id":"SAMPLE_TYPE","description":"The type of sample (i.e., normal, primary, met, recurrence).","patient_attribute":0},{"attr_id":"SEX","description":"Sex","patient_attribute":1},{"attr_id":"SOMATIC_STATUS","description":"Somatic Status","patient_attribute":0},{"attr_id":"THERAPY","description":"therapy","patient_attribute":1},{"attr_id":"TMB_NONSYNONYMOUS","description":"TMB (nonsynonymous)","patient_attribute":0}]} ◀ result # Study Guide: Glioblastoma (TCGA, Cell 2013) **Study ID:** `gbm_tcga_pub2013` **Cancer Type:** difg **Description:** Whole-exome and/or whole-genome sequencing of 291 of the 577 glioblastoma tumor/normal pairs. The Cancer Genome Atlas (TCGA) Glioblastoma Project. ## Cohort Statistics - **Patients:** 577 - **Samples:** 578 ## Available Data Types - **PROTEIN_LEVEL**: Protein expression (RPPA) - **PROTEIN_LEVEL**: Protein expression z-scores (RPPA) - **COPY_NUMBER_ALTERATION**: Putative copy-number alterations from GISTIC - **COPY_NUMBER_ALTERATION**: Capped relative linear copy-number values - **MUTATION_EXTENDED**: Mutations - **MRNA_EXPRESSION**: mRNA expression (RNA Seq V2 RSEM) - **MRNA_EXPRESSION**: mRNA expression z-scores relative to diploid samples (RNA Seq V2 RSEM) - **MRNA_EXPRESSION**: mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM) ## Gene Panels - **WES** (Whole Exome): 572 samples — all genes profiled ## Available Clinical Attributes | Attribute | Samples with Data | |-----------|------------------| | METHYLATION_STATUS | 577 | | IDH1_MUTATION | 577 | | CANCER_TYPE_DETAILED | 577 | | MUTATION_COUNT | 577 | | ONCOTREE_CODE | 577 | | SAMPLE_TYPE | 577 | | CANCER_TYPE | 577 | | EXPRESSION_SUBTYPE | 577 | | G_CIMP_METHYLATION | 577 | | MGMT_STATUS | 577 | | TMB_NONSYNONYMOUS | 577 | | FRACTION_GENOME_ALTERED | 577 | | SOMATIC_STATUS | 577 | | AGE | 1 | | OS_MONTHS | 1 | | DFS_STATUS | 1 | | SEX | 1 | | SAMPLE_COUNT | 1 | | DFS_MONTHS | 1 | | THERAPY | 1 | ## Top Mutated Genes | Gene | Altered Samples | |------|----------------| | PTEN | 69 | | EGFR | 61 | | TP53 | 59 | | TTN | 58 | | MUC16 | 36 | | PIK3R1 | 32 | | NF1 | 27 | | SPTA1 | 26 | | PIK3CA | 26 | | PCLO | 24 | ## Sample Types - **Primary**: 543 samples - **Unknown**: 34 samples ## Query Tips for gbm_tcga_pub2013 ```sql -- Get all samples in this study SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'gbm_tcga_pub2013'; -- 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 = 'gbm_tcga_pub2013' 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 = 'gbm_tcga_pub2013' AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'OS_MONTHS'); ``` ▶ clickhouse_run_select_query { "query": "WITH m AS (SELECT sample_unique_id, patient_unique_id, anyIf(attribute_value, attribute_name='MGMT_STATUS') AS mgmt FROM clinical_data_derived WHERE cancer_study_identifier='gbm_tcga_pub2013' AND attribute_name='MGMT_STATUS' GROUP BY sample_unique_id, patient_unique_id), os AS (SELECT patient_unique_id, maxIf(toFloat64OrNull(attribute_value), attribute_name='OS_MONTHS') AS osm, maxIf(attribute_value, attribute_name='OS_STATUS') AS oss, maxIf(attribute_value, attribute_name='THERAPY') AS th FROM clinical_data_derived WHERE cancer_study_identifier='gbm_tcga_pub2013' AND attribute_name IN ('OS_MONTHS','OS_STATUS','THERAPY') GROUP BY patient_unique_id) SELECT m.mgmt, count() AS n_samples, countIf(os.oss!='') AS with_os, countIf(startsWith(os.oss,'1')) AS events, countIf(startsWith(os.oss,'0')) AS censored, countIf(os.th!='') AS with_therapy FROM m LEFT JOIN os ON m.patient_unique_id=os.patient_unique_id GROUP BY m.mgmt" } ◀ result {"rows":[{"n_samples":227,"with_os":193,"events":171,"censored":22,"with_therapy":193},{"mgmt":"UNMETHYLATED","n_samples":180,"with_os":179,"events":131,"censored":48,"with_therapy":180},{"mgmt":"METHYLATED","n_samples":170,"with_os":169,"events":115,"censored":54,"with_therapy":170}]} ▶ clickhouse_run_select_query { "query": "SELECT attribute_name, attribute_value, count() c FROM clinical_data_derived WHERE cancer_study_identifier='gbm_tcga_pub2013' AND attribute_name IN ('THERAPY','OS_STATUS','DFS_STATUS') GROUP BY 1,2 ORDER BY 1,3 DESC LIMIT 30" } ◀ result {"rows":[{"attribute_name":"DFS_STATUS","attribute_value":"1:Recurred/Progressed","c":344},{"attribute_name":"DFS_STATUS","attribute_value":"0:DiseaseFree","c":199},{"attribute_name":"DFS_STATUS","c":34},{"attribute_name":"OS_STATUS","attribute_value":"1:DECEASED","c":417},{"attribute_name":"OS_STATUS","attribute_value":"0:LIVING","c":124},{"attribute_name":"OS_STATUS","c":36},{"attribute_name":"THERAPY","attribute_value":"TMZ Chemoradiation, TMZ Chemo","c":217},{"attribute_name":"THERAPY","attribute_value":"Standard Radiation, TMZ Chemo","c":88},{"attribute_name":"THERAPY","attribute_value":"Unspecified Radiation","c":60},{"attribute_name":"THERAPY","attribute_value":"Standard Radiation","c":57},{"attribute_name":"THERAPY","c":34},{"attribute_name":"THERAPY","attribute_value":"Nonstandard Radiation","c":30},{"attribute_name":"THERAPY","attribute_value":"Standard Radiation, Alkylating Chemo","c":28},{"attribute_name":"THERAPY","attribute_value":"Nonstandard Radiation, TMZ Chemo","c":20},{"attribute_name":"THERAPY","attribute_value":"Unspecified Therapy","c":18},{"attribute_name":"THERAPY","attribute_value":"Alkylating Chemo","c":7},{"attribute_name":"THERAPY","attribute_value":"Nonstandard Radiation, Alkylating Chemo","c":6},{"attribute_name":"THERAPY","attribute_value":"TMZ Chemo","c":4},{"attribute_name":"THERAPY","attribute_value":"Alkylating Chemoradiation, Alkylating Chemo","c":4},{"attribute_name":"THERAPY","attribute_value":"Alkylating Chemoradiation, TMZ Chemo","c":2},{"attribute_name":"THERAPY","attribute_value":"Unspecified Radiation, Alkylating Chemo","c":1},{"attribute_name":"THERAPY","attribute_value":"Unspecified Radiation, TMZ Chemo","c":1}]} ▶ clickhouse_run_select_query { "query": "WITH m AS (SELECT patient_unique_id, anyIf(attribute_value, attribute_name='MGMT_STATUS') AS mgmt 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 osm, maxIf(attribute_value, attribute_name='OS_STATUS') AS oss, maxIf(attribute_value, attribute_name='THERAPY') AS th FROM clinical_data_derived WHERE cancer_study_identifier='gbm_tcga_pub2013' AND attribute_name IN ('OS_MONTHS','OS_STATUS','THERAPY') GROUP BY patient_unique_id) SELECT m.mgmt, if(positionCaseInsensitive(os.th,'TMZ')>0,'TMZ','No TMZ') AS tmz, count() n, countIf(startsWith(os.oss,'1')) events, countIf(startsWith(os.oss,'0')) censored, min(os.osm) mn, max(os.osm) mx FROM m JOIN os ON m.patient_unique_id=os.patient_unique_id WHERE m.mgmt IN ('METHYLATED','UNMETHYLATED') AND os.oss!='' GROUP BY 1,2 ORDER BY 1,2" } ◀ result {"rows":[{"mgmt":"METHYLATED","tmz":"No TMZ","n":48,"events":40,"censored":8,"mn":0.1,"mx":100},{"mgmt":"METHYLATED","tmz":"TMZ","n":121,"events":75,"censored":46,"mn":2,"mx":92.6},{"mgmt":"UNMETHYLATED","tmz":"No TMZ","n":56,"events":47,"censored":9,"mn":0.1,"mx":127.6},{"mgmt":"UNMETHYLATED","tmz":"TMZ","n":123,"events":84,"censored":39,"mn":0.9,"mx":82.6}]} ▶ get_studyviewfilter_options { "studyId": "gbm_tcga_pub2013", "attributeIds": [ "MGMT_STATUS" ] } ◀ result {"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"gbm_tcga_pub2013","attributes":[{"attributeId":"MGMT_STATUS","displayName":"MGMT Status","description":"MGMT Status","datatype":"STRING","values":["NA","METHYLATED","UNMETHYLATED"]}]}} ▶ 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":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6abb41e6e497aad53593ca4c","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6abb41e6e497aad53593ca4c","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)."}} ◀ 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) I can't give you a survival or treatment-response conclusion from this cohort alone. That needs Kaplan-Meier and log-rank tests, which I can't run here. What I can give you is the group sizes and event counts, and links that run the analysis in cBioPortal. **Cohort:** Glioblastoma (TCGA, Cell 2013), 577 patients. MGMT promoter status is recorded for 350 of them. The other 227 have no status. Counts below are patients with both MGMT status and overall survival data. | MGMT promoter | Patients | Deaths | Living (censored) | |---|---|---|---| | Methylated | 169 | 115 | 54 | | Unmethylated | 179 | 131 | 48 | **Split by temozolomide (TMZ) exposure.** The study's therapy field marks whether a patient's recorded therapy includes TMZ. This is not a randomized comparison. | MGMT promoter | Therapy | Patients | Deaths | Living (censored) | |---|---|---|---|---| | Methylated | Includes TMZ | 121 | 75 | 46 | | Methylated | No TMZ | 48 | 40 | 8 | | Unmethylated | Includes TMZ | 123 | 84 | 39 | | Unmethylated | No TMZ | 56 | 47 | 9 | - **What the counts show:** the methylated, TMZ-treated group has the lowest proportion of deaths and the highest share of patients still alive at last follow-up. The other three groups look more alike in raw proportions. - **What they don't show:** raw proportions ignore follow-up time. They don't establish a prognostic effect, or a predictive effect (a difference in benefit from TMZ between methylated and unmethylated tumors). - **Median survival, hazard ratios and p-values:** I haven't reported any, because I can't compute them here. The Survival tab in the first link below gives the Kaplan-Meier curves and log-rank p-value for methylated versus unmethylated. To test whether TMZ benefit differs by MGMT status, you would need a Cox model with an MGMT-by-treatment interaction. You could run that in R (`survival::coxph`) or Python (`lifelines`). - **Caveats:** - Therapy is a patient-level label, and treatment assignment isn't randomized. Confounding by age, performance status and other factors is possible. - I didn't check whether the "no TMZ" group is small or unusual in some other way. - MGMT status is missing for 227 patients. - **General background, not from this data:** MGMT promoter methylation is widely described in the glioblastoma literature as associated with better outcomes and greater benefit from temozolomide. I haven't verified that claim in this dataset. **Links** - [Survival comparison: MGMT methylated (170 samples) vs unmethylated (180 samples)](https://www.cbioportal.org/comparison/survival?comparisonId=6abb41e6e497aad53593ca4c) - [Study overview filtered to MGMT-methylated and unmethylated samples](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)