Q (haiku): are there different outcomes for idh1 mutant vs egfr amp in lgg?

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

▶ list_studies
{
 "search": "LGG",
 "limit": 10
}

◀ 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
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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)
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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
{"result":[{"cancer_study_identifier":"lgggbm_tcga_pub","name":"Merged Cohort of LGG and GBM (TCGA, Cell 2016)","type_of_cancer_id":"difg","sample_count":1122,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgggbm_tcga_pub"},{"cancer_study_identifier":"lgg_tcga","name":"Brain Lower Grade Glioma (TCGA, Firehose Legacy)","type_of_cancer_id":"difg","sample_count":530,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_tcga"},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","type_of_cancer_id":"difg","sample_count":514,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lgg_ucsf_2014","name":"Low-Grade Gliomas (UCSF, Science 2014)","type_of_cancer_id":"difg","sample_count":61,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_ucsf_2014"},{"cancer_study_identifier":"lgg_ctf_synodos_2025","name":"Pediatric Low-Grade Glioma (CTF, Acta Neuropathol 2021)","type_of_cancer_id":"difg","sample_count":31,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_ctf_synodos_2025"}]}

═ answer (success)
Good — I found LGG studies. Before I run the analysis, let me clarify what you mean by "different outcomes":

**What outcome are you interested in comparing?**
- **Overall survival (OS)** — time to death?
- **Progression-free survival (PFS)** — time to tumor progression?
- **Clinical grade/grade progression** — tumor grade or malignant transformation?
- **Other clinical features** — age at diagnosis, tumor size, recurrence?

Once you specify, I'll use the largest TCGA LGG study and:
1. Pull the raw data for IDH1-mutant vs EGFR-amplified samples
2. Return the summary statistics (N, event counts, follow-up range for survival)
3. Provide a direct link to cBioPortal's Group Comparison tab where you can run the statistical test with visual Kaplan-Meier or hazard ratio comparison

Which outcome would you like to compare?