Q (sonnet): Is KRAS G12C more aggressive than G12D?

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 "uri": "cbioportal://statistical-tests-guide"
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Statistical Test Selection Guide
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Purpose
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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)

═ answer (success)
"Aggressive" is ambiguous — it could refer to overall survival, metastasis rate, tumor grade/stage, TMB, or other molecular features. I don't want to silently assume you mean survival.

**Could you clarify which comparison you'd like between KRAS G12C and G12D?**

- **Overall/disease-free survival** (would require Kaplan-Meier + log-rank — I can pull the raw survival data and per-group N/events/censored counts, but the actual KM curve and significance test need to run in cBioPortal's Group Comparison → Survival tab, R `survival`, or Python `lifelines`)
- **Metastasis rate / stage at diagnosis**
- **Tumor mutational burden (TMB)**
- **Co-occurring alterations** (e.g., STK11, KEAP1, TP53 co-mutation patterns, which some literature associates with outcome differences)
- Something else specific to a particular cancer type (e.g., LUAD)?

Also note: I can't produce a p-value or hazard ratio directly from the database — ClickHouse doesn't run statistical tests — but I can build the contingency tables or survival summaries and point you to the exact test to run.

Which definition of "aggressive" would you like, and is there a specific cancer type/study in mind (e.g., LUAD, colorectal)?