The prognostic impact of KRAS, TP53, STK11 and KEAP1 mutations and their influence on the NLR in NSCLC patients treated with immunotherapy
Bibliographic record
Abstract
PD-L1 expression is used to predict NSCLC response to ICIs, but its performance is suboptimal. The impact of KRAS mutations in these patients is unclear. Studies evaluating co-mutations in TP53, STK11 and KEAP1 as well as the NLR showed that they may predict the benefit of ICIs. This is a retrospective study of patients with NSCLC treated with ICIs at the CHUM between July 2015 and June 2020. OS and PFS were compared using Kaplan-Meier and logrank methods. Co-mutations in TP53, STK11 and KEAP1 as well as the NLR were accounted for. ORR and safety were compared using Wald method. From 100 patients with known KRAS status, 50 were mutated (KRASMut). Mutation in TP53, STK11 and KEAP1 were present, and their status known in, respectively, 19/40 (47.5%), 8/39 (20.5%) and 4/38 (10.5%) patients. STK11Mut and KEAP1Mut were associated with shorter overall survival when compared with wild type tumors (respectively median OS of 3.3 vs 20.4, p=0.0001 and 10.1 vs 17.7, p=0.24). When KRAS status was compounded with STK11/KEAP1, KRASMut trended to a better prognosis in STK11+KEAP1WT tumors (median OS 21.1 vs 15.8 for KRASWT, p=0.15), but not for STK11+/-KEAP1Mut tumors. The NLR was strongly impacted by STK11 (6.0Mut vs 3.6WT, p=0.014) and TP53 (3.2Mut vs 4.8WT, p=0.048), but not by KEAP1 or KRAS mutations. STK11Mut and KEAP1Mut are adverse predictors of ICI therapy benefit. The NLR is strongly impacted by STK11Mut but not by KEAP1Mut, suggesting differences in their resistance mechanism. In STK11-KEAP1WT tumors, KRASMut seem associated with improved survival in NSCLC patients treated with ICIs. Response of NSCLC to immunotherapy is not easily predictable. We conducted a retrospective study in 100 patients with NSCLC and a known KRAS status. By accounting for different co-mutations, KRAS mutation was found to be associated with a better median overall survival in STK11 and KEAP1 wild-type tumors (21.1 vs 15.8, p=0.15). NLR was impacted by STK11, but not KEAP1 mutation, suggesting a difference in their resistance mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".