The prognostic impact of KRAS, TP53, STK11 and KEAP1 mutations and the influence of the NLR in NSCLC patients treated with immunotherapy.
Bibliographic record
Abstract
e21010 Background: ICIs changed the way NSCLC is treated, but not all patients benefit from it. PD-L1 level is used to predict response to therapy, but its performance is sub-optimal. KRAS is important in NSCLC tumorigenesis, but the impact of its mutations in patients treated with ICIs is unclear. Similarly, studies evaluating co-mutations in TP53, STK11 and KEAP1 as well as the NLR showed that they may predict the benefit of ICIs. Methods: We conducted a retrospective study including all consenting patients with NSCLC treated with ICIs at the CHUM between July 2015 and June 2020. OS and PFS were compared in co-mutation subgroups using Kaplan-Meier and logrank methods. Co-mutations in TP53, STK11 and KEAP1 as well as the NLR were accounted for. Overall response rate (ORR) and safety data was also compared in subgroups and will be detailed at the meeting. Results: We included 100 patients with known KRAS status. From these, 50 were wild-type ( KRASWT) and 50 were mutated ( KRASMut). The most frequent mutation was G12C (54%). Co-mutation status for TP53, STK11 and KEAP1 were known for, respectively, 40, 39 and 38 patients. Co-mutations for these genes were present in respectively 19 (47.5%), 8 (20.5%) and 4 (10.5%). Data comparing KRASMut and KRASWT showed non-significant differences in survival (median OS of respectively 21.1 vs. 17.7 months, p = 0.27). The presence of STK11 and/or KEAP1 mutations was associated with a negative impact on survival when compared with wild-type (median OS 7.4 vs 20.4 months, p = 0.001). When the presence of a KRAS mutation was compounded with STK11 and KEAP1, KRASMut (vs KRASWT) trended to a better prognosis in STK11+KEAP1WT tumors (median OS of 21.1 for KRASMut vs 15.8 for KRASWT, p = 0.15), but not in STK11+/-KEAP1Mut tumors (7.4 for KRASMut vs 7.0 for KRASWT). No influence on survival was seen in relationship to the TP53 co-mutation. Interestingly, the NLR was significantly higher with STK11 mutations (6.66Mut vs 3.59WT, p = 00012), slightly lower with TP53 mutations (3.23Mut vs 4.82WT, p = 0.047) but not impacted by KEAP1 (3.72Mut vs 4.20WT, p = 0.72) or KRAS mutations (4.32Mut vs 5.21WT, p = 0.34). Conclusions: The STK11 and KEAP1 mutations are significant adverse predictors of ICI therapy benefit. The NLR is strongly impacted by STK11 mutations but not by KEAP1 mutations suggesting marked differences in the resistance mechanism for both mutations. In STK11-KEAP1WT tumors, KRAS mutations seems to be associated with improved survival in NSCLC patient treated with ICIs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".