Machine learning to identify biomarkers of response to immunotherapy in <i>KRAS</i> wildtype ( <i>KRASwt)</i> non-small cell lung cancer (NSCLC).

Abstract

e20609 Background: KEAP1 and STK11 are associated with resistance to immune checkpoint inhibitors (ICIs) in NSCLC in KRAS -mutant disease, and are used to guide treatment selection. In contrast, biomarkers to guide treatment selection in KRAS wt NSCLC are poorly defined. We aimed to develop predictive models to guide immunotherapy selection and to identify predictive clinicogenomic biomarkers of response to immunotherapy in KRAS wt NSCLC. Methods: We analyzed patients with metastatic KRAS wt non-squamous NSCLC (nsNSCLC) treated with ICIs at multiple centers, with external validation using the US-based de-identified Flatiron Health–Foundation Medicine NSCLC Clinico-Genomic Database. XGBoost models were trained to predict progression-free survival (PFS) &gt; 6 months following first-line anti-PD-1 monotherapy (ICI-mono) or chemo-immunotherapy (ICI-chemo). Feature importance was assessed using SHAP values. The academic cohort was split 80/20 into training and test sets, and frontline-treated patients in Flatiron served as an external validation cohort. PD-L1 ≥50% (ICI-mono) and PD-L1 ≥1% (ICI-chemo) were used as baseline comparators. Results: The academic and flatiron cohorts included 1,183 and 4087 patients, respectively. The ICI-mono model demonstrated strong discrimination (test AUC = 0.71 vs PD-L1 ≥50% AUC = 0.59) and validated externally (Flatiron real-world time to next treatment [rwTTNT] HR = 0.60, p &lt; 0.001; rwOS HR = 0.60, p &lt; 0.001). The ICI-chemo model did not perform as well internally (test AUC = 0.62 vs PD-L1 ≥1% AUC = 0.53) or externally (Flatiron rwTTNT HR = 0.80, p = 0.039; rwOS HR = 0.80, p = 0.026). In both models, higher tumor mutational burden and PD-L1 were associated with improved outcomes, whereas ECOG and liver metastases were associated with worse outcomes. Genomic features were weakly predictive and did not validate in Flatiron. None of the top predictive genomic features validated in Flatiron, underscoring the importance of external validation. Given prior associations in KRAS -mutant NSCLC, KEAP1 and STK11 were evaluated in KRASwt patients: compared with double–wildtype tumors, KEAP1 -only tumors treated with ICI-mono had improved outcomes (PFS HR = 0.80, p = 0.023; Flatiron rwTTNT HR = 0.80, p = 0.031), STK11-only tumors showed no significant difference, while KEAP1 / STK11 co-mutation was associated with worse outcomes (PFS HR = 1.3, p = 0.048; Flatiron rwTTNT HR = 1.3, p = 0.013). Conclusions: In integrated modeling of KRAS wt nsNSCLC treated with ICIs, no genomic alterations were consistently predictive of response. However, univariate analyses suggest that KEAP1 and STK11 mutations exert distinct and non-additive effects in KRAS wt disease, contrasting with their role in KRAS -mutant NSCLC. These findings have potential implications for treatment intensification strategies, including selective use of anti-CTLA-4 therapy in KRAS wt NSCLC.