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KRAS mutations are one of the most prevalent oncogenic alterations in cancer. Until recently, drug development targeting KRAS did not convey clinical benefits to patients. Specific KRASG12C inhibitors, such as sotorasib and adagrasib, have been designed to bind to the protein’s mutant structure and block KRASG12C in its GDP-bound inactive state. Phase 1/2 trials have shown promising anti-tumor activity, especially in pretreated non-small cell lung cancer patients. As expected, both primary and secondary resistance to KRASG12C inhibitors invariably occurs, and molecular mechanisms have been characterized in pre-clinical models and patients. Several mechanisms such as tyrosine kinase receptors (RTKs) mediated feedback reactivation of ERK-dependent signaling can result in intrinsic resistance to KRAS target therapy. Acquired resistance to KRASG12C inhibitors include novel KRAS mutations such as Y96D/C and other RAS-MAPK effector protein mutations. This review focuses on the intrinsic and acquired mechanisms of resistance to KRASG12C inhibitors in KRASG12C mutant non-small cell lung cancer and the potential clinical strategies to overcome or prevent it.

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Background Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far. Methods Capitalizing data from the TERAVOLT registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure and a tree-based model to screen and optimize a broad panel of demographics, clinical COVID-19 and cancer characteristics. Results As of April 15, 2021, 1491 consecutive evaluable patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened approximately 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection then identified seven major determinants of death ECOG-PS (OR 2.47 1.87-3.26), neutrophil count (OR 2.46 1.76-3.44), serum procalcitonin (OR 2.37 1.64-3.43), development of pneumonia (OR 1.95 1.48-2.58), c-reactive protein (CRP) (OR 1.90 1.43-2.51), tumor stage at COVID-19 diagnosis (OR 1.97 1.46-2.66) and age (OR 1.71 1.29-2.26). The ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 0.75 – 0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90% and the tree-based model recognized ECOG-PS, neutrophil count and CRP as the major determinants of prognosis. Conclusion From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated the strongest association with poor outcome from COVID-19. With our analysis we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19.