Elucidation of the Application of Blood Test Biomarkers to Predict Immune-Related Adverse Events in Atezolizumab-Treated NSCLC Patients Using Machine Learning Methods

Background Development of severe immune-related adverse events (irAEs) is a major predicament to stop treatment with immune checkpoint inhibitors, even though tumor progression is suppressed. However, no effective early phase biomarker has been established to predict irAE until now. Method This study retrospectively used the data of four international, multi-center clinical trials to investigate the application of blood test biomarkers to predict irAEs in atezolizumab-treated advanced non-small cell lung cancer (NSCLC) patients. Seven machine learning methods were exploited to dissect the importance score of 21 blood test biomarkers after 1,000 simulations by the training cohort consisting of 80%, 70%, and 60% of the combined cohort with 1,320 eligible patients. Results XGBoost and LASSO exhibited the best performance in this study with relatively higher consistency between the training and test cohorts. The best area under the curve (AUC) was obtained by a 10-biomarker panel using the XGBoost method for the 8:2 training:test cohort ratio (training cohort AUC = 0.692, test cohort AUC = 0.681). This panel could be further narrowed down to a three-biomarker panel consisting of C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), and thyroid-stimulating hormone (TSH) with a small median AUC difference using the XGBoost method [for the 8:2 training:test cohort ratio, training cohort AUC difference = −0.035 (p < 0.0001), and test cohort AUC difference = 0.001 (p=0.965)]. Conclusion Blood test biomarkers currently do not have sufficient predictive power to predict irAE development in atezolizumab-treated advanced NSCLC patients. Nevertheless, biomarkers related to adaptive immunity and liver or thyroid dysfunction warrant further investigation

Definition of a new blood cell count score for early survival prediction for non-small cell lung cancer patients treated with atezolizumab: Integrated analysis of four multicenter clinical trials

Importance Blood cell count test (BCT) is a robust method that provides direct quantification of various types of immune cells to reveal the immune landscape to predict atezolizumab treatment outcomes for clinicians to decide the next phase of treatment. Objective This study aims to define a new BCTscore model to predict atezolizumab treatment benefits in non-small lung cell cancer (NSCLC) patients. Design, Setting, and Participants This study analyzed four international, multicenter clinical trials (OAK, BIRCH, POPLAR, and FIR trials) to conduct post-hoc analyses of NSCLC patients undergoing atezolizumab (anti–PD-L1) single-agent treatment (n = 1,479) or docetaxel single-agent treatment (n = 707). BCT was conducted at three time points: pre-treatment (T1), the first day of treatment cycle 3 (T2), and first day of treatment cycle 5 (T3). Univariate and multivariate Cox regression analyses were conducted to identify early BCT biomarkers to predict atezolizumab treatment outcomes in NSCLC patients. Main Outcomes and Measures Overall survival (OS) was used as the primary end point, whereas progression-free survival (PFS) according to Response Evaluation Criteria in Solid Tumors (RECIST), clinical benefit (CB), and objective response rate (ORR) were used as secondary end points. Results The BCT biomarkers of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) at time point T3 and neutrophil-to-monocyte ratio (NMR) at time point T2 with absolute cutoff values of NLR_T3 = 5, PLR_T3 = 180, and NMR_T2 = 6 were identified as strong predictive biomarkers for atezolizumab (Ate)–treated NSCLC patients in comparison with docetaxel (Dtx)–treated patients regarding OS (BCTscore low risk: HR Ate vs. Dtx = 1.54 (95% CI: 1.04–2.27), P = 0.031; high risk: HR Ate vs. Dtx = 0.84 (95% CI: 0.62–1.12), P = 0.235). The identified BCTscore model showed better OS AUC in the OAK (AUC12month = 0.696), BIRCH (AUC12month = 0.672) and POPLAR+FIR studies (AUC12month = 0.727) than that of each of the three single BCT biomarkers. Conclusion and Relevance The BCTscore model is a valid predictive and prognostic biomarker for early survival prediction in atezolizumab-treated NSCLC patients

Candida albicans gains azole resistance by altering sphingolipid composition

The fungal pathogen Candida albicans is diploid, which hinders genome-wide studies. Here, Gao et al. present a piggyBac transposon-mediated mutagenesis system using stable haploid C. albicans strains, and use it to identify genes and mechanisms underlying azole resistance

Pph3 Dephosphorylation of Rad53 Is Required for Cell Recovery from MMS-Induced DNA Damage in <em>Candida albicans</em>

<div><p>The pathogenic fungus <em>Candida albicans</em> switches from yeast growth to filamentous growth in response to genotoxic stresses, in which phosphoregulation of the checkpoint kinase Rad53 plays a crucial role. Here we report that the Pph3/Psy2 phosphatase complex, known to be involved in Rad53 dephosphorylation, is required for cellular responses to the DNA-damaging agent methyl methanesulfonate (MMS) but not the DNA replication inhibitor hydroxyurea (HU) in <em>C. albicans</em>. Deletion of either <em>PPH3</em> or <em>PSY2</em> resulted in enhanced filamentous growth during MMS treatment and continuous filamentous growth even after MMS removal. Moreover, during this growth, Rad53 remained hyperphosphorylated, MBF-regulated genes were downregulated, and hypha-specific genes were upregulated. We have also identified S461 and S545 on Rad53 as potential dephosphorylation sites of Pph3/Psy2 that are specifically involved in cellular responses to MMS. Therefore, our studies have identified a novel molecular mechanism mediating DNA damage response to MMS in <em>C. albicans</em>.</p> </div