Nicholas D. Socci

The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uniexponential vs. multiexponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous disease with respect to presentation and clinical outcome. The prognostic value of recently identified somatic mutations has not been systematically evaluated in a phase 3 trial of treatment for AML. METHODS: We performed a mutational analysis of 18 genes in 398 patients younger than 60 years of age who had AML and who were randomly assigned to receive induction therapy with high-dose or standard-dose daunorubicin. We validated our prognostic findings in an independent set of 104 patients. RESULTS: We identified at least one somatic alteration in 97.3% of the patients. We found that internal tandem duplication in FLT3 (FLT3-ITD), partial tandem duplication in MLL (MLL-PTD), and mutations in ASXL1 and PHF6 were associated with reduced overall survival (P=0.001 for FLT3-ITD, P=0.009 for MLL-PTD, P=0.05 for ASXL1, and P=0.006 for PHF6); CEBPA and IDH2 mutations were associated with improved overall survival (P=0.05 for CEBPA and P=0.01 for IDH2). The favorable effect of NPM1 mutations was restricted to patients with co-occurring NPM1 and IDH1 or IDH2 mutations. We identified genetic predictors of outcome that improved risk stratification among patients with AML, independently of age, white-cell count, induction dose, and post-remission therapy, and validated the significance of these predictors in an independent cohort. High-dose daunorubicin, as compared with standard-dose daunorubicin, improved the rate of survival among patients with DNMT3A or NPM1 mutations or MLL translocations (P=0.001) but not among patients with wild-type DNMT3A, NPM1, and MLL (P=0.67). CONCLUSIONS: We found that DNMT3A and NPM1 mutations and MLL translocations predicted an improved outcome with high-dose induction chemotherapy in patients with AML. These findings suggest that mutational profiling could potentially be used for risk stratification and to inform prognostic and therapeutic decisions regarding patients with AML. (Funded by the National Cancer Institute and others.).

Purpose Treatment of advanced non-small-cell lung cancer with immune checkpoint inhibitors (ICIs) is characterized by durable responses and improved survival in a subset of patients. Clinically available tools to optimize use of ICIs and understand the molecular determinants of response are needed. Targeted next-generation sequencing (NGS) is increasingly routine, but its role in identifying predictors of response to ICIs is not known. Methods Detailed clinical annotation and response data were collected for patients with advanced non-small-cell lung cancer treated with anti-programmed death-1 or anti-programmed death-ligand 1 [anti-programmed cell death (PD)-1] therapy and profiled by targeted NGS (MSK-IMPACT; n = 240). Efficacy was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, and durable clinical benefit (DCB) was defined as partial response/stable disease that lasted > 6 months. Tumor mutation burden (TMB), fraction of copy number-altered genome, and gene alterations were compared among patients with DCB and no durable benefit (NDB). Whole-exome sequencing (WES) was performed for 49 patients to compare quantification of TMB by targeted NGS versus WES. Results Estimates of TMB by targeted NGS correlated well with WES (ρ = 0.86; P < .001). TMB was greater in patients with DCB than with NDB ( P = .006). DCB was more common, and progression-free survival was longer in patients at increasing thresholds above versus below the 50th percentile of TMB (38.6% v 25.1%; P < .001; hazard ratio, 1.38; P = .024). The fraction of copy number-altered genome was highest in those with NDB. Variants in EGFR and STK11 associated with a lack of benefit. TMB and PD-L1 expression were independent variables, and a composite of TMB plus PD-L1 further enriched for benefit to ICIs. Conclusion Targeted NGS accurately estimates TMB and elevated TMB further improved likelihood of benefit to ICIs. TMB did not correlate with PD-L1 expression; both variables had similar predictive capacity. The incorporation of both TMB and PD-L1 expression into multivariable predictive models should result in greater predictive power.