Granzyme B is correlated with clinical outcome after PD-1 blockade in patients with stage IV non-small-cell lung cancer

BACKGROUND: A minority of patients with advanced non-small-cell lung cancer (NSCLC) benefit from treatment with immune checkpoint inhibitors (ICIs). Ineffective effector function of activated T and NK cells may lead to reduced tumor cell death, even when these activated effector cells are released from their immune checkpoint brake. Hence, in this study we aimed to assess the association of baseline serum granzyme B, as well as germline variation of the GZMB gene, with clinical outcome to programmed cell death protein 1 (PD-1) blockade. METHODS: A total of 347 patients with stage IV NSCLC who started nivolumab treatment between June 2013 and June 2017 were prospectively included. Baseline serum and whole blood was available, allowing for protein quantification and targeted DNA sequencing. Clinical outcome was based on best overall response (BOR) according to Response Evaluation Criteria in Solid Tumors, V.1.1, progression-free survival (PFS), and overall survival (OS). RESULTS: Patients with low serum levels of granzyme B had worse PFS (HR: 1.96; 95% CI: 1.12 to 3.43; p=0.018) and worse OS (HR: 2.08; 95% CI: 1.12 to 3.87; p=0.021) than patients with high baseline serum levels. To validate the findings, germline variation of GZMB rs8192917 was assessed. Patients with homozygous and heterozygous variants of GZMB rs8192917 had worse BOR (OR: 1.60; 95% CI: 1.01 to 2.52; p=0.044) and worse PFS (HR: 1.38; 95% CI:1.02 to 1.87; p=0.036) than wild types. CONCLUSIONS: A low baseline serum level of granzyme B and germline variation of GZMB was associated with worse clinical outcome in NSCLC, emphasizing the relevance and additional value of monitoring germline genetic variations which mirror cytotoxic functions of T cells in ICI therapy. TRAIL REGISTRATION NUMBER: Dutch Trial Registry (NL6828)

Association between single-nucleotide polymorphisms and adverse events in nivolumab-treated non-small cell lung cancer patients

Background: Treatment with PD-1 inhibitors can be hampered by severe auto-immune-related toxicities. Our objective was to identify single-nucleotide polymorphisms (SNPs) in genes previously associated with auto-immunity, which are associated with toxicities in nivolumab-treated NSCLC patients. This was in order to identify patients prone to develop severe toxicities and to gain more insight into the underlying pathobiology. Methods: We analysed 322 nivolumab-treated patients and assessed the association with toxicities for seven SNPs in four genes, which are considered contributors to PD-1-directed T-cell responses, i.e., PDCD1, PTPN11, ZAP70 and IFNG. Every SNP was tested for its association with toxicity endpoints. Significant associations were tested in a validation cohort. Results: A multivariable analysis in the exploration cohort showed that homozygous variant patients for PDCD1 804C>T (rs2227981) had decreased odds for any grade treatment-related toxicities (n = 96; OR 0.4; 95% CI 0.2–1.0; p = 0.039). However, this result could not be validated (n = 85; OR 0.9; 95% CI 0.4–1.9; p = NS). Conclusions: Our results show that it is unlikely that the investigated SNPs have a clinical implication in predicting toxicity. A finding, even though negative, that is considered timely and instructive towards further research in biomarker development for checkpoint inhibitor treatments

A prospective cohort study on the pharmacokinetics of nivolumab in metastatic non-small cell lung cancer, melanoma, and renal cell cancer patients

Background: Nivolumab is administered in a weight-based or fixed-flat dosing regimen. For patients with non-small cell lung cancer (NSCLC), a potential exposure-response relationship has recently been reported and may argue against the current dosing strategies. The primary objectives were to determine nivolumab pharmacokinetics (PK) and to assess the relationship between drug clearance and clinical outcome in NSCLC, melanoma, and renal cell cancer (RCC). Methods: In this prospective observational cohort study, individual estimates of nivolumab clearance and the impact of baseline covariates were determined using a population-PK model. Clearance was related to best overall response (RECISTv1.1), and stratified by tumor type. Results: Two-hundred-twenty-one patients with metastatic cancer receiving nivolumab-monotherapy were included of whom 1,715 plasma samples were analyzed. Three baseline parameters had a significant effect on drug clearance and were internally validated in the population-PK model: gender, BSA, and serum albumin. Women had 22% lower clearance compared to men, while the threshold of BSA and albumin that led to > 20% increase of clearance was > 2.2m2 and < 37.5 g/L, respectively. For NSCLC, drug clearance was 42% higher in patients with progressive disease (mean: 0.24; 95% CI: 0.22-0.27 L/day) compared to patients with partial/complete response (mean: 0.17; 95% CI: 0.15-0.19 L/day). A similar trend was observed in RCC, however, no clearance-response relationship was observed in melanoma. Conclusions: Based on the first real-world population-PK model of nivolumab, covariate analysis revealed a significant effect of gender, BSA, and albumin on nivolumab clearance. A clearance-response relationship was observed in NSCLC, with a non-significant trend in RCC, but not in melanoma. Individual pharmacology of nivolumab in NSCLC appears important and should be prospectively studied

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Associations between patient and disease characteristics and severe adverse events during immune checkpoint inhibitor treatment: An observational study

Aim: With increasing use of immune checkpoint inhibitors (ICIs) more patients will develop severe and potentially life-threatening immune-related adverse events (irAEs). So far, predictive models for the occurrence of grade ≥3 irAEs are lacking. Therefore, we analysed associations between patient and disease characteristics, and the occurrence of grade ≥3 irAEs. Methods: Patients with cancer who were treated with anti-PD-1 (+/−anti-CTLA-4) between July 2015 and February 2020, and who were prospectively included in the MULTOMAB-trial, were eligible for this cohort study. Time to and occurrence of grade ≥3 irAEs according to CTCAE v5.0 were retrospectively registered. The associations between patient and disease characteristics and irAE occurrence were analysed using the competing risk cox-regression model of Fine and Gray. Analyses were performed separately in patients treated with monotherapy (anti-PD-1) and combination therapy (anti-PD-1 + anti-CTLA-4). Subgroup analyses were performed in tumour types with the highest number of patients; melanoma and NSCLC. Results: Out of 641 patients, 106 patients (17%) experienced grade ≥3 irAEs. None of the analysed factors were associated with grade ≥3 irAE occurrence in the monotherapy (n = 550) or the combination therapy (n = 91) groups, nor in the subgroup analyses. Of interest, none of the patients with NSCLC with a WHO performance status of 0 (n = 34) experienced grade ≥3 irAEs. Most common NSCLC histology types were adenocarcinoma (n = 99/55%) and squamous cell carcinoma (n = 39/22%). Concluding statement: This study shows that patient and disease characteristics are not able to predict the occurrence of serious AEs in patients treated with ICIs

Final report for %22High performance computing for advanced national electric power grid modeling and integration of solar generation resources%22, LDRD Project No. 149016.

Design and operation of the electric power grid (EPG) relies heavily on computational models. High-fidelity, full-order models are used to study transient phenomena on only a small part of the network. Reduced-order dynamic and power flow models are used when analysis involving thousands of nodes are required due to the computational demands when simulating large numbers of nodes. The level of complexity of the future EPG will dramatically increase due to large-scale deployment of variable renewable generation, active load and distributed generation resources, adaptive protection and control systems, and price-responsive demand. High-fidelity modeling of this future grid will require significant advances in coupled, multi-scale tools and their use on high performance computing (HPC) platforms. This LDRD report demonstrates SNL's capability to apply HPC resources to these 3 tasks: (1) High-fidelity, large-scale modeling of power system dynamics; (2) Statistical assessment of grid security via Monte-Carlo simulations of cyber attacks; and (3) Development of models to predict variability of solar resources at locations where little or no ground-based measurements are available