Clinical roles of soluble PD-1 and PD-L1 in plasma of NSCLC patients treated with immune checkpoint inhibitors.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) have significantly improved the prognosis of non-small cell lung cancer (NSCLC). However, only a limited proportion of patients can benefit from this therapy, and clinically useful predictive biomarkers remain to be elucidated. METHODS: Blood was collected from 189 patients with NSCLC before and six weeks after the initiation of ICI treatment (anti-PD-1 or anti-PD-L1 antibody). Soluble PD-1 (sPD-1) and PD-L1 (sPD-L1) in plasma before and after treatment were analyzed to evaluate their clinical significance. RESULTS: Cox regression analysis demonstrated that higher sPD-L1 levels before treatment significantly predicted unfavorable progression-free survival (PFS; HR 15.4, 95% CI 1.10-86.7, P = 0.009) and overall survival (OS; HR 11.4, 95% CI 1.19-52.3, P = 0.007) in NSCLC patients treated with ICI monotherapy (n = 122) but not in those treated with ICIs combined with chemotherapy (n = 67: P = 0.729 and P = 0.155, respectively). In addition, higher sPD-1 levels after treatment were significantly associated with better OS (HR 0.24, 95% CI 0.06-0.91, P = 0.037) in patients treated with anti-PD-1 monotherapy, whereas higher sPD-L1 levels after treatment were significantly associated with worse PFS (HR 6.09, 95% CI 1.42-21.0, P = 0.008) and OS (HR 42.6, 95% CI 6.83-226, P < 0.001). The levels of sPD-L1 at baseline closely correlated with those of other soluble factors, such as sCD30, IL-2Ra, sTNF-R1, and sTNF-R2, which are known to be released from the cell surface by zinc-binding proteases ADAM10/17. CONCLUSIONS: These findings suggest the clinical significance of pretreatment sPD-L1 as well as posttreatment sPD-1 and sPD-L1 in NSCLC patients treated with ICI monotherapy.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation.

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Circulating IL-6 and not its circulating signaling components sIL-6R and sgp130 demonstrate clinical significance in NSCLC patients treated with immune checkpoint inhibitors.

Introduction: Clinical roles of plasma IL-6 levels have been reported in patients with various cancers, including non-small cell lung cancer (NSCLC), treated with immune checkpoint inhibitors (ICIs). However, the roles of other IL-6 signaling components, soluble IL-6 receptor (sIL-6R) and soluble gp130 (sgp130), in the plasma have not been elucidated. Methods: Blood was collected from 106 patients with NSCLC before initiation of ICI treatment (anti-PD-1 or anti-PD-L1 antibody). Plasma levels of IL-6, sIL-6R, sgp130, and their complexes were assessed by Cox regression hazard model to evaluate their clinical significance. The clinical role of IL-6 or IL-6R genetic polymorphisms was also analyzed. Results: Cox regression analysis showed that higher plasma IL-6 levels significantly predicted unfavorable overall survival (OS; hazard ratio [HR] 1.34, 95% confidence interval [CI] 1.05-1.68, p = 0.012) in NSCLC patients treated with ICIs. However, plasma sIL-6R and sgp130 levels showed no prognostic significance (p = 0.882 and p = 0.934, respectively). In addition, the estimated concentrations of binary IL-6:sIL-6R and ternary IL-6:sIL-6R:sgp130 complexes and their ratios (binary/ternary complex) were not significantly associated with OS (p = 0.647, p = 0.727, and p = 0.273, respectively). Furthermore, the genetic polymorphisms of IL-6 (-634G>C) and IL-6R (48892A>C) showed no clinical role by Kaplan-Meier survival analysis (p = 0.908 and p = 0.639, respectively). Discussion: These findings demonstrated the clinical significance of plasma levels of IL-6, but not of other IL-6 signaling components, sIL-6R and sgp130, suggesting that classical IL-6 signaling, but not trans-signaling, may be related to anti-tumor immune responses in cancer patients treated with ICIs.

Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures.

BACKGROUND: Immune checkpoint inhibitor (ICI) therapy has substantially improved the overall survival (OS) in patients with non-small-cell lung cancer (NSCLC); however, its response rate is still modest. In this study, we developed a machine learning-based platform, namely the Cytokine-based ICI Response Index (CIRI), to predict the ICI response of patients with NSCLC based on the peripheral blood cytokine profiles. METHODS: We enrolled 123 and 99 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy or combined chemotherapy in the training and validation cohorts, respectively. The plasma concentrations of 93 cytokines were examined in the peripheral blood obtained from patients at baseline (pre) and 6 weeks after treatment (early during treatment: edt). Ensemble learning random survival forest classifiers were developed to select feature cytokines and predict the OS of patients undergoing ICI therapy. RESULTS: Fourteen and 19 cytokines at baseline and on treatment, respectively, were selected to generate CIRI models (namely preCIRI14 and edtCIRI19), both of which successfully identified patients with worse OS in two completely independent cohorts. At the population level, the prediction accuracies of preCIRI14 and edtCIRI19, as indicated by the concordance indices (C-indices), were 0.700 and 0.751 in the validation cohort, respectively. At the individual level, patients with higher CIRI scores demonstrated worse OS [hazard ratio (HR): 0.274 and 0.163, and p<0.0001 and p=0.0044 in preCIRI14 and edtCIRI19, respectively]. By including other circulating and clinical features, improved prediction efficacy was observed in advanced models (preCIRI21 and edtCIRI27). The C-indices in the validation cohort were 0.764 and 0.757, respectively, whereas the HRs of preCIRI21 and edtCIRI27 were 0.141 (p<0.0001) and 0.158 (p=0.038), respectively. CONCLUSIONS: The CIRI model is highly accurate and reproducible in determining the patients with NSCLC who would benefit from anti-PD-1/PD-L1 therapy with prolonged OS and may aid in clinical decision-making before and/or at the early stage of treatment.

[Current challenges and future perspectives of pharmacological testing using new approach methodologies].

The mission of regulatory science is to promote human longevity by providing safer and more effective drugs and ensuring human health. At present, various in vitro and in vivo evaluation methods are used for drug development, and no major problems have been observed. However, there is still room for improvement in terms of risk prediction in humans. Thus, new approaches and methodologies (NAMs) have recently been developed to predict adverse events in humans more accurately. Based on the animal alternative methods and the current COVID-19 pandemic, in vitro methods, such as human iPS cells, and computational approach are accelerated to improve the efficiency of drug development, ensure the patients' safety and speed up the review process. In this review, we would like to summarize the current status and future perspectives of pharmacological assay system using NAM in drug development.

Development of alveolar and airway cells from human iPS cells: toward SARS-CoV-2 research and drug toxicity testing.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). SARS-CoV-2 enters host cells by binding with the receptor angiotensin-converting enzyme 2 (ACE2). While ACE2 is expressed in multiple cell types, it has been implicated in the clinical progression of COVID-19 as an entry point for SARS-CoV-2 into respiratory cells. Human respiratory cells, such as airway and alveolar epithelial type II (ATII) cells, are considered essential for COVID-19 research; however, primary human respiratory cells are difficult to obtain. In the present study, we generated ATII and club cells from human induced pluripotent stem cells (hiPSCs) for SARS-CoV-2 infection and drug testing. The differentiated cells expressed ATII markers (SFTPB, SFTPC, ABCA3, SLC34A2) or club cell markers (SCGB1A1 and SCGB3A2). Differentiated cells, which express ACE2 and TMPRSS2, were infected with SARS-CoV-2. Remdesivir treatment decreased intracellular SARS-CoV-2 viral replication and, furthermore, treatment with bleomycin showed cytotoxicity in a concentration-dependent manner. These data suggest that hiPSC-derived AT2 and club cells provide a useful in vitro model for drug development.

Are integrin alpha(2)beta(1), glycoprotein Ib and vWf levels correlated with their contributions to platelet adhesion on collagen under high-shear flow?

Platelets in flowing blood at high-shear stress are recruited to exposed subendothelial collagen of injured vessels by GPIb-von Willebrand factor (vWf) and integrin alpha(2)beta(1) (alpha(2)beta(1))-collagen interactions. Platelet adhesion to type I collagen depends mainly on the alpha(2)beta(1)-collagen interaction and that to type III collagen depends on the GPIb-vWf interaction due to vWf's weak affinity for type I collagen. Contributions of these two interactions would differ depending on expressions of alpha(2)beta(1), vWf, or GPIb. We quantitated platelet adhesion to low- and high-density collagen under high-shear flow conditions in the presence of anti-alpha(2)beta(1) (Gi9) and anti-GPIb (NNKY5-5) antibodies to determine if their inhibitory effects were correlated with the amounts of alpha(2)beta(1), GPIb and vWf. Gi9 inhibition of adhesion to type I collagen was decreased in platelets with more integrin alpha(2)beta(1). Gi9 and NNKY5-5 are more inhibitory against adhesion to low-density type III and I, respectively. Higher alpha(2)beta(1) expression decreases adhesion to low-density type III and increases Gi9 inhibition of adhesion to high-density type III, suggesting crosstalk between the alpha(2)beta(1)-collagen and GPIb-vWf interactions in adhesion to type III. Integrin alpha(2)beta(1)-collagen and GPIb-vWf interactions both contribute to platelet adhesion to collagen under high-shear flow. In adhesion under high-shear stress, the two interactions would compensate for each other, when there is a deficiency in one or the other. The alpha(2)beta(1)-collagen interaction was also suggested to have an inhibitory effect on platelet adhesion to type III collagen, through a yet undefined mechanism.

Novel approach for formation of platelet-like particles from mouse embryonic stem cells without using feeder cells.

Megakaryocytes (MKs) and platelet-like particles (PLPs) have generally been obtained by culturing embryonic stem (ES) cells over feeder cells. However, using feeder cells need many labor-consuming processes and the MK and PLP fractions obtained are often contaminated by such cells and their fragments. Here we describe our new culture system for differentiating mouse ES cells to MKs and PLPs without using feeder cells. ES cells are differentiated to cells with MK-like morphology and properties, including proplatelet formation, high ploidy (>8N), and CD41 expression. The culture medium contained PLPs expressing platelet glycoproteins, CD41 and GPIb. Integrin alpha(IIb)beta(3) of PLPs can be activated by thrombin. Addition of the metalloproteinase inhibitor TAPI-2 to the culture increased the surface expression of GPIbalpha and augmented the adhesion of PLPs to immobilized von Willebrand factor through decreasing the shedding of GPIbalpha. Thus our mouse ES cells culture system is a suitable and efficient method for obtaining MKs and functional PLPs that obviates the need for feeder cells.