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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.

Neutrophil extracellular traps (NETs) released from neutrophils are related to cancer progression. However, the relationship between the therapeutic effects of immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-PD-L1 antibodies and plasma NET concentration in patients with non-small cell lung cancer (NSCLC) is poorly understood. In this study, concentrations of citrullinated histone H3 (CitH3), a surrogate marker of NETs, in plasma before/after treatment were examined in patients with advanced or recurrent NSCLC undergoing ICI treatment (n = 185). The clinical significances of NET levels before/after treatment and posttreatment changes were statistically evaluated. As a result, multivariate Cox analysis showed that high NET levels before treatment were statistically significant predictors of unfavorable overall survival (OS; p < 0.001, HR 1.702, 95% CI 1.356–2.137) and progression-free survival (PFS; p < 0.001, HR 1.566, 95% CI 1.323–1.855). The Kaplan-Meier curves showed significant separation between the high- and low-NET groups in OS (p = 0.002) and PFS (p < 0.001). Additionally, high NET levels after treatment were also significantly associated with worse OS (p < 0.001) and PFS (p < 0.001) by multivariate Cox analysis. Notably, the pretreatment NET levels were significantly correlated with the plasma levels of NET-related inflammatory cytokines, such as IL-6 and IL-8, and with NET-related gene expression and immune-suppressive profile in peripheral blood mononuclear cells. Our findings suggest that NETs released from activated neutrophils might reduce the clinical efficacy of ICIs in patients with NSCLC.

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.

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

We aimed to introduce a novel semi-automatic design approach for fabricating individualized, three-dimensional printed template dedicated to interstitial brachytherapy in vaginal tumor treatment. The central component of this concept involved the development of a cylindrical template with strategically placed tunnels to optimize applicator placement. These tunnels originated from the template's base, meticulously designed to prevent any potential overlap or interference. For precise tumor localization, we employed a method wherein the tumor's mask image was projected onto a spherical surface. Subsequently, we employed the k-means algorithm to segment the terminal points, with each cluster's center serving as the terminal point. To ensure the optimal starting point for the tunnels, we utilized the conjugate gradient method, considering the following factors: inter-starting point distance, angles between tunnels, inter-tunnel distance, and the starting point's position relative to the base of the template (inside or outside). We established a semi-automatic design paradigm for fabricating three-dimensional printed template tailored for vaginal brachytherapy. While our initial findings are promising, further comprehensive investigations are imperative to validate the clinical efficacy of this innovative concept.

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. [ABSTRACT FROM AUTHOR]

Abstract Background Some studies have demonstrated that concurrent chemo-radiotherapy is an effective salvage treatment for isolated para-aortic lymph node (PALN) recurrence. However, no studies have compared multi-treatment modalities, such as radiation therapy (RT), concurrent chemoradiotherapy (CCRT), surgery, chemotherapy, and best supportive care (BSC), across a sufficient number of patients with PALN recurrence. We thus aimed to evaluate the clinical outcomes of multi-treatment modalities for isolated PALN recurrence in uterine cervical cancer. Methods Records of 50 patients who were first diagnosed with isolated PALN recurrence after definitive cervical cancer treatment from 2002 to 2016 at our institution were reviewed retrospectively. The initial definitive cervical cancer therapies included RT alone, CCRT, or surgery with or without post-operative RT. The median follow-up time was 33 months. The median age at recurrence diagnosis was 57 years (range, 26–84 years). The median duration between the end of initial treatment and recurrence was 10 months (range, 1–91 months). The median maximum metastatic lesion size was 17 mm (range, 8–60 mm). Twenty-four patients had one or two PALN metastases, while 26 had 3 or more. Eighteen patients were treated for recurrence with RT alone, seven with CCRT, three with surgery, 17 with chemotherapy, and five with BSC. Potential prognostic factors included histopathology, initial FIGO stage, initial treatment, age at recurrence, tumor markers (serum SCC-Ag and CEA) at recurrence, time to recurrence, maximum size of the metastatic lesion, number of metastases, and the recurrence treatment method. Results The 3-year overall survival (OS) rates of all patients were 47.0%. The 3-year OS rate of patients who underwent CCRT for recurrence was 85.7%; surgery, 66.7%; chemotherapy, 48.8%; RT, 41.3%; and BSC, 0% (p = 0.014). Univariate analysis revealed that only the recurrence treatment method was significantly associated with OS. The 3-year local control rate (LCR) and progression free survival (PFS) rate for CCRT were 100 and 71.4%; for surgery, 100 and 66.7%; for chemotherapy, 33.6 and 13.7%; and for RT, 55.5 and 14.1%, respectively (LCR: p = 0.028, PFS: p = 0.059). The number of metastatic lesions, SCC-Ag levels and recurrence treatment method were significantly associated with LCR. Age at recurrence, SCC-Ag levels, and number of metastatic lesions were significantly associated with PFS. Conclusions Although our patient cohort size was small, our results suggest that CCRT may be effective in preventing local disease recurrence in the PALN and may improve OS.

Abstract Background The selection of radiation therapy dose fractionation schedules for bone metastases is often based on the estimation of life expectancy. Therefore, accurate prognosis prediction is an important issue. It is reported that the Katagiri scoring system can be used to predict the survival of patients with bone metastases. We aimed to assess prognostic factors and validate the Katagiri scoring system in patients who were treated with radiation therapy for bone metastases. Materials/Methods We retrospectively reviewed data of all patients who were treated with radiation therapy for bone metastases between 2004 and 2013. Age, sex, Karnofsky performance status (KPS), Eastern Cooperative Oncology Group performance status (ECOG PS), primary site (lesions and characteristics), visceral metastases, laboratory data, previous chemotherapy, and multiple bone metastases were analyzed for associations with overall survival (OS). Katagiri scores were calculated for each patient and were used to compare OS. Results Out of the 616 patients included in this analysis, 574 had died and 42 remained alive. The median follow-up time for survivors was 42 months. Univariate analysis revealed that age (P = 0.604) and multiple bone metastases (P = 0.691) were not significantly associated with OS. Multivariate analysis revealed that sex, ECOG PS, KPS, primary characteristics, visceral metastases, laboratory data, and previous chemotherapy were significantly associated with OS. The survival rates at 3, 6, 12, and 24 months, categorized by Katagiri score, were as follows: score 0–3, 94.4, 77.8, and 61.1%, respectively; score 4–6, 67.7, 48.7, and 31.2%, respectively; and score 7–10, 39.1, 22.1, and 9.0%, respectively (P

There are currently no reliable, established serum biomarkers to predict the prognosis of radiotherapy for advanced cervical cancer. We aimed to identify serum biomarkers for survival after radiotherapy for cervical cancer. In this multicenter prospective cohort study, the usefulness of pre- and posttreatment serum protein levels of potential biomarkers, including squamous cell carcinoma antigen (SCC-Ag), apolipoprotein C-II (ApoC-II), matrix metalloproteinase (MMP)1, and MMP2, were evaluated together with clinical factors in 145 cervical cancer patients in order to determine their suitability to predict survival. Progression-free survival (PFS) was the primary endpoint, and overall survival (OS), pelvic PFS (PPFS), and distant metastasis-free survival (DMFS) were the secondary endpoints. Blood samples were collected before and 1 month after radiotherapy to measure serum biomarker levels. ApoC-II was measured using a monoclonal antibody-based enzyme-linked immunosorbent assay, which was developed for this purpose. Kaplan-Meier method, log-rank test, and univariate and multivariate Cox proportional hazards models were used for statistical analyses. In multivariate analysis, larger tumor size was independently associated with shorter PFS, OS, PPFS, and DMFS, while longer overall treatment time was independently associated with shorter PPFS. Higher pretreatment SCC-Ag (P < 0.001) was associated with shorter DMFS. Higher posttreatment SCC-Ag (P = 0.017) was also associated with shorter DMFS. Pretreatment ApoC-II was associated with PPFS in univariate analysis (P = 0.048), but not in multivariate analysis. Patients with pretreatment ApoC-II levels ≤ 25.8 μg/ml had shorter PPFS than those with pretreatment ApoC-II levels > 25.8 μg/ml (P = 0.023, log-rank test). Pre- and posttreatment serum SCC-Ag and pretreatment serum ApoC-II levels may be important biomarkers to predict survival outcomes of patients with cervical cancer after radiotherapy. Pre- and posttreatment SCC-Ag and pretreatment ApoC-II might be useful in clinical settings for screening patients to improve treatment strategies in cervical cancer.