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Objective Current biomarkers for predicting immunotherapy response in non-small-cell lung cancer (NSCLC) are derived from invasive procedures with limited predictive accuracy. Thus, identifying a non-invasive predictive biomarker would improve patient stratification and precision immunotherapy.Methods and analysis In this retrospective multicohort study, the discovery cohort included 205 NSCLC patients screened from ORIENT-11 and an external validation (EV) cohort included 99 real-world NSCLC patients. The ‘onion-mode segmentation’ method was developed to extract ‘onion-mode perfusion’ (OMP) from contrast-enhanced CT images. The predictive performance of OMP or its combination with the PD-L1 Tumour Proportion Score (TPS) was evaluated by the area under the curve (AUC).Results High baseline OMP was associated with significantly longer survival and predicted patient response to combination anti-PD-(L)1 therapy in the discovery and EV cohorts. OMP complemented the PD-L1 TPS with superior predictive sensitivity (p=0.02). In the PD-L1 TPS

The detection of the impurity rate in machine-picked seed cotton is crucial for precision agriculture. This study proposes a novel Cotton-YOLO-Seg cotton-impurity instance segmentation algorithm based on the you only look once version 8 small segmentation model (Yolov8s-Seg). The algorithm achieves precise pixel-level segmentation of cotton and impurities in seed cotton images and establishes a detection model for the impurity rate, enabling accurate detection of the impurity rate in machine-picked cotton. The proposed algorithm removes the Pyramid 4 (P4) feature layer and incorporates Multi-Scale Convolutional Block Attention (MSCBCA) that integrates the Convolutional Block Attention Module (CBAM) and Multi-Scale Convolutional Attention (MSCA) into the Faster Implementation of Cross Stage Partial Bottleneck with 2 Convolutions (C2f) module of the feature extraction network, forming a novel C2f_MSCBCA module. The SlimNeck structure is introduced in the feature fusion network by replacing the P4 feature layer with the small-target detection layer Pyramid 2 (P2). Additionally, transfer learning is employed using the Common Objects in Context (COCO) instance segmentation dataset. The analysis of 100 groups of cotton image samples shows that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) for impurity rate detection are 0.29%, 0.33%, and 3.70%, respectively, which are reduced by 52.46%, 48.44%, and 53.75% compared to the Yolov8s-seg model. The Precision (P), Recall (R), and mean Average Precision at an intersection over union of 0.5 (mAP@0.5) are 85.4%, 78.4%, and 80.8%, respectively, which are improved by 4.2%, 6.2%, and 6.4% compared to Yolov8s-seg model, significantly enhancing the segmentation performance of minor impurities. The Cotton-YOLO-Seg model demonstrates practical significance for precisely detecting the impurity rate in machine-picked seed cotton.

Abstract The prognostic indexes based on the combination of preoperative fibrinogen and systemic inflammatory indexes may have greater predictive value in esophageal squamous cell carcinoma (ESCC). It was found that the predictive ability of F-NLR was more valuable than other systemic inflammatory indexes. The preoperative F-NLR score was closely related to the TNM stage, and could be used as an important independent prognostic index for patients with ESCC. Then the nomogram model constructed by F-NLR and TNM stage had higher prognostic ability than that of AJCC stage for ESCC patients. Preoperative F-NLR is a new independent prognostic index and a potential marker for treatment response monitoring in patients with ESCC.

Claudin 18.2 (CLDN18.2)-targeting chimeric antigen receptor (CAR)-modified T cells are one of the few cell therapies currently producing an impressive therapeutic effect in treating solid tumors; however, their long-term therapeutic efficacy is not satisfactory with a short duration of response. Transgenic expression of interleukin (IL)-15 has been reported to promote T-cell expansion, survival, and function and enhance the antitumor activity of engineered T cells in vitro and in vivo. Therefore, this study aimed to explore whether IL-15 modification would increase the antitumor activity of CLDN18.2-targeting CAR-modified T (CAR-T) cells in immunocompetent murine tumor models. CLDN18.2-specific CAR-T cells with (H9 CAR-IL15) or without transgenic IL-15 expression (H9 CAR) were generated by retroviral transduction of mouse splenic T cells. In vitro, compared with H9 CAR T cells, H9 CAR-IL15 T cells exhibited better expansion and viability in the absence of antigen stimulation, with a less differentiated and T-cell exhausted phenotype; although IL-15 modification did not affect the production of effector cytokines and cytotoxic activity in the short-term killing assay, it moderately improved the in vitro recursive killing activity of CAR-T cells against CLDN18.2-expressing tumor cells. In vivo, H9 CAR T cells showed no antitumor activity against CLDN18.2-expressing pancreatic tumors in immunocompetent mice without lymphodepleting pretreatment; however, H9 CAR-IL15 T cells produced significant tumor-suppressive effects. Furthermore, H9 CAR-IL15 T cells exhibited greater in vivo expansion and tumor infiltration when combined with lymphodepleting preconditioning, resulting in superior antitumor activity in two murine tumor models and a survival advantage in one tumor model. We further demonstrated that recurrent tumors following H9 CAR-IL15 T-cell therapy downregulated CLDN18.2 expression, suggesting immune escape through the selection of antigen-negative cells under persistent CAR-T-cell immune pressure. In conclusion, our findings provide preclinical evidence supporting the clinical evaluation of IL-15-expressing CLDN18.2 CAR-T cells in patients with CLDN18.2-positive tumors.

Abstract The ratio of mean platelet volume (MPV) to count (PC) (MPV/PC) has been applied in the diagnosis and prognosis of various malignancies. However, the prognostic value of MPV/PC in gastric cancer has not been studied yet. This study aims to explore the prognostic value of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), combined neutrophil-platelet score (CNPS), systemic immune-inflammation index (SII) and MPV/PC in patients with resectable gastric cancer. In this study, the medical records of patients with gastric cancer in two centers were retrospectively analyzed. Kaplan–Meier and log-rank were tests applied to analyze the survival differences of patients with various inflammation indexes. A nomogram prognostic model was established to predict the 3- and 5-year survival rate of patients with resectable gastric cancer. In the two cohorts, Kaplan–Meier analysis that the postoperative survival time of gastric cancer patients with low MPV/PC, high NLR, high PLR and high SII was significantly shorter than that of patients with high MPV/PC, low NLR, low PLR or low SII. Compared with NLR, PLR, SII and CNPS, MPV/PC was more accurate in determining the prognosis of patients with gastric cancer than other indexes, and multivariate analysis confirmed that MPV/PC was an independent prognostic factor for patients with resectable gastric cancer. The nomogram model established based on tumor size, TNM stage and MPV/PC was more accurate than TNM stage in predicting the 3- and 5-year survival rate of patients with resectable gastric cancer. Preoperative MPV/PC is a new independent prognostic index and a potential marker for treatment response monitoring in patients with resectable gastric cancer. The nomogram model for postoperative prognosis of gastric cancer established based on MPV/PC, tumor size and TNM stage is helpful for developing more accurate and timely individualized therapeutic regimens.

Abstract Background A higher vitamin D intake improves the prognosis of early stage breast cancer (BC) patients. We hypothesized that vitamin D intake should refer to vitamin D receptor (VDR) expression. In order to prove this hypothesis, we first intend to evaluate the correlation between VDR expression and prognosis of BC patients using meta-analysis. Methods Literatures from PubMed, Embase, and the Cochrane Library (last update by May 20, 2020) were retrieved to find studies assessing the prognostic role of VDR in BC. The hazard ratios (HRs) for patients’ survival were extracted for pooled analyses. Subgroup analysis, sensitivity analysis and meta-regression were performed to explore the sources of heterogeneity. Results Seven articles containing eight studies with 2503 patients were enrolled. The results from the pooled analyses showed that the VDR expression generally had no relationship with BC patients’ overall survival (OS), disease-free survival (DFS), cancer-specific survival (CSS), and progression-free survival (PFS) (P > 0.05). Because only the number of studies exploring the relationship between VDR expression and OS is greater than five and there is heterogeneity, we explored the sources of heterogeneity of these studies. Subgroup analyses showed that the VDR expression in the nucleus had no relationship with OS, but high total VDR expression in the nucleus and cytoplasm was related to a better OS (pooled HR = 0.41; 95% CI = 0.18–0.95; P = 0.038). In addition, in subgroup of studies using cut-off values other than ‘immunoreactive score (IRS)>5’ and ‘IRS > 25′, high VDR expression was associated with a better OS (pooled HR = 0.47; 95% CI = 0.30–0.74; P = 0.001). Sensitivity analysis showed that the result pattern was not obviously affected by any single study. Meta-regression showed that the source of heterogeneity was not country (P = 0.657), pathological type (P = 0.614), molecular type (P = 0.423), staining location (P = 0.481), or cut-off value (P = 0.509). Conclusions The protein expression level of VDR in entire BC cells evaluated by immunohistochemistry is related to the OS of BC patients. It is expected that a more individualized vitamin D intake and a more accurate prognosis assessment can be recommended for BC patients based on the VDR expression. Of course, more preclinical and clinical studies are needed.

Abstract Background In recent years, the role of pre-treatment C-reactive protein/albumin ratio (CAR) in prognosis of esophageal cancer (EC) has been investigated by several studies. This meta-analysis aimed to provide a more accurate and objective assessment of the prognostic value of pre-treatment CAR in EC. Methods Studies assessing the role of pre-treatment CAR in prognosis of EC were searched from PubMed, Embase and the Cochrane Library (last update by April 16, 2019). The hazard ratios (HRs) of CAR and the corresponding 95% CIs for overall survival (OS) or cancer-specific survival (CSS) in EC were extracted for pooled analysis. Results A total of eight observational studies including 2255 patients were collected. The pooled analysis showed that high CAR was related to worse OS in EC (pooled HR = 1.81; 95% CI = 1.40–2.35; P

The systemic inflammation response index (SIRI) has been revealed to be closely related to the prognosis of a variety of tumors. Whether the dynamic change in SIRI before and after surgery can be used to judge the prognosis of patients after radical gastrectomy has not yet been studied. In this study, the predictive ability of preoperative SIRI and changes in SIRI before and after surgery for the survival rate of gastric cancer patients was evaluated in two independent cohorts. It was found that SIRI was closely related to TNM staging. The higher the TNM stage, the higher the proportion of patients with a high SIRI. However, SIRI was not related to any other clinicopathological parameters. Kaplan-Meier survival analysis showed that a high SIRI was associated with poor prognosis in gastric cancer patients in the original cohort and in the validation cohort. SIRI, NLR, PLR, and MLR could be used to judge the prognosis of patients with operable gastric cancer. However, multivariate analysis suggested that only SIRI was an independent prognostic factor for patients with operable gastric cancer. In addition, the change in SIRI at 4 to 6 weeks after surgery compared with SIRI before surgery was closely related to the survival of gastric cancer patients. Compared with the unchanged group (absolute variation 50% had a worse OS, while patients with a SIRI decrease >50% had a better prognosis. In conclusion, SIRI can be used as a reliable index to evaluate the prognosis of patients with operable gastric cancer, and the dynamic change in SIRI before and after surgery is significantly related to the prognosis of patients with gastric cancer.

Background. The systemic immune-inflammation index (SII), based on peripheral lymphocytes, neutrophils, and platelet count, has been used as a prognostic marker for several tumors. However, use of the SII has not been reported for gastric cancer. Methods. We evaluated the prognostic value of the SII in primary and validation cohorts. We also established an effective prognostic nomogram for gastric cancer based on R language. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C index) and a calibration curve and were compared with TNM classifications. Results. The Kaplan-Meier survival analysis results showed that the high SII was associated with poor prognosis of gastric cancer patients in the primary and validation cohorts. SII proved to be related to tumor location, histological grade, tumor size, TNM stage, and perineural infiltration in patients with gastric cancer and was an independent prognostic factor for patients with gastric cancer. SII has a better predictive ability than other existing prognostic indexes based on inflammation, such as NLR, PLR, and MLR. The nomogram established can accurately predict the 3- and 5-year survival rates of patients with gastric cancer after operation, and its accuracy is significantly higher than that of the 8th edition of the AJCC staging system. Conclusion. SII can independently predict the overall survival of patients with gastric cancer after operation, which is superior to the existing systemic inflammatory indexes. The prognostic nomogram based on SII is a reliable model for predicting the postoperative survival of patients with gastric cancer. [ABSTRACT FROM AUTHOR]