CD3, CD8, IFN-γ, tumor and stroma inflammatory cells as prognostic indicators for surgically resected SCLC: evidences from a 10-year retrospective study and immunohistochemical analysis.

Current clinical guidelines limit surgical intervention to patients with cT1-2N0M0 small cell lung cancer (SCLC). Our objective was to reassess the role of surgery in SCLC management, and explore novel prognostic indicators for surgically resected SCLC. We reviewed all patients diagnosed with SCLC from January 2011 to April 2021 in our institution. Survival analysis was conducted using the Kaplan-Meier method, and independent prognostic factors were assessed through the Cox proportional hazard model. In addition, immunohistochemistry (IHC) staining was performed to evaluate the predictive value of selected indicators in the prognosis of surgically resected SCLC patients. In the study, 177 SCLC patients undergoing surgical resection were ultimately included. Both univariate and multivariate Cox analysis revealed that incomplete postoperative adjuvant therapy emerged as an independent risk factor for adverse prognosis (p < 0.001, HR 2.96). Survival analysis revealed significantly superior survival among pN0-1 patients compared to pN2 patients (p < 0.0001). No significant difference in postoperative survival was observed between pN1 and pN0 patients (p = 0.062). Patients with postoperative stable disease (SD) exhibited lower levels of tumor inflammatory cells (TIC) (p = 0.0047) and IFN-γ expression in both area and intensity (p < 0.0001 and 0.0091, respectively) compared to those with postoperative progressive disease (PD). Conversely, patients with postoperative SD showed elevated levels of stromal inflammatory cells (SIC) (p = 0.0453) and increased counts of CD3+ and CD8+ cells (p = 0.0262 and 0.0330, respectively). Survival analysis indicated that high levels of SIC, along with low levels of IFN-γ+ cell area within tumor tissue, may correlate positively with improved prognosis in surgically resected SCLC (p = 0.017 and 0.012, respectively). In conclusion, the present study revealed that the patients with pT1-2N1M0 staging were a potential subgroup of SCLC patients who may benefit from surgery. Complete postoperative adjuvant therapy remains an independent factor promoting a better prognosis for SCLC patients undergoing surgical resection. Moreover, CD3, CD8, IFN-γ, TIC, and SIC may serve as potential indicators for predicting the prognosis of surgically resected SCLC.

CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer.

BACKGROUND: Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC. METHOD: Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application. RESULTS: A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC. CONCLUSION: The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.

Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury.

BACKGROUND AND AIMS: Some drug-induced liver injury (DILI) cases may become chronic, even after drug withdrawal. Radiomics can predict liver disease progression. We established and validated a predictive model incorporating the clinical characteristics and radiomics features for predicting chronic DILI. METHODS: One hundred sixty-eight DILI patients who underwent liver gadolinium-diethylenetriamine pentaacetate-enhanced magnetic resonance imaging were recruited. The patients were clinically diagnosed using the Roussel Uclaf causality assessment method. Patients who progressed to chronicity or recovery were randomly divided into the training (70%) and validation (30%) cohorts, respectively. Hepatic T1-weighted images were segmented to extract 1672 radiomics features. Least absolute shrinkage and selection operator regression was used for feature selection, and Rad-score was constructed using support vector machines. Multivariable logistic regression analysis was performed to build a clinic-radiomics model incorporating clinical characteristics and Rad-scores. The clinic-radiomics model was evaluated for its discrimination, calibration, and clinical usefulness in the independent validation set. RESULTS: Of 1672 radiomics features, 28 were selected to develop the Rad-score. Cholestatic/mixed patterns and Rad-score were independent risk factors of chronic DILI. The clinic-radiomics model, including the Rad-score and injury patterns, distinguished chronic from recovered DILI patients in the training (area under the receiver operating characteristic curve [AUROC]: 0.89, 95% confidence interval [95% CI]: 0.87-0.92) and validation (AUROC: 0.88, 95% CI: 0.83-0.91) cohorts with good calibration and great clinical utility. CONCLUSION: The clinic-radiomics model yielded sufficient accuracy for predicting chronic DILI, providing a practical and non-invasive tool for managing DILI patients.

The potential effect of HBV vaccination on off-treatment HBsAg reversion after interferon-induced HBsAg clearance.

Off-treatment HBsAg reversion occurs in a considerable number of chronic hepatitis B(CHB) patients after IFN(interferon)-induced HBsAg clearance. HBV vaccination protects the general population against HBV infection. However, it remains unclear whether HBV vaccination could prevent off-treatment HBsAg reversion in CHB patients with HBsAg clearance. CHB patients (n = 199) with HBsAg clearance were included in the current study, comprising spontaneous HBsAg clearance group (n = 51), NA (nucleoside/nucleotide analogues)-induced group (n = 36) and IFN-induced group (n = 112). Log-rank test was performed to compare the cumulative incidences of HBsAg reversion between groups. Cox regression model was used to identify the factors associated with off-treatment HBsAg reversion. The 5-year cumulative incidence of HBsAg reversion in IFN-induced group was significantly higher than that in NA-induced group or spontaneous group (27.6% vs. 3.3% vs. 8.1%, both p < .05). In IFN-induced group, 66.7% of CHB patients received HBV vaccination. The cumulative incidence of HBsAg reversion in individuals with strong responses to HBV vaccination (HBsAb level >100mIU/ml) was significantly lower than that in those with weak responses to HBV vaccination (HBsAb level ≤100mIU/ml) or without HBV vaccination in IFN-induced group (7.7% vs. 58.5% vs. 31.9%, both p < .05). Multivariate Cox regression analysis confirmed strong responses to HBV vaccination were independently associated with a lower cumulative incidence of HBsAg reversion after IFN-induced HBsAg clearance (HR = 0.246, 95%CI: 0.066-0.907, p = .035). HBV vaccination has potential to prevent off-treatment HBsAg reversion in CHB patients after IFN-induced HBsAg clearance via a sufficiently high level of HBsAb, helping clinicians optimize the clinical management of such patients.

qHBsAg for the Identification of Liver Histological Abnormalities in HBeAg-Negative Chronic Hepatitis B Patients with Normal and Mildly Elevated ALT Levels.

Backgrounds: Noninvasive detection of histological abnormalities remains challenging in patients with HBeAg-negative chronic HBV infection with normal or mildly elevated levels of alanine aminotransferase (ALT). This study aimed to assess the utility of serum quantitative hepatitis B surface antigen (qHBsAg) in identifying significant histological lesions in this population. Methods: This is a single-center study with retrospective analysis of 392 treatment-naive patients of HBeAg-negative chronic HBV infection with normal or mildly elevated levels of ALT. Results: In this cohort, significant necroinflammation and fibrosis were found in 69.4% and 61.5% of patients, respectively. Patients with qHBsAg >1000 IU/mL (N = 236) had more hepatic inflammation of ≥G2 (75.4% vs. 60.9%, P < 0.01) or fibrosis ≥ S2 (66.1% vs. 54.5%, P < 0.05) compared to those without (N = 156). Serum HBsAg (cutoff point = 1000 IU/mL), aspartate aminotransferase (AST) level (cutoff point = 25 IU/L), age (cutoff point = 40 years), and HBV family history were identified as independent predictors of significant histological abnormalities in multivariate logistic analysis. Conclusions: A significantly higher proportion of patients with histological abnormalities were found in patients with qHBsAg >1000 IU/mL than those without. The qHBsAg level together with age, AST, and family history of HBV infection could be used as an algorithm to help noninvasive patient selection for antiviral therapy.

Alteration of Bile Acids and Omega-6 PUFAs Are Correlated With the Progression and Prognosis of Drug-Induced Liver Injury.

Background & Aims: Drug-induced liver injury (DILI) is one of the leading causes of liver failure with some of the patients progressed to chronic DILI. The mechanisms underlying the severity and chronicity of DILI are poorly elucidated and the biomarkers are limited. Metabolites and gut microbiota played a crucial role in the development of various liver diseases. Herein, a systematic analysis of serum metabolites and gut microbiota was performed in DILI patients, aiming to identify metabolites correlated with the progression and clinical prognosis of DILI. Methods: Various serum metabolites were quantitated using a metabolite array technology in this prospective study. Gut microbiome compositions and the expression profiles of liver genes were determined in patients with DILI and healthy controls. Results: Metabolomic analysis revealed that bile acids (BAs) and polyunsaturated fatty acids (PUFAs) were closely related to DILI severity and chronicity respectively. The ratios of serum primary/secondary BAs and omega-6/omega-3 PUFAs were elevated in DILI patients. A model established by adrenic acid (AdA) and aspartic acid (Asp) exerts good performance for predicting the chronicity of DLIL. Hepatic transcriptome revealed enhanced expression of PUFA peroxidation and supressed expression of BA synthesis related genes in DILI patients. In addition, Lactic acid bacteria and BA converting bacteria were increased in gut of DILI patients. Besides, elevated serum malondialdehyde (MDA) and fibroblast growth factor 19 (FGF19) was observed in DILI patients. Conclusion: BAs and PUFAs could be potent markers for the severity and chronicity of DILI respectively. The panel of AdA and Asp could be ideal predictive model for the risk of chronicity at the acute stage of DILI. Gut microbiota might act as a negative feedback mechanism to maintain the homeostasis of BAs and PUFAs via FGF19 signalling and PUFA saturation, respectively. Our study revealed novel biomarkers for severe and chronic DILI and provided new therapeutic targets for DILI.