Association between clinicopathological features and prognosis significance of PD-L1 expression in small cell lung cancer patients: a systemic review and meta-analysis

Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images

Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims to develop a deep learning radiomics (DLR)-based approach for predicting programmed death-ligand 1 (PD-L1) expression in patients with non-small cell lung cancer (NSCLC). Data from 352 NSCLC patients with known PD-L1 expression were collected, of which 48.29% (170/352) were tested positive for PD-L1 expression. Tumor regions of interest (ROI) were semi-automatically segmented based on CT images, and DL features were extracted using Residual Network 50. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and dimensionality reduction. Seven algorithms were used to build models, and the most optimal ones were identified. A combined model integrating DLR with clinical data was also developed. The predictive performance of each model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve analysis. The DLR model, based on CT images, demonstrated an AUC of 0.85 (95% confidence interval (CI), 0.82-0.88), sensitivity of 0.80 (0.74-0.85), and specificity of 0.73 (0.70-0.77) for predicting PD-L1 status. The integrated model exhibited superior performance, with an AUC of 0.91 (0.87-0.95), sensitivity of 0.85 (0.82-0.89), and specificity of 0.75 (0.72-0.80). Our findings indicate that the DLR model holds promise as a valuable tool for predicting the PD-L1 status in patients with NSCLC, which can greatly assist in clinical decision-making and the selection of personalized treatment strategies.

Prognostic value of PD-L1 expression in patients with anal cancer: a meta-analysis

Background: The present meta-analysis was performed to evaluate the prognostic and clinicopathological significance of PD-L1 in anal cancer (AC). Methods: Hazard ratios (HRs) and 95% CIs regarding overall survival (OS) and progression-free survival (PFS) were calculated based on PD-L1 levels. Results: According to the combined data, PD-L1 showed no significant relationship with OS (HR = 0.76; 95% CI = 0.35-1.67; p = 0.502) or PFS (HR = 0.88; 95% CI = 0.35-2.33; p = 0.789) in patients with AC. Based on subgroup analysis, PD-L1 overexpression significantly predicted prolonged OS (HR = 0.38; 95% CI = 0.17-0.84; p = 0.017) in tumor node metastasis stages I-III and inferior PFS (HR = 2.73; 95% CI = 1.32-5.65; p = 0.007) in patients with stage I-IV AC. Conclusion: PD-L1 level assessed by immunohistochemistry did not significantly predict survival outcomes in AC cases.

The role of TGF-β pathway alterations in immune regulation as a potential pan-cancer biomarker in immunotherapy

BACKGROUND

Depending on the context, the transforming growth factor beta (TGF-β) signaling pathway is involved in opposing cell processes of tumor suppression and tumor promotion. However, the effects of TGF-β pathway on immunotherapy efficacy have not yet been systematically investigated.

METHODS

In this study, we have extracted the available data of whole-exome sequencing, messenger RNA (mRNA) expression, baseline characterization, and prognosis information of 10,912 pan-cancer patients from The Cancer Genome Atlas to explore the role of TGF-β pathway in immune regulation. Formalin-fixed, paraffin-embedded tissue samples from 6,717 Chinese cancer patients assayed by next-generation sequencing (NGS) were used as a validation cohort (3DMed cohort). Data sets from the public MSK (Memorial Sloan Kettering Cancer Center) cohort (N=1,610) were used to explore the association of TGF-β pathway with immunotherapy effects.

RESULTS

The results showed that TGF-β pathway alteration was significantly correlated with high microsatellite instability (MSI), high tumor mutational burden, and high neoantigen burden (TNB) (P<0.001 for each). Consistently, the pathway mutations were associated with distinct patterns of immune-related gene expression and tumor-infiltrating immune cells. Patients with TGF-β pathway mutations exhibited significantly worse prognosis than did the wild-type patients regardless of the interventions [overall survival (OS): hazard ratio (HR) 1.20; 95% confidence interval (CI): 1.08-1.33; P=0.001]. However, when treated with immune checkpoint inhibitors (ICIs), superior survival benefit was observed in patients from the mutation group versus the wild-type group (OS: HR 0.73; 95% CI: 0.61-0.88; P=0.001).

CONCLUSIONS

Collectively, our study suggested that mutations in TGF-β pathway may be associated with positive immune regulation and better efficacy of immunotherapy.