Prevalence and prognostic role of PD-L1 in patients with gynecological cancers: A systematic review and meta-analysis

Effectiveness and safety of PD-1/PD-L1 inhibitors monotherapy in patients with endometrial cancer

BACKGROUND

Studies evaluating the effectiveness of immune checkpoint inhibitors (ICI) for endometrial cancer (EC) are limited. This study aimed to assess the efficacy of PD-1/PD-L1 inhibitors as monotherapy for EC by conducting a meta-analysis. The predictive significance of MMR status, a biomarker for ICI response, also required further investigation.

METHODS

A systematic literature search was conducted in English databases until September 2023. The analysis included objective response rate (ORR), disease control rate (DCR), adverse events (AEs), and odds ratios (OR), along with their corresponding 95% confidence intervals (CI).

RESULTS

There were twelve trials totaling 685 individuals. PD-1/PD-L1 inhibitor monotherapy resulted in an ORR for 34% (95% CI = 24-44%) of the pooled EC patients. Subgroup analysis revealed a significantly higher ORR in dMMR EC (45%) compared to pMMR EC (8%), with an OR of 6.36 (95% CI = 3.64-11.13). The overall DCR was 42%, with dMMR EC at 51% and pMMR EC at 30% (OR = 2.61, 95% CI = 1.69-4.05). Grade three or higher adverse events (AEs) occurred in 15% of cases (95% CI = 9-24%) of the pooled incidence of AEs, which was 68% (95% CI = 65-72%).

CONCLUSIONS

This meta-analysis provides significant evidence for the effectiveness of PD-1/PD-L1 inhibitors as monotherapy for EC. Notably, dMMR EC patients demonstrated superior treatment efficacy with PD-1/PD-L1 inhibitor immunotherapy. Further research is required to explore subclassifications of EC based on dMMR molecular subtypes, enabling improved treatment strategies and outcomes for EC patients.

The emerging and challenging role of PD-L1 in patients with gynecological cancers: An updating review with clinico-pathological considerations

Over recent years, there has been significant progress in the development of immunotherapeutic molecules designed to block the PD-1/PD-L1 axis. These molecules have demonstrated their ability to enhance the immune response by prompting T cells to identify and suppress neoplastic cells. PD-L1 is a type 1 transmembrane protein ligand expressed on T lymphocytes, B lymphocytes, and antigen-presenting cells and is considered a key inhibitory checkpoint involved in cancer immune regulation. PD-L1 immunohistochemical expression in gynecological malignancies is extremely variable based on tumor stage and molecular subtypes. As a result, a class of monoclonal antibodies targeting the PD-1 receptor and PD-L1, known as immune checkpoint inhibitors, has found successful application in clinical settings. In clinical practice, the standard method for identifying suitable candidates for immune checkpoint inhibitor therapy involves immunohistochemical assessment of PD-L1 expression in neoplastic tissues. The most commonly used PD-L1 assays in clinical trials are SP142, 28-8, 22C3, and SP263, each of which has been rigorously validated on specific platforms. Gynecologic cancers encompass a wide spectrum of malignancies originating from the ovaries, uterus, cervix, and vulva. These neoplasms have shown variable response to immunotherapy which appears to be influenced by genetic and protein expression profiles, including factors such as mismatch repair status, tumor mutational burden, and checkpoint ligand expression. In the present paper, an extensive review of PD-L1 expression in various gynecologic cancer types is discussed, providing a guide for their pathological assessment and reporting.

Bridging the divide: unveiling mutual immunological pathways of cancer and pregnancy

The juxtaposition of two seemingly disparate physiological phenomena within the human body-namely, cancer and pregnancy-may offer profound insights into the intricate interplay between malignancies and the immune system. Recent investigations have unveiled striking similarities between the pivotal processes underpinning fetal implantation and successful gestation and those governing tumor initiation and progression. Notably, a confluence of features has emerged, underscoring parallels between the microenvironment of tumors and the maternal-fetal interface. These shared attributes encompass establishing vascular networks, cellular mobilization, recruitment of auxiliary tissue components to facilitate continued growth, and, most significantly, the orchestration of immune-suppressive mechanisms.Our particular focus herein centers on the phenomenon of immune suppression and its protective utility in both of these contexts. In the context of pregnancy, immune suppression assumes a paramount role in shielding the semi-allogeneic fetus from the potentially hostile immune responses of the maternal host. In stark contrast, in the milieu of cancer, this very same immunological suppression fosters the transformation of the tumor microenvironment into a sanctuary personalized for the neoplastic cells.Thus, the striking parallels between the immunosuppressive strategies deployed during pregnancy and those co-opted by malignancies offer a tantalizing reservoir of insights. These insights promise to inform novel avenues in the realm of cancer immunotherapy. By harnessing our understanding of the immunological events that detrimentally impact fetal development, a knowledge grounded in the context of conditions such as preeclampsia or miscarriage, we may uncover innovative immunotherapeutic strategies to combat cancer.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.