Immune checkpoint inhibitors (ICIs) are a major class of immuno-oncology therapeutics that have significantly improved the prognosis of various cancers, both in (neo)adjuvant and metastatic settings. Unlike other conventional therapies, ICIs elicit antitumor effects by enhancing host immune systems to eliminate cancer cells. There are 3 approved ICI classes by the U.S. Food and Drug Administration: inhibitors targeting cytotoxic T lymphocyte associated antigen 4, programmed death 1/programmed death-ligand 1, and lymphocyte-activation gene 3, with many more in development. ICIs are commonly associated with distinct toxicities, known as immune-related adverse events, which can arise during treatment or less frequently be of late onset, usually relating to excessive activation of the immune system. Acute cardiovascular immune-related adverse events such as myocarditis are rare; however, data suggesting chronic cardiovascular sequelae are emerging. This review presents the current landscape of ICIs in oncology, with a focus on important aspects relevant to cardiology. 

https://doi.org/10.1016/j.jaccao.2022.09.004

Immune Checkpoint Inhibitor Therapy in Oncology

Background There is a crosstalk between gut microbiota (GM) and cancer immunotherapy (CI). The purpose of this study is to use bibliometric analysis to identify the highly cited papers relating to GM/CI and explore the research status and development trends of the GM/CI research. Methods A literature search regarding GM/CI publications from 2012 to 2021 was undertaken on July 4, 2022. The article titles, journals, authors, institutions, countries, total citations, keywords, and other information were extracted from the Science Citation Index Expanded (SCIE) of Web of Science Core Collection (WoSCC). The Bibliometrix of R package and VOSviewer were used for bibliometric analysis. Results A total of 665 papers were extracted. The number of papers has increased rapidly over the past decade, especially after 2018. The United States and China had the most publications and made great contributions to this field. Th5e Univ Texas MD Anderson Canc Ctr and Univ Paris Saclay were absolutely in the leading position in GM/CI. The most influential authors were Zitvogel L and Routy B. Frontiers in Immunology had the most publications and Science had the most total citations. Historical direct citation analysis explained the historical evolution in GM/CI. Highly cited papers and high-frequency keywords illustrated the current status and trends of GM/CI. Four clusters were identified and the important topics included the role of GM and antibiotics in CI, the methods of targeting GM to improve CI outcomes, the mechanism by which GM affects CI and the application of ICIs in melanoma. “Tumor microbiome”, “proton pump inhibitors” and “prognosis” may be the new focus of attention in the next few years. Conclusion This study filtered global publications on GM/CI correlation and analyzed their bibliometric characteristics, identified the most cited papers in GM/CI, and gained insight into the status, hotspots and trends of global GM/CI research, which may inform researchers and practitioners of future directions.

/pdf/global-research-trends-on-the-links-between-gut-microbiota-byth3cw0.pdf

Global research trends on the links between gut microbiota and cancer immunotherapy: A bibliometric analysis (2012-2021)

In recent years, Onco-immunotherapies (OIMTs) have been shown to be a potential therapy option for cancer. Several immunotherapies have received regulatory approval, while many others are now undergoing clinical testing or are in the early stages of development. Despite this progress, a large number of challenges to the broad use of immunotherapies to treat cancer persists. To make immunotherapy more useful as a treatment while reducing its potentially harmful side effects, we need to know more about how to improve response rates to different types of immunotherapies. Nanocarriers (NCs) have the potential to harness immunotherapies efficiently, enhance the efficiency of these treatments, and reduce the severe adverse reactions that are associated with them. This article discusses the necessity to incorporate nanomedicines in OIMTs and the challenges we confront with current anti-OIMT approaches. In addition, it examines the most important considerations for building nanomedicines for OIMT, which may improve upon current immunotherapy methods. Finally, it highlights the applications and future scenarios of using nanotechnology. 

Exploring nanocarriers as innovative materials for advanced drug delivery strategies in onco-immunotherapies.

Numerous studies have reported the vital roles of circular RNA (circRNA)-based competitive endogenous RNA (ceRNA) regulatory networks in cancers. Here, we established a non-small-cell lung cancer (NSCLC)-related circRNA–miRNA–mRNA axis and estimated its diagnostic value in NSCLC. The circ_0061235–miR-3180-5p–PPM1L axis was constructed by small RNA deep sequencing, bioinformatics databases, and preliminary testing. The serum levels of the selected circ_0061235, miR-3180-5p, and PPM1L were quantified using quantitative polymerase chain reaction. Receiver operating characteristic analyses were conducted to evaluate the diagnostic power. The levels of circ_0061235, miR-3180-5p, and PPM1L showed close correlations according to the ceRNA regulation rule. They were significantly dysregulated in NSCLC and showed the diagnostic ability to discriminate between healthy and NSCLC, and remarkably, between benign lung tumors and NSCLC. Additionally, the down-regulated levels of hsa_circ_0061235, the up-regulated levels of miR-3180-5p, and the decreased levels of PPM1L were correlated to more aggressive features of NSCLC, such as lymph node metastasis, distant metastasis, and higher stages. Intriguingly, compared to the single circ_0061235, miR-3180-5p, PPM1L, and traditional tumor markers, the diverse combinations of circ_0061235, miR-3180-5p, and PPM1L showed much higher sensitivity and specificity to differentiate greater or lesser severity of NSCLC. GO annotation and KEGG pathway analyses revealed the underlying role of the circ_0061235–miR-3180-5p–PPM1L axis in NSCLC. We established a specific circRNA–miRNA–mRNA network with higher sensitivity and specificity to diagnose NSCLC, particularly more aggressive NSCLC, providing a new strategy for further developing tumor biomarkers. 

A circRNA-based ceRNA network shows its diagnostic value in non-small cell lung cancer.

Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR-TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors. We propose a deep learning based CoxCC model based on quantitative brain magnetic resonance imaging (MRI), a prognostic index and clinical data; the model can be used to predict progression-free survival (PFS) after EGFR-TKI therapy in advanced EGFR-mutant NSCLC.This retrospective single-center study included 271 patients receiving first-line EGFR-TKI targeted therapy in 2018-2019. Among them, 72 patients who had brain metastases before receiving first-line EGFR-TKI treatment. Three radiomic features were extracted from pretreatment brain MRI images. A CoxCC model for the progression risk stratification of EGFR-TKI treatment was proposed on the basis of MRI radiomics, clinical features, and a prognostic index. We performed time-dependent PFS predictions to evaluate the performance of the CoxCC model.The CoxCC model based on a prognostic index, clinical features, and radiomic features of brain metastasis exhibited higher performance than clinical features combined with indexes previously proposed for determining the prognosis of brain metastasis, including recursive partitioning analysis, diagnostic-specific graded prognostic assessment, graded prognostic assessment for lung cancer using molecular markers (lung-molGPA), and modified lung-molGPA, with c-index values of 0.75, 0.67, 0.66, 0.65, and 0.65, respectively. The model achieved areas under the curve of 0.88, 0.73, 0.92, and 0.90 for predicting PFS at 3, 6, 9 and 12 months, respectively. PFS significantly differed between the high- and low-risk groups (p < 0.001).For patients with advanced-stage NSCLC with brain metastasis, MRI radiomics of brain metastases may predict PFS. The CoxCC model integrating brain metastasis radiomics, clinical features, and a prognostic index provided reliable multi-time-point PFS predictions for patients with advanced NSCLC and brain metastases receiving EGFR-TKI treatment. 

Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors

Simple Summary The current meta-analysis highlighted that proton pump inhibitors (PPIs) and histamine-2-receptor antagonists (H2RAs) could impact immune checkpoint inhibitors (ICIs) efficacy in NSCLC patients, highlighting the need for a deeper comprehension of factors involved in treatment response or resistance. Since the number of indications and NSCLC patients receiving ICIs is supposed to increase further soon, identifying the impact of these agents on NSCLC immunotherapy represents a compelling and urgent need regarding NSCLC. Abstract (1) Background: In recent years, immunotherapy has revolutionized the treatment landscape of non-small cell lung cancer (NSCLC), representing a therapeutic breakthrough in this field. Antacid agents such as proton pump inhibitors (PPIs) and histamine-2-receptor antagonists (H2RAs) are commonly prescribed for extended periods in NSCLC patients, and these drugs have the potential to modify the efficacy of immune checkpoint inhibitors (ICIs). (2) Materials and Methods: Herein, we conducted a systematic review and meta-analysis to investigate the impact of PPIs and H2RAs on progression-free survival (PFS) and overall survival (OS) among patients receiving immunotherapy for metastatic NSCLC. Effect measures for OS were Hazard Ratios (HRs) and 95% Confidence Intervals (CIs), which were extracted from available studies. Forest plots were used to assess HRs to describe the relationship between treatment and OS in the specified cohorts of patients. (3) Results: Six studies were included in the analysis, involving 2267 patients. The pooled HRs for OS and PFS were 1.4 (95% CI, 1.25–1.58) and 1.29 (95% CI, 1.17–1.43), respectively, suggesting that PPIs and H2RAs administration was negatively associated with PFS and OS. (4) Conclusion: Concomitant antacid use could modify the activity of ICIs in NSCLC patients.

/pdf/impact-of-proton-pump-inhibitors-and-histamine-2-receptor-qfkayfqn.pdf

Impact of Proton Pump Inhibitors and Histamine-2-Receptor Antagonists on Non-Small Cell Lung Cancer Immunotherapy: A Systematic Review and Meta-Analysis

In the last decade, Process Mining has become a significant field to help healthcare process experts understand and gain relevant insights about the processes they execute. One of the most challenging questions in Process Mining, and particularly in healthcare, typically is: how good are the discovered models? Previous studies have suggested approaches for comparing the (few) available discovery algorithms and measure their quality. However, a general and clear comparison framework is missing, and none of the analyzed algorithms exploits Markov Chains-based Models. 
In this paper, we propose and discuss effective ways for assessing both quality and performance of discovered models. This is done by focusing on a case study, where the pMiner tool is used for generating Markov Chains-based models, on a large set of real Clinical Guidelines and workflows.

pdf/on-the-comparison-of-markov-chains-based-models-in-process-37m9szce.pdf

On the Comparison of Markov Chains-based Models in Process Mining for Healthcare: A Case Study

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Impact of Proton Pump Inhibitors and Histamine-2-Receptor Antagonists on Non-Small Cell Lung Cancer Immunotherapy: A Systematic Review and Meta-Analysis

On the Comparison of Markov Chains-based Models in Process Mining for Healthcare: A Case Study