scholarly journals Tackling Immune Targets for Breast Cancer: Beyond PD-1/PD-L1 Axis

2021 ◽  
Vol 11 ◽  
Author(s):  
Yasser Tabana ◽  
Isobel S. Okoye ◽  
Arno Siraki ◽  
Shokrollah Elahi ◽  
Khaled H. Barakat
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Checkpoint Inhibitors ◽  
Clinical Results ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Tumor Immune Microenvironment ◽  
Immunosuppressive Microenvironment ◽  
Problem Drug ◽  
Treat Breast Cancer

The burden of breast cancer is imposing a huge global problem. Drug discovery research and novel approaches to treat breast cancer have been carried out extensively over the last decades. Although immune checkpoint inhibitors are showing promising preclinical and clinical results in treating breast cancer, they are facing multiple limitations. From an immunological perspective, a recent report highlighted breast cancer as an “inflamed tumor” with an immunosuppressive microenvironment. Consequently, researchers have been focusing on identifying novel immunological targets that can tune up the tumor immune microenvironment. In this context, several novel non-classical immune targets have been targeted to determine their ability to uncouple immunoregulatory pathways at play in the tumor microenvironment. This article will highlight strategies designed to increase the immunogenicity of the breast tumor microenvironment. It also addresses the latest studies on targets which can enhance immune responses to breast cancer and discusses examples of preclinical and clinical trial landscapes that utilize these targets.

scholarly journals Therapeutic Associations Comprising Anti-PD-1/PD-L1 in Breast Cancer: Clinical Challenges and Perspectives

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 5999
Author(s):  
Fanny Ledys ◽  
Laura Kalfeist ◽  
Loick Galland ◽  
Emeric Limagne ◽  
Sylvain Ladoire
Keyword(s):  
Breast Cancer ◽  
Tumor Targeting ◽  
Checkpoint Inhibitors ◽  
Metastatic Breast ◽  
Therapeutic Approaches ◽  
Cytotoxic Effects ◽  
Tumor Immune Microenvironment ◽  
Immunological Effects ◽  
Immunosuppressive Microenvironment ◽  
Immune Exclusion

Despite a few cases of long-responder patients, immunotherapy with anti-PD-(L)1 has so far proved rather disappointing in monotherapy in metastatic breast cancer, prompting the use of synergistic therapeutic combinations incorporating immunotherapy by immune-checkpoint inhibitors. In addition, a better understanding of both the mechanisms of sensitivity and resistance to immunotherapy, as well as the immunological effects of the usual treatments for breast cancer, make it possible to rationally consider this type of therapeutic combination. For several years, certain treatments, commonly used to treat patients with breast cancer, have shown that in addition to their direct cytotoxic effects, they may have an impact on the tumor immune microenvironment, by increasing the antigenicity and/or immunogenicity of a “cold” tumor, targeting the immunosuppressive microenvironment or counteracting the immune-exclusion profile. This review focuses on preclinical immunologic synergic mechanisms of various standard therapeutic approaches with anti-PD-(L)1, and discusses the potential clinical use of anti-PD-1/L1 combinations in metastatic or early breast cancer.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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