Precision Oncology in Checkpoint Immunotherapy: Leveraging Predictive Biomarkers for Personalized Treatment

This Research Topic is apart of the series "The Future of Checkpoint Immunotherapy: New Roads Ahead". Please see the series here.

We are pleased to announce a special collection focusing on the dynamic field of precision oncology within the domain of checkpoint immunotherapy. As cancer treatment evolves, there is increasing recognition of the significance of personalized medicine approaches that tailor therapies to individual patients based on specific biomarkers and molecular characteristics of their tumors. This collection aims to explore the investigation of predictive biomarkers, including tumor mutational burden (TMB), microsatellite instability (MSI), and immune cell profiling, for patient stratification and personalized treatment selection in the context of checkpoint immunotherapy.

Immunotherapy has revolutionized cancer treatment by harnessing the immune system to target and eliminate cancer cells. However, response rates vary among patients, necessitating biomarkers that can accurately predict treatment outcomes and guide clinical decision-making. TMB, MSI, and immune cell profiling have emerged as promising biomarkers offering valuable insights into the tumor microenvironment and host immune response.

We invite submissions of original research, reviews, and clinical trials exploring the utility of these biomarkers in checkpoint immunotherapy. Topics may include the predictive value of TMB and MSI in various cancer types, and the role of immune cell profiling techniques such as flow cytometry, single-cell RNA sequencing, and spatial transcriptomics, which will be examined for their ability to characterize the immune landscape of tumors and inform treatment decisions. and advances in computational modeling and machine learning algorithms for developing predictive signatures for immunotherapy response.

Furthermore, this collection will highlight advances in computational modeling and machine learning algorithms for integrating multi-omics data and developing predictive signatures for immunotherapy response. By leveraging big data analytics and artificial intelligence, researchers aim to refine existing biomarker panels and identify novel predictive markers that enhance the precision and efficacy of checkpoint immunotherapy.

This special collection seeks to provide a comprehensive resource for clinicians, researchers, and industry stakeholders invested in advancing personalized medicine approaches in checkpoint immunotherapy. Through collaborative efforts and interdisciplinary research endeavors, we aim to accelerate the translation of predictive biomarkers into clinical practice, ultimately improving outcomes for cancer patients worldwide.

Topic Editor Prof. Aristotelis Chatziioannou is the founder and CEO of e-NIOS Applications PC. The other Topic Editors declare no competing interests with regard to the Research Topic subject.