Cancer immunotherapy is a current intervention that aims to augment, reverse suppression or modify immune cells towards anti-cancer functions. T-cells, important effectors from the adaptive immunity, respond to tumor-associated genetic changes exemplified by newly expressed patient-specific neoantigens that, like microbial antigens, are not subject to central immune tolerance. Frequencies of neoantigen-specific T-cells located in the tumor tissue, the sentinel lymphatics and circulation are low and, thus, difficult to track and to exploit for therapeutic benefit. The recent development of monoclonal antibody-based immunotherapy targeting inhibitory receptors on T-cells, the so-called “immune checkpoint blockade therapy,” has fundamentally changed the outlook on metastatic cancer offering new hope for long-term survival in several malignancies.
More recently, innate immune cells (e.g. cytotoxic natural killer (NK) cells, ILC2 or macrophages) have also been suggested as potential immunotherapy targets with the aim to enhance their anti-cancer efficacies. Nevertheless, clinical efficiencies of T cell targeted immunotherapies remain low and vary across different malignant diseases. While treatment efficacy commonly occurs in patients whose tumors are infiltrated by high numbers of effector cells, multiple primary and acquired mechanisms of therapy resistance have recently been described. Patients with primary resistance mechanisms do not respond to immunotherapy, while patients with acquired resistance mechanisms initially respond, but subsequently acquire resistance to immunotherapy, eventually leading to relapse. Multiple tumor intrinsic and extrinsic acquired resistance mechanisms have been described, but despite intensive research efforts, many of them are still poorly understood. Tumor- and host-intrinsic properties are both key to guide treatment selection and to estimate risk of relapse due to primary or acquired resistance mechanisms. With the advancement of technologies providing high-dimensional data, artificial intelligence and machine learning algorithms, we slowly uncover the complexity of the highly heterogenous landscapes of malignant diseases. A better understanding of resistance mechanisms will allow clinicians and scientists to design novel targeting approaches or to optimally orchestrate combinatory treatments aiming to overcome resistance mechanisms with the ultimate goal to improve clinical outcomes.
In this Research Topic, we aim to provide a current overview of the field with original contributions on the mechanisms of treatment efficacy or primary/acquired treatment resistance across divers malignant disease types. We welcome submissions of Original Research articles, Reviews and Perspectives covering efficacy and resistance mechanisms to immunotherapies in human malignancies and pre-clinical cancer models, and including bioinformatic-based studies, with a focus on the following subtopics:
• Primary versus acquired resistance mechanisms (e.g. TGFß and TNF superfamily, shedding of tumor cell-related immunosurveillance ligands, ß-catenin, immune checkpoint molecule heterogeneity and landscape, IFN pathways, effector cytotoxic lymphocyte epigenetics, myeloid and NK cell tumor infiltration, mutational burden)
• Tumor- (e.g. tumor immunogenicity, oncogenic drivers, PD-L1 expression), immune- (e.g. hot tumors vs cold tumors) or microbiota-related (e.g. modulation of anti-tumor immune responses and how they relate to anti-PD-1 and anti-CTLA-4 treatments) efficacy or resistance
• Chronic inflammation, immunosenescence, impact of age, context of different T and ILC subsets, breadth of the immune response in tumor (B-cells, Trm etc).
Dr. Koguchi receives funding from Tesaro, a GSK company, Bristol-Myers Squibb, and Shimadzu Corporation.. Dr. Vujanovic is a co-inventor of a methodology licensed to INmune Bio, Inc. where a selective inhibitor of soluble TNF can be used to prevent or treat malignancies. Dr. Hansen is the recipient of a private grant from Eurostars Eureka. The other Topic Editors declare no competing interests.
Cancer immunotherapy is a current intervention that aims to augment, reverse suppression or modify immune cells towards anti-cancer functions. T-cells, important effectors from the adaptive immunity, respond to tumor-associated genetic changes exemplified by newly expressed patient-specific neoantigens that, like microbial antigens, are not subject to central immune tolerance. Frequencies of neoantigen-specific T-cells located in the tumor tissue, the sentinel lymphatics and circulation are low and, thus, difficult to track and to exploit for therapeutic benefit. The recent development of monoclonal antibody-based immunotherapy targeting inhibitory receptors on T-cells, the so-called “immune checkpoint blockade therapy,” has fundamentally changed the outlook on metastatic cancer offering new hope for long-term survival in several malignancies.
More recently, innate immune cells (e.g. cytotoxic natural killer (NK) cells, ILC2 or macrophages) have also been suggested as potential immunotherapy targets with the aim to enhance their anti-cancer efficacies. Nevertheless, clinical efficiencies of T cell targeted immunotherapies remain low and vary across different malignant diseases. While treatment efficacy commonly occurs in patients whose tumors are infiltrated by high numbers of effector cells, multiple primary and acquired mechanisms of therapy resistance have recently been described. Patients with primary resistance mechanisms do not respond to immunotherapy, while patients with acquired resistance mechanisms initially respond, but subsequently acquire resistance to immunotherapy, eventually leading to relapse. Multiple tumor intrinsic and extrinsic acquired resistance mechanisms have been described, but despite intensive research efforts, many of them are still poorly understood. Tumor- and host-intrinsic properties are both key to guide treatment selection and to estimate risk of relapse due to primary or acquired resistance mechanisms. With the advancement of technologies providing high-dimensional data, artificial intelligence and machine learning algorithms, we slowly uncover the complexity of the highly heterogenous landscapes of malignant diseases. A better understanding of resistance mechanisms will allow clinicians and scientists to design novel targeting approaches or to optimally orchestrate combinatory treatments aiming to overcome resistance mechanisms with the ultimate goal to improve clinical outcomes.
In this Research Topic, we aim to provide a current overview of the field with original contributions on the mechanisms of treatment efficacy or primary/acquired treatment resistance across divers malignant disease types. We welcome submissions of Original Research articles, Reviews and Perspectives covering efficacy and resistance mechanisms to immunotherapies in human malignancies and pre-clinical cancer models, and including bioinformatic-based studies, with a focus on the following subtopics:
• Primary versus acquired resistance mechanisms (e.g. TGFß and TNF superfamily, shedding of tumor cell-related immunosurveillance ligands, ß-catenin, immune checkpoint molecule heterogeneity and landscape, IFN pathways, effector cytotoxic lymphocyte epigenetics, myeloid and NK cell tumor infiltration, mutational burden)
• Tumor- (e.g. tumor immunogenicity, oncogenic drivers, PD-L1 expression), immune- (e.g. hot tumors vs cold tumors) or microbiota-related (e.g. modulation of anti-tumor immune responses and how they relate to anti-PD-1 and anti-CTLA-4 treatments) efficacy or resistance
• Chronic inflammation, immunosenescence, impact of age, context of different T and ILC subsets, breadth of the immune response in tumor (B-cells, Trm etc).
Dr. Koguchi receives funding from Tesaro, a GSK company, Bristol-Myers Squibb, and Shimadzu Corporation.. Dr. Vujanovic is a co-inventor of a methodology licensed to INmune Bio, Inc. where a selective inhibitor of soluble TNF can be used to prevent or treat malignancies. Dr. Hansen is the recipient of a private grant from Eurostars Eureka. The other Topic Editors declare no competing interests.
