Over the decades, cancer immunotherapy has been a burgeoning science in medicine and an established pillar of therapeutic options for diverse cancer types. Leading the way to success is mainly the discovery and development of immune checkpoint inhibitors (ICIs) and chimeric antigen receptor (CAR) T cell-based therapies. Under immunotherapy, a portion of cancer patients enables to achieve a significant and durable remission with a longer survival time, particularly in those with lung cancer, melanoma, genitourinary cancer, etc. However, striking disparities still exist in the effectiveness of anticancer immunotherapy and cause a great challenge to select appropriate candidates. A large part of cancer patients presents limited or no response to immunotherapy, mainly attributable to immunotherapy resistance. Despite of great strides already made, the exact mechanism underlying immunotherapy resistance is still enigmatic. Immunotherapy resistance is extremely complex and involves voluminous aspects such as tumor-intrinsic factors (aberrant expression of tumor antigens, alterations of signaling pathways, etc.), tumor-extrinsic factors (local tumor microenvironment, etc.) and host-related factors (gender, age, etc.). More efforts should be made to characterize the underlying mechanism and uncover robust targets for a better response rate.
The aim of this research topic is to highlight state-of-art findings related to immunotherapy resistance in a broad spectrum of solid and hematologic cancers. In this topic, we warmly welcome basic science research, clinical research, bioinformatic research, and review articles, in which new techniques and new theories are recommended. We hope this topic will present fresh biomarkers for cancer immunotherapy resistance, shed light on potential mechanistic pathways, and provide convincing evidence for clinical application to overcome this process.
The submissions may refer to, but are not only limited to:
• The potential tumor-intrinsic mechanisms of immunotherapy resistance such as alternations in antitumor immune response pathways and signaling pathways in cancer cells, as well as the formation of an immunosuppressive microenvironment.
• The possible tumor-extrinsic mechanisms of immunotherapy resistance including the alternation in the local tumor microenvironment (abnormal neovascularization, immunosuppressive cells, and molecules, etc.)
• The relationship between immunotherapy resistance and individual signatures (age, gender, race, hormone, diet, etc.)
• Adopt effective therapeutic approaches to combine immunotherapy with chemotherapy, radiotherapy, anti-angiogenesis therapy, or other treatments to improve the effectiveness of immunotherapy in different clinical scenarios.
• Fresh combinations of immune checkpoint inhibitors, or immune checkpoint inhibitors with tyrosine kinase inhibitors.
• Novel therapeutic approaches involving nanomedicine-based immunotherapy and engineering stimuli-responsive nanotherapeutics.
• New strategies to predict immunotherapy resistance, such as using computational methods to identify robust biomarkers.
Over the decades, cancer immunotherapy has been a burgeoning science in medicine and an established pillar of therapeutic options for diverse cancer types. Leading the way to success is mainly the discovery and development of immune checkpoint inhibitors (ICIs) and chimeric antigen receptor (CAR) T cell-based therapies. Under immunotherapy, a portion of cancer patients enables to achieve a significant and durable remission with a longer survival time, particularly in those with lung cancer, melanoma, genitourinary cancer, etc. However, striking disparities still exist in the effectiveness of anticancer immunotherapy and cause a great challenge to select appropriate candidates. A large part of cancer patients presents limited or no response to immunotherapy, mainly attributable to immunotherapy resistance. Despite of great strides already made, the exact mechanism underlying immunotherapy resistance is still enigmatic. Immunotherapy resistance is extremely complex and involves voluminous aspects such as tumor-intrinsic factors (aberrant expression of tumor antigens, alterations of signaling pathways, etc.), tumor-extrinsic factors (local tumor microenvironment, etc.) and host-related factors (gender, age, etc.). More efforts should be made to characterize the underlying mechanism and uncover robust targets for a better response rate.
The aim of this research topic is to highlight state-of-art findings related to immunotherapy resistance in a broad spectrum of solid and hematologic cancers. In this topic, we warmly welcome basic science research, clinical research, bioinformatic research, and review articles, in which new techniques and new theories are recommended. We hope this topic will present fresh biomarkers for cancer immunotherapy resistance, shed light on potential mechanistic pathways, and provide convincing evidence for clinical application to overcome this process.
The submissions may refer to, but are not only limited to:
• The potential tumor-intrinsic mechanisms of immunotherapy resistance such as alternations in antitumor immune response pathways and signaling pathways in cancer cells, as well as the formation of an immunosuppressive microenvironment.
• The possible tumor-extrinsic mechanisms of immunotherapy resistance including the alternation in the local tumor microenvironment (abnormal neovascularization, immunosuppressive cells, and molecules, etc.)
• The relationship between immunotherapy resistance and individual signatures (age, gender, race, hormone, diet, etc.)
• Adopt effective therapeutic approaches to combine immunotherapy with chemotherapy, radiotherapy, anti-angiogenesis therapy, or other treatments to improve the effectiveness of immunotherapy in different clinical scenarios.
• Fresh combinations of immune checkpoint inhibitors, or immune checkpoint inhibitors with tyrosine kinase inhibitors.
• Novel therapeutic approaches involving nanomedicine-based immunotherapy and engineering stimuli-responsive nanotherapeutics.
• New strategies to predict immunotherapy resistance, such as using computational methods to identify robust biomarkers.
