About this Research Topic
Artificial intelligence (AI) technologies and machine learning (ML) algorithms are witnessing tremendous growth in immunology from identifying inflammatory markers related to varying immune disorders and frailty, potential application in designing next generation monoclonal antibodies and vaccines, and to uncover the complex patterns in the human’s immune repertoires.
The goal of this Research Topic is to highlight the key role of AI/ML in cancer immunotherapy. It is recognized that treatment with immune modulators and cellular therapies whether alone or in conjunction with traditional cancer treatment modalities (chemo or radiotherapy) have transformed the standard of care of many advanced malignancies. Nevertheless, many patients do not benefit from these advances and still experience primary or secondary tumour resistance to immunotherapy. Therefore, there is scientific and clinical need to develop improved biomarkers and treatment strategies for immunotherapy. Given the wealth of existing immunotherapy data and its complex multi-modal heterogenous nature from clinical, molecular to pathological and diagnostic imaging, AI can play a vital role in navigating through this complexity. For instance, AI can identify candidate biomarkers that can be applied clinically to select patients for the appropriate immunotherapy regimens and save others the unwanted toxicities and costs.
This Research Topic is soliciting original and review articles for AI application in cancer with potential topics including, but not limited to AI technologies and strategies to:
• Identify new biomarkers of immunotherapy response and individualization of systemic versus focal therapies in the metastatic setting.
• Contribute to understanding the complex immunotherapy signaling pathways.
• Model cell-cell interaction, immune cell trafficking and respective balance between anti-tumor immune response and immune-suppression.
• Optimize application of combination immune-based therapeutics.
• Engineering design of cellular immunotherapy regimens such as characterizing tumor immunogenicity and to direct specific tumor antigen directed approaches (CAR-T cells modelling).
• Integrate the immunological dimension into the clinical diagnostic, prognostic, and therapeutic workflow. This includes and does not restrict to the analysis of large-scale datasets, multi-parametric and multi-dimensional imaging, and omics.
Topic Editor Dr. Eric Deutsch is in receipt of institutional grants from Roche Genentech, Boehringer, Astrazeneca, Merck Serono, BMS, and MSD, and has previously been in receipt of consulting fees from Merck Serono and Boehringer. Dr. Pilon-Thomas is listed as a co-inventor on a patent application with Provectus Biopharmaceuticals and participates in sponsored research agreements with Provectus Biopharmaceuticals, Iovance Biotherapeutics, Intellia Therapeutics, Dyve Biosciences, Turnstone Biologics, and Celgene. Dr. Pilon-Thomas has received consulting fees from Seagen Inc., Morphogenesis Inc., and KSQ Therapeutics. Dr. El Naqa acts as scientific advisor for Endectra LLC, is a co-founder of iRAI technologies LLC, and receives grants from NIH and DoD.
Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this topic.
The goal of this Research Topic is to highlight the key role of AI/ML in cancer immunotherapy. It is recognized that treatment with immune modulators and cellular therapies whether alone or in conjunction with traditional cancer treatment modalities (chemo or radiotherapy) have transformed the standard of care of many advanced malignancies. Nevertheless, many patients do not benefit from these advances and still experience primary or secondary tumour resistance to immunotherapy. Therefore, there is scientific and clinical need to develop improved biomarkers and treatment strategies for immunotherapy. Given the wealth of existing immunotherapy data and its complex multi-modal heterogenous nature from clinical, molecular to pathological and diagnostic imaging, AI can play a vital role in navigating through this complexity. For instance, AI can identify candidate biomarkers that can be applied clinically to select patients for the appropriate immunotherapy regimens and save others the unwanted toxicities and costs.
This Research Topic is soliciting original and review articles for AI application in cancer with potential topics including, but not limited to AI technologies and strategies to:
• Identify new biomarkers of immunotherapy response and individualization of systemic versus focal therapies in the metastatic setting.
• Contribute to understanding the complex immunotherapy signaling pathways.
• Model cell-cell interaction, immune cell trafficking and respective balance between anti-tumor immune response and immune-suppression.
• Optimize application of combination immune-based therapeutics.
• Engineering design of cellular immunotherapy regimens such as characterizing tumor immunogenicity and to direct specific tumor antigen directed approaches (CAR-T cells modelling).
• Integrate the immunological dimension into the clinical diagnostic, prognostic, and therapeutic workflow. This includes and does not restrict to the analysis of large-scale datasets, multi-parametric and multi-dimensional imaging, and omics.
Topic Editor Dr. Eric Deutsch is in receipt of institutional grants from Roche Genentech, Boehringer, Astrazeneca, Merck Serono, BMS, and MSD, and has previously been in receipt of consulting fees from Merck Serono and Boehringer. Dr. Pilon-Thomas is listed as a co-inventor on a patent application with Provectus Biopharmaceuticals and participates in sponsored research agreements with Provectus Biopharmaceuticals, Iovance Biotherapeutics, Intellia Therapeutics, Dyve Biosciences, Turnstone Biologics, and Celgene. Dr. Pilon-Thomas has received consulting fees from Seagen Inc., Morphogenesis Inc., and KSQ Therapeutics. Dr. El Naqa acts as scientific advisor for Endectra LLC, is a co-founder of iRAI technologies LLC, and receives grants from NIH and DoD.
Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this topic.
Keywords: Artificial intelligence, machine learning, immunotherapy, cancer
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
