This Research Topic is the second volume of the “Immune Responses Against Tumors - From the Bench to the Bedside” Community Series. Please see Volume I
here..
Traditionally, surgery, radiotherapy, chemotherapy and hormonal therapy have been used in clinical practice to treat cancer patients. However, these methods are either not completely tumor free or without collateral damage to health tissues. Therefore, tumor recurrence, partial remission, treatment side effect and poor tolerance in elderly patients, and lack of effective strategy to treat late-stage cancers have been the major hurdles faced by clinicians in health care.
Although cancer immunotherapy including checkpoint blockade, CAR-T, oncolytic viruses, and recombinant cytokines has taken center stage in mainstream oncology because of its specifically targeting tumor cells without affecting surrounding normal cells, only a proportion of patients receiving treatment respond and others relapse after an initial response. Different tumor indications respond differently, and even in cancer types that respond, unresponsiveness are still observed. This resistance suggests that either lack of sufficient host recognition and immunity (intrinsic) or active immune suppression by tumor complex (acquired).
During cancer evolution, most tumors employ multiple strategies to escape and/or attenuate immune response. Therefore, controlling both tumor including all tumor cells and tumor microenvironment (TME) is critical for development of next stages of cancer treatment. In this topic, we would like to focus on researches that tackle the mechanisms which cancer cells employed to evade immunosurveillance in order to induce complete and long-lasting tumor regression. To target all tumor cells, truncal mutations that occurs in every single tumor cell need to be identified. Novel methods in stimulating and expanding of T cells recognising clonal neoantigens and therefore killing all cancer cells including in vitro cell culture and in vivo manipulations such as via nanoparticles are promising therapeutic tools. In addition, earlier, quicker and cost effective diagnosis of early-stage cancer still provides golden opportunity and cure-like therapeutic window. There is an emerging field of identifying unique amino acid changes in cancer metabolism by artificial intelligence. It is easier to implement than NGS and could be an ideal supplementation to existing prognostic and therapy guiding tools in clinic and hospitals.
This Research Topic welcomes the submission of Original Research, Systematic Review, Methods, Review, Mini Review, Perspective, Clinical Trial, Case Report, Brief Research Report and Opinion articles covering, but not limited to the following sub-topics:
• Pre-clinical studies using transgenic, gene knock-in/out, humanized animal models of solid organ tumor to reveal novel anti-tumor pathways against tumor and TME for potential targets of immunotherapy
• Prognostic model using machine learning to screen characteristic amino acid metabolism and identify patients with positive response to anti-tumor immunotherapy and subsequent clinical benefits which provides guidance for designing treatment strategy
• Identification of neoantigens derived from patient specific or driver mutations and neoantigen-specific T-cell receptors (TCRs) via bioinformatic platforms for personalised or off-the-shelf autologous adoptive cell therapy (ACT) for treating solid organ cancers.
• Methods to isolate and expand neoantigen-specific tumor infiltrating lymphocytes (TILs) from blood, lymph node and tumor with defined fitness framework for long-lived protective cancer immunity
• Novel delivery systems of immune stimulatory gene, checkpoint inhibitor and tumor microenvironment modulators via targeted nanoparticles (NP) based technology to strengthen anti-tumor immunity either alone or in combination with other immunomodulatory agents
Please note that manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation are considered out of scope for this Research Topic.