Neoantigens play important roles in cancer immunotherapy. However, current methods are insufficient for reliable neoantigen prediction and screening. Artificial intelligence (AI)-based tumor neoantigens screening is rapidly developed and is going to play more and more important roles in cancer immunotherapy. Committed to AI application in medicine over years, Creative Biolabs has rich experience accumulated from practice. With solid experimental foundations and excellent scientific staff, we are confident in offering high-quality AI-based tumor neoantigens screening development services to global researchers.
The most important class of tumor antigens are tumor-specific antigens (TSAs), these antigens are truly distinguished from normal tissue and have the greatest potential to elicit a strictly tumor-specific immune response. TSAs are generally referred to as signature tumor mutations (somatic mutations) in the tumor genome that can be translated into mutant proteins only expressed in tumors but not in the normal tissue. The mutant proteins, ideally to be the common oncogenes are processed by the proteasome to generate the peptides which are subsequently presented by major histocompatibility complex (MHC) on the tumor cell surfaces. These antigen peptides are typically referred to as neoantigens. Since the neoantigens are directly recognized by TCRs which are highly sensitive and specific for their epitopes, neoantigens are the most ideal targets for T cell immunotherapy.
A significant challenge for the translation of TSA therapies is the ability to select the subset of clinically relevant epitopes from all computationally predicted neoantigens. The development of a machine-learning algorithm to predict the immunogenicity of neoantigen peptides (i.e., variant peptides predicted to bind MHC) would be valuable for screening predicted neoantigens for clinical application.
Fig.1 A paradigm of neoantigen utility. (Desrichard, 2016)
Neopepsee, a machine-learning-based neoantigen prediction program, integrates nine immunogenicity features and was able to determine immunogenic neoantigens in melanoma and chronic lymphocytic leukemia (CLL).
Luksza et al. combined estimations of the probability that a neoantigen will be presented on MHC I and the probability that presented neoantigens will be recognized by the TCR repertoire based on tumor clonality, MHC I binding affinity, and microbial epitope homology. This model was applied to two melanoma cohorts and one non-small cell lung cancer (NSCLC) cohort undergoing anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4) and anti-PD-1 targeting therapy, respectively, and predicted survival in each cohort.
Snyder et al. developed a bioinformatic pipeline incorporating MHC class I binding probability, TCR binding probability, patient-specific HLA genotype, and epitope-homology analysis to identify putative neoepitopes associated with clinical outcome in advanced melanoma patients undergoing anti-CTLA-4 targeting therapy. Among predicted neoantigens, conserved stretches of amino acids were identified that were shared by patients with a clinical benefit exceeding six months. These neoepitope signatures were significantly associated with survival in the discovery as well as in the validation set.
Committed to AI application in medicine over years, Creative Biolabs has accumulated extensive precious experience. With step-by-step practice, we gradually equipped our technology platform with advanced facilities, Ph.D. level specialists, and mature technicians. With the comprehensive platform, we are capable of providing reliable AI-based tumor neoantigens screening development services to global clients.
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