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Artificial Intelligence is Revolutionizing Cancer Research
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
B. Sudha, K. Suganya, K. Swathi, S. Sumathi
The implementation of AI increases the possibility of effective cancer immunotherapy by predicting the pharmacological activity based on the development of predictive immunotherapy ratings, including immunophenoscore and immunoscore (Angell and Galon, 2013). These two scoring systems have been designed to predict immune blocking point (ICB) reactions. Implementing an AI-based therapeutic framework with the descriptions of doctors can be strongly associated with improving diagnostic precision for indistinguishable subsets of cancer (Rajkomar et al. 2019). Consequently, the use of AI in cancer immunotherapy can bring oneself closer to good results in patients. A machine learning-based AI platform was created to accurately anticipate the therapeutic efficacy of programmed cell death protein 1 (PD-1) inhibitors (Sun et al. 2018). In patients with advanced solid tumors that are immune to PD-1 inhibitors, this platform can accurately measure the effects of immunotherapy. A machine learning method based on a human leukocyte antigen (HLA) mass spectrometry database was developed to enhance the detection of neoantigen cancer and the efficacy of cancer immunotherapy (Bulik-Sullivan et al. 2018).
Immunomodulatory Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Successful production of TILs has also been achieved from solid tumor types other than melanoma (e.g., breast, cervical, gastrointestinal, head and neck, ovarian, and non-small-cell lung tumors). Thus, the challenge for the development of TIL therapies for other tumor types is not obtaining and expanding the TILs but the heterogenicity of the mutational and neoantigen burden. This highlights the need to develop tools to accurately select and enrich for subset T-cell populations. Nonetheless, numerous clinical trials are underway utilizing studying the efficacy of TIL therapies in bladder, lung (i.e., NSCLC), and ovarian cancers. For example, Iovance Biotherapeutics recently presented the results of an ongoing Phase II clinical trial investigating the use of TIL therapy in cervical cancer. This trial achieved an ORR and PR of 44.4% and 33.3%, respectively, with 78% of patients achieving a reduction in tumor burden.
Biomarkers for the Immune Checkpoint Inhibitors
Published in Sherry X. Yang, Janet E. Dancey, Handbook of Therapeutic Biomarkers in Cancer, 2021
Weijie Ma, Sixi Wei, Eddie C. Tian, Tianhong Li
Antitumor T cells recognize not only tumor-specific mutant peptides like neoantigens, but also cancer germline antigens (CGAs), germline proteins whose expression is typically restricted to germline cells but is upregulated in tumor cells. Tumor types harboring higher numbers of nonsynonymous somatic variants have higher response rates to immunotherapy, due to greater numbers of neoantigens [28]. In some cases of MMR deficiency, whole-exome sequencing confirmed a 20-fold-higher level of nonsynonymous mutation-associated neoantigen load compared to MMR-proficient patients; this is consistent with other reports demonstrating an association between higher mutational load and response to anti-PD-1 in NSCLC [27]. In one example, a patient with metastatic lung adenocarcinoma showed an exceptional response to atezolizumab (anti-PD-L1). Whole-exome sequencing of the patient’s tumor and blood revealedgain-of-function somatic alterations in Janus kinase 3 (JAK3) as well as germline mutations in the same allele [78]. These studies demonstrate the impact of germline mutations that can influence neoantigen formation or immune signaling and may serve as future predictors of sensitivity to immunotherapy.
What’s next in cancer immunotherapy? - The promise and challenges of neoantigen vaccination
Published in OncoImmunology, 2022
Alec J. Redwood, Ian M. Dick, Jenette Creaney, Bruce W. S. Robinson
The mutational landscape of a tumor is not homogenous55 meaning that genomic or proteomic neoantigen identification from a single biopsy is likely to be subject to sampling bias. This represents a potential barrier for successful neoantigen vaccination because vaccines that target poorly represented, sub-clonal, neoantigens could drive tumor escape through the process of immunoediting.56 Therefore, neoantigen vaccines would ideally consist of clonal neoantigens, i.e., those that arose early in the tumorigenesis and are expressed by the majority of cells. Sampling bias will affect the identification of clonal antigens meaning that multiple samples may need to be taken from each patient.57,58 This places significant logistical limits on neoantigen prediction and others have sought to use computational approaches for the identification of clonal neoantigens.59
Nano-sized drug delivery systems to potentiate the immune checkpoint blockade therapy
Published in Expert Opinion on Drug Delivery, 2022
Man Kyu Shim, Su Kyung Song, Seong Ik Jeon, Kwang Yeon Hwang, Kwangmeyung Kim
Neoantigens, which are tumor-specific antigens produced by somatic mutations, have emerged as a promising cancer immunotherapy strategy to induce antitumor immune responses owing to their extremely high specificity, strong immunogenicity, and low toxicity [46]. Because neoantigens are only expressed on tumor cells and are absent from in healthy tissues, they provide a potential approach for personalized cancer vaccination, which has been verified in preclinical and clinical trials [47]. There are certain levels of homology between different types of tumors, but neoantigens from parent tumor cells are mainly used for the more precise, personalized treatment with fewer side effects [48]. However, conventional neoantigens trigger limited immunogenicity due to their rapid in vivo clearance [49]. Therefore, the overall clinical responses of neoantigen therapy are still unsatisfactory to achieve complete tumor regression without metastasis and recurrence [50]. Thus, combination with other modalities is needed to improve its potency. To overcome these challenges, various Nano-DDS have been developed for the combination of ICD with neoantigens.
Towards customized cancer vaccines: a promising filed in personalized cancer medicine
Published in Expert Review of Vaccines, 2021
Xiaoling Xu, Zichao Zhou, Hui Li, Yun Fan
There are various of the strengths of neoantigen vaccines. First, the neoantigen vaccine contains many neoantigens of different tumors, using the natural ability of the immune system to detect and attack the target antigen, reducing the occurrence of drug resistance. Secondly, neoantigen vaccines are customized for each patient, using antigens produced by the patient’s unique mutations in cancer, and are only present on cancer cells. Targeted tumor vaccines can generate an immune response that only attacks cancer cells, bypassing natural immune tolerance process. Finally, ‘off-target’ effects are rarely seen, with only mild side effects. However, individualized tumor vaccines are designed based on the analysis of individual somatic cell mutations through artificial intelligence software. Complicated preparation procedures and biometric analysis usually takes 2–3 months and the cost is relatively high. Besides, there are several barriers to the successful application of neoantigen-based vaccines. First, it remains difficult to precisely predict which mutated proteins are digested into short peptides by the proteasome, transported into the endoplasmic reticulum by antigen processing transporters, and loaded onto newly synthesized MHCs for recognition by CD8 + T-cells. Second, a rapid assay is needed to validate the predicted neoantigens. Third, rapid manufacturing and timely delivery of neoantigen vaccines remain problematic, as ≥3 months are often required from the initial mutation analysis to clinical vaccine administration. Finally, the optimal clinical setting remains unclear for neoantigen vaccines.