癌症是一种复杂的疾病,会导致细胞异常生长和扩散。DNA突变,化学或环境暴露,病毒感染,慢性炎症,激素异常,等。,是可能导致癌症的潜在因素。耐药性和毒性使癌症治疗复杂化。此外,癌症的变异性使得建立通用治疗指南变得困难。下一代测序使基因检测变得便宜。这揭示了可以用特殊药物治疗的基因突变。AI(人工智能),机器学习,活检,下一代测序,数字病理学提供个性化的癌症治疗。这允许患者特异性的生物靶标和癌症治疗。单克隆抗体,CAR-T,和癌症疫苗是有前途的癌症治疗。结合这些疗法的最新试验数据显示,在临床结果和药物耐受性方面优于常规化疗。这些疗法与新技术的结合可以改变癌症治疗并帮助许多人。这篇综述讨论了靶向治疗如单克隆抗体(mAb)的发展和挑战,双特异性抗体(BsAbs),双特异性T细胞衔接器(BiTE),双可变域(DVD)抗体,CAR-T疗法,癌症疫苗,溶瘤病毒,基于脂质纳米颗粒的mRNA癌症疫苗,以及它们在各种癌症中的临床结果。我们还将研究人工智能和机器学习如何帮助寻找新的癌症治疗目标。
Cancer is a complex disease that causes abnormal cell growth and spread. DNA mutations, chemical or environmental exposure, viral infections, chronic inflammation, hormone abnormalities, etc., are underlying factors that can cause cancer. Drug resistance and toxicity complicate cancer treatment. Additionally, the variability of cancer makes it difficult to establish universal treatment guidelines. Next-generation sequencing has made genetic testing inexpensive. This uncovers genetic mutations that can be treated with specialty drugs. AI (artificial intelligence), machine learning, biopsy, next-generation sequencing, and digital pathology provide personalized cancer treatment. This allows for patient-specific biological targets and cancer treatment. Monoclonal antibodies, CAR-T, and cancer vaccines are promising cancer treatments. Recent trial data incorporating these therapies have shown superiority in clinical outcomes and drug tolerability over conventional chemotherapies. Combinations of these therapies with new technology can change cancer treatment and help many. This review discusses the development and challenges of targeted therapies like monoclonal antibodies (mAbs), bispecific antibodies (BsAbs), bispecific T cell engagers (BiTEs), dual variable domain (DVD) antibodies, CAR-T therapy, cancer vaccines, oncolytic viruses, lipid nanoparticle-based mRNA cancer vaccines, and their clinical outcomes in various cancers. We will also study how artificial intelligence and machine learning help find new cancer treatment targets.