Structural insights into antibody-based immunotherapy for hepatocellular carcinoma.

Masaud Shah, Muhammad Hussain, Hyun Goo Woo
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Abstract

Hepatocellular carcinoma (HCC) is one of the most common types of primary liver cancer and remains a leading cause of cancer-related deaths worldwide. While traditional approaches like surgical resection and tyrosine kinase inhibitors struggle against the tumor's immune evasion, monoclonal antibody (mAb)-based immunotherapies have emerged as promising alternatives. Several therapeutic antibodies that counter the immunosuppressive tumor microenvironment have demonstrated efficacy in clinical trials, leading to FDA approvals for advanced HCC treatment. A crucial aspect of advancing these therapies lies in understanding the structural interactions between antibodies and their targets. Recent findings indicate that mAbs and bispecific antibodies (bsAbs) can target different, non-overlapping epitopes on immune checkpoints such as PD-1 and CTLA-4. This review delves into the epitope-paratope interactions of structurally unresolved mAbs and bsAbs, and discusses the potential for combination therapies based on their non-overlapping epitopes. By leveraging this unique feature, combination therapies could enhance immune activation, reduce resistance, and improve overall efficacy, marking a new direction for antibody-based immunotherapy in HCC.

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基于抗体的肝细胞癌免疫治疗的结构见解。
肝细胞癌(HCC)是最常见的原发性肝癌类型之一,并且仍然是全球癌症相关死亡的主要原因。虽然手术切除和酪氨酸激酶抑制剂等传统方法与肿瘤的免疫逃避斗争,但基于单克隆抗体(mAb)的免疫疗法已成为有希望的替代方法。几种对抗免疫抑制肿瘤微环境的治疗性抗体已在临床试验中证明有效,导致FDA批准晚期HCC治疗。推进这些疗法的一个关键方面在于了解抗体与其靶标之间的结构相互作用。最近的研究表明,单抗和双特异性抗体(bsAbs)可以靶向PD-1和CTLA-4等免疫检查点上不同的、不重叠的表位。这篇综述深入研究了结构不确定的单克隆抗体和双克隆抗体的表位-旁位相互作用,并讨论了基于它们的非重叠表位的联合治疗的潜力。利用这一独特的特点,联合治疗可以增强免疫激活,降低耐药性,提高整体疗效,标志着以抗体为基础的免疫治疗HCC的新方向。
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Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort. Structural insights into antibody-based immunotherapy for hepatocellular carcinoma. DeepDoublet identifies neighboring cell-dependent gene expression. Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features. Rare disease genomics and precision medicine.
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