PO.CH01.02 · 化学
通过一套易于使用的噬菌体展示分析工具加速选择性癌症配体的发现
Accelerating selective cancer ligand discovery through an accessible phage display analysis suite
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
鉴定新型可成药靶点仍是癌症研究中的一大挑战。在通过各种测序技术发现的约5,000个潜在可成药癌症基因中,94%已被充分表征,但只有14.3%拥有获批药物。这一差距凸显了间接的、由序列驱动的方法的局限性,此类方法只能推断靶点,而非直接测量功能性结合。像噬菌体展示这样的直接发现平台通过在生物学相关的背景下筛选配体,有助于克服这一障碍。噬菌体展示是一种高通量筛选技术,它在噬菌体表面呈现海量的随机肽或蛋白文库,以鉴定能选择性结合癌症相关靶点的配体。该技术的广泛应用受限于高多样性文库的获取受限,以及缺乏标准化、易于使用的用于分析下一代测序(NGS)噬菌体筛选数据的分析流程。这些障碍使得难以将真正的结合物与由非特异性结合、扩增偏倚或测序噪声导致的伪影区分开来。这些挑战在全细胞和基于组织的筛选中尤为突出,因为在这些筛选中,鉴定真正的结合物对于在真实的癌症环境中发现靶点至关重要。为解决这些局限,我们开发了一个高多样性(>10^8)、二硫键约束、可变环长的肽文库,专为癌症靶点发现进行了优化,并配套开发了一个统一的噬菌体筛选NGS分析流程。该文库设计支持高亲和力结合物,并能够分析跨靶点的序列和环大小偏倚。该软件流程提供了一个标准化的处理工作流程,直接影响下游分析。基于机器学习的降噪和稳健的基序发现进一步提高了对富集序列和真正结合物的鉴定能力。该平台已通过成熟的蛋白靶点筛选进行了验证,并成功用于鉴定对多发性骨髓瘤中已知蛋白靶点具有选择性的配体。这些结果为正在进行的针对耐药多发性骨髓瘤细胞系的全细胞淘选(panning)奠定了坚实基础,以发现具有治疗和诊断潜力的配体。我们的长期目标是构建一个由社区驱动的、使用此统一流程处理的噬菌体筛选数据库,类似于用于RNA-seq的TCGA,从而允许跨研究比较以及计算机模拟(in-silico)鉴定对癌症(而非周围健康组织)具有选择性的配体或结合基序。这个标准化平台降低了癌症研究中可操作配体发现的门槛,并加速了用于癌症治疗的选择性药物的开发。
查看英文原文 English abstract
Identifying novel druggable targets remains a major challenge in cancer research. Of the approximately 5,000 potentially druggable cancer genes found through various sequencing techniques, 94% are well characterized, yet only 14.3% have approved drugs. This gap highlights the limitations of indirect, sequence-driven approaches, which infer targets rather than directly measuring functional binding. Direct discovery platforms like phage display help overcome this barrier by screening ligands in biologically relevant contexts. Phage display is a high-throughput screening technology that presents vast libraries of randomized peptides or proteins on bacteriophage surfaces to identify ligands that selectively bind cancer associated targets. Broader use of this technique is limited by restricted access to high-diversity libraries and the absence of standardized, user-friendly pipelines for analyzing next-generation sequencing (NGS) phage screen data. These barriers make it difficult to distinguish true binders from artifacts caused by non-specific binding, amplification bias, or sequencing noise. These challenges are especially pronounced in whole-cell and tissue-based screens, where identifying true binders is critical for discovering targets in authentic cancer environments. To address these limitations, we developed a high-diversity (>10 8 ), disulfide-constrained, variable-loop-length peptide library optimized for cancer target discovery, along with a unified phage screen NGS analysis pipeline. The library design supports high-affinity binders and enables analysis of sequence and loop-size biases across targets. The software pipeline provides a standardized processing workflow which directly influences downstream analysis. Machine-learning-based denoising and robust motif discovery further improve identification of enriched sequences and true binders. The platform has been validated with established protein-target screens and successfully used to identify ligands selective for known protein targets in multiple myeloma. These results provide a strong foundation for ongoing whole-cell panning on drug-resistant multiple myeloma lines to discover ligands with therapeutic and diagnostic potential. Our long-term aim is to build a community-driven database of phage screens processed with this unified pipeline, analogous to TCGA for RNA-seq, allowing cross-study comparisons and in-silico identification of ligands or binding motifs selective for cancer instead of surrounding healthy tissue. This standardized platform lowers barriers to actionable ligand discovery in cancer research and accelerates the development of selective agents for cancer therapeutics.
利益披露 Disclosure
S. Lees, None.