人工智能在免疫肿瘤学预测生物标志物发现中的应用：系统评价。

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review.

机构信息

Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.

Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy.

出版信息

Ann Oncol. 2024 Jan;35(1):29-65. doi: 10.1016/j.annonc.2023.10.125. Epub 2023 Oct 23.

DOI:10.1016/j.annonc.2023.10.125

PMID:37879443

Abstract

BACKGROUND: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

摘要

背景：免疫检查点抑制剂（ICI）的广泛应用彻底改变了多种癌症的治疗方式。然而，选择可能从 ICI 中获益的患者仍然具有挑战性。人工智能（AI）方法允许在肿瘤学的高维数据中进行研究，并开发精准免疫肿瘤学。

材料和方法：我们对五项数据模式（基因组学（包括基因组学、转录组学和表观基因组学）、放射组学、数字病理学（病组学）、真实世界和多模态数据）中癌症患者ICI 疗效预测的同行评审原始研究进行了系统文献回顾。

结果：本系统综述共纳入 90 项研究，其中 80%的研究发表于 2021-2022 年。其中，37 项研究包含基因组学数据，20 项研究包含放射组学数据，8 项研究包含病组学数据，20 项研究包含真实世界数据，5 项研究包含多模态数据。72%的研究使用了标准机器学习（ML）方法，22%使用了深度学习（DL）方法，6%同时使用了 ML 和 DL 方法。研究最多的癌症类型是非小细胞肺癌（36%），其次是黑色素瘤（16%），25%的研究为泛癌研究。没有前瞻性研究设计从一开始就纳入 AI 方法，而是都将 AI 作为事后分析来实施。通过 AI 方法在放射组学和病组学中确定了ICI 的新生物标志物，并且分子生物标志物已从基因组学扩展到转录组学和表观基因组学。最后，通过整合多模态/多组学数据，新兴的复杂算法和新型 AI 标志物（如元生物标志物）正在涌现。

结论：基于 AI 的方法为生物标志物的发现开辟了新的视野，展示了整合现有数据集的多模态数据以发现新的元生物标志物的能力。虽然大多数纳入的研究都为基于 AI 的免疫治疗获益预测提供了希望，但没有一项研究为立即改变实践提供了高级别的证据。需要前瞻性的、预先计划的临床试验设计来涵盖这些软件生物标志物的所有生命周期步骤，从开发和验证到整合到临床实践中。