Worldwide Medical Oncology, Bristol Myers Squibb, Princeton, New Jersey.
Immunai, New York, New York.
Cancer Immunol Res. 2022 Apr 1;10(4):372-383. doi: 10.1158/2326-6066.CIR-20-0586.
Immune-checkpoint inhibitors (ICI), although revolutionary in improving long-term survival outcomes, are mostly effective in patients with immune-responsive tumors. Most patients with cancer either do not respond to ICIs at all or experience disease progression after an initial period of response. Treatment resistance to ICIs remains a major challenge and defines the biggest unmet medical need in oncology worldwide. In a collaborative workshop, thought leaders from academic, biopharma, and nonprofit sectors convened to outline a resistance framework to support and guide future immune-resistance research. Here, we explore the initial part of our effort by collating seminal discoveries through the lens of known biological processes. We highlight eight biological processes and refer to them as immune resistance nodes. We examine the seminal discoveries that define each immune resistance node and pose critical questions, which, if answered, would greatly expand our notion of immune resistance. Ultimately, the expansion and application of this work calls for the integration of multiomic high-dimensional analyses from patient-level data to produce a map of resistance phenotypes that can be utilized to guide effective drug development and improved patient outcomes.
免疫检查点抑制剂(ICI)虽然在提高长期生存结果方面具有革命性意义,但主要对免疫应答性肿瘤患者有效。大多数癌症患者要么根本对 ICI 没有反应,要么在初始反应期后出现疾病进展。ICI 治疗耐药性仍然是一个主要挑战,也是全球肿瘤学领域最大的未满足医疗需求。在一次合作研讨会上,来自学术、生物制药和非营利部门的思想领袖齐聚一堂,制定了一个耐药性框架,以支持和指导未来的免疫耐药性研究。在这里,我们通过从已知生物学过程的角度来整理开创性发现,来探讨我们努力的初始部分。我们强调了八个生物学过程,并将它们称为免疫抵抗节点。我们研究了定义每个免疫抵抗节点的开创性发现,并提出了关键问题,如果这些问题得到回答,将极大地扩展我们对免疫抵抗的认识。最终,这项工作的扩展和应用需要整合来自患者水平数据的多组学高维分析,以生成耐药表型图谱,用于指导有效的药物开发和改善患者预后。