Pan-cancer, morphology-based prediction of therapy response to immune checkpoint inhibitors using deep learning

使用深度学习对免疫检查点抑制剂的治疗反应进行基于形态学的泛癌预测

• 批准号: 504101714
• 负责人: Dr. Sebastian Försch
• 金额: --
• 依托单位: Institut für allgemeine Pathologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/504101714?language=en
• 关键词: Pan; cancer; morphology; based; prediction

Malignant transformation of somatic cells is accompanied by genetic changes that often lead to the expression of neoantigens and qualify tumor cells as a possible target of an adaptive immune response. Tumors can only grow and manifest clinically if they develop immunological escape mechanisms under the constant selection pressure by the patient’s immune system. Immune checkpoint inhibitors (ICI) aim to interrupt these escape mechanisms and restore functional anti-tumor immunity. Although ICIs are extremely effective in some patients, the average response rate is only about 13% and there are still no reliable biomarkers for predicting a therapy response to ICI.The aim of this project is to predict the therapy response to ICI using artificial intelligence (AI) and machine learning (ML). Various deep learning (DL) models will be trained on histopathological and immunohistochemical image data to predict response to treatment. The hypothesis is that various stages of anti-tumor immunity have distinct morphological features and can thus can be detected by the intelligent algorithms on a microscopic scale. Molecular changes relevant to the response to ICI, such as microsatellite instability and tumor mutation load, have already been successfully predicted using DL methods. In addition, there are a number of morphological categorizations of the tumor-associated immune reaction, each of which responds differently to ICI.We will first train a series of specially established network architectures on a retrospective cohort of 551 patients who were treated with a PD-1 / PD-L1 inhibitor in the period 2015-2020 (retrospective training and validation cohort). The six tumor entities most frequently treated with ICI at the Mainz University Medical Center are included in the study. The target variable will be the best objective response according to RECIST. We will then prospectively check the predictive accuracy of our DL models in all patients with the above-mentioned tumor entities who are treated with ICI during the two-year funding period (prospective test cohort). Furthermore, we will identify histopathological features that influenced the decision-making of the DL model with the aim of identifying new morphological characteristics that determine a therapy response to ICI.

体细胞的恶性转化伴随着遗传变化，其通常导致新抗原的表达并使肿瘤细胞成为适应性免疫应答的可能靶点。肿瘤只有在患者免疫系统的持续选择压力下发展免疫逃逸机制，才能生长并临床表现。免疫检查点抑制剂（ICI）旨在中断这些逃逸机制并恢复功能性抗肿瘤免疫。尽管ICI在某些患者中非常有效，但平均应答率仅为13%左右，并且仍然没有可靠的生物标志物来预测ICI的治疗应答。本项目的目的是使用人工智能（AI）和机器学习（ML）来预测ICI的治疗应答。将在组织病理学和免疫组织化学图像数据上训练各种深度学习（DL）模型，以预测对治疗的反应。假设抗肿瘤免疫的各个阶段具有不同的形态学特征，因此可以通过微观尺度上的智能算法进行检测。与ICI反应相关的分子变化，如微卫星不稳定性和肿瘤突变负荷，已经使用DL方法成功预测。此外，肿瘤相关免疫反应有许多形态学分类，每一种对ICI的反应都不同，我们将首先在2015-2020年期间接受PD-1 / PD-L1抑制剂治疗的551例患者的回顾性队列（回顾性训练和验证队列）上训练一系列专门建立的网络架构。美因茨大学医学中心最常接受ICI治疗的六种肿瘤实体被纳入研究。目标变量将是根据RECIST的最佳客观缓解。然后，我们将前瞻性地检查我们的DL模型在两年资助期内接受ICI治疗的所有上述肿瘤实体患者中的预测准确性（前瞻性测试队列）。此外，我们将确定影响DL模型决策的组织病理学特征，目的是确定确定对ICI的治疗反应的新形态学特征。