人工智能的进步为利用苏木精和伊红染色的肿瘤切片进行精准肿瘤学铺平了道路。我们提出了 ENLIGHT-DeepPT，一种间接的两步方法，包括 (1) DeepPT，一种深度学习框架，可预测载玻片中的全基因组肿瘤 mRNA 表达，以及 (2) ENLIGHT，可预测载玻片对靶向和免疫疗法的反应。推断的表达式值。我们表明 DeepPT 成功预测了所有 16 个癌症基因组图谱队列中测试的转录组学，并很好地推广到两个独立数据集。 ENLIGHT-DeepPT 成功预测了五个独立患者队列中的真正反应者，涉及涵盖六种癌症类型的四种不同治疗方法，总体优势比为 2.28，预测反应者的反应率与基线率相比增加了 39.5%。值得注意的是，其在没有对治疗数据进行任何训练的情况下获得的预测准确性与通过直接预测图像响应所获得的预测准确性相当，后者需要对治疗评估队列进行特定培训。© 2024。这是美国政府的工作，而不是受美国版权保护；可能适用外国版权保护。

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.