深度学习人工智能从组织学切片预测同源重组缺陷和铂反应

IF 41.9 1区 医学 Q1 ONCOLOGY
Journal of Clinical Oncology Pub Date : 2024-10-20 Epub Date: 2024-07-31 DOI:10.1200/JCO.23.02641
Erik N Bergstrom, Ammal Abbasi, Marcos Díaz-Gay, Loïck Galland, Sylvain Ladoire, Scott M Lippman, Ludmil B Alexandrov
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引用次数: 0

摘要

目的:同源重组缺陷(HRD)癌症患者可从铂盐和多聚(ADP-核糖)聚合酶抑制剂中获益。检测HRD的标准诊断测试需要分子图谱分析,而分子图谱分析并非普遍可用:我们利用《癌症基因组图谱》(TCGA)中的原发性乳腺癌(n = 1,008 例)和卵巢癌(n = 459 例),训练了用于从苏木精和伊红(H&E)染色的组织病理切片中预测 HRD 的深度学习平台 DeepHRD。利用来自多个独立数据集的乳腺癌(n = 349)和卵巢癌(n = 141),将DeepHRD与四种标准的HRD分子检测进行了比较,这些数据集包括铂治疗的临床队列,以RECIST无进展生存期(PFS)、完全反应(CR)和总生存期(OS)为终点:DeepHRD可预测TCGA中H&E染色乳腺癌切片的HRD,AUC为0.81(95% CI,0.77至0.85)。这一结果在两个独立的原发性乳腺癌队列中得到了证实(AUC,0.76 [95% CI,0.71 至 0.82])。在外用铂治疗的转移性乳腺癌队列中,预测为 HRD 的样本具有更高的完全 CR(AUC,0.76 [95% CI,0.54 至 0.93]),中位 PFS 增加了 3.7 倍(14.4 个月对 3.9 个月;P = .0019),危险比 (HR) 为 0.45(P = .0047)。在三个乳腺癌队列中,非铂类治疗结果与预测的HRD状态没有明显差异,其中包括紫杉类药物治疗的转移性乳腺癌的CR(AUC,0.39)和PFS(HR,0.98,P = .95)。通过对高级别浆液性卵巢癌的迁移学习,在两个队列中,DeepHRD预测的HRD样本在一线(HR,0.46;P = .030)和新辅助(HR,0.49;P = .015)铂治疗后具有更好的OS:结论:DeepHRD可直接从常规H&E切片预测乳腺癌和卵巢癌的HRD,适用于多个外部队列、切片扫描仪和组织固定变量。与分子检测相比,DeepHRD对HRD患者的分类率提高了1.8至3.1倍,在高级别浆液性卵巢癌中显示出更好的OS,在转移性乳腺癌中显示出铂特异性PFS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides.

Purpose: Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available.

Methods: We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints.

Results: DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI, 0.77 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 0.82]). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC, 0.76 [95% CI, 0.54 to 0.93]) with 3.7-fold increase in median PFS (14.4 v 3.9 months; P = .0019) and hazard ratio (HR) of 0.45 (P = .0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC, 0.39) and PFS (HR, 0.98, P = .95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR, 0.46; P = .030) and neoadjuvant (HR, 0.49; P = .015) platinum therapy in two cohorts.

Conclusion: DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.

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来源期刊
Journal of Clinical Oncology
Journal of Clinical Oncology 医学-肿瘤学
CiteScore
41.20
自引率
2.20%
发文量
8215
审稿时长
2 months
期刊介绍: The Journal of Clinical Oncology serves its readers as the single most credible, authoritative resource for disseminating significant clinical oncology research. In print and in electronic format, JCO strives to publish the highest quality articles dedicated to clinical research. Original Reports remain the focus of JCO, but this scientific communication is enhanced by appropriately selected Editorials, Commentaries, Reviews, and other work that relate to the care of patients with cancer.
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