Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205.
Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205.
Proc Natl Acad Sci U S A. 2024 Nov 5;121(45):e2410911121. doi: 10.1073/pnas.2410911121. Epub 2024 Oct 28.
Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.
转移性三阴性乳腺癌（TNBC）患者对 PD-1 抑制的反应各不相同。通过预测性生物标志物进行有效的患者选择是理想的，但受到现有生物标志物性能有限的阻碍。在这里，我们利用基于转录组和临床数据的转移性 TNBC 定量系统药理学模型生成的计算机模拟患者队列，探索了改善患者选择的潜在方法。我们通过基于截止值的生物标志物测试算法结合基于机器学习的特征选择，评估和量化了 90 种生物标志物候选物的性能，包括各种细胞和分子种类，在不同截止值下的表现。在降低敏感性的代价下，预处理生物标志物的组合提高了特异性。另一方面，治疗开始后两周内从基线测量的肿瘤直径的相对变化等早期治疗生物标志物，具有显著更高的敏感性和特异性。此外，基于血液的生物标志物在识别应答者亚组方面与基于肿瘤或淋巴结的生物标志物具有相当的能力，可能表明一种侵入性较小的患者选择方法。
