PO.TB04.03 · 肿瘤生物学
利用AI驱动的图像分析对患者来源类器官和微类器官球体进行患者药物敏感性的功能评估
Functional assessment of patient drug sensitivity using AI-powered image analysis on patient-derived organoids and micoorganoid spheres
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
近来，人们一直在推动开发比传统模型更能准确代表人类生物学的新型实验室模型。这些模型包括微型肿瘤模型，如患者来源类器官（PDO）和微类器官球体（MOS），它们可作为患者肿瘤和治疗反应的功能化身1,2。尽管近期研究首次提供证据表明患者对标准治疗的反应可被再现，但这些研究仅能预测一部分患者的临床反应3。这一局限主要源于所用的分析方法，即CellTiter-Glo 3D4，其依赖于批量、终点分析，仅提取PDO所提供的临床相关洞见的一小部分5。因此，我们假设使用动态、更高维度的分析方法可进一步提高PDO的预测性能。我们将活体成像技术与AI驱动的分析相结合，以捕获动态药物反应。使用一个来自胰腺导管腺癌（PDAC）患者的完全表征的PDO panel（n=8）和一个来自结直肠癌患者的完全表征的MOS panel（n=43），我们将多参数分析与匹配患者对标准治疗（如吉西他滨-紫杉醇、FOLFIRINOX、奥沙利铂）的回顾性临床反应进行了匹配。我们的PDO分析量化了患者体内耐药和敏感的PDO克隆，并鉴定出与匹配患者无进展生存期一致的患者特异性治疗敏感性（R=0.97）6。这较CellTiter-Glo3D的相对活力读数（R2=0.26）有显著改进。我们的MOS分析与患者对奥沙利铂的敏感性和耐药性相关。综上所述，我们的工作凸显了使用精密分析方法来测量MOS和PDO等新型实验室模型复杂性的重要性。我们正在进行的工作包括利用我们分析平台的多参数读数开发更稳健的预测模型。1. Hadj Bachir, E., et al. Biol Cell 114 (2021) 2. Ding, S., et al. Cell Stem Cell 29 (2022) 3. Driehuis, E., et al. Proc Natl Acad Sci 116 (2019) 4. Sachs, N., et al. Cell 172 (2018) 5. Phan, N., et al. Commun Biol 2 (2019) 6. Le Compte, M., et al. npj Precis Oncol (2023)
查看英文原文 English abstract
Lately, there has been a push for new laboratory models that more accurately represent human biology than traditional models. These include miniature tumor models such as, patient-derived organoids (PDOs) and MicorOrganoidSpheres (MOS), which act as functional avatars of patient tumors and treatment response 1,2 . While recent studies provided first evidence that patient responses to standard-of-care therapies could be recapitulated, these studies were only able to predict clinical responses in a subset of patients 3 . This limitation largely stems from the analytical methods used, namely CellTiter-Glo 3D 4 , which rely on a bulk, endpoint analysis and only extracts a fraction of clinically relevant insights that PDOs provide 5 . Therefore, we hypothesized that using kinetic, higher-dimensional analysis methods, further improves the predictive performance of PDOs. We combined live-imaging techniques with AI-driven analysis to capture dynamic drug responses. Using a fully characterized a PDO panel (n=8) from patients with pancreatic ductal adenocarcinoma (PDAC) and a fully characterized MOS panel (n=43) from patients with colorectal cancer, we matched our multiparametric analysis with retrospective clinical patient response to standard of care therapies (e.g. gemcitabine-paclitaxel, FOLFIRINOX, oxaliplatin). Our PDO analysis quantified resistant and sensitive PDO clones within the patient, and identified patient-specific sensitives to therapy that were in-line with progression-free survival of matched patients (R=0.97) 6 . This was a significant improvement to the relative viability readouts from CellTiter-Glo3D (R 2 =0.26). Our MOS analysis correlated with patient sensitivity and resistance to oxaliplatin. Taken together, our work highlights the importance of using sophisticated analysis methods to measure the complexity new laboratory models, such as MOS and PDOs. Our ongoing work include developing more robust predictive models, using the multiparametric readouts of our analysis platform. 1. Hadj Bachir, E., et al. Biol Cell 114 (2021) 2. Ding, S., et al. Cell Stem Cell 29 (2022) 3. Driehuis, E., et al. Proc Natl Acad Sci 116 (2019) 4. Sachs, N., et al. Cell 172 (2018) 5. Phan, N., et al. Commun Biol 2 (2019) 6. Le Compte, M., et al. npj Precis Oncol (2023)
利益披露 Disclosure
A. Lin, None..
M. Le Compte, None..
D. L. Dayanidhi, None..
E. Cardenas De La Hoz, None..
R. Stone, None..
T. Gilcrest, None..
G. Roeyen, None..
F. Lardon, None..
C. Deben, None.