LBPO.TB03 · 肿瘤生物学 · Late-Breaking

用于精准癌症治疗的胃肠道恶性肿瘤人源肿瘤切片基准评估

Benchmarking human tumor slices from gastrointestinal malignancies for precision cancer therapy

海报缩略图:用于精准癌症治疗的胃肠道恶性肿瘤人源肿瘤切片基准评估
编号 LB483 展板 2 时间 4/22 09:00–12:00 区域 Section 54 主讲 Jonathan Weitz, PhD
分会场 Late-Breaking Research: Tumor Biology 3
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作者与单位 Authors & Affiliations

Kevin Gulay, Rithika Medari, Isabella Ng, Jingjing Zou, Ethan Tabibzadeh, Elias Warren, Brian Wishart, Rebekah White, Herve Tiriac, Andrew Lowy, Jonathan Weitz

UCSD Moores Cancer Center, La Jolla, CA

摘要 Abstract

中文摘要
患者来源肿瘤切片(PDS)已被用于评估多种人类肿瘤类型的药物敏感性。尽管如此,将PDS确立为精准医学工具的标准化参数仍然缺乏。在此,我们对来自40例胃肠道癌标本的增殖率和标准差进行了基准评估,界定了正常的生物学变异。此处,胰腺导管腺癌(PDAC)、阑尾癌和结直肠腺癌的平均标准差率分别为10%、6%和15%。利用这些数据,我们建立了以3个标准差的z-score为阈值的标准,以便将对pan-RAS(On)抑制剂RMC-6236有意义的药物敏感性与正常生物学变异区分开来。该框架使得能够将PDAC和阑尾癌PDS培养物分类为敏感或耐药。为评估切片检测的预测价值,我们比较了配对的患者来源类器官(PDO)和异种移植(PDX)中的RAS抑制剂活性,并进一步确定基于切片的检测敏感性可预测匹配的正交肿瘤模型中的敏感性和生存结局。
查看英文原文 English abstract
Patient-derived tumor slices (PDS) have been used to evaluate drug sensitivity in multiple human tumor types. Despite this, standardized parameters to establish PDS as a precision medicine tool are lacking. Here we benchmarked proliferation rates and standard deviations from 40 gastrointestinal cancer specimens, defining normal biological variation. Here the mean standard deviation rates in pancreatic duct adenocarcinoma (PDAC), appendiceal cancer, and colorectal adenocarcinomas were 10%, 6%, and 15%, respectively. Using these data, we established threshold criteria set at a z-score of 3 standard deviations in order to distinguish meaningful drug sensitivity to the pan-RAS(On) inhibitor RMC-6236, from that of normal biological variation. This framework enabled classification of PDAC and appendiceal PDS cultures as sensitive or resistant. To benchmark the predictive value of slice assays, we compared RAS-inhibitor activity across matched patient-derived organoids (PDOs) and xenografts (PDXs), and further determined that slice-based assay sensitivity predicted sensitivity and survival outcomes in matching orthogonal tumor models.
利益披露 Disclosure
K. Gulay, None.. R. Medari, None.. I. Ng, None.. J. Zou, None.. E. Tabibzadeh, None.. E. Warren, None.. B. Wishart, None.. R. White, None.. H. Tiriac, None.. A. Lowy, None.. J. Weitz, None.

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