Integrating Spatial Omics and Drug Imaging to Dissect the Role of Pancreatic Tumor Microenvironment in Drug Resistance

项目总结 这份提案描述了一项职业发展计划，为卢博士的独立研究生涯做好准备 它的重点是开发计算和实验方法，以改进癌症检测、诊断、 和治疗。该计划将为卢博士提供单细胞空间组学方面的新专业知识，与 她的背景是基于机器学习的图像计算(研究生获得)和临床单项- 细胞药物成像(作为博士后研究人员获得)，以促进我们对 导致胰腺癌的抗药性。卢博士将由加里·诺兰博士指导，他发明了 Codex技术用于高度多路复用的单细胞成像，并由Eben Rosenthal博士共同指导，A 内科医生和科学家，开创了人类第一次荧光引导癌症手术的临床研究，以及 罗伯特·韦斯特博士，他开发了用于空间转录的Smart-3SEQ技术。K99期的Dr。 卢的培训将包括(I)由主要导师和共同导师进行有组织的指导，(Ii)密切互动 与咨询委员会和合作者合作，(3)技术和学术培训，(4)挑衅性研究 项目，以及(V)职业过渡计划。 阐明肿瘤微环境(TME)在耐药中的作用对于开发有效的耐药机制至关重要 癌症治疗，但量化药物传递和作用与宿主环境因素一起 临床肿瘤在技术上仍然具有挑战性。以抗体为基础的疗法，如抗体-药物结合物 抗肿瘤药物(ADC)和免疫检查点抑制剂(ICIS)尤其容易受到TME屏障的封锁。这个 该项目的总体目标是确定导致胰管耐药的TME因素。 将治疗性抗体的单细胞地理空间定位与深度定位相结合的腺癌 TME的空间轮廓。核心假设是Periostin和肿瘤相关巨噬细胞(TAMs) 在抑制PDAC的药物传递和反应方面发挥关键作用。核心假说将通过追查来检验 三个目标：(目标1)通过整合Codex和Smart-3SEQ建立计算空间组学平台，以 以公正的方式绘制PDAC TME的基线架构图；(目标2)将单细胞药物成像与 空间组学：确定间质屏障对抗体进入PDAC的影响，并评估 抑制Periostin改善患者来源的异种移植小鼠抗EGFR抗体和ADC的输送 模型；以及(目标3)检查化疗在改变人类TAMs表型和功能中的作用 和小鼠PDAC；识别化疗诱导的PDAC患者-脑梗塞相互作用的改变 抗PD-L1荧光抗体；并验证抑制与脑梗塞的相互作用是否能改善脑梗塞的反应 在PDAC转基因小鼠模型中加用化疗。该项目将提供新的计算工具来 量化临床肿瘤中细胞-细胞和细胞-药物的相互作用，为耐药提供新的机制见解 在胰腺癌，并导致新的治疗策略，以提高患者的生存。

PROJECT SUMMARY This proposal describes a career development program to prepare Dr. Lu for an independent research career that focuses on developing computational and experimental methods to improve cancer detection, diagnosis, and treatment. This program will provide Dr. Lu with new expertise in single-cell spatial omics, integrating with her background in machine learning-based image computation (gained as a graduate student) and clinical single- cell drug imaging (gained as a postdoctoral researcher) to advance our understanding of the mechanism that drives drug resistance of pancreatic cancer. Dr. Lu will be mentored by Dr. Garry Nolan, who invented the CODEX technology for highly multiplexed single-cell imaging, and co-mentored by Dr. Eben Rosenthal, a physician-scientist who pioneered the first-in-human clinical studies for fluorescence-guided cancer surgery, and Dr. Robert West, who developed the Smart-3SEQ technology for spatial transcriptomics. The K99 phase of Dr. Lu’s training will consist of (i) structured mentorship by the primary mentor and co-mentors, (ii) close interactions with advisory committee and collaborators, (iii) technical and academic training, (iv) a provocative research project, and (v) a program of career transition. Elucidating the role of the tumor microenvironment (TME) in drug resistance is critical to developing effective cancer therapies, but quantifying the drug delivery and action together with host environment factors within clinical tumors remains technically challenging. Antibody-based therapeutics, such as antibody-drug conjugates (ADCs) and immune checkpoint inhibitors (ICIs), are especially susceptible to blockade by TME barriers. The overall objective of this project is to identify the TME factors driving drug resistance in pancreatic ductal adenocarcinoma (PDAC) by integrating single-cell geospatial mapping of therapeutic antibodies with the deep spatial profiling of the TME. The central hypothesis is that periostin and tumor-associated macrophages (TAMs) play a key role in inhibiting drug delivery and response in PDAC. The central hypothesis will be tested by pursuing three aims: (Aim 1) establish a computational spatial omics platform by integrating CODEX and Smart-3SEQ to chart the baseline architecture of PDAC TME in an unbiased way; (Aim 2) combine single-cell drug imaging with spatial omics to determine the impact of stromal barriers to antibody delivery into PDAC and evaluate whether inhibiting periostin improves the delivery of anti-EGFR antibodies and ADCs in patient-derived xenograft mouse models; and (Aim 3) examine the role of chemotherapy in altering the phenotype and function of TAMs in human and mouse PDAC; identify chemo-induced alterations in TAM-ICI interactions in PDAC patients infused with a fluorescent anti-PD-L1 antibody; and validate whether inhibiting TAM-ICI interactions improves response to ICI plus chemotherapy in a transgenic mice model of PDAC. This project will provide novel computational tools to quantify cell-cell and cell-drug interactions in clinical tumors, offer new mechanistic insights on drug resistance in pancreatic cancer, and lead to new treatment strategies to improve patient survival.