Integrative Prediction of Therapeutic Response in T-cell Lymphoma by Omic and Spatial Modeling

通过组学和空间模型综合预测 T 细胞淋巴瘤的治疗反应

• 批准号: 10115190
• 负责人: Ajit Johnson Nirmal
• 金额: $ 13.61万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10115190
• 关键词: 1-Phosphatidylinositol 3-Kinase; Adoption; Aftercare; Architecture; Area; Biological Assay; Biological Markers; Biology; Cancer Biology; Cancer Center; Cell Communication; Cells; Characteristics; Classification; Clinical; Clinical Trials; Communication; Complex; Computational Biology; Computer Analysis; Conditioned Reflex; Data; Data Science; Development; Drug Combinations; Drug resistance; Ecosystem; Engineering; Exposure to; Genetic; Genetic Transcription; Immune; JAK1 gene; JAK2 gene; Knowledge; Lead; Leadership; Learning; Link; Logistic Regressions; Machine Learning; Malignant - descriptor; Medical Genetics; Methods; Modality; Modeling; Molecular; Mutation; Neighborhoods; Non-Malignant; Patients; Pattern; Performance; Pharmaceutical Preparations; Phase; Phenotype; Play; Positioning Attribute; Prediction of Response to Therapy; Prognosis; Recurrent disease; Relapse; Research; Resistance; Resistance development; Role; Sampling; Sensitivity and Specificity; Signal Transduction; Specimen; T-Cell Lymphoma; Techniques; Technology; Testing; Therapeutic; Training; Training Programs; Transcription Alteration; Validation; Voting; base; cancer type; clinical practice; cohort; computerized tools; data integration; design; exome sequencing; experience; immunoregulation; improved; inhibitor/antagonist; innovation; interest; liquid crystal polymer; mRNA Expression; molecular modeling; multiplexed imaging; neoplastic cell; neural network; novel; outcome prediction; paracrine; patient response; patient stratification; precision oncology; predicting response; prediction algorithm; predictive marker; predictive modeling; programs; random forest; resistance mechanism; response; single cell technology; skills; spatial integration; spatial relationship; spectrograph; statistical and machine learning; support vector machine; therapy resistant; tool; transcriptome sequencing; transcriptomics; translational research program; treatment response; tumor

ABSTRACT The tumor ecosystem plays a critical role in tumor development, progression and therapeutic response. Previous studies have utilized dissociative and single-cell omics technologies to profile the tumor ecosystem, specifically to understand therapeutic resistance and identify predictive biomarkers for precision cancer medicine. Yet, very few of these biomarkers have adequate performance characteristics for adoption in clinical practice. We hypothesize that a fundamental facet of the tumor ecosystem, i.e., the spatial organization of cells, which encodes key information involving paracrine and juxtracrine interactions that drive “neighborhood- level” biology, can further inform predictive models. Recent technological breakthroughs in highly multiplexed imaging and spatial transcriptomics offer an unprecedented opportunity to delineate the therapeutic consequences of spatial relationships within clinical tumor samples. Quantitative spatial features can provide independent valuable information, which is unlikely to be captured by clinical, genetic and bulk-transcriptional predictors. Hence, we propose to integrate highly multiplexed imaging data with omic approaches to delineate mechanisms of resistance and build predictive models of response for patients with T-cell lymphoma, who have a desperate unmet clinical need. In Aim 1 (K99 phase), I will build automated computational tools to robustly quantify spatial features from highly multiplexed imaging data and integrate it with exome and RNA- Seq. I will utilize >100 primary specimens collected pre-, on- and after-treatment with the PI3K-δγ inhibitor duvelisib to nominate mechanisms of de novo and acquired resistance. In Aim 2 (K99 phase), I will build an integrated machine-learning model to predict which patients are most likely to benefit from duvelisib and evaluate the impact of spatial features towards model performance. In Aim 3 (R00 phase), I will validate the model in an independent cohort and extend to samples from patients treated with additional agents, to identify consistent and parsimonious signatures of spatial features that could be developed for broader use. My extensive background in computational biology and experimental biology puts me in a unique position to accomplish this proposal. During the K99 phase, I will be supported by an outstanding and interdisciplinary team of advisors and collaborators (Drs. David Weinstock, Peter Sorger, Jon Aster, Allon Klein, Peter Park, and Steven Horwitz) with expertise in all aspects of the proposed research. I will acquire new skills in (1) computational analysis of highly multiplexed imaging to model molecular and spatial information, (2) data integration methods to delineate regulatory programs for designing effective drug combinations and (3) analysis of predictive biomarkers in clinical trial samples from clinical trials. Together with institutional support from Dana Farber Cancer Center and formal coursework and training, I will bridge my knowledge gap in cancer biology and gain the communication and leadership skills vital to transition into an independent position and establish an independent, data science-driven, translational research program.

摘要 肿瘤生态系统在肿瘤的发生、发展和治疗反应中起着至关重要的作用。 以前的研究已经利用解离和单细胞组学技术来描述肿瘤生态系统， 特别是为了了解治疗耐药性和识别预测精确癌症的生物标记物 医药。然而，这些生物标志物中很少有足够的性能特征可以在临床上采用。 练习一下。我们假设，肿瘤生态系统的一个基本方面，即 细胞，它编码涉及旁分泌和旁分泌相互作用的关键信息，这些相互作用驱动着“邻里-- 水平“生物学，可以进一步为预测模型提供信息。最近在高度多路复用方面的技术突破 成像和空间转录学提供了一个前所未有的机会来描绘治疗 临床肿瘤样本中空间关系的后果。量化的空间要素可以提供 独立的有价值的信息，这些信息不太可能被临床、遗传和批量转录捕获 预测者。因此，我们建议将高度多路传输的成像数据与OMIC方法相结合来描绘 T细胞淋巴瘤患者耐药机制及预测模型的建立 有一个令人绝望的未得到满足的临床需求。在目标1(K99阶段)中，我将构建自动化计算工具来 从高度多元化的成像数据中稳健地量化空间特征，并将其与外显子组和RNA- 序列号。我将利用在使用PI3K-δγ抑制剂治疗前、治疗后和治疗后收集的100个初级样本 Duvelisib用于命名从头耐药和获得性耐药的机制。在目标2(K99阶段)中，我将建立一个 集成的机器学习模型，以预测哪些患者最有可能受益于duvelisib和 评估空间要素对模型性能的影响。在目标3(R00阶段)中，我将验证 在一个独立的队列中建立模型，并扩展到接受其他药物治疗的患者的样本，以确定 一致和简约的空间要素签名，可以开发用于更广泛的用途。我的 在计算生物学和实验生物学方面的广泛背景使我处于独特的地位 完成这项建议。在K99阶段，我将得到一位杰出的跨学科人员的支持 顾问和合作者团队(David Weinstock、Peter Sorger、Jon Aster、Allon Klein、Peter Park、 和史蒂文·霍维茨)，拥有拟议研究的所有方面的专业知识。我将在(1)中获得新技能 模拟分子和空间信息的高度多路复用成像的计算分析，(2)数据 为设计有效的药物组合而制定管制计划的整合方法和(3) 临床试验样本中预测生物标志物的分析。与机构支持一起 来自Dana Farber癌症中心的正式课程和培训，我将弥合我在癌症方面的知识差距 生物学，并获得至关重要的沟通和领导技能，以过渡到独立的职位和 建立一个独立的、由数据科学驱动的转化性研究计划。