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: 10746892
• 负责人: Ajit Johnson Nirmal
• 金额: $ 24.9万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 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 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10746892
• 关键词: 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; Institution; JAK1 gene; JAK2 gene; Knowledge; Lead; Leadership; Learning; Link; Logistic Regressions; Malignant - descriptor; Methods; Modality; Modeling; Molecular; Mutation; Neighborhoods; Non-Malignant; PIK3CG gene; 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; cancer type; clinical practice; cohort; computerized tools; data integration; design; exome sequencing; experience; gradient boosting; immunoregulation; improved; inhibitor; innovation; interest; liquid crystal polymer; mRNA Expression; machine learning model; model building; 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.

抽象的 肿瘤生态系统在肿瘤发生、进展和治疗反应中发挥着关键作用。 先前的研究利用解离和单细胞组学技术来描绘肿瘤生态系统， 专门用于了解治疗耐药性并识别精准癌症的预测生物标志物 然而，这些生物标志物很少有足够的性能特征可供临床采用。 我们认为这是肿瘤生态系统的一个基本方面，即肿瘤的空间组织。 细胞，编码涉及驱动“邻里- 水平”生物学，可以进一步为高度多重的预测模型提供最新的技术突破。 成像和空间转录组学为描述治疗提供了前所未有的机会 临床肿瘤样本内空间关系的结果可以提供定量空间特征。 独立的有价值的信息，不太可能被临床、遗传和批量转录捕获 因此，我们建议将高度多重成像数据与组学方法相结合来描述。 耐药机制并为 T 细胞淋巴瘤患者建立反应预测模型， 在目标 1（K99 阶段）中，我将构建自动化计算工具来满足迫切的未满足的临床需求。 稳健地量化高度多重成像数据的空间特征，并将其与外显子组和 RNA 集成 Seq. I 将利用 PI3K-δγ 抑制剂治疗前、治疗中和治疗后收集的 >100 个原始样本 duvelisib 指定从头和获得性耐药机制 在目标 2（K99 阶段）中，我将建立一个机制。 集成机器学习模型来预测哪些患者最有可能从 duvelisib 和 评估空间特征对模型性能的影响。在目标 3（R00 阶段）中，我将验证 在独立队列中建立模型，并扩展到接受其他药物治疗的患者的样本，以确定 空间特征的一致且简约的特征可以开发用于更广泛的用途。 计算生物学和实验生物学的广泛背景使我处于独特的地位 在K99阶段，我将得到一位杰出的跨学科人士的支持。 顾问和合作者团队（David Weinstock 博士、Peter Sorger、Jon Aster、Allon Klein、Peter Park、 和史蒂文·霍维茨（Steven Horwitz）在拟议研究的各个方面都具有专业知识，我将获得（1）方面的新技能。 高度多重成像的计算分析以模拟分子和空间信息，(2) 数据 整合方法来描述设计有效药物组合的监管计划和（3） 分析临床试验样本中的预测生物标志物以及机构支持。 来自达纳法伯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 生物学并获得对于过渡到独立职位至关重要的沟通和领导技能 建立一个独立的、数据科学驱动的转化研究项目。