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.

抽象的 肿瘤生态系统在肿瘤发育，进展和热反应中起关键作用。 先前的研究已利用解离和单细胞的奥元技术来介绍肿瘤生态系统， 专门了解治疗性耐药性并确定精确癌症的预测生物标志物 药品。然而，这些生物标志物中很少有足够的性能特征用于临床 实践。我们假设肿瘤生态系统的基本方面，即 细胞，该细胞编码涉及旁分泌和Juxtracrine相互作用的关键信息，这些信息驱动“邻居 - 水平”生物学，可以进一步告知预测模型。高度多重的技术突破 成像和空间转录组学为描述治疗性提供了前所未有的机会 临床肿瘤样品中空间关系的后果。定量空间特征可以提供 独立的有价值信息，不太可能被临床，遗传和批量转录捕获 预测指标。因此，我们建议将高度多路复用的成像数据与OMIC方法进行划定 耐药机制和为T细胞淋巴瘤患者的反应的预测模型， 有迫切未满足的临床需求。在AIM 1（K99阶段）中，我将构建自动化计算工具 从高度多重成像数据中量化空间特征，并将其与外显子组和RNA-集成 seq。我将使用PI3K-Δγ抑制剂的> 100个主要标本，并在处理前，进行后处理和后处理 Duvelisib提名从头的机制并获得了抵抗。在AIM 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从Duvelisib和 评估空间特征对模型性能的影响。在AIM 3（R00阶段）中，我将验证 在独立队列中模型，并扩展到接受其他药物治疗的患者的样本，以识别 可以为更广泛使用而开发的空间特征的一致和简约的签名。我的 计算生物学和实验生物学的广泛背景使我处于独特的位置 完成此建议。在K99阶段，我将得到一个杰出和跨学科的支持 顾问和合作者团队（David Weinstock博士，Peter Sorger，Jon Aster，Allon Klein，Peter Park， 和史蒂芬·霍维茨（Steven Horwitz））在拟议研究的各个方面都有专业知识。我将在（1）中获得新技能 高度多路复用成像的计算分析，以建模分子和空间信息，（2）数据 划定设计有效药物组合的监管计划的整合方法和（3） 临床试验中临床试验样品中预测性生物标志物的分析。与机构支持一起 从达纳·法伯（Dana Farber）癌症中心以及正式的课程和培训中，我将弥合癌症的知识差距 生物学并获得过渡到独立立场至关重要的沟通和领导能力 建立一个独立的，数据科学驱动的翻译研究计划。