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.

摘要 肿瘤生态系统在肿瘤发生、发展和治疗反应中起着关键作用。 以前的研究已经利用解离和单细胞组学技术来描绘肿瘤生态系统， 特别是了解治疗耐药性和识别精确癌症的预测生物标志物 药然而，这些生物标志物中很少有足够的性能特征用于临床应用。 实践我们假设肿瘤生态系统的一个基本方面，即，的空间组织 细胞，它编码的关键信息涉及旁分泌和外分泌的相互作用，驱动"邻里， 生物学水平，可以进一步为预测模型提供信息。高度多路复用技术的最新技术突破 影像学和空间转录组学提供了一个前所未有的机会， 临床肿瘤样本内空间关系的后果。定量空间特征可以提供 独立的有价值的信息，这是不太可能被捕获的临床，遗传和批量转录 预测器因此，我们建议将高度复用的成像数据与组学方法相结合， 耐药机制，并建立T细胞淋巴瘤患者的反应预测模型， 有迫切的临床需求在目标1（K99阶段），我将构建自动化计算工具， 从高度多路复用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整合， seq.本人将使用> 100份在PI3K-δ γ抑制剂治疗前、治疗中和治疗后采集的原始标本 duvelisib提名从头和获得性耐药机制。在目标2（K99阶段），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中获益， 评估空间特征对模型性能的影响。在目标3（R00阶段），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样本，以确定 一致和简约的空间特征特征，可以开发用于更广泛的用途。我 在计算生物学和实验生物学的广泛背景使我在一个独特的位置， 完成这个提案。在K99阶段，我将得到一位杰出的跨学科专家的支持。 顾问和合作者团队（大卫温斯托克博士，彼得索格，乔恩阿斯特，阿隆克莱因，彼得公园， 和史蒂文·霍维茨）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程序，以及（3） 分析来自临床试验的临床试验样品中的预测性生物标志物。加上机构支持 从达纳法伯癌症中心和正式的课程和培训，我将弥合我的知识差距，在癌症 生物学和获得沟通和领导技能至关重要的过渡到一个独立的立场， 建立一个独立的、数据科学驱动的转化研究项目。