Machine Learning to identify Biomarkers for Risk of Chronic Graft-Versus-Host Disease

机器学习识别慢性移植物抗宿主病风险的生物标志物

基本信息

批准号：
10533823
负责人：
Brent R Logan
金额：
$ 59.64万
依托单位：
MEDICAL UNIVERSITY OF SOUTH CAROLINA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-12-02 至 2026-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10533823
关键词：
Address Adult Algorithms Allogenic Archives B-Cell Activation Biological Biological Markers Biology Blood Blood Tests Blood specimen Bone Marrow Transplantation CCR5 gene CD276 gene CSF1 gene CXCL10 gene CXCR3 gene Cells Child Childhood Clinical Clinical Trials Network Complication Cryopreservation Cytometry Dana-Farber Cancer Institute Data Dendritic Cells Development Dimensions Disease Disease susceptibility Enzyme-Linked Immunosorbent Assay FOXP3 gene Frequencies Helper-Inducer T-Lymphocyte Hematologic Neoplasms Hispanic IL17 gene Immunosuppression Incidence Inherited Interleukins Ligands MCAM gene Machine Learning Malignant - descriptor Measures Modeling Patients Peripheral Blood Mononuclear Cell Plasma Plasma Proteins Population Positioning Attribute Proteomics Publishing RANTES Random Allocation Regimen Regulatory T-Lymphocyte Relapse Research Risk Sample Size Sampling Sensitivity and Specificity Steroids Stromelysin 1 Symptoms T-Lymphocyte Techniques Testing Thinness Tissue Banks Training Transplant Recipients Trees Validation biobank biomarker identification biomarker panel biomarker validation candidate identification chemokine chronic graft versus host disease cohort curative treatments deep learning disorder control disorder risk experimental study hematopoietic cell transplantation high dimensionality high risk improved machine learning algorithm novel novel marker osteopontin personalized medicine predictive marker receptor risk prediction risk stratification statistics success tandem mass spectrometry

项目摘要

Major barriers to chronic graft-versus-host disease (cGVHD) research and preemptive treatment are the inability to predict early following allogeneic hematopoietic cell transplantation (HCT), who will develop cGVHD, and lack of specific and sensitive risk biomarkers of cGVHD before onset is detectable by clinical symptoms. This project will use already collected plasma and PBMCs samples from BMTCTN 0201, 1202 and multicenter pediatric and adults studies (NCT00075816, NCT01879072, and NCT02194439) and the Pasquarello tissue bank at the Dana–Farber Cancer Institute to analyze proteomic and cellular signatures associated with impending onset of clinical cGVHD, and overall survival using machine learning (ML) versus established statistics. Proposed markers are based on previous published and unpublished studies and will include other novel or hypothesized factors. We will use the tow BMT CTN and NCT02194439 biorepositories with sample size totaling ~1300 HCT patients (669 cGVHD in comparison to 664 non-cGVHD controls) at day +90 post- HCT and 14 plasma proteins [Stimulation 2 (ST2; the interleukin (IL)-33 receptor), chemokine (C-X-C motif) ligand 9 (CXCL9), matrix metalloproteinase 3 (MMP3), osteopontin (OPN), and C-C motif chemokine 15 (CCL15), CD163, CXCL10, IL17, BAFF, B7H3, DKK3, IL1RACP, MCSF, CCL5] as well as 35 markers on 10+ populations totaling up to 300 parameters in a cohort of 200 patients with available PBMCs and paired plasma at day +90±10 post-HCT with mass cytometry. We will then be in a unique position in the field of cGVHD to address major questions: (a) Are plasma biomarkers or cellular biomarkers or the combination of both more amenable to provide better specificity/sensitivity? (b) Can we increase sensitivity and specificity of cGVHD biomarkers panels by using ML statistics? (c) Can we discover new key biologic drivers of cGVHD using ML algorithms? As ML techniques are likely to provide better prediction when large amount of data with high- dimensional covariates and nonlinear relationships are used, we hypothesize that ML analysis will increase sensitivity and specificity of our panels as well as increase biology granularity. Specific Aim 1 will address if a day-90 fourteen-plasma biomarker panel on 1300 patients’ samples, using ML, predicts risk of cGVHD with higher specificity and sensitivity than established statistics. Specific Aim 2 will address if a day-90±10 thirty- five-cellular biomarker panel, using single-cell mass cytometry and ML, is predictive of development of cGVHD in a 30 cases vs 30 controls discovery cohort. Specific Aim 3 will address if a comprehensive day-90±10 proteomic biomarker panel only, or cellular biomarker panel only, or a combined proteomic and cellular biomarker panel in a validation cohort of 200 paired plasma/PBMCs samples, will improve prediction of cGVHD risk. Upon completion, these studies will result in novel biomarker panels that may facilitate cGVHD risk stratification for HCT patients and identify candidates for new preemptive approaches.

慢性移植物抗宿主病（cGVHD 障碍）研究和先发性治疗的主要领域是无法早期预测同种异体造血细胞移植 (HCT) 后谁会发生 cGVHD，通过临床症状可检测到发病前缺乏特异性且敏感的 cGVHD 风险生物标志物。该项目将使用从 BMTCTN 0201、1202 和多中心收集的血浆和 PBMC 样本儿科和成人研究（NCT00075816、NCT01879072 和 NCT02194439）和 Pasquarello 组织达纳法伯癌症研究所的银行分析与即将发生的临床 cGVHD 以及使用机器学习 (ML) 与已建立的数据的总体生存率拟议的标记基于之前发表和未发表的研究，并将包括其他内容。我们将使用两个 BMT CTN 和 NCT02194439 生物样本库。术后第 90 天，总共约 1300 名 HCT 患者（669 名 cGVHD 对照和 664 名非 cGVHD 对照） HCT 和 14 种血浆蛋白 [刺激 2（ST2；白细胞介素 (IL)-33 受体）、趋化因子（C-X-C 基序）配体 9 (CXCL9)、基质金属蛋白酶 3 (MMP3)、骨桥蛋白 (OPN) 和 C-C 基序趋化因子 15 (CCL15)、CD163、CXCL10、IL17、BAFF、B7H3、DKK3、IL1RACP、MCSF、CCL5] 以及 10+ 上的 35 个标记在 200 名具有可用 PBMC 和配对血浆的患者队列中，总共有多达 300 个参数在 HCT 后+90±10 天进行质谱流式细胞术，我们将在 cGVHD 领域处于独特的地位。解决主要问题：(a) 血浆生物标志物或细胞生物标志物或两者的组合是否更有效？能够提供更好的特异性/敏感性吗？ (b) 我们可以提高 cGVHD 的敏感性和特异性吗？使用 ML 统计的生物标志物面板？ (c) 我们能否使用 ML 发现 cGVHD 的新关键生物驱动因素？当大量数据具有较高的精度时，机器学习技术可能会提供更好的预测。使用维度协变量和非线性关系，我们追求ML分析会增加我们的小组作为颗粒的敏感性和特异性以及增加生物学数量将解决以下问题。第 90 天，使用 ML 对 1300 名患者样本进行 14 种血浆生物标志物检测，预测 cGVHD 风险比已建立的统计数据更高的特异性和敏感性将解决如果一天90±10三十-。使用单细胞质谱流式细胞术和机器学习的五细胞生物标志物组可预测 cGVHD 的发展在 30 个病例与 30 个对照发现队列中，具体目标 3 将解决综合日 90±10 的问题。仅蛋白质组生物标志物组，或仅细胞生物标志物组，或组合的蛋白质组和细胞 200 个配对血浆/PBMC 样本验证队列中的生物标志物组合将改善 cGVHD 的预测完成后，这些研究将产生可能促进 cGVHD 风险的新型生物标志物组合。对 HCT 患者进行分层并确定新的先发性方法的候选者。