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

• 批准号: 10390896
• 负责人: Brent R Logan
• 金额: $ 62.82万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 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/10390896
• 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; CXCL9 gene; CXCR3 gene; Cells; Child; Childhood; Clinical; Clinical Trials Network; Complication; Cryopreservation; Custom; 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; 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; base; biobank; biomarker panel; 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; predictive test; preempt; receptor; risk stratification; statistics; success; tandem mass spectrometry

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.

慢性移植抗宿主病（CGVHD）研究和先发制人治疗的主要障碍是 在同种异体造血细胞移植（HCT）后，无法早日预测，他们将发展CGVHD， 临床符号可以检测到发作前CGVHD的特异性风险生物标志物。 该项目将使用BMTCTN 0201、1202和多中心的已经收集的血浆和PBMCS样品 儿科和成人研究（NCT00075816，NCT01879072和NCT02194439）和Pasquarello组织 Dana -Farber癌症研究所的银行分析与蛋白质组学和细胞特征 即将开始使用机器学习（ML）与已建立的临床CGVHD发作以及总体生存 统计数据。拟议的标记基于以前发表和未发表的研究，将包括其他 新颖或假设的因素。我们将使用带有样品的Tow BMT CTN和NCT02194439生物库 大小约1300例HCT患者（与664个非CGVHD对照相比，669 CGVHD）。 HCT和14个血浆蛋白[刺激2（ST2；白介素（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名可用PBMC和配对等离子体的患者的队列中，总计多达300个参数的人群 在第 +90±10天后进行质量细胞仪。然后，我们将在CGVHD领域处于独特的位置 解决主要问题：（a）是血浆生物标志物或细胞生物标志物或两者的组合 可以提供更好的特异性/敏感性？ （b）我们可以提高CGVHD的灵敏度和特异性 使用ML统计数据的生物标志物面板？ （c）我们可以使用ML发现CGVHD的新关键生物驱动器 算法？由于ML技术可能会提供更好的预测，当 使用尺寸协变量和非线性关系，我们假设ML分析将增加 面板的敏感性和特异性以及增加生物学粒度。具体目标1如果一个 第90天使用ML的14个患者样本上的14种系以上的生物标志物面板可预测使用CGVHD的风险 比确定的统计数据更高的特异性和灵敏度。特定的目标2如果一天-90±10三十 - 使用单细胞质量细胞术和ML的五细胞生物标志物面板可预测CGVHD的发展 在30个案例中，30个控制发现队列。特定的目标3将解决如果全面的90±10 仅蛋白质组学生物标志物面板或仅细胞生物标志物面板或组合蛋白质组学和细胞 在200个配对等离子体/PBMCS样品的验证队列中的生物标志物面板将改善CGVHD的预测 风险。完成后，这些研究将导致新型的生物标志物面板，以促进CGVHD风险 HCT患者的分层并确定新的先发制人方法的候选人。