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: 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
• 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

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）研究和抢先治疗的主要障碍是 不能早期预测异基因造血细胞移植（HCT）后谁将发展cGVHD， 并且在发病前缺乏特异性和敏感性的cGVHD风险生物标志物可通过临床症状检测。 本项目将使用已从BMTCTN 0201、1202和多中心采集的血浆和PBMC样本 儿童和成人研究（NCT 00075816、NCT 01879072和NCT 02194439）和Pasquarello组织 Dana-Farber癌症研究所的一家银行分析了与癌症相关的蛋白质组和细胞特征， 临床cGVHD即将发作，使用机器学习（ML）与已建立的 统计建议的标志物是基于以前发表和未发表的研究，并将包括其他 新的或假设的因素。我们将使用两个BMT CTN和NCT 02194439生物储存库， 在给药后第+90天，总计约1300例HCT患者（669例cGVHD，664例非cGVHD对照） HCT和14种血浆蛋白[刺激2（ST 2;白细胞介素（IL）-33受体），趋化因子（C-X-C基序） 配体9（CXCL 9）、基质金属蛋白酶3（MMP 3）、骨桥蛋白（OPN）和C-C基序趋化因子15 （CCL 15）、CD 163、CXCL 10、IL 17、BAFF、B7 H3、DKK 3、IL 1 RACP、MCSF、CCL 5]以及10+细胞上的35种标志物。 在200例具有可用PBMC和配对血浆的患者队列中，总计多达300个参数的人群 在HCT后第+90天至10天，用质谱细胞术测定。然后，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 是否能够提供更好的特异性/灵敏度？(b)我们能否提高cGVHD的敏感性和特异性 生物标志物面板使用ML统计？(c)我们能否利用ML发现cGVHD的新的关键生物驱动因素 算法由于ML技术可能会在大量数据具有较高的预测能力时提供更好的预测， 使用多维协变量和非线性关系，我们假设ML分析将增加 灵敏度和特异性以及增加生物学粒度。具体目标1将解决以下问题： 使用ML，对1300名患者的样本进行90天的14种血浆生物标志物面板，预测cGVHD的风险， 特异性和灵敏度高于已建立的统计学。具体目标2将解决如果一天-90±10 30- 使用单细胞质量细胞计数和ML的五细胞生物标志物面板可预测cGVHD的发展 在30个病例与30个对照的发现队列中。具体目标3将解决如果综合日-90±10 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生物标志物组。 在200个配对血浆/PBMC样品的验证队列中的生物标志物组，将改善cGVHD的预测 风险完成后，这些研究将产生可能促进cGVHD风险的新生物标志物组 对HCT患者进行分层，并确定新的先发制人方法的候选者。