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

慢性移植物抗宿主病(CGVHD)研究和预防性治疗的主要障碍是 无法预测异基因造血细胞移植(HCT)后早期谁会患上cGVHD， 发病前缺乏特异性和敏感性的cGVHD危险生物标记物可从临床症状中发现。 该项目将使用已经从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 Bank在Dana-Farber癌症研究所分析与以下相关的蛋白质和细胞签名 临床cGVHD即将发作，使用机器学习(ML)与已建立的总体存活率 统计数据。建议的标记基于以前已发表和未发表的研究，并将包括其他 新奇的或假设的因素。我们将使用两个BMT CTN和NCT02194439生物信息库和样品 在+90天时，总共约1300名HCT患者(669名cGVHD与664名非cGVHD对照) Hct和14种血浆蛋白[刺激因子2(ST2；白细胞介素33受体)、趋化因子(C-X-C基序)] 配体9、基质金属蛋白酶3、骨桥蛋白和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 具有可用的PBMC和配对血浆的200名患者队列中总计多达300个参数的人群 血细胞比色仪检测+90±10天。到那时，我们将在cGVHD领域处于独特的地位 解决主要问题：(A)是血浆生物标记物还是细胞生物标记物，还是两者的组合 是否愿意提供更好的特异性/敏感性？(B)我们能否提高cGVHD的敏感性和特异性 使用ML统计的生物标记物小组？(C)我们能否使用ML发现cGVHD的新关键生物驱动因素 算法？由于ML技术可能会在大量具有高可靠性的数据时提供更好的预测 使用维协变量和非线性关系，我们假设最大似然分析将增加 我们的专家小组的敏感性和特异性以及增加了生物粒度。具体目标1将解决以下问题 使用ML对1300名患者样本进行90天14血浆生物标记物小组检查，预测cGVHD的风险 比已有的统计数据更具特异性和敏感性。具体目标2将解决如果一天-90±10 30- 应用单细胞质量细胞术和ML的五细胞生物标志物组合可预测慢性移植物抗宿主病的发展 在30个病例与30个对照发现队列中。具体目标3将解决是否全面的一天-90±10 仅蛋白质组生物标记组，或仅细胞生物标记组，或蛋白质组和细胞组合 200对血浆/外周血单核细胞样本的验证队列中的生物标志物小组将改善对cGVHD的预测 风险。完成后，这些研究将产生可能促进cGVHD风险的新型生物标志物面板。 对HCT患者进行分层，并确定新的先发制人方法的候选者。

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 Proposed markers are based on previous published and unpublished studies and will include other novel or hypothesized factors.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.