Machine Learning and Network Science for Predicting Kidney Transplant Survival

用于预测肾移植存活率的机器学习和网络科学

基本信息

批准号：
10221053
负责人：
Stanislaw M Stepkowski
金额：
$ 27.78万
依托单位：
UNIVERSITY OF TOLEDO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-12-31
项目状态：
已结题

项目摘要

Chronic kidney disease affects about 10% of adults in the United States and 7-12% of the population worldwide. It may lead to irreversible loss of kidney function, known as end-stage renal disease (ESRD). For patients with ESRD, kidney transplantation is the preferred treatment compared to dialysis in terms of patient survival, quality of life and cost. Despite the advantages of kidney transplants, most patients with ESRD are treated with dialysis primarily because there exist an insufficient number of compatible donors for patients. The human leukocyte antigens (HLAs) of the organ donor and recipient are known to be a significant contributing factor to transplanted organ survival times due to immunogenicity, the immune response of the recipient to the transplanted organ. Mismatches between donor and recipient HLAs are associated with shorter survival times; however, it is extremely rare to identify donors that have a perfect match with recipients, so most transplants involve mismatched HLAs. Our main objective is to accurately predict survival times for kidney transplants by incorporating both data- driven models of HLA compatibility based on outcomes of past transplants and biologically-driven models of HLA immunogenicity. Accurate prediction of survival times can improve patient transplant outcomes by enabling more efficient allocation of donors and recipients, particularly by reducing the number of repeat transplants due to graft failure with a poorly matched donor. We propose to estimate HLA compatibilities using high-dimensional variable selection techniques applied to outcomes of past transplants and through a novel donor-recipient latent space model for the HLA compatibility network. We then propose to incorporate these predicted compatibilities along with biologically-driven models of HLA immunogenicity using amino acid sequences and epitopes into a multi-task classification-based survival prediction algorithm. Our proposed approach for learning integrated data- and biologically-driven models of transplant survival generalizes broadly to organ transplantation (liver, heart, pancreas, lungs) and possibly to bone marrow transplantation.

慢性肾病影响美国约 10% 的成年人和 7-12% 的人口全世界。它可能导致不可逆的肾功能丧失，称为终末期肾病（ESRD）。对于 ESRD 患者，与透析相比，肾移植是首选治疗方法患者生存率、生活质量和费用。尽管肾移植有诸多优点，但大多数患者 ESRD 采用透析治疗主要是因为兼容供体数量不足对于患者。已知器官捐献者和接受者的人类白细胞抗原（HLA）是由于免疫原性、免疫接受者对移植器官的反应。供者和受者 HLA 之间的不匹配与较短的生存时间相关；然而，很难找到拥有完美基因的捐赠者。与受者匹配，因此大多数移植涉及不匹配的 HLA。我们的主要目标是通过整合这两种数据来准确预测肾移植的生存时间- 基于过去移植结果和生物驱动模型的 HLA 相容性驱动模型 HLA 免疫原性。准确预测生存时间可以通过以下方式改善患者移植结果使捐助者和受援者的分配更加有效，特别是通过减少重复的次数由于供体不匹配而导致移植失败。我们建议估计 HLA 相容性使用应用于过去移植结果的高维变量选择技术，并通过 HLA 兼容性网络的新型供体-受体潜在空间模型。然后我们建议合并这些预测的兼容性以及使用氨基的 HLA 免疫原性生物驱动模型酸序列和表位进入基于多任务分类的生存预测算法。我们的提出的学习移植存活的综合数据和生物驱动模型的方法广泛适用于器官移植（肝脏、心脏、胰腺、肺），也可能适用于骨髓移植。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation.

肾移植中 HLA 相容性网络的潜在空间模型。

DOI：
发表时间：
2022-12
期刊：
影响因子：
0
作者：
Huang, Zhipeng;Xu, Kevin S
通讯作者：
Xu, Kevin S

Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs.

使用 HLA 的多个特征表示来预测肾移植存活率。

DOI：
发表时间：
2021-06
期刊：
影响因子：
0
作者：
Nemati, Mohammadreza;Zhang, Haonan;Sloma, Michael;Bekbolsynov, Dulat;Wang, Hong;Stepkowski, Stanislaw;Xu, Kevin S
通讯作者：
Xu, Kevin S

Predicting kidney transplant survival using multiple feature representations for HLAs.

使用 HLA 的多个特征表示来预测肾移植存活率。

DOI：
发表时间：
2023-11
期刊：
影响因子：
0
作者：
Nemati, Mohammadreza;Zhang, Haonan;Sloma, Michael;Bekbolsynov, Dulat;Wang, Hong;Stepkowski, Stanislaw;Xu, Kevin S
通讯作者：
Xu, Kevin S

Empirical Comparison of Continuous and Discrete-time Representations for Survival Prediction

生存预测的连续时间表示和离散时间表示的实证比较