About this Research Topic
Hematopoietic stem cell transplantation (HSCT) is a sophisticated, resource-intensive therapy that is increasingly used to treat malignant and non-malignant diseases. HSCT may use cells from healthy donors (allotransplants) or from patients themselves (autotransplants). Allotransplants have historically been performed with a goal of matching alleles of the HLA genes as well as optimizing other donor characteristics (e.g. KIR, cell counts, donor age). For patients who lack a suitable related donor, registries of volunteer potential donors and public banks of umbilical cord blood units have been established and provide cells for the majority of allotransplants.
Large scale efforts are in place to optimize the outcome of HSCT by understanding and affecting the immunology driving the immune response, optimize clinical decision making and optimize the development of global HSCT donor registries. The interplay between these efforts makes HCT one of the most interdisciplinary domains in current science.
Large scale HLA typing registries were developed over the last decades with over 33 million registered potential donors and 770 thousand banked cord blood units. Such registries are useful, but often fail to provide a solution for under-represented populations, or complex genetic backgrounds, such as patients with mixed ancestries. To maximize the utility of such registries targeted recruitment methods were proposed. Such methods require detailed human population genetics and models for immune system alleles and haplotype distribution in different populations.
Another factor limiting the usage of registries and the possibility of finding optimal donors is the typing resolution. While most modern HLA typing is based on high resolution genotyping, most of the data in registries comes from low resolution genotyping or serology. Such low-resolution typing requires imputation to estimate the detailed donor genotypes. In the KIR locus, the situation is even more complex, with low precision for most KIR typing, and limited information on existing KIR haplotype frequencies. Haplotype imputation algorithms are thus required to produce precisely detailed models of the immune gene compositions in different populations, and the relation between different populations.
Assessing HSCT outcomes is challenging. Individual indications for HSCT are uncommon, sources for grafts are diverse, the early HSCT period is characterized by multiple competing risks, and technologies evolve rapidly. There is a need for long-term follow-up of recipients, since some HSCT effects, e.g., therapy-related cancers, occur many years after treatment. Outcomes registries facilitate understanding of treatment outcomes by addressing questions not amenable to randomized trials or single-center series. These include results in various disease states and patient groups, determining prognostic factors, defining inter-center variability in diagnosis, practice, and outcome, evaluating long-term outcomes, and developing new analytic approaches. Although outcomes registries have been useful for hypothesis-driven research they have been underutilized for discovery-based research and for informing machine learning models.
To summarize, HSCT stands at the junction of multiple domains: clinical research, immunology, population genetics, and advanced modeling and algorithms. To address the interplay between all these issues, this collection welcomes contributions that deal with the different domains affecting HSCT and the interaction between them.
Keywords: Population genetics, Bone marrow transplants, Bone marrow registries, HLA, KIR
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