Strategies to advance the HCT source selection process

Delivering tools and matching features to improve event-free survival

Many initiatives are underway at the National Marrow Donor Program® (NMDP)/Be The Match® to equip your transplant center with evidence-based research through tools that can lead to optimal outcomes for patients. Our initiative in optimizing transplant success aims to provide you with up-to-date research to improve allogeneic hematopoietic cell transplantation (HCT) source selection.

Ultimately, our goal is to help improve event-free survival by applying data and research findings to clinical decision making and optimizing source selection.

During The ONE Forum® 2020, panelists discussed a few of the tools and matching features being evaluated as part of the initiative to aid transplant centers in selecting the a source for HCT.

Making the donor selection process more predictable

Eric Williams is a Senior Bioinformatics Scientist at the CIBMTR® (Center for International Blood and Marrow Transplant Research®), a research collaboration between the NMDP/Be The Match and the Medical College of Wisconsin. Williams explained how information used to calculate four key components of donor matching can be used to identify the best donor candidates. Those components include the:

The DRS has been in use for MatchSource users since April 2020. It was created in collaboration with the University of Minnesota using a machine learning approach. Your donor search results in MatchSource® include a DRS.

According to Williams, the DRS is about 70% accurate in predicting donor availability at CT. It uses factors such as age, gender and years that the donor has been on the Be The Match Registry®. Williams explained that the model is currently most useful as a tiebreaker when there are multiple equally matched donors. It can also indicate the need to explore alternatives when there are too few HLA matched unrelated donors.

The NMDP/Be The Match will continue to update the DRS as new data points are obtained and to keep it up to date with ongoing advancements in machine learning techniques.

Examining DPB1’s impact on overall survival and GVHD 

Martin Maiers is the Vice President of Innovation at the NMDP/Be The Match. He discussed efforts to advance HLA class II matching and particularly to increase capabilities around seven classical HLA genes in the class II region. The class II region includes the DR, DP and DQ gene families.

As reported in the NMDP/Be The Match and CIBMTR donor selection guidelines published in Blood in September 2019, a DPB1 is an important factor in overall survival following transplant. Therefore, it warrants further study.

Research papers using the DPB1 T-cell epitope model were published in Blood and Lancet Oncology. This research has shed light on how to determine which DPB1 mismatches are permissive (i.e., not likely to have an impact on survival) or nonpermissive (i.e., lead to increased mortality). Findings from another model which assesses DPB1 expression have determined that the level of expression also impacts the likelihood that a patient could develop graft-versus-host disease (GVHD) following transplant.

We are working to integrate learnings from both these models by developing a new, nuanced tool for predicting DPB1 matching. We are also examining the impact of low-expression loci, which include others in the DP, DQ and DR gene families, that have been shown to impact both mortality and GVHD following transplant.

Developing an HLA-B matching assessment tool 

Ray Sajulga is a Bioinformatics Scientist at the NMDP/Be The Match. He shared an update on research into HLA-B peptide matching. Sajulga described how exons, particularly exon 1, can encode “leader” peptides that regulate downstream immune responses. Exon 1 is a region outside of the peptide binding region which is typically considered for matching.

Research from Effie Petersdorf, MD, has identified which HLA-B mismatches are most optimal and which cause negative effects following transplant. The distinction depends on whether the HLA-B mismatched allele of the donor codes for the same leader peptide as the recipient and has a T leader shared allotype.

Sajulga has collaborated with Dr. Petersdorf to develop an automated HLA-B matching assessment tool. Integrating this tool into our systems and across the cell therapy industry will help avoid future mismatching of leader peptides between patients and donors. This can reduce the likelihood of transplant side effects like GVHD.

“There’s a lot of possibilities with B-Leader that can help improve the lives of a lot more patients. That’s what makes this novel gene feature so exciting,” Sajulga said.

As your partner, we are committed to consistently leveraging research outcomes in ways that allow your team to make data-driven decisions. Through our initiative in optimizing transplant success, we will continue to provide you with data and research to aid in clinical decision making, optimize donor and patient matching, and improve event-free survival.