Precision medicine may mitigate racial biases

In this issue of Blood, Akahoshi et al present an elegantly designed new grading system for acute graft-versus-host disease (aGVHD) that integrates symptoms and biomarkers to improve prognostic power and predicts response to treatment at day 28 after allogeneic hematopoietic cell transplantation (HCT).¹ aGVHD, a significant complication of HCT, is marked by a complex interplay of donor and recipient immune systems. Traditional graft-versus-host disease (GVHD) grading systems primarily rely on clinical symptoms, leading to varied prognostic outcomes and treatment responses. Recognizing the need for a more nuanced and accurate predictive model to guide treatment, Akahoshi et al developed the Mount Sinai Acute GVHD International Consortium (MAGIC) composite scores, incorporating both clinical symptoms and biomarkers to enhance treatment outcome predictions and support a personalized approach to treatment.

aGVHD occurs early after transplant during the period of greatest nonrelapse mortality (NRM) risk and infectious complication risks. High-dose corticosteroid therapy, the standard in firstline treatment, has the potential to reduce quality of life, increase morbidity because of adverse effects, and raise mortality through its association with infective complications, particularly in endemic settings. The integration of biomarkers into GVHD grading represents a substantial advancement in the field, acknowledging the heterogeneity of GVHD manifestations and the necessity for precision medicine in improving patient outcomes. This paves the way for personalized treatment strategies, potentially reducing the reliance on high-dose systemic immunosuppressive therapies that often come with significant adverse effects.

Data from 1863 patients treated for GVHD within MAGIC were analyzed using a classification and regression tree algorithm to create 3 risk groups based on clinical symptoms alone. These groups demonstrated a significantly higher area under the receiver operating characteristic curve for 6-month NRM compared with the traditional Minnesota risk classification.¹ By integrating serum GVHD biomarker scores, 3 MAGIC composite scores were identified. These composite scores significantly improved the prediction of NRM and day 28 treatment response, highlighting a robust model that transcends the limitations of symptom-only–based systems. The utility of a composite score has the potential to address several unmet needs in the field of aGVHD diagnosis, treatment, and research that may not be immediately apparent.

Recent advances in prophylaxis have reduced the incidence of severe aGVHD, making mild to moderate symptoms the dominant phenotype. This reduced incidence and the higher incidence of more subtle forms of aGVHD are likely to challenge clinicians in recognizing and staging aGVHD, especially in atypical presentations.¹ Furthermore, with the advent of posttransplant cyclophosphamide and haploidentical transplants, the donor pool has expanded for patients with diverse ethnic backgrounds, for whom data on manifestations and phenotypes of aGVHD are limited. These populations are at higher risk of aGVHD, with higher incidence and severity in less homogeneous ethnicities, such as African Americans vs Scandinavian and Japanese populations.^2-5 In clinical practice, a challenge for educators and practitioners is the absence of images or definitions of skin manifestations in different ethnicities.^6,7 This further highlights the need and role of staging systems that include biomarkers to augment symptoms and signs-based models that are limited by clinical training and exposure that is not generally inclusive of minoritized populations. In clinical research, even the recent advances in GVHD therapeutics are significantly challenged by the lack of diversity in patient populations. For instance, the landmark ruxolitinib studies in corticosteroid-refractory aGVHD had ≈70% White patients and 0% Black patients in the treatment arm.^8,9 Although it is critical that progress is made in the inclusion of diverse patient populations in clinical trials, tools that support objective diagnostic and prognostic models will improve precision in diagnosis, staging, and treatment even if the original research data set on which the tools were based was not diverse.

The goal of improving diversity in clinical research is to enhance the recognition of (or to better identify) biological differences in different populations, not to address the cultural constructs of racism. Researchers need to find opportunities to identify or use scientific tools that can mitigate biases in our assumptions that predicate our knowledge base, clinical practice, and research. Traditional clinical symptom-based grading systems may inadvertently perpetuate disparities because of data gaps in the variations in symptoms and presentations across different ethnic groups. The incorporation of biomarkers, which are less likely to be influenced by such biases, offers a more equitable approach to patient care even if the study cohorts are not inclusive of diverse populations. This is because although there may be variations in genetic drivers of disease and disease phenotypes, this is less likely to be a concern in biomarkers of disease.

This study sets a precedent for addressing racial biases in clinical practice and research by acknowledging data gaps for ethnic minority populations both historically and within this study (5% African American) presented.¹ This allows the reader and future researchers the opportunity to consider the limits in the application of the proposed prognostic tool in clinical practice and potential improvement in future research. Interestingly, despite the limitation of a lack of diversity in the training and validation cohorts of this prognostic model, the integration of biomarkers offers clinical tools that benefit these populations. This exemplifies the importance of deliberate consideration of who is missing in the data, who will be impacted by this in the communities we serve, and most important, how they will be impacted. Only then can we aspire to equity in clinical care with a continuous improvement mindset. Integrating equity, diversity, and inclusion in all aspects of clinical research is central to high-quality clinical research.

Conflict-of-interest disclosure: The author declares no competing financial interests.