Immunogenetic Diversity and Cancer Immunotherapy Disparities Free

HLA class I molecules are highly polymorphic and exhibit extensive diversity within the human population, boasting more than 25,000 variants (1). Specifically, the diversity within the classical HLA class I molecules (HLA-A, -B, and -C) has played a crucial role in the survival of our species when faced with a myriad of pathogens. HLA class I molecules act as “cellular informants” presenting peptides derived from other cellular proteins to CD8⁺ T cells, playing a crucial role by enabling a distinction between “self” and “nonself” by the immune system. The polymorphisms in HLA class I molecules often result in variations in the size, shape, and electrostatic properties of their peptide-binding pockets, influencing the affinity of an HLA class I molecule to the peptides that it presents. Consequently, each individual HLA class I allele typically binds a unique repertoire of peptides. The large diversity in HLA class I alleles allows that at least part of the human population has the capacity to generate competent T cell–based immune responses against a specific pathogen. This is essential for the survival of our species, especially during pandemics with high mortality rates and reinforces the increased probability that individuals within these populations possess adequate immune protection.

It is crucial to acknowledge that the frequencies of HLA class I alleles show notable variations among diverse ethnic groups. For instance, HLA-A*02–01, a frequently studied HLA class I allele is particularly common in Caucasian populations but less prevalent in populations of African or Asian descent (Fig. 1A; ref. 2). HLA class I–restricted therapies leverage T-cell receptors (TCR) that specifically recognize antigens presented by a particular HLA class I allele. Each TCR is highly specific for a unique combination of HLA and antigen, allowing for targeted modulation of immune responses in therapeutic applications in an HLA class I–restricted manner. These therapies hold promise in treating conditions such as viral infections and cancer because they enable precise targeting of diseased cells based on their presentation of disease-associated peptides on HLA class I.

Currently, there is only one HLA class I restricted TCR-based therapy approved by the FDA, namely Tebentafusp (3). This is a soluble TCR, targeting a peptide from the melanoma-specific antigen gp100 protein presented by HLA-A*02:01. The soluble TCR is fused to an antibody specific for CD3, resulting in a bispecific T-cell engager that recruits and activates T cells against cells expressing the HLA-A*02:01/gp100 peptide complex (Fig. 1B). It is currently used for systemic therapy of advanced uveal melanoma in patients who carry the HLA-A*02:01 allele. It represents the first and currently only HLA class I—restricted agent that improves clinical outcomes of patients with cancer. Nevertheless, there are numerous ongoing clinical trials that are leveraging the recognition of peptide complexes associated with specific HLA class I alleles by TCRs. Tebentafusp is currently under clinical investigation for both uveal and skin melanoma, in a relapsed and advanced state. There are currently six ongoing clinical trials exploring soluble TCRs fused to an antibody or therapeutic antibodies that specifically recognize HLA class I/peptide complexes targeting tumor antigens, namely NY-ESO-1, PRAME, MAGE-A4/8, and WT1. All these therapies are designed for the specific targeting of HLA-A*02:01/peptide complexes (4).

The second class of HLA class I–restricted therapies that are in clinical trials focuses on the adoptive transfer of TCR engineered T cells. In this therapy, T cells of a patient are engineered ex vivo to express an antigen-specific TCR and are transferred back to the patient (Fig. 1B). This approach is analogous to chimeric antigen receptor (CAR) T-cell therapies, in which T cells are engineered ex vivo to express a CAR (5). Whereas CAR T cells only recognize surface proteins using the antigen recognition domain of an antibody and are therefore not limited by specific HLA alleles, TCRs recognize HLA-associated peptides derived from cellular proteins, including intracellular ones. This form of adoptive cellular therapy has gained increased attention, specifically for the treatment of solid cancers. There are currently 118 adoptive TCR T-cell clinical trials registered at clinicaltrials.gov, of which 97 (82%) are specific for HLA-A*02 [Research Square (https://doi.org/10.21203/rs.3.rs-3534388/v1)].

There is an additional layer of complexity within the field of HLA class I–restricted biomedical research, which relates to the identification of immunogenic antigens and the finding of their cognate TCRs. The identification of immunogenic antigens is a crucial and initial step for the success of HLA-restricted therapies and diagnostics. Often, the identification of immunogenic antigens in cancer, such as neoantigens or tumor-associated antigens, along with their cognate TCRs, relies on the utilization of HLA class I multimer technologies (Fig. 1C; ref. 6). The current gold standard involves flow cytometric analysis using fluorescently labeled HLA class I reagents loaded with peptides. This approach is used for the detection and eventual isolation of antigen-specific T cells. However, this method is time-consuming and requires specialized expertise. Although these technologies are not yet widely implemented in clinical diagnosis, emerging tools designed to simplify the detection of antigen-specific T cells using HLA class I multimer technologies hold significant potential to impact cancer immunotherapy. It is important to note that these technologies make use of patient-specific HLA class I alleles, which are not only needed for diagnostics but also the source of clinically relevant TCRs. Therefore, it is essential that within the biomedical field the importance of using a wide range of HLA class I allele reagents is acknowledged, which is essential for widespread implementation of HLA class I–restricted diagnostics and therapies.

The concentration on a select few HLA class I alleles in biomedical research and clinical development can be justified by a number of reasons. First, considering the immunogenetic diversity of HLA class I, expanding all research and clinical development efforts to all HLA class I alleles would be a herculean task. Second, preclinical and clinical research on TCR-engineered therapies, including clinical trials, are developed considering the most prevalent and accessible population at their respective geographical location and first have to demonstrate their merits for a specific HLA class I allele. However, it is important to acknowledge that these approaches have the potential to accentuate disparities regarding the access to therapies depending on the immunogenetic background of an individual.

To move this field forward, it will be essential to ensure that clinical breakthroughs are accessible to a diverse range of patients, irrespective of their HLA class I genotype. Successful approaches developed for specific alleles should be extended to others, fostering inclusivity in cancer treatment strategies and actively precluding disparities regarding access to innovative treatments. The consideration of a few HLA class I alleles that have high frequencies in different populations would considerably expand the applicability of HLA class I–restricted approaches irrespective of one's ancestry. Alleles such as HLA-A*34:01, HLA-A*20:01, and HLA-A*11:01, which are common alleles in Australia, Asia, and Africa, respectively, could be considered (2). However, insufficient participation of racial and ethnic minorities in clinical studies is often observed, and this potentially limits the progress of HLA class I–restricted therapies and highlights a disparity in access to novel therapies in health care. Overcoming challenges in enrolling a diverse range of patients in clinical trials is complex, involving various levels of barriers. Although interventions at individual levels have shown some success, comprehensive, multilevel interventions hold the highest potential for success. Another consideration is to reevaluate the existing regulations governing the medical approval of HLA class I–restricted therapies. Including additional HLA alleles for clinical development can be challenging because the patient population of some alleles can be too small to undergo the normal development of a novel therapy. The unique challenges associated with small patient populations necessitate tailored approaches to trial design, statistical analysis, and regulatory considerations. The solution could be that for already established HLA class I–restricted therapies, additional alleles get an orphan-like regulation. Fostering an environment that encourages flexibility and adaptation on the regulatory level by the FDA and European Medicines Agency (EMA), we can enhance the regulatory framework, expanding the development and approval of innovative HLA class I–restricted therapies that hold immense promise for all patients. We also advocate for a proactive attitude within the scientific and clinical community, fostering the belief that a future of genetically diverse and inclusive HLA class I–restricted clinical therapies and diagnostics is possible and needed. There are several considerations and novel developments that will help in this development. The most important one is that there is a general feeling of urgency of taking genetical diversity seriously in biomedical research. Within human genetic studies, the genetic diversity of different populations is becoming an important consideration (7). It is acknowledged that the majority of discoveries are focused on data from populations of European ancestry (8). This holds true for genome-wide association studies (GWAS) that play a pivotal role in numerous biomedical investigations and drug development endeavors. Importantly, there is a concerted effort to incorporate genetic diversity within these studies, reflecting the recognition of the significance of diverse population representation. In addition, the genetic diversity plays a crucial role in CRISPR gene editing, in which DNA sequences in cells of patients are modified to treat a specific disease. Currently, CRISPR gene engineering is transitioning from research toward clinical therapies, and recently, UK regulators approved the first CRISPR genome-editing therapy for the genetic blood disorder sickle cell disease. Genetic variants have a substantial effect on the off-target landscape of CRISPR-based therapeutics, which are specific on the population and the individual level. It was recently shown that off-target prediction of CRISPR technology is mainly focused on Western-based reference genomes (9), demonstrating the significance of incorporating genetic diversity in clinical developments.

Access to health care and cost for HLA class I–restricted therapies presents a substantial challenge in ensuring global access to these therapies. Although the consideration of available HLA class I alleles is crucial on the genetic level, broader issues such affordability and logistics are at least equally important. Affordability is not only the cost of the therapy itself but also encompasses associated expenses like monitoring for potential side effects, such as cytokine release syndrome (CRS). Monitoring and managing side effects require a robust health care infrastructure that includes trained personnel, equipment, and ongoing support. Solving the challenges of logistics and making therapies financially feasible in diverse regions are crucial steps in achieving equitable access to health care and ensuring that genetic diversity does not become a barrier to receiving cutting-edge medical treatments.

We believe that novel advancements in HLA-restricted therapies have the potential to be transformative, particularly from the vantage point of including additional alleles within therapies. For TCR-centered therapies, the identification of antigen-specific TCR is a critical aspect and often HLA class I multimer technology is essential. The HLA class I multimer technology enables the detection and characterization of TCRs by utilizing multimers that mimic the natural interaction between TCRs and the peptide–HLA complex. The selection of peptides with a high affinity for a specific HLA class I allele often relies on in silico predictions (10). The accuracy of these prediction models is notably higher for alleles with corresponding real-world data, such as data derived from mass spectrometry analysis of HLA class I immunopeptidome. The immunopeptidome refers to the collection of peptide ligands that are experimentally found to be presented by HLA class I alleles. Inclusion of diverse HLA class I alleles for the immunopeptidome is currently happening, making the in silico prediction more reliable for a more diverse set of HLA alleles (11). It is worth noting that approaches for identifying immunogenic peptide antigens and TCRs with therapeutic potential are also being developed in an HLA class I–agnostic manner. The recently published HANSolo protocol makes use of large minigene libraries encoding candidate cancer antigens, which are transduced into patient-derived immortalized B-cell lines (12). Sequencing the libraries from control B-cell cultures, exposed or not exposed to T-cell populations carrying antigen-reactive T cells, enables an ingenious screen based on the ability of T cells to eliminate B cells carrying specific minigenes. Although these approaches can be labor-intensive and costly, they offer the potential to develop personalized T-cell therapies that are independent of HLA class I considerations.

Recent advances in protein structure predictions, driven by neural networks and artificial intelligence (AI), have demonstrated remarkable capabilities in modeling protein complexes. The emergence of AI in predicting TCR structures that recognize specific HLA class I/peptide complexes is an exciting development. With the development of AlphaFold, accurate protein structure prediction has become accessible to all (13). It can not only predict the structure of monomeric proteins but also protein complexes. Currently, AlphaFold can generate models of TCR:peptide–HLA interactions that effectively discriminate between peptide epitopes that result in TCR binding or do not result in binding with substantial accuracy (10). Moreover, a recent preprint describes the generation of de novo TCRs against a known peptide and HLA class I complex. In addition, this AI-based technology can predict HLA class I restriction of a specific TCR (bioRxiv 2023.09.12.557285). Similar to in silico HLA class I–specific peptide predictions, these AI systems rely on real-world data of peptide HLA class I allele complex and the cognate TCR. It will be intriguing to see how AI will influence HLA class I–restricted therapies and diagnostics. However, the dependence of technological developments on the availability of real-world data raises concerns about further disparities in resources and knowledge related to specific HLA class I alleles and populations. On the other hand, AI advancements may enable predicting peptide–HLA class I allele complexes and TCR interactions with minimal input data, potentially facilitating accurate predictions for HLA alleles with limited data. This could pave the way for developing HLA-restricted therapies, especially for alleles less commonly studied in biomedical research, potentially extending the benefits of immunotherapy to populations in Asia and Africa.

In conclusion, HLA class I–restricted therapies and diagnostics have the potential to expand the benefits to a larger proportion of patients with cancer when the large HLA class I allele diversity is taken along in the preclinical and clinical development. A reevaluation of existing regulations, heightened awareness, and continuous exploration of novel developments are essential to overcome the current challenges and facilitate the effective implementation of HLA-restricted immunotherapies, thereby addressing disparities in the development of cancer immunotherapies.