By Jon Smith
They don’t take up much room in your DNA – a mere 4 megabases on the short arm of Chromosome 6 – but Human Leukocyte Antigen (HLA) genes play a defining role in whether you will develop an autoimmune disorder, fend off an infectious disease, or have an adverse reaction to potentially life-saving treatments.
Donor-recipient HLA matching benefits from nextgen sequencing.
Take blood and bone marrow transplants, for instance, which are used to treat more than 70 different diseases including the number one childhood cancer - leukemia. Some 50,000 patients worldwide receive blood stem cell transplants each year, and their fate may well lie in how quickly clinicians are able to find them a well-matched donor through HLA testing. In many cases finding that perfect match is far from easy – it requires timely and accurate analysis of a highly complex and variable region of the human genome. With new therapeutic technologies and better disease detection, the number of transplant patients and potential donors rises year on year, and with it the burden on registries and testing laboratories to coordinate donor-recipient matching more efficiently, coping with an immense volume of sample processing while keeping costs down. Fortunately, the groundbreaking technology of massively parallel DNA sequencing, more commonly known as Next Generation Sequencing (NGS), promises to ease this burden. Once considered cost effective only for large-scale operations, NGS-based HLA testing is becoming ever more accessible through improvements on numerous fronts including laboratory automation, upstream sample preparation, reagent validation and data analytics. With these new developments, NGS solutions are poised to become routine in labs offering HLA sequencing services, taking us one step closer to realizing the vision of affordable precision medicine.
Raising the alarm - for better or worse
The HLA genes encode a set of proteins that mark cells as ‘self’ and present peptide antigens at the cell surface for inspection by T-lymphocytes, which raise an alarm if their cognate receptors detect a foreign HLA or antigen. This mechanism forms an integral part of the immune system, enabling the body to eliminate damaged cells and invading pathogens like bacteria and viruses. Unfortunately, the very system responsible for our protection can sometimes work against us, for example when the body mistakenly identifies its own cells as foreign (autoimmune disease), becomes sensitized to a particular drug or rejects a transplant that is not a close enough match.
The HLA region of the human genome, historically named the Major Histocompatibility Complex (MHC), is a complicated mash-up of over 220 closely packed and highly variable (polymorphic) genes, a direct consequence of evolutionary selection pressure in the relentless arms race between humans and pathogens. Well over 100 diseases and many drug sensitivities have been correlated with specific variants of HLA and non-HLA genes in the MHC, and for this reason, in addition to being important for transplantation medicine, accurate genotyping of this region also has diverse applications in fields such as disease research, pharmacogenomics and population genetics.
Needle in a haystack - HLA typing challenges
Because genes of the MHC cluster so tightly, they tend to segregate as a unit, so that most people inherit one set of HLA alleles from each parent. This means that transplant patients typically have a 30% chance of a sibling match, while the remaining 70% must turn to an unrelated donor. For organ transplants, where viability can deteriorate in as little as 6 hours, immunosuppressant drugs are sufficient to cope with a degree of incompatibility, so mismatches can be tolerated even though not ideal. However, for some procedures such as hematopoietic stem cell transplantation even a single mismatch can profoundly affect the outcome. Unfortunately, finding a close unrelated donor match for a rare HLA type can be as difficult as finding a needle in a haystack.
Six ‘classical transplantation antigens’ are most associated with poorer outcomes in blood, bone marrow and organ transplantations: Class I HLA-A, -B and -C, and Class II HLA-DR, -DQ, -DP. As DNA analysis techniques have improved, the number of known alleles for these 6 antigens has increased dramatically, from just a handful catalogued in 1987 to nearly 15,000 reported in the latest release of the international IPD-IMGT/HLA database (release 3.25, June 2016). While some alleles are not translated into protein (null), or encode the same protein as another allele, many are expressed as unique proteins that could impact histocompatibility. For example, there are currently 2,480 different versions of the HLA-A gene product in the population. As the number of known alleles has grown, so have the ambiguities in HLA typing results, presenting a daunting challenge for effective donor-recipient matching and pushing conventional typing methods to their limit.
The perfect partner - the promise of HLA typing and NGS
For decades serological and cell based methods prevailed for histocompatibility testing. Advances in molecular DNA analysis techniques, including the introduction of polymerase chain reaction (PCR) in the ‘80s, gave rise to the more definitive and robust DNA-based approaches that have been widely adopted in both research and clinical laboratories. The most common techniques combine PCR with large panels of sequence-specific primers (PCR-SSP) or oligonucleotide probes (PCR-SSOP) designed based on known allele sequences. While such technologies have matured to produce rapid and reliable results, developments in automated DNA sequencing and analysis technologies have led to the adoption of sequence-based typing (SBT) as the ‘gold standard’ for high resolution HLA testing.
NGS technology takes first-generation Sanger-based methods to the next level. Its massively parallel approach has increased data output per run 1000-fold, at the same time reducing cost and enabling multiplexing of many samples in a single run. In addition, NGS takes a clonal approach that can deal with linked polymorphisms in heterozygous samples, eliminating the need to run additional costly and time-consuming processing or confirmatory tests to resolve cis-trans ambiguities. Despite clear advantages of NGS for HLA typing, it has yet to be widely adopted for routine testing in histocompatibility and immunogenetics laboratories. Developments across the NGS workflow, from upstream sample preparation onwards, are changing this. In the next article of this series we’ll look at some of the solutions that are now emerging to make NGS the norm for routine HLA testing.
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