How Knowledge of T cell Epitopes Can Improve the Efficacy of Cancer Treatment
Modern medicine is quickly moving away from generic treatments and toward the personalization of therapy. In other words, people are more interested in “the right treatment for the right person at the right time.”1 These days, the “right treatment” often involves biologics in the form of monoclonal antibodies, which cover the surface of cancer cells and trigger their destruction by activating the immune system. Other monoclonal antibodies destroy cancers by targeting proteins necessary for growth or by directly activating immune cells.2
However, the point remains that though monoclonal antibodies have revolutionized cancer treatment, they can trigger very dangerous, life-threatening immune reactions in some individuals. To prevent such adverse reactions, research teams creating monoclonal antibodies should be especially careful to consider tests that may identify those least likely to respond well to specific monoclonal antibodies.
Monoclonal Antibodies as Foe
Despite the success of biologic treatments, there have been instances where the use of these treatments led to serious injuries or death. In particular, autoimmune thrombocytopenia, a condition when the immune system begins to attack itself, has been observed in patients exposed to recombinant thrombopoietin and can occur in cancer patients exposed to monoclonal antibodies.3 Scientists believe that this reaction is most clearly associated with T cell epitopes, which are presented on the surface of antigen-presenting cells (APCs) and bound to MHC or HLA molecules.4 T cell epitopes play an essential role in immunogenicity and predict a person’s likelihood to reject a biologic or to develop adverse side effects due to anti-drug antibodies or immune responses. These anti-drug immune responses are first triggered when monoclonal antibodies against cancer, for example, are digested by APCs.5 Inside of endocytic vesicles, the protein is degraded into peptides, which then fuse to MHC vesicles. Depending on the binding affinity of the protein and MHC, they form peptide-MHC complexes that are transported to the surface of the cell where it is now available to TCRs.
Generally, researchers have realized that therapeutic peptides with a high affinity for MHC are more likely to form the complexes that are eventually displayed at the cell surface, thus triggering an immune reaction. How can pharmaceutical companies decrease the chances that their monoclonal antibodies are similarly processed and displayed on cell surfaces to trigger dangerous reactions?
Personalized Medicine in the Form of Epitope Mapping
The idea of “personalized medicine” comes to play in considering how pharmaceutical companies can reduce the potential immunogenicity of monoclonal antibodies. One common approach is to divide a protein sequence into overlapping 9-mer peptide components and to use software to estimate HLA binding affinities for these short sequences. For a given patient of African, Middle Eastern or European descent, the HLA alleles tested should be those that are commonly seen in people of that genetic background. Beyond actually determining which peptide sequences are likely to bind with high affinity to MHC/HLA molecules (and are thus likely to induce an immune response), pharmaceutical companies can also use this information to determine which parts of a protein can be substituted for alternative amino acids. In other words, there could be particular regions of monoclonal antibodies that are predicted to result in a variety of “reactive peptides.” If this area is altered, this could reduce the likelihood that specific epitope sequences bind to HLA molecules and trigger an immune response. Beyond HLA phenotypes and predicting the peptides that will bind most strongly to specific HLA molecules, other factors that could be used to predict the immunogenicity of monoclonal antibodies include Ig-RF subgroups.6
Determine Appropriate Workflow with Biologics Software
Given the great variety of patients, pharmaceutical companies can start with personalizing medical options by organizing patients into HLA clusters. Monoclonal antibodies should then exist for patients within each of these cluster to increase the safety and efficacy of antibody treatments. But to generate this variety of monoclonal antibodies requires extensive software capable of processing and maintaining high volume sequence data about antibodies that can be used for epitope mapping and to predict the likelihood of MHC/HLA high affinity binding.
BIOVIA Biologics Solution streamlines efforts and removes barriers to innovation. As unified research and development software, it is designed to help researchers modify and discover new therapeutic compounds, while optimizing their overall workflow, especially important if one were to modify existing biologics based on HLA profiles. In the process, the movement of data from the discovery phase into the manufacturing stage is quicker and more easily accomplished. If you are considering how to similarly modify existing monoclonal antibodies, please contact us today.
- “Personalized medicine: What it means for patient-centered healthcare and how to address its current challenges,” http://www.pwc.com/us/en/view/issue-13/what-it-means-for-patient-centered-healthcare-and-how-to-address-its-current-challenges.htm ↩
- “Biological Therapies for Cancer,” June 12, 2013, http://www.cancer.gov/about-cancer/treatment/types/immunotherapy/bio-therapies-fact-sheet ↩
- “Thrombocytopenia caused by the development of antibodies to thrombopoietin,” December 1, 2001, http://www.bloodjournal.org/content/98/12/3241.abstract?sso-checked=true ↩
- “Personalized Medicine for Biologics,” March 5, 2015, http://www.epivax.com/blog/personalized-medicine-for-biologics/ ↩
- “T cell epitope: Friend or Foe? Immunogenicity of biologics in context,” July 6, 2009, http://www.epivax.com/wp-content/uploads/2013/09/T_cell_epitope_friend_foe_EpiVax_ADR_2009.pdf ↩
- ”Immunoglobulin subtypes predict therapy response to the biologics in patients with rheumatoid arthritis,” June 2013, http://www.ncbi.nlm.nih.gov/pubmed/23179259 ↩