By Dr. Ravi Gupta, PhD, Chief Scientist, Bioinformatics
High throughput sequencing of cancer patients has enabled rapid identification of somatic coding mutations that could generate neoantigens1,2. The tumor neoantigens are ideal targets for immunotherapy because they are expressed only by the tumor cells3. Several studies have suggested that neoantigens are important targets for effective antitumor immune response and their use for developing personalized vaccines4-6. Many studies have been published that showed that higher mutation burden is linked to stronger T-cell responses and better survival of the patients7,8. Associations have been reported in endometrial cancers, melanoma, non-small cell lung cancer (NSCLC) and colorectal cancer 9-12. The neoantigens-specific T cell population have been also found to be expanded in effective antitumor immunity9,10. Both animal and human studies have shown that the tumor cell presenting immunogenic peptides can be selectively targeted by T cells which leads to complete or partial regression of tumor13-15.
Challenges in developing cancer vaccine
The vaccine has to be designed such that the patient’s immune cell (T cells) selectively hunt and kill only those specific tumor cells that present the targeted neoantigens. Finding a solution to train patient’s immune systems to specifically target and kill cancer cells has proven to be a difficult task. The first success of molecular identification of neoantigen was reported by Plaen et al. in 199816.
Identification of right immunogenic neoantigens is one of the central problems in the successful development of cancer vaccine. A patient’s tumor contains candidate neoantigens ranging from few hundred to several thousand. The real challenge is in selecting the candidate that would be best for stimulating the patient’s T cells. Computational algorithms have been developed but these programs suffer from lack of sensitivity and specificity because they rely heavily on features associated with antigen presentation alone, without considering features required for T cell receptor (TCR) binding. A recent paper describes a novel approach of quantifying neoantigen fitness in tumors to predict immunogenic peptides, in which both HLA presentation and TCR recognition are used as fitness components17. The neoantigen fitness model predicts immunogenic epitopes without examining structural features in a peptide that enables interaction with TCR. The model was used to predict long-term survivors of pancreatic cancer in a recent study18.
MedGenome computational group has developed a highly accurate new method (IPepPredicT) to select immunogenic peptide19. IPepPredicT applies ensemble voting-based machine learning approach to identify immunogenic peptides from patient’s somatic mutations. Our method is the first in-silico model that combines physicochemical properties of amino acids favorable for TCR binding with features relevant for antigen presentation and processing. IPepPredicT is trained on MHC Class I HLA-A*02:01 9mer peptides present in IEDB data. Our analysis revealed enrichment of helix/turn features at TCR contact residues along with hydrophobicity features enriched at the HLA-binding anchor residues. Our analysis also provides a feature spatial enrichment map that provides a guideline for selecting immunogenic peptides. While developing the method we also analyzed MHC-peptide-TCR complex crystal structures. Our analysis revealed that many of the features selected by our prediction algorithm are in agreement with finding from crystal structure analysis.
Promising results from recent clinical trials
Recently two clinical trials reported have shown encouraging results. The first study was conducted at Boston’s Dana-Farber Cancer Institute on six melanoma patients4. The cancer vaccine targeted 20 neoantigens. Of the six patients given cancer vaccine, four of them are disease free for 25 months. For the remaining two patients, the disease reoccurred and was treated with anti-PD-1 therapy. The neoantigen specific T cells was found to be expanded that lead to complete tumor regression. The second clinical trial was conducted on 13 melanoma patients by Biopharmaceutical New Technologies (BioNTech) in Germany5. The cancer vaccine in this trial targeted 10 neoantigens for each patient. Eight patients were disease free for 12–23 months. These studies clearly indicate that the personalized vaccine has the ability to make cancer patient disease free.
By accurately predicting neoantigens, we believe IPepPredicT could help in effective personalized cancer vaccines.
- Snyder, A. & Chan, T. A. Immunogenic peptide discovery in cancer genomes. Curr Opin Genet Dev 30, 7-16, doi:10.1016/j.gde.2014.12.003 (2015).
- van Rooij, N. et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol 31, e439-442, doi:10.1200/JCO.2012.47.7521 (2013).
- Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69-74, doi:10.1126/science.aaa4971 (2015).
- Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217-221, doi:10.1038/nature22991 (2017).
- Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222-226, doi:10.1038/nature23003 (2017).
- Vasquez, M., Tenesaca, S. & Berraondo, P. New trends in antitumor vaccines in melanoma. Ann Transl Med 5, 384, doi:10.21037/atm.2017.09.09 (2017).
- Yarchoan, M., Johnson, B. A., 3rd, Lutz, E. R., Laheru, D. A. & Jaffee, E. M. Targeting neoantigens to augment antitumour immunity. Nat Rev Cancer 17, 209-222, doi:10.1038/nrc.2016.154 (2017).
- Hu, Z., Ott, P. A. & Wu, C. J. Towards personalized, tumour-specific, therapeutic vaccines for cancer. Nat Rev Immunol, doi:10.1038/nri.2017.131 (2017).
- Chan, T. A., Wolchok, J. D. & Snyder, A. Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma. N Engl J Med 373, 1984, doi:10.1056/NEJMc1508163 (2015).
- Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124-128, doi:10.1126/science.aaa1348 (2015).
- Le, D. T. et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med 372, 2509-2520, doi:10.1056/NEJMoa1500596 (2015).
- Howitt, B. E. et al. Association of Polymerase e-Mutated and Microsatellite-Instable Endometrial Cancers With Neoantigen Load, Number of Tumor-Infiltrating Lymphocytes, and Expression of PD-1 and PD-L1. JAMA Oncol 1, 1319-1323, doi:10.1001/jamaoncol.2015.2151 (2015).
- Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400-404, doi:10.1038/nature10755 (2012).
- DuPage, M., Mazumdar, C., Schmidt, L. M., Cheung, A. F. & Jacks, T. Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482, 405-409, doi:10.1038/nature10803 (2012).
- Castle, J. C. et al. Exploiting the mutanome for tumor vaccination. Cancer Res 72, 1081-1091, doi:10.1158/0008-5472.CAN-11-3722 (2012).
- De Plaen, E. et al. Immunogenic (tum-) variants of mouse tumor P815: cloning of the gene of tum- antigen P91A and identification of the tum- mutation. Proc Natl Acad Sci U S A 85, 2274-2278 (1988).
- Luksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517-520, doi:10.1038/nature24473 (2017).
- Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512-516, doi:10.1038/nature24462 (2017).
- Priyanka Shah, R. G., Anand Kumar Maurya, Ravi Gupta, Amit Chaudhuri. A machine learning approach for accurate prediction of immunogenic peptides from somatic mutations. Under Review (2017).