Background
Checkpoint inhibitors (CPIs) have significantly enhanced cancer treatment, yet formation of antidrug antibodies (ADA) can reduce drug efficacy and lead to increased immune toxicity.1 Identifying biomarkers predictive of ADA formation is crucial to optimize administration of CPIs. Human leukocyte antigen (HLA) genes are responsible for presentation of antigens to T cells and harbor substantial inter-patient variation. A recent study2 identified allelic variation in the HLA-DRB1 gene as a risk factor for ADA formation in CPI. For non-CPI-directed cancer immunotherapies (CITs), aggregated clinical data is scarce, and the role of HLA in ADA formation remains largely unexplored.
Methods
To investigate associations between HLA alleles and ADA formation, we established a harmonized data mart from 23 early-phase CIT trials, encompassing 12 molecules with diverse mode of actions and various cancer indications. This comprehensive dataset includes a total of 3568 patients, both CPI-naïve and CPI-experienced, and features clinical and two-field class I and II HLA alleles imputed from genotyping arrays.3
Treatment-induced ADA status was determined using a standardized set of definitions based on therapeutic antibody-specific titers collected longitudinally.4 We adopted an HLA-wide approach5 to assess associations with persistent ADA formation, focusing on a patient population of European ethnicity to account for differences in HLA allele frequencies and correcting for potential confounders of ADA such as age and gender.
Results
Associations with the HLA alleles are summarized on a per molecule basis. After Bonferroni correction for HLA alleles, no statistically significant association was identified. However, the results for CPI as a standard-of-care combination therapy showed an odds ratio of 1.84 (CI 95% 1.02–3.21) for the DRB1*01:01 variant, consistent with the main finding previously reported in a set of larger atezolizumab monotherapy trials.2 Furthermore, consistency in the ranking of HLA alleles associated with ADA across the different therapeutic antibodies was low, with Spearman correlations ranging from 0.02 to 0.15.
Conclusions
This dataset has enabled a systematic investigation of the HLA region’s role in ADA formation linked to non-standard CITs. Low correlations amongst rankings of the associations suggest that genetic predisposition to ADA formation is likely molecule-specific. However, these results may also be attributed to low statistical power and other confounding factors. As a next step, we aim to integrate in-silico prediction algorithms6 for T cell epitopes to factor in the immunogenicity cascade and elucidate the differences seen across the therapeutic antibodies.
Acknowledgements
Members of the Enhanced Data Insights and Sharing community at Genentech and Roche who developed, curated, and integrated data for this work in the Cancer Immunotherapy Data Mart.
References
van Brummelen EM, Ros W, Wolbink G, Beijnen JH, Schellens JH. Antidrug antibody formation in oncology: clinical relevance and challenges. Oncologist 2016 Oct;21(10):1260–1268.doi: 10.1634/theoncologist.2016-0061. Epub 2016 Jul 20. PMID: 27440064;PMCID: PMC5061540.
Hammer C, Ruppel J, Kamen L, Hunkapiller J, Mellman I, Quarmby V. Allelic variation in HLA-DRB1 is associated with development of antidrug antibodies in cancer patients treated with atezolizumab that are neutralizing in vitro. Clin Transl Sci 2022 Jun;15(6):1393–1399. doi: 10.1111/cts.13264. Epub 2022 Apr 8. PMID: 35263013; PMCID: PMC9199883.
Zheng X, Shen J, Cox C, Wakefield JC, Ehm MG, Nelson MR, Weir BS. HIBAG–HLA genotype imputation with attribute bagging. Pharmacogenomics J 2014 Apr;14(2):192–200. doi: 10.1038/tpj.2013.18. Epub 2013 May 28. PMID: 23712092; PMCID: PMC3772955.
Shankar G, Devanarayan V, Amaravadi L, Barrett YC, Bowsher R, Finco-Kent D, Fiscella M, Gorovits B, Kirschner S, Moxness M, Parish T, Quarmby V, Smith H, Smith W, Zuckerman LA, Koren E. Recommendations for the validation of immunoassays used for detection of host antibodies against biotechnology products. J Pharm Biomed Anal 2008 Dec 15;48(5):1267–81.doi: 10.1016/j.jpba.2008.09.020. Epub 2008 Sep 19. PMID: 18993008.
Migdal M, Ruan DF, Forrest WF, Horowitz A, Hammer C. MiDAS-meaningful immunogenetic data at scale. PLoS Comput Biol 2021 Jul 6;17(7):e1009131.doi: 10.1371/journal.pcbi.1009131. PMID: 34228721; PMCID: PMC8284797.
Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 2020 Jul 2;48(W1):W449-W454. doi: 10.1093/nar/gkaa379. PMID: 32406916; PMCID: PMC7319546.