Abstract
Purpose :
All major clinical trials have shown that only 27-40% of diabetic macular edema (DME) patients show vision improvement of >15 letters with intravitreal anti-VEGF injections. Thus, a substantial proportion of patients are “poor” responders to anti-VEGF therapy. Our goal was to identify genetic variants, if any, that may help determine “good” vs “poor” response to anti-VEGF therapy.
Methods :
We conducted whole-exome sequencing (WES) on two cohorts, "Good” Responders (reduction of baseline central retinal thickness, CRT >25% after three monthly anti-VEGF injections; n=10) and “Poor” Responders (reduction of CRT <10% or increase in CRT after three monthly injections; n=10). DNA was isolated from white blood cells followed by quality evaluation and quantification. DNA libraries were constructed using HiFi Library Amplification Kit followed by exome region capturing by Agilent SureSelect XTLI and Human All Exon V7 capture. Exome sequencing was performed on the Illumina NovaSeq 6000 for 100X coverage. The sequenced data were processed and aligned to the reference genome (Hg19/GRCh38) using TGen pipeline and Constitutional variant calling with high functional impact performed with DeepVariant caller.
Results :
After comprehensive exome variant analysis to identify coding variants with functional impact, we identified that “Poor” responders had increased burden of missense mutation. Further, gene enrichment analysis revealed enrichment of ultra-rare variants in Sphingomyelin Phosphodiesterase 4 (SMPD4), Golgi Associated, Gamma Adaptin Ear Containing, ARF Binding Protein 2 (GGA2), and Mucin 12 (MUC12) in the “poor” responder cohort. These genes have been shown to play an important role in signaling of cell proliferation, apoptosis, and cell adhesion.
Conclusions :
Our data provides evidence for protein-coding genetic variants that may influence patient response to anti-VEGF therapy. A thorough understanding and functional validation of the mechanistic consequences of these variants using cell and animal model of diabetes will help in the identification of risk genes that play a role in the anti-VEGF response in DME patients and may help identify personalized treatment strategies.
This is a 2021 ARVO Annual Meeting abstract.