Geographic EBV variants confound disease-specific variant interpretation and predict variable immune therapy responses.

ABSTRACT: Epstein-Barr virus (EBV) is a potent carcinogen linked to hematologic and solid malignancies and causes significant global morbidity and mortality. Therapy using allogeneic EBV-specific lymphocytes shows promise in certain populations, but the impact of EBV genome variation on these strategies remains unexplored. To address this, we sequenced 217 EBV genomes, including hematologic malignancies from Guatemala, Peru, Malawi, and Taiwan, and analyzed them alongside 1307 publicly available EBV genomes from cancer, nonmalignant diseases, and healthy individuals across Africa, Asia, Europe, North America, and South America. These included, to our knowledge, the first natural killer (NK)/T-cell lymphoma (NKTCL) EBV genomes reported outside of East Asia. Our findings indicate that previously proposed EBV genome variants specific to certain cancer types are more closely tied to geographic origin than to cancer histology. This included variants previously reported to be specific to NKTCL but were prevalent in EBV genomes from other cancer types and healthy individuals in East Asia. After controlling for geographic region, we did identify multiple NKTCL-specific variants associated with a 7.8-fold to 21.9-fold increased risk. We also observed frequent variations in EBV genomes that affected peptide sequences previously reported to bind common major histocompatibility complex alleles. Finally, we found several nonsynonymous variants spanning the coding sequences of current vaccine targets BALF4, BKRF2, BLLF1, BXLF2, BZLF1, and BZLF2. These results highlight the need to consider geographic variation in EBV genomes when devising strategies for exploiting adaptive immune responses against EBV-related cancers, ensuring greater global effectiveness and equity in prevention and treatment.

Low-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries.

Inadequate diagnostics compromise cancer care across lower- and middle-income countries (LMICs). We hypothesized that an inexpensive gene expression assay using paraffin-embedded biopsy specimens from LMICs could distinguish lymphoma subtypes without pathologist input. We reviewed all biopsy specimens obtained at the Instituto de Cancerología y Hospital Dr. Bernardo Del Valle in Guatemala City between 2006 and 2018 for suspicion of lymphoma. Diagnoses were established based on the World Health Organization classification and then binned into 9 categories: nonmalignant, aggressive B-cell, diffuse large B-cell, follicular, Hodgkin, mantle cell, marginal zone, natural killer/T-cell, or mature T-cell lymphoma. We established a chemical ligation probe-based assay (CLPA) that quantifies expression of 37 genes by capillary electrophoresis with reagent/consumable cost of approximately $10/sample. To assign bins based on gene expression, 13 models were evaluated as candidate base learners, and class probabilities from each model were then used as predictors in an extreme gradient boosting super learner. Cases with call probabilities < 60% were classified as indeterminate. Four (2%) of 194 biopsy specimens in storage <3 years experienced assay failure. Diagnostic samples were divided into 70% (n = 397) training and 30% (n = 163) validation cohorts. Overall accuracy for the validation cohort was 86% (95% confidence interval [CI]: 80%-91%). After excluding 28 (17%) indeterminate calls, accuracy increased to 94% (95% CI: 89%-97%). Concordance was 97% for a set of high-probability calls (n = 37) assayed by CLPA in both the United States and Guatemala. Accuracy for a cohort of relapsed/refractory biopsy specimens (n = 39) was 79% and 88%, respectively, after excluding indeterminate cases. Machine-learning analysis of gene expression accurately classifies paraffin-embedded lymphoma biopsy specimens and could transform diagnosis in LMICs.