NeoScore Integrates Characteristics of the Neoantigen:MHC Class I Interaction and Expression to Accurately Prioritize Immunogenic Neoantigens.

Accurate prioritization of immunogenic neoantigens is key to developing personalized cancer vaccines and distinguishing those patients likely to respond to immune checkpoint inhibition. However, there is no consensus regarding which characteristics best predict neoantigen immunogenicity, and no model to date has both high sensitivity and specificity and a significant association with survival in response to immunotherapy. We address these challenges in the prioritization of immunogenic neoantigens by (1) identifying which neoantigen characteristics best predict immunogenicity; (2) integrating these characteristics into an immunogenicity score, the NeoScore; and (3) demonstrating a significant association of the NeoScore with survival in response to immune checkpoint inhibition. One thousand random and evenly split combinations of immunogenic and nonimmunogenic neoantigens from a validated dataset were analyzed using a regularized regression model for characteristic selection. The selected characteristics, the dissociation constant and binding stability of the neoantigen:MHC class I complex and expression of the mutated gene in the tumor, were integrated into the NeoScore. A web application is provided for calculation of the NeoScore. The NeoScore results in improved, or equivalent, performance in four test datasets as measured by sensitivity, specificity, and area under the receiver operator characteristics curve compared with previous models. Among cutaneous melanoma patients treated with immune checkpoint inhibition, a high maximum NeoScore was associated with improved survival. Overall, the NeoScore has the potential to improve neoantigen prioritization for the development of personalized vaccines and contribute to the determination of which patients are likely to respond to immunotherapy.

Targeted enrichment of whole-genome SNPs from highly burned skeletal remains.

Genetic assessment of highly incinerated and/or degraded human skeletal material is a persistent challenge in forensic DNA analysis, including identifying victims of mass disasters. Few studies have investigated the impact of thermal degradation on whole-genome single-nucleotide polymorphism (SNP) quality and quantity using next-generation sequencing (NGS). We present whole-genome SNP data obtained from the bones and teeth of 27 fire victims using two DNA extraction techniques. Extracts were converted to double-stranded DNA libraries then enriched for whole-genome SNPs using unpublished biotinylated RNA baits and sequenced on an Illumina NextSeq 550 platform. Raw reads were processed using the EAGER (Efficient Ancient Genome Reconstruction) pipeline, and the SNPs filtered and called using FreeBayes and GATK (v. 3.8). Mixed-effects modeling of the data suggest that SNP variability and preservation is predominantly determined by skeletal element and burn category, and not by extraction type. Whole-genome SNP data suggest that selecting long bones, hand and foot bones, and teeth subjected to temperatures <350°C are the most likely sources for higher genomic DNA yields. Furthermore, we observed an inverse correlation between the number of captured SNPs and the extent to which samples were burned, as well as a significant decrease in the total number of SNPs measured for samples subjected to temperatures >350°C. Our data complement previous analyses of burned human remains that compare extraction methods for downstream forensic applications and support the idea of adopting a modified Dabney extraction technique when traditional forensic methods fail to produce DNA yields sufficient for genetic identification.

The Impact of Sample Size and Population History on Observed Mutational Spectra: A Case Study in Human and Chimpanzee Populations.

Recent studies have highlighted variation in the mutational spectra among human populations as well as closely related hominoids-yet little remains known about the genetic and nongenetic factors driving these rate changes across the genome. Pinpointing the root causes of these differences is an important endeavor that requires careful comparative analyses of population-specific mutational landscapes at both broad and fine genomic scales. However, several factors can confound such analyses. Although previous studies have shown that technical artifacts, such as sequencing errors and batch effects, can contribute to observed mutational shifts, other potentially confounding parameters have received less attention thus far. Using population genetic simulations of human and chimpanzee populations as an illustrative example, we here show that the sample size required for robust inference of mutational spectra depends on the population-specific demographic history. As a consequence, the power to detect rate changes is high in certain hominoid populations while, for others, currently available sample sizes preclude analyses at fine genomic scales.