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Summary: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related deaths worldwide, and a large proportion is attributable to viral causes, including hepatitis B (HBV) and C viruses (HCV). The pathogenesis of viral-mediated HCC can differ between HBV and HCV, but it is unclear how much these differences influence the tumors’ final molecular and immune profiles. Additionally, there are known sex differences in the molecular etiology of HCC, but sex differences have not been explored in the context of viral-mediated HCC. To determine the extent to which the viral status and sex impact the molecular and immune profiles of HCC, we performed differential expression and immune cell deconvolution analyses. We identified a large number of differentially expressed genes unique to the HBV or HCV tumor:tumor-adjacent comparison. Pathway enrichment analyses demonstrated that changes unique to the HCV tumor:tumor-adjacent tissue were dominated by changes in immune pathways. Immune cell deconvolution demonstrated that HCV tumor-adjacent tissue had the largest immune cell infiltrate, with no difference in the immune profiles within HBV and HCV tumor samples. Overall, this work demonstrates the convergence of HBV- and HCV-mediated HCC on a similar transcriptomic landscape and immune profile despite differences in the surrounding tissue.

Vesiculoviruses are attractive oncolytic virus platforms due to their rapid replication, appreciable transgene capacity, broad tropism, limited preexisting immunity, and tumor selectivity through type I interferon response defects in malignant cells. We developed a synthetic chimeric virus (VMG) expressing the glycoprotein (G) from Morreton virus (MorV) and utilizing the remaining structural genes from vesicular stomatitis virus (VSV). VMG exhibited in vitro efficacy by inducing oncolysis in a broad range of sarcoma subtypes across multiple species. Notably, all cell lines tested showed the ability of VMG to yield productive infection with rapid replication kinetics and induction of apoptosis. Furthermore, pilot safety evaluations of VMG in immunocompetent, non-tumor-bearing mice showed an absence of toxicity with intranasal doses as high as 1e10 50% tissue culture infectious dose (TCID50)/kg. Locoregional administration of VMG in vivo resulted in tumor reduction in an immunodeficient Ewing sarcoma xenograft at doses as low as 2e5 TCID50. In a murine syngeneic fibrosarcoma model, while no tumor inhibition was achieved with VMG, there was a robust induction of CD8+ T cells within the tumor. The studies described herein establish the promising potential for VMG to be used as a novel oncolytic virotherapy platform with anticancer effects in sarcoma.

Abstract Background Neanderthal introgressed DNA has been linked to different normal and disease traits including immunity and metabolism—two important functions that are altered in liver cancer. However, there is limited understanding of the relationship between Neanderthal introgression and liver cancer risk. The aim of this study was to investigate the relationship between Neanderthal introgression and liver cancer risk. Methods Using germline and somatic DNA and tumor RNA from liver cancer patients from The Cancer Genome Atlas, along with ancestry-match germline DNA from unaffected individuals from the 1000 Genomes Resource, and allele specific expression data from normal liver tissue from The Genotype-Tissue Expression project we investigated whether Neanderthal introgression impacts cancer etiology. Using a previously generated set of Neanderthal alleles, we identified Neanderthal introgressed haplotypes. We then tested whether somatic mutations are enriched or depleted on Neanderthal introgressed haplotypes compared to modern haplotypes. We also computationally assessed whether somatic mutations have a functional effect or show evidence of regulating expression of Neanderthal haplotypes. Finally, we compared patterns of Neanderthal introgression in liver cancer patients and the general population. Results We find Neanderthal introgressed haplotypes exhibit an excess of somatic mutations compared to modern haplotypes. Variant Effect Predictor analysis revealed that most of the somatic mutations on these Neanderthal introgressed haplotypes are not functional. We did observe expression differences of Neanderthal alleles between tumor and normal for four genes that also showed a pattern of enrichment of somatic mutations on Neanderthal haplotypes. However, gene expression was similar between liver cancer patients with modern ancestry and liver cancer patients with Neanderthal ancestry at these genes. Provocatively, when analyzing all genes, we find evidence of Neanderthal introgression regulating expression in tumor from liver cancer patients in two genes, ARK1C4 and OAS1. Finally, we find that most genes do not show a difference in the proportion of Neanderthal introgression between liver cancer patients and the general population. Conclusion Our results suggest that Neanderthal introgression provides opportunity for somatic mutations to accumulate, and that some Neanderthal introgression may impact liver cancer risk.

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Abstract Background Sex-differences in cancer occurrence and mortality are evident across tumor types; men exhibit higher rates of incidence and often poorer responses to treatment. Targeted approaches to the treatment of tumors that account for these sex-differences require the characterization and understanding of the fundamental biological mechanisms that differentiate them. Hepatocellular Carcinoma (HCC) is the second leading cause of cancer death worldwide, with the incidence rapidly rising. HCC exhibits a male-bias in occurrence and mortality, but previous studies have failed to explore the sex-specific dysregulation of gene expression in HCC. Methods Here, we characterize the sex-shared and sex-specific regulatory changes in HCC tumors in the TCGA LIHC cohort using combined and sex-stratified differential expression and eQTL analyses. Results By using a sex-specific differential expression analysis of tumor and tumor-adjacent samples, we uncovered etiologically relevant genes and pathways differentiating male and female HCC. While both sexes exhibited activation of pathways related to apoptosis and cell cycle, males and females differed in the activation of several signaling pathways, with females showing PPAR pathway enrichment while males showed PI3K, PI3K/AKT, FGFR, EGFR, NGF, GF1R, Rap1, DAP12, and IL-2 signaling pathway enrichment. Using eQTL analyses, we discovered germline variants with differential effects on tumor gene expression between the sexes. 24.3% of the discovered eQTLs exhibit differential effects between the sexes, illustrating the substantial role of sex in modifying the effects of eQTLs in HCC. The genes that showed sex-specific dysregulation in tumors and those that harbored a sex-specific eQTL converge in clinically relevant pathways, suggesting that the molecular etiologies of male and female HCC are partially driven by differential genetic effects on gene expression. Conclusions Sex-stratified analyses detect sex-specific molecular etiologies of HCC. Overall, our results provide new insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and provide a framework for future studies on sex-biased cancers.

A low percentage of actinic keratoses progress to develop into cutaneous squamous cell carcinoma. The immune mechanisms that successfully control or eliminate the majority of actinic keratoses and the mechanisms of immune escape by invasive squamous cell carcinoma are not well-understood. Here, we took a systematic approach to evaluate the neoantigens present in actinic keratosis and cutaneous squamous cell carcinoma specimens. We compared the number of mutations, the number of neoantigens predicted to bind MHC class I, and the number of neoantigens that are predicted to bind MHC class I and be recognized by a T cell receptor in actinic keratoses and cutaneous squamous cell carcinomas. We also considered the relative binding strengths to both MHC class I and the T cell receptor in a fitness cost model that allows for a comparison of the immune recognition potential of the neoantigens in actinic keratosis and cutaneous squamous cell carcinoma samples. The fitness cost was subsequently adjusted by the expression rates of the neoantigens to examine the role of neoantigen expression in tumor immune evasion. Our analyses indicate that, while the number of mutations and neoantigens are not significantly different between actinic keratoses and cutaneous squamous cell carcinomas, the predicted immune recognition of the neoantigen with the highest expression-adjusted fitness cost is lower for cutaneous squamous cell carcinomas compared with actinic keratoses. These findings suggest a role for the down-regulation of expression of highly immunogenic neoantigens in the immune escape of cutaneous squamous cell carcinomas. Furthermore, these findings highlight the importance of incorporating additional factors, such as the quality and expression of the neoantigens, rather than focusing solely on tumor mutational burden, in assessing immune recognition potential.

Several recent studies have shown a genetic influence on gene expression variation, including variation between the two chromosomes within an individual and variation between individuals at the population level. We hypothesized that genetic inheritance may also affect variation in chromatin states. To test this hypothesis, we analyzed chromatin states in 12 lymphoblastoid cells derived from two Centre d'Etude du Polymorphisme Humain families using an allele-specific chromatin immunoprecipitation (ChIP-on-chip) assay with Affymetrix 10K SNP chip. We performed the allele-specific ChIP-on-chip assays for the 12 lymphoblastoid cells using antibodies targeting at RNA polymerase II and five post-translation modified forms of the histone H3 protein. The use of multiple cell lines from the Centre d'Etude du Polymorphisme Humain families allowed us to evaluate variation of chromatin states across pedigrees. These studies demonstrated that chromatin state clustered by family. Our results support the idea that genetic inheritance can determine the epigenetic state of the chromatin as shown previously in model organisms. To our knowledge, this is the first demonstration in humans that genetics may be an important factor that influences global chromatin state mediated by histone modification, the hallmark of the epigenetic phenomena.

Cancer is recognized to be a family of gene-based diseases whose causes are to be found in disruptions of basic biologic processes. An increasingly deep catalogue of canonical networks details the specific molecular interaction of genes and their products. However, mapping of disease phenotypes to alterations of these networks of interactions is accomplished indirectly and non-systematically. Here we objectively identify pathways associated with malignancy, staging, and outcome in cancer through application of an analytic approach that systematically evaluates differences in the activity and consistency of interactions within canonical biologic processes. Using large collections of publicly accessible genome-wide gene expression, we identify small, common sets of pathways - Trka Receptor, Apoptosis response to DNA Damage, Ceramide, Telomerase, CD40L and Calcineurin - whose differences robustly distinguish diverse tumor types from corresponding normal samples, predict tumor grade, and distinguish phenotypes such as estrogen receptor status and p53 mutation state. Pathways identified through this analysis perform as well or better than phenotypes used in the original studies in predicting cancer outcome. This approach provides a means to use genome-wide characterizations to map key biological processes to important clinical features in disease.

Identification of single nucleotide polymorphisms (SNPs) and mutations is important for the discovery of genetic predisposition to complex diseases. PCR resequencing is the method of choice for de novo SNP discovery. However, manual curation of putative SNPs has been a major bottleneck in the application of this method to high-throughput screening. Therefore it is critical to develop a more sensitive and accurate computational method for automated SNP detection. We developed a software tool, SNPdetector, for automated identification of SNPs and mutations in fluorescence-based resequencing reads. SNPdetector was designed to model the process of human visual inspection and has a very low false positive and false negative rate. We demonstrate the superior performance of SNPdetector in SNP and mutation analysis by comparing its results with those derived by human inspection, PolyPhred (a popular SNP detection tool), and independent genotype assays in three large-scale investigations. The first study identified and validated inter- and intra-subspecies variations in 4,650 traces of 25 inbred mouse strains that belong to either the Mus musculus species or the M. spretus species. Unexpected heterozygosity in CAST/Ei strain was observed in two out of 1,167 mouse SNPs. The second study identified 11,241 candidate SNPs in five ENCODE regions of the human genome covering 2.5 Mb of genomic sequence. Approximately 50% of the candidate SNPs were selected for experimental genotyping; the validation rate exceeded 95%. The third study detected ENU-induced mutations (at 0.04% allele frequency) in 64,896 traces of 1,236 zebra fish. Our analysis of three large and diverse test datasets demonstrated that SNPdetector is an effective tool for genome-scale research and for large-sample clinical studies. SNPdetector runs on Unix/Linux platform and is available publicly (http://lpg.nci.nih.gov).

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.

We introduce a novel technique to determine the expression state of a gene from quantitative information measuring its expression. Adopting a productive abstraction from current thinking in molecular biology, we consider two expression states for a gene--Up or Down. We determine this state by using a statistical model that assumes the data behaves as a combination of two biological distributions. Given a cohort of hybridizations, our algorithm predicts, for the single reading, the probability of each gene's being in an Up or a Down state in each hybridization. Using a series of publicly available gene expression data sets, we demonstrate that our algorithm outperforms the prevalent algorithm. We also show that our algorithm can be used in conjunction with expression adjustment techniques to produce a more biologically sound gene-state call. The technique we present here enables a routine update, where the continuously evolving expression level adjustments feed into gene-state calculations. The technique can be applied in almost any multi-sample gene expression experiment, and holds equal promise for protein abundance experiments.