Effect of Race and Ethnicity on Risk of Radiotherapy Toxicity and Implications for Radiogenomics

AimsPatient factors affect the risk of radiotherapy toxicity, but many are poorly defined. Studies have shown that race affects cancer incidence, survival, drug response, molecular pathways and epigenetics. Effects on radiosensitivity and radiotherapy toxicity are not well studied. The aim of the present study was to identify the effects of race and ethnicity on the risk of radiotherapy toxicity.Materials and methodsA systematic review was carried out of PubMed, Ovid Medline and Ovid Embase with no year limit. PRISMA 2020 guidelines were followed. Two independent assessors reviewed papers.ResultsOf 607 papers screened, 46 fulfilled the inclusion criteria. Papers were published between 1996 and 2021 and involved 30–28,354 individuals (median 433). Most involved patients with prostate (33%), breast (26%) and lung (9%) cancer. Both early and late toxicities were studied. Some studies reported a higher risk of toxicity in White men with prostate cancer compared with other races and ethnicities. For breast cancer patients, some reported an increased risk of toxicity in White women compared with other race and ethnic groups. In general, it was difficult to draw conclusions due to insufficient reporting and analysis of race and ethnicity in published literature.ConclusionsReporting of race and ethnicity in radiotherapy studies must be harmonised and improved and frameworks are needed to improve the quality of reporting. Further research is needed to understand how ancestral heritage might affect radiosensitivity and risk of radiotherapy toxicity.     

The genetics of platelet count and volume in humans

The last decade has witnessed an explosion in the depth, variety, and amount of human genetic data that can be generated. This revolution in technical and analytical capacities has enabled the genetic investigation of human traits and disease in thousands to now millions of participants. Investigators have taken advantage of these advancements to gain insight into platelet biology and the platelet’s role in human disease. To do so, large human genetics studies have examined the association of genetic variation with two quantitative traits measured in many population and patient based cohorts: platelet count (PLT) and mean platelet volume (MPV). This article will review the many human genetic strategies—ranging from genome-wide association study (GWAS), Exomechip, whole exome sequencing (WES), to whole genome sequencing (WGS)—employed to identify genes and variants that contribute to platelet traits. Additionally, we will discuss how these investigations have examined and interpreted the functional implications of these newly identified genetic factors and whether they also impart risk to human disease. The depth and size of genetic, phenotypic, and other -omic data are primed to continue their growth in the coming years and provide unprecedented opportunities to gain critical insights into platelet biology and how platelets contribute to disease.     

Lost in Translation (LiT)

Translational medicine is a roller coaster with occasional brilliant successes and a large majority of failures. Lost in Translation 1 (‘LiT1’), beginning in the 1950s, was a golden era built upon earlier advances in experimental physiology, biochemistry and pharmacology, with a dash of serendipity, that led to the discovery of many new drugs for serious illnesses. LiT2 saw the large-scale industrialization of drug discovery using high-throughput screens and assays based on affinity for the target molecule. The links between drug development and university sciences and medicine weakened, but there were still some brilliant successes. In LiT3, the coverage of translational medicine expanded from molecular biology to drug budgets, with much greater emphasis on safety and official regulation. Compared with R&D expenditure, the number of breakthrough discoveries in LiT3 was disappointing, but monoclonal antibodies for immunity and inflammation brought in a new golden era and kinase inhibitors such as imatinib were breakthroughs in cancer. The pharmaceutical industry is trying to revive the LiT1 approach by using phenotypic assays and closer links with academia. LiT4 faces a data explosion generated by the genome project, GWAS, ENCODE and the ‘omics’ that is in danger of leaving LiT4 in a computerized cloud. Industrial laboratories are filled with masses of automated machinery while the scientists sit in a separate room viewing the results on their computers. Big Data will need Big Thinking in LiT4 but with so many unmet medical needs and so many new opportunities being revealed there are high hopes that the roller coaster will ride high again.     

Two novel genetic variants in the STK38L and RAB27A genes are associated with glioma susceptibility

Glioma is the most common malignant primary brain tumors with poor prognosis. Genome wide association studies (GWAS) of glioma in populations with Western European ancestry were completed in the US and UK. However, our previous results strongly suggest the genetic heterogeneity could be important in glioma risk. To systematically investigate glioma risk–associated variants in Chinese population, we performed a multistage GWAS of glioma in the Han Chinese population, with a total of 3,097 glioma cases and 4,362 controls. In addition to confirming two associations reported in other ancestry groups, this study identified one new risk-associated locus for glioma on chromosome 12p11.23 (rs10842893, p_meta = 2.33x10-12, STK38L) as well as a promising association at 15q15-21.1 (rs4774756, p_meta = 6.12x10-8, RAB27A) in 3,097 glioma cases and 4,362 controls. Our findings demonstrate two novel association between the glioma risk region marked by variant rs10842893 and rs4774756) and glioma risk. These findings may advance the understanding of genetic susceptibility to glioma.     

Genetic variation associated with chromosomal aberration frequency: A genome-wide association study

Chromosomal aberrations (CAs) in human peripheral blood lymphocytes (PBL) measured with the conventional cytogenetic assay have been used for human biomonitoring of genotoxic exposure for decades. CA frequency in peripheral blood is a marker of cancer susceptibility. Previous studies have shown associations between genetic variants in metabolic pathway, DNA repair and major mitotic checkpoint genes and CAs. We conducted a genome-wide association study on 576 individuals from the Czech Republic and Slovakia followed by a replication in two different sample sets of 482 (replication 1) and 1288 (replication 2) samples. To have a broad look at the genetic susceptibility associated with CA frequency, the sample sets composed of individuals either differentially exposed to smoking, occupational/environmental hazards, or they were untreated cancer patients. Phenotypes were divided into chromosome- and chromatid-type aberrations (CSAs and CTAs, respectively) and total chromosomal aberrations (CAtot). The arbitrary cutoff point between individuals with high and low CA frequency was 2% for CAtot and 1% for CSA and CTA. The data were analyzed using age, sex, occupation/cancer and smoking history as covariates. Altogether 11 loci reached the P-value of 10⁻⁵ in the GWAS. Replication 1 supported the association of rs1383997 (8q13.3) and rs2824215 (21q21.1) in CAtot and rs983889 (5p15.1) in CTA analysis. These loci were found to be associated with genes involved in mitosis, response to environmental and chemical factors and genes involved in syndromes linked to chromosomal abnormalities. Identification of new genetic variants for the frequency of CAs offers prediction tools for cancer risk in future. Environ. Mol. Mutagen. 60:17–28, 2019. © 2018 Wiley Periodicals, Inc.     

Fine‐Mapping Additive and Dominant SNP Effects Using Group‐LASSO and Fractional Resample Model Averaging

Genomewide association studies (GWAS) sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple single‐nucleotide polymorphisms (SNPs) simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow‐up studies. Current multi‐SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA‐dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights. It estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing an SNP prioritization that best identifies underlying true signals, we show the following: our method easily outperforms a single‐marker analysis; when additive‐only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive‐only effects; and when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation.     

Powerful Set‐Based Gene‐Environment Interaction Testing Framework for Complex Diseases

Identification of gene‐environment interaction (G × E) is important in understanding the etiology of complex diseases. Based on our previously developed Set Based gene EnviRonment InterAction test (SBERIA), in this paper we propose a powerful framework for enhanced set‐based G × E testing (eSBERIA). The major challenge of signal aggregation within a set is how to tell signals from noise. eSBERIA tackles this challenge by adaptively aggregating the interaction signals within a set weighted by the strength of the marginal and correlation screening signals. eSBERIA then combines the screening‐informed aggregate test with a variance component test to account for the residual signals. Additionally, we develop a case‐only extension for eSBERIA (coSBERIA) and an existing set‐based method, which boosts the power not only by exploiting the G‐E independence assumption but also by avoiding the need to specify main effects for a large number of variants in the set. Through extensive simulation, we show that coSBERIA and eSBERIA are considerably more powerful than existing methods within the case‐only and the case‐control method categories across a wide range of scenarios. We conduct a genome‐wide G × E search by applying our methods to Illumina HumanExome Beadchip data of 10,446 colorectal cancer cases and 10,191 controls and identify two novel interactions between nonsteroidal anti‐inflammatory drugs (NSAIDs) and MINK1 and PTCHD3.