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Repair of DNA damage is critical for maintaining the genomic integrity of cells. DNA polymerase lambda (POLL/Pol λ) is suggested to function in base excision repair (BER) and nonhomologous end-joining (NHEJ), and is likely to play a role in damage tolerance at the replication fork. Here, using next-generation sequencing, it was discovered that the POLL rs3730477 single-nucleotide polymorphism (SNP) encoding R438W Pol λ was significantly enriched in the germlines of breast cancer patients. Expression of R438W Pol λ in human breast epithelial cells induces cellular transformation and chromosomal aberrations. The role of estrogen was assessed as it is commonly used in hormone replacement therapies and is a known breast cancer risk factor. Interestingly, the combination of estrogen treatment and the expression of the R438W Pol λ SNP drastically accelerated the rate of transformation. Estrogen exposure produces 8-oxoguanine lesions that persist in cells expressing R438W Pol λ compared with wild-type (WT) Pol λ-expressing cells. Unlike WT Pol λ, which performs error-free bypass of 8-oxoguanine lesions, expression of R438W Pol λ leads to an increase in mutagenesis and replicative stress in cells treated with estrogen. Together, these data suggest that individuals who carry the rs3730477 POLL germline variant have an increased risk of estrogen-associated breast cancer.ImplicationsThe Pol λ R438W mutation can serve as a biomarker to predict cancer risk and implicates that treatment with estrogen in individuals with this mutation may further increase their risk of breast cancer. Mol Cancer Res; 14(11); 1068-77. ©2016 AACR.

Background: Emerging data suggest variability in susceptibility and outcome to coronavirus disease 2019 (COVID-19) infection. Identifying risk factors associated with infection and outcomes in cancer patients is necessary to develop healthcare recommendations.Methods: We analyzed electronic health records of the US Veterans Affairs Healthcare System and assessed the prevalence of COVID-19 infection in cancer patients. We evaluated the proportion of cancer patients tested for COVID-19 who were positive, as well as outcome attributable to COVID-19, and stratified by clinical characteristics including demographics, comorbidities, cancer treatment, and cancer type. All statistical tests are 2-sided.Results: Of 22 914 cancer patients tested for COVID-19, 1794 (7.8%) were positive. The prevalence of COVID-19 was similar across age. Higher prevalence was observed in African American (15.0%) compared with White (5.5%; P < .001) and in patients with hematologic malignancy compared with those with solid tumors (10.9% vs 7.8%; P < .001). Conversely, prevalence was lower in current smokers and patients who recently received cancer therapy (<6 months). The COVID-19-attributable mortality was 10.9%. Higher attributable mortality rates were observed in older patients, those with higher Charlson comorbidity score, and in certain cancer types. Recent (<6 months) or past treatment did not influence attributable mortality. Importantly, African American patients had 3.5-fold higher COVID-19-attributable hospitalization; however, they had similar attributable mortality as White patients.Conclusion: Preexistence of cancer affects both susceptibility to COVID-19 infection and eventual outcome. The overall COVID-19-attributable mortality in cancer patients is affected by age, comorbidity, and specific cancer types; however, race or recent treatment including immunotherapy do not impact outcome. [ABSTRACT FROM AUTHOR]

Abstract Background High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. Results We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. Conclusion Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.