Novel bioinformatic analyses of somatic cell contamination in sperm samples.

The assessment of epigenetic profiles in sperm is sensitive to somatic cell contamination, which can influence methylation signals at gene promoters. This contamination is particularly problematic in the assessment of DNA methylation in samples with low sperm counts, where fractional amounts of somatic cell DNA can lead to significant shifts in measured methylation state. In this study, a new method of detecting possible somatic cell contamination is proposed through two multi-region bioinformatic models: a traditional differential methylation analysis and a machine learning logistic regression model. These models were trained on publicly available sperm (n = 489) and blood (n = 1029) DNA methylation array data and tested on a contamination set, wherein the sperm of four donors with normal sperm counts were run on a 450k methylation array with four permutations each, including pure blood, half blood and half sperm by DNA concentration, half blood and half sperm by cell count, and pure sperm (n = 16). The DMR and logistic regression model classified the contamination testing set with 100% and 94% accuracy, respectively. These new methods of detecting the effects of somatic cell contamination allow for more accurate differentiation between epigenetic profiles that contain a biological somatic-like shift and those that have somatic-like signatures because of contamination.

Epigenetic determinants of reproductive potential augment the predictive ability of the semen analysis.

OBJECTIVE: To investigate the power of DNA methylation variability in sperm cells in assessing male fertility potential. DESIGN: Retrospective cohort. SETTING: Fertility care centers. PATIENTS: Male patients seeking infertility treatment and fertile male sperm donors. INTERVENTION: None. MAIN OUTCOME MEASURES: Sperm DNA methylation data from 43 fertile sperm donors were analyzed and compared with the data from 1344 men seeking fertility assessment or treatment. Methylation at gene promoters with the least variable methylation in fertile patients was used to create 3 categories of promoter dysregulation in the infertility treatment cohort: poor, average, and excellent sperm quality. RESULTS: After controlling for female factors, there were significant differences in intrauterine insemination pregnancy and live birth outcomes between the poor and excellent groups across a cumulative average of 2-3 cycles: 19.4% vs. 51.7% (P=.008) and 19.4% vs. 44.8% (P=.03), respectively. Live birth outcomes from in vitro fertilization, primarily with intracytoplasmic sperm injection, were not found to be significantly different among any of the 3 groups. CONCLUSION: Methylation variability in a panel of 1233 gene promoters could augment the predictive ability of semen analysis and be a reliable biomarker for assessing intrauterine insemination outcomes. In vitro fertilization with intracytoplasmic sperm injection appears to overcome high levels of epigenetic instability in sperm.

Real-world pan-cancer landscape of frameshift mutations and their role in predicting responses to immune checkpoint inhibitors in cancers with low tumor mutational burden.

BACKGROUND: Pembrolizumab is FDA approved for tumors with tumor mutational burden (TMB) of ≥10 mutations/megabase (mut/Mb). However, the response to immune checkpoint inhibitors (ICI) varies significantly among cancer histologies. We describe the landscape of frameshift mutations (FSs) and evaluated their role as a predictive biomarker to ICI in a clinical cohort of patients. METHODS: Comprehensive genomic profiling was performed on a cohort of solid tumor samples examining at least 324 genes. The clinical cohort included patients with metastatic solid malignancies who received ICI monotherapy and had tumor sequencing. Progression-free survival (PFS), overall survival, and objective response rates (ORR) were compared between the groups. RESULTS: We analyzed 246,252 microsatellite stable (MSS) and 4561 samples with microsatellite instability across solid tumors. Histologies were divided into groups according to TMB and FS. MSS distribution: TMB-L (<10 mut/Mb)/FS-A (absent FS) (N=111,065, 45%), TMB-H (≥10 mut/Mb)/FS-A (N=15,313, 6%), TMB-L/FS-P (present ≥1 FS) (N=98,389, 40%) and TMB-H/FS-P (N=21,485, 9%). FSs were predominantly identified in the p53 pathway. In the clinical cohort, 212 patients were included. Groups: TMB-L/FS-A (N=80, 38%), TMB-H/FS-A (N=36, 17%), TMB-L/FS-P (N=57, 27%), TMB-H/FS-P (N=39, 18%). FSs were associated with a higher ORR to ICI, 23.8% vs 12.8% (p=0.02). TMB-L/FS-P had superior median PFS (5.1 months) vs TMB-L/FS-A (3.6 months, p<0.01). The 12-month PFS probability was 34% for TMB-L/FS-P vs 17.1% for TMB-L/FS-A. CONCLUSIONS: FSs are found in 47% of patients with MSS/TMB-L solid tumors in a pan-cancer cohort. FS may complement TMB in predicting immunotherapy responses, particularly for tumors with low TMB.

Altered Epigenetic Profiles in the Placenta of Preeclamptic and Intrauterine Growth Restriction Patients.

Intrauterine growth restriction (IUGR) and preeclampsia (PE) are placental pathologies known to complicate pregnancy and cause neonatal disorders. To date, there is a limited number of studies on the genetic similarity of these conditions. DNA methylation is a heritable epigenetic process that can regulate placental development. Our objective was to identify methylation patterns in placental DNA from normal, PE and IUGR-affected pregnancies. DNA was extracted, and bisulfite was converted, prior to being hybridized for the methylation array. Methylation data were SWAN normalized and differently methylated regions were identified using applications within the USEQ program. UCSC's Genome browser and Stanford's GREAT analysis were used to identify gene promoters. The commonality among affected genes was confirmed by Western blot. We observed nine significantly hypomethylated regions, two being significantly hypomethylated for both PE and IGUR. Western blot confirmed differential protein expression of commonly regulated genes. We conclude that despite the uniqueness of methylation profiles for PE and IUGR, the similarity of some methylation alterations in pathologies could explain the clinical similarities observed with these obstetric complications. These results also provide insight into the genetic similarity between PE and IUGR and suggest possible gene candidates plausibly involved in the onset of both conditions.