Describing patterns of familial cancer risk in subfertile men using population pedigree data.

STUDY QUESTION: Can we simultaneously assess risk for multiple cancers to identify familial multicancer patterns in families of azoospermic and severely oligozoospermic men? SUMMARY ANSWER: Distinct familial cancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in familial cancer risk by both type of subfertility and within subfertility type. WHAT IS KNOWN ALREADY: Subfertile men and their relatives show increased risk for certain cancers including testicular, thyroid, and pediatric. STUDY DESIGN, SIZE, DURATION: A retrospective cohort of subfertile men (N = 786) was identified and matched to fertile population controls (N = 5674). Family members out to third-degree relatives were identified for both subfertile men and fertile population controls (N = 337 754). The study period was 1966-2017. Individuals were censored at death or loss to follow-up, loss to follow-up occurred if they left Utah during the study period. PARTICIPANTS/MATERIALS, SETTING, METHODS: Azoospermic (0 × 106/mL) and severely oligozoospermic (<1.5 × 106/mL) men were identified in the Subfertility Health and Assisted Reproduction and the Environment cohort (SHARE). Subfertile men were age- and sex-matched 5:1 to fertile population controls and family members out to third-degree relatives were identified using the Utah Population Database (UPDB). Cancer diagnoses were identified through the Utah Cancer Registry. Families containing ≥10 members with ≥1 year of follow-up 1966-2017 were included (azoospermic: N = 426 families, 21 361 individuals; oligozoospermic: N = 360 families, 18 818 individuals). Unsupervised clustering based on standardized incidence ratios for 34 cancer phenotypes in the families was used to identify familial multicancer patterns; azoospermia and severe oligospermia families were assessed separately. MAIN RESULTS AND THE ROLE OF CHANCE: Compared to control families, significant increases in cancer risks were observed in the azoospermia cohort for five cancer types: bone and joint cancers hazard ratio (HR) = 2.56 (95% CI = 1.48-4.42), soft tissue cancers HR = 1.56 (95% CI = 1.01-2.39), uterine cancers HR = 1.27 (95% CI = 1.03-1.56), Hodgkin lymphomas HR = 1.60 (95% CI = 1.07-2.39), and thyroid cancer HR = 1.54 (95% CI = 1.21-1.97). Among severe oligozoospermia families, increased risk was seen for three cancer types: colon cancer HR = 1.16 (95% CI = 1.01-1.32), bone and joint cancers HR = 2.43 (95% CI = 1.30-4.54), and testis cancer HR = 2.34 (95% CI = 1.60-3.42) along with a significant decrease in esophageal cancer risk HR = 0.39 (95% CI = 0.16-0.97). Thirteen clusters of familial multicancer patterns were identified in families of azoospermic men, 66% of families in the azoospermia cohort showed population-level cancer risks, however, the remaining 12 clusters showed elevated risk for 2-7 cancer types. Several of the clusters with elevated cancer risks also showed increased odds of cancer diagnoses at young ages with six clusters showing increased odds of adolescent and young adult (AYA) diagnosis [odds ratio (OR) = 1.96-2.88] and two clusters showing increased odds of pediatric cancer diagnosis (OR = 3.64-12.63). Within the severe oligozoospermia cohort, 12 distinct familial multicancer clusters were identified. All 12 clusters showed elevated risk for 1-3 cancer types. An increase in odds of cancer diagnoses at young ages was also seen in five of the severe oligozoospermia familial multicancer clusters, three clusters showed increased odds of AYA diagnosis (OR = 2.19-2.78) with an additional two clusters showing increased odds of a pediatric diagnosis (OR = 3.84-9.32). LIMITATIONS, REASONS FOR CAUTION: Although this study has many strengths, including population data for family structure, cancer diagnoses and subfertility, there are limitations. First, semen measures are not available for the sample of fertile men. Second, there is no information on medical comorbidities or lifestyle risk factors such as smoking status, BMI, or environmental exposures. Third, all of the subfertile men included in this study were seen at a fertility clinic for evaluation. These men were therefore a subset of the overall population experiencing fertility problems and likely represent those with the socioeconomic means for evaluation by a physician. WIDER IMPLICATIONS OF THE FINDINGS: This analysis leveraged unique population-level data resources, SHARE and the UPDB, to describe novel multicancer clusters among the families of azoospermic and severely oligozoospermic men. Distinct overall multicancer risk and familial multicancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in cancer risk by type of subfertility and within subfertility type. Describing families with similar cancer risk patterns provides a new avenue to increase homogeneity for focused gene discovery and environmental risk factor studies. Such discoveries will lead to more accurate risk predictions and improved counseling for patients and their families. STUDY FUNDING/COMPETING INTEREST(S): This work was funded by GEMS: Genomic approach to connecting Elevated germline Mutation rates with male infertility and Somatic health (Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): R01 HD106112). The authors have no conflicts of interest relevant to this work. TRIAL REGISTRATION NUMBER: N/A.

Fertility preservation practices for female oncofertility differ significantly across the USA: results of a survey of SREI members.

PURPOSE: The field of oncofertility has maintained an important focus on improving access, yet standardized practices are lacking. To assess how female cancer patients are provided oncofertility care, we sought to determine provider-level differences and whether there are physician or practice characteristics that predict these variations. METHODS: A cross-sectional survey was sent to SREI members. The survey included fifteen questions about physician practice characteristics and oncofertility cryopreservation protocols. Topics included ovarian stimulation protocols, fertilization techniques, stage of embryo cryopreservation, routine use of pre-implantation genetic testing for aneuploidy (PGT-A), and ovarian tissue cryopreservation (OTC). Statistical analyses assessed whether practice setting, geographic region, time in practice, and mandatory state insurance coverage had effects on cryopreservation protocols. RESULTS: A total of 141 (17%) from diverse REI practice backgrounds completed the survey. The median number of new female oncofertility consults per year was 30 (range 1 to 300). Providers in academic settings treated more patients (median 40 vs. 15, p < 0.001). Providers in academic settings more often use gonadotropin-releasing hormone agonists (85% vs. 52%, p < 0.001) and perform OTC (41% vs. 4%, p < 0.001). Providers in academic practices were less likely to perform intracytoplasmic sperm injection in every cycle (37% vs. 55%, p = 0.032) and less likely to usually advise PGT-A (21% vs. 36%, p = 0.001). Mandated state insurance coverage had no effect on oncofertility practices. CONCLUSION: Oncofertility practices vary among providers. Factors such as practice setting and region may affect the services provided. We do not yet know the best practices in oncofertility patients, and future research is needed.

The sperm epigenome: implications for the embryo.

Recent advances, including the human genome project and numerous studies of cancer and other diseases, have shown that the genetic code is not simply limited to the sequence of the four bases of DNA but also includes epigenetic programming, heritable changes that affect gene expression [Riggs A, Martinssen R, Russo V (2007) Introduction. In: Riggs A, Martinssen R, Russo V (eds) Epigenetics mechanisms of gene regulation. Cold Spring Harbor Press, New York]. The science of epigenetics is important in understanding many diseases and biological processes, including in identifying the causes of disease and better understanding the mechanisms by which the environment can affect gene expression [Carrell Fertil Steril 97 (2):267-274, 2012]. This chapter will focus on the epigenome of sperm and particularly highlight the potential role of the sperm epigenome in embryogenesis.The sperm epigenome is unique and highly specialized because of the unique nature and function of sperm and because of the diverse requirements for successful fertilization. Due to the need for motility, sperm chromatin must be compacted and highly organized. During spermiogenesis the chromatin is packaged tightly into the sperm head by the replacement of most histones with protamines. This allows for protection of the DNA from the hostile environment in the female reproductive tract. Remaining histones can have chemical modifications to the tails of the protein that either facilitate or repress gene transcription. Sperm, like embryonic stem cells, have a unique pattern of histone modifications that includes both activating and silencing marks in the promoters of genes associated with development. These bivalent marks, along with DNA hypomethylation, comprise a unique state in which the key genes are "poised" for possible activation in embryogenesis. Sperm epigenetic abnormalities have been linked with multiple diseases including male factor infertility and poor embryogenesis.

Disease progression and solid tumor survival: a transcriptome decoherence model.

Networks of genes are typically generated from expression changes observed between control and test conditions. Nevertheless, within a single control state many genes show expression variance across biological replicates. These transcripts, typically termed unstable, are usually excluded from analyses because their behavior cannot be reconciled with biological constraints. Grouped as pairs of covariant genes they can however show a consistent response to the progression of a disease. We present a model of coherence arising from sets of covariant genes that was developed in-vitro then tested against a range of solid tumors. DGPMs, Decoherence Gene Pair Models, showed changes in network topology reflective of the metastatic transition. Across a range of solid tumor studies the model generalizes to reveal a richly connected topology of networks in healthy tissues that becomes sparser as the disease progresses reaching a minimum size in the advanced tumors with minim survivability.