Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database

Shared genomic segments analysis identifies MHC class I and class III molecules as genetic risk factors for juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is a complex rheumatic disease encompassing several clinically defined subtypes of varying severity. The etiology of JIA remains largely unknown, but genome-wide association studies (GWASs) have identified up to 22 genes associated with JIA susceptibility, including a well-established association with HLA-DRB1. Continued investigation of heritable risk factors has been hindered by disease heterogeneity and low disease prevalence. In this study, we utilized shared genomic segments (SGS) analysis on whole-genome sequencing of 40 cases from 12 multi-generational pedigrees significantly enriched for JIA. Subsets of cases are connected by a common ancestor in large extended pedigrees, increasing the power to identify disease-associated loci. SGS analysis identifies genomic segments shared among disease cases that are likely identical by descent and anchored by a disease locus. This approach revealed statistically significant signals for major histocompatibility complex (MHC) class I and class III alleles, particularly HLA-A∗02:01, which was observed at a high frequency among cases. Furthermore, we identified an additional risk locus at 12q23.2-23.3, containing genes primarily expressed by naive B cells, natural killer cells, and monocytes. The recognition of additional risk beyond HLA-DRB1 provides a new perspective on immune cell dynamics in JIA. These findings contribute to our understanding of JIA and may guide future research and therapeutic strategies.

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.

Familial Associations of Prevalence and Cause-Specific Mortality for Thoracic Aortic Disease and Bicuspid Aortic Valve in a Large-Population Database

BACKGROUND

Thoracic aortic disease and bicuspid aortic valve (BAV) likely have a heritable component, but large population-based studies are lacking. This study characterizes familial associations of thoracic aortic disease and BAV, as well as cardiovascular and aortic-specific mortality, among relatives of these individuals in a large-population database.

METHODS

In this observational case-control study of the Utah Population Database, we identified probands with a diagnosis of BAV, thoracic aortic aneurysm, or thoracic aortic dissection. Age- and sex-matched controls (10:1 ratio) were identified for each proband. First-degree relatives, second-degree relatives, and first cousins of probands and controls were identified through linked genealogical information. Cox proportional hazard models were used to quantify the familial associations for each diagnosis. We used a competing-risk model to determine the risk of cardiovascular-specific and aortic-specific mortality for relatives of probands.

RESULTS

The study population included 3 812 588 unique individuals. Familial hazard risk of a concordant diagnosis was elevated in the following populations compared with controls: first-degree relatives of patients with BAV (hazard ratio [HR], 6.88 [95% CI, 5.62-8.43]); first-degree relatives of patients with thoracic aortic aneurysm (HR, 5.09 [95% CI, 3.80-6.82]); and first-degree relatives of patients with thoracic aortic dissection (HR, 4.15 [95% CI, 3.25-5.31]). In addition, the risk of aortic dissection was higher in first-degree relatives of patients with BAV (HR, 3.63 [95% CI, 2.68-4.91]) and in first-degree relatives of patients with thoracic aneurysm (HR, 3.89 [95% CI, 2.93-5.18]) compared with controls. Dissection risk was highest in first-degree relatives of patients who carried a diagnosis of both BAV and aneurysm (HR, 6.13 [95% CI, 2.82-13.33]). First-degree relatives of patients with BAV, thoracic aneurysm, or aortic dissection had a higher risk of aortic-specific mortality (HR, 2.83 [95% CI, 2.44-3.29]) compared with controls.

CONCLUSIONS

Our results indicate that BAV and thoracic aortic disease carry a significant familial association for concordant disease and aortic dissection. The pattern of familiality is consistent with a genetic cause of disease. Furthermore, we observed higher risk of aortic-specific mortality in relatives of individuals with these diagnoses. This study provides supportive evidence for screening in relatives of patients with BAV, thoracic aneurysm, or dissection.

Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes

BACKGROUND

Transcriptome studies are gaining momentum in genomic epidemiology, and the need to incorporate these data in multivariable models alongside other risk factors brings demands for new approaches.

METHODS

Here we describe SPECTRA, an approach to derive quantitative variables that capture the intrinsic variation in gene expression of a tissue type. We applied the SPECTRA approach to bulk RNA sequencing from malignant cells (CD138+) in patients from the Multiple Myeloma Research Foundation CoMMpass study.

RESULTS

A set of 39 spectra variables were derived to represent multiple myeloma cells. We used these variables in predictive modeling to determine spectra-based risk scores for overall survival, progression-free survival, and time to treatment failure. Risk scores added predictive value beyond known clinical and expression risk factors and replicated in an external dataset. Spectrum variable S5, a significant predictor for all three outcomes, showed pre-ranked gene set enrichment for the unfolded protein response, a mechanism targeted by proteasome inhibitors which are a common first line agent in multiple myeloma treatment. We further used the 39 spectra variables in descriptive modeling, with significant associations found with tumor cytogenetics, race, gender, and age at diagnosis; factors known to influence multiple myeloma incidence or progression.

CONCLUSIONS

Quantitative variables from the SPECTRA approach can predict clinical outcomes in multiple myeloma and provide a new avenue for insight into tumor differences by demographic groups.

IMPACT

The SPECTRA approach provides a set of quantitative phenotypes that deeply profile a tissue and allows for more comprehensive modeling of gene expression with other risk factors.

Familial risk of epithelial ovarian cancer after accounting for gynaecological surgery: a population-based study

BACKGROUND

Uptake of risk-reducing surgery has increased among women at high risk of epithelial ovarian cancer. We sought to characterise familial risk of epithelial ovarian cancer histotypes in a population-based study after accounting for gynaecological surgeries, including bilateral oophorectomy.

METHODS

We compared risk of epithelial ovarian cancer in relatives of 3536 epithelial ovarian cancer cases diagnosed in 1966-2016 and relatives of 35 326 matched controls. We used Cox competing risk models, incorporating bilateral oophorectomy as a competing risk, to estimate the relative risk of ovarian cancer in first-degree (FDR), second-degree (SDR) and third-degree (TDR) relatives from 1966 to 2016. We also estimated relative risks in time periods before (1966-1994, 1995-2004) and after (2005-2016) formal recommendations were made for prophylactic oophorectomy among women with pathogenic variants in BRCA1/2.

RESULTS

The relative risks of epithelial ovarian cancer in FDRs, SDRs and TDRs of cases versus controls were 1.68 (95% CI 1.39 to 2.04), 1.51 (95% CI 1.30 to 1.75) and 1.34 (95% CI 1.20 to 1.48), respectively. Relative risks were greatest for high-grade serous, mucinous and 'other epithelial' histotypes. Relative risks were attenuated for case FDRs, but not for SDRs or TDRs, from 2005 onwards, consistent with the timing of recommendations for prophylactic surgery.

CONCLUSION

Familial risk of epithelial ovarian cancer extends to TDRs, especially for high-grade serous and mucinous histotypes. Distant relatives share genes but minimal environment, highlighting the importance of germline inherited genetics in ovarian cancer aetiology. Increased ovarian cancer risk in distant relatives has implications for counselling and recommendations for prophylactic surgeries that, from our data, appear only to reach FDRs.

Geographic Proximity of Family Members and Healthcare Utilization After Complex Surgical Procedures

OBJECTIVE

We sought to determine the relationship between a patient's proximal familial social support, defined as the geographic proximity of family members, and healthcare utilization after complex cardiovascular and oncologic procedures.

BACKGROUND

Social support mechanisms are increasingly identified as modifiable risk factors for healthcare utilization.

METHODS

We performed a retrospective cohort study of 60,895 patients undergoing complex cardiovascular procedures or oncologic procedures. We defined healthcare utilization outcomes as 30-day all-cause readmission unplanned readmission, nonindex hospital readmission, index hospital length of stay, and home discharge disposition. For each patient, we aggregated the number of first-degree relatives (FDR) living within 30 miles of the patient's home address at the time of the surgical procedure into the following categories: 0 to 1, 2 to 3, 4 to 5, 6+ FDRs. We developed hierarchical multivariable regression models to determine the relationship between the number of FDR living within 30 miles of the patient and the healthcare utilization outcomes.

RESULTS

Compared with patients with 0 to 1 FDRs, patients with 6+ FDRs living in close proximity had significantly lower rates of all-cause readmission (12.1% vs 13.5%, P <0.001), unplanned readmission (10.9% vs 12.0%, P =0.001), nonindex readmission (2.6% vs 3.2%, P =0.003); higher rates of home discharge (88.0% vs 85.3%, P <0.001); and shorter length of stay (7.3 vs 7.5 days, P =0.02). After multivariable adjustment, a larger number of FDRs living within 30 miles of the patient was significantly associated with a lower likelihood of all-cause readmission ( P <0.001 for trend), 30-day unplanned readmission ( P <0.001), nonindex readmission ( P <0.001); higher likelihood of home discharge ( P <0.001); and shorter index length of stay ( P <0.001).

CONCLUSIONS

The geographic proximity of family members is significantly associated with decreased healthcare utilization after complex cardiovascular and oncologic surgical procedures.

Shared genomic segment analysis in a large high-risk chronic lymphocytic leukemia pedigree implicates CXCR4 in inherited risk

AIM

Chronic lymphocytic leukemia (CLL) has been shown to cluster in families. First-degree relatives of individuals with CLL have an ~8 fold increased risk of developing the malignancy. Strong heritability suggests pedigree studies will have good power to localize pathogenic genes. However, CLL is relatively rare and heterogeneous, complicating ascertainment and analyses. Our goal was to identify CLL risk loci using unique resources available in Utah and methods to address intra-familial heterogeneity.

METHODS

We identified a six-generation high-risk CLL pedigree using the Utah Population Database. This pedigree contains 24 CLL cases connected by a common ancestor. We ascertained and genotyped eight CLL cases using a high-density SNP array, and then performed shared genomic segment (SGS) analysis - a method designed for extended high-risk pedigrees that accounts for heterogeneity.

RESULTS

We identified a genome-wide significant region (P = 1.9 × 10-7, LOD-equivalent 5.6) at 2q22.1. The 0.9 Mb region was inherited through 26 meioses and shared by seven of the eight genotyped cases. It sits within a ~6.25 Mb locus identified in a previous linkage study of 206 small CLL families. Our narrow region intersects two genes, including CXCR4 which is highly expressed in CLL cells and implicated in maintenance and progression.

CONCLUSION

SGS analysis of an extended high-risk CLL pedigree identified the most significant evidence to-date for a 0.9 Mb CLL disease locus at 2q22.1, harboring CXCR4. This discovery contributes to a growing literature implicating CXCR4 in inherited risk to CLL. Investigation of the segregating haplotype in the pedigree will be valuable for elucidating risk variant(s).

Duo Shared Genomic Segment analysis identifies a genome-wide significant risk locus at 18q21.33 in myeloma pedigrees

AIM

High-risk pedigrees (HRPs) are a powerful design to map highly penetrant risk genes. We previously described Shared Genomic Segment (SGS) analysis, a mapping method for single large extended pedigrees that also addresses genetic heterogeneity inherent in complex diseases. SGS identifies shared segregating chromosomal regions that may inherit in only a subset of cases. However, single large pedigrees that are individually powerful (at least 15 meioses between studied cases) are scarce. Here, we expand the SGS strategy to incorporate evidence from two extended HRPs by identifying the same segregating risk locus in both pedigrees and allowing for some relaxation in the size of each HRP.

METHODS

Duo-SGS is a procedure to combine single-pedigree SGS evidence. It implements statistically rigorous duo-pedigree thresholding to determine genome-wide significance levels that account for optimization across pedigree pairs. Single-pedigree SGS identifies optimal segments shared by case subsets at each locus across the genome, with nominal significance assessed empirically. Duo-SGS combines the statistical evidence for SGS segments at the same genomic location in two pedigrees using Fisher's method. One pedigree is paired with all others and the best duo-SGS evidence at each locus across the genome is established. Genome-wide significance thresholds are determined through distribution-fitting and the Theory of Large Deviations. We applied the duoSGS strategy to eleven extended, myeloma HRPs.

RESULTS

We identified one genome-wide significant region at 18q21.33 (0.85 Mb, P = 7.3 × 10-9) which contains one gene, CDH20. Thirteen regions were genome-wide suggestive: 1q42.2, 2p16.1, 3p25.2, 5q21.3, 5q31.1, 6q16.1, 6q26, 7q11.23, 12q24.31, 13q13.3, 18p11.22, 18q22.3 and 19p13.12.

CONCLUSION

Our results provide novel risk loci with segregating evidence from multiple HRPs and offer compelling targets and specific segment carriers to focus a future search for functional variants involved in inherited risk formyeloma.

Prognosis prediction signature of seven immune genes based on HPV status in cervical cancer

Cervical cancer (CC) has a high incidence and mortality rate, with a low 5-year survival rate, and human papillomavirus (HPV) is one of its carcinogenic risks. However, little evidence exists on the impact of HPV infection on the survival of patients with CC. In the present study, the CC cohort and immune genes were downloaded from the TCGA database and the ImmPort database, respectively. Subsequently, the Gene Set Enrichment Analysis was performed and found that HPV status was involved in multiple immune signaling pathways, which revealed that HPV infection might play critical roles in the immune response. Then seven prognostic immune genes were identified according to HPV status in CC. Using the seven immune genes, we established an immune risk score (IRS) signature and the Kaplan-Meier curve showed that high IRS was significantly correlated with poor prognosis of CC in both the training sets (HR = 2.32, 95% CI = 1.66-3.33; AUC = 0.712) and the validation sets (HR = 1.38, 95% CI = 1.02-1.85 and AUC = 0.583 in TCGA-HNSCC; HR = 2.58, 95% CI = 1.364-4.893, AUC = 0.676 in GSE44001). A nomogram of IRS combined with clinical features was established, and further analyses demonstrated that the power of the nomogram to predict the prognosis of CC was more reliable than that of a single independent factor. In conclusion, this study provided a more comprehensive understanding of the correlation between HPV and immune mechanisms as well as a novel signature that can effectively predict the prognosis of CC patients.

Charting the life course: Emerging opportunities to advance scientific approaches using life course research

Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.