A common IGF1R gene variant predicts later life breast cancer risk in women with preeclampsia.

PURPOSE: Preeclampsia has been inconsistently associated with altered later life risk of cancer. This study utilizes the Nurses' Health Study 2 (NHS2) to determine if the future risk of breast and non-breast cancers in women who experience preeclampsia is modified by carrying a protective variant of rs2016347, a functional insulin-like growth factor receptor-1 (IGF1R) single nucleotide polymorphism. METHODS: This retrospective cohort study completed within the NHS2 evaluated participants enrolled in 1989 and followed them through 2015, with a study population of 86,751 after exclusions. Cox proportional hazards models both with and without the impact of rs2016347 genotype were used to assess the risk of invasive breast cancer, hormone receptor-positive (HR+) breast cancer, and non-breast cancers. RESULTS: Women with preeclampsia had no change in risk of all breast, HR+ breast, or non-breast cancers when not considering genotype. However, women carrying at least one T allele of rs2016347 had a lower risk of HR+ breast cancer, HR 0.67, 95% CI: 0.47-0.97, P = 0.04, with interaction term P = 0.06. For non-breast cancers as a group, women carrying a T allele had an HR 0.76, 95% CI: 0.53-1.08, P = 0.12, with interaction term P = 0.26. CONCLUSIONS: This retrospective cohort study found that women with preeclampsia who carry a T allele of IGF1R rs2016347 had a reduced future risk of developing HR+ breast cancer, and a reduced but not statistically significant decreased risk of non-breast cancers suggesting a possible role for the IGF-1 axis in the development of cancer in these women.

PFAS concentrations in early and mid-pregnancy and risk of gestational diabetes mellitus in a nested case-control study within the ethnically and racially diverse PETALS cohort.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals and are commonly found in everyday items. PFAS have been linked to disrupting glucose homeostasis, however, whether they are associated with gestational diabetes mellitus (GDM) risk remains inconclusive. We examined prospective associations of PFAS concentrations measured twice in pregnancy with GDM risk. METHODS: In the PETALS pregnancy cohort, a nested case-control study which included 41 GDM cases and 87 controls was conducted. PFAS analytes were measured in blood serum collected in both early and mid-pregnancy (mean [SD]: 13.9 [2.2] and 20.2 [2.2] gestational weeks, respectively), with cumulative exposure calculated by the area-under-the-curve (AUC) to integrate both the PFAS concentration and the timing of the exposure. Individual adjusted weighted unconditional logistic regression models examined seven PFAS in association with GDM risk. P-values were corrected using the false-discovery-rate (FDR). Mixture models were analyzed with Bayesian kernel machine regression (BKMR). RESULTS: PFDA, PFNA and PFOA were individually associated with higher GDM risk per interquartile range (IQR) in early pregnancy (OR [95% CI]: 1.23 [1.09, 1.38]), 1.40 [1.24, 1.58]), and 1.15 [1.04, 1.27], respectively), mid-pregnancy (1.28 [1.15, 1.43], 1.16 [1.05, 1.28], and 1.20 [1.09, 1.33], respectively), and with cumulative exposure (1.23 [1.09, 1.38], 1.21 [1.07, 1.37], and 1.19 [1.09, 1.31], respectively). PFOS in mid-pregnancy and with cumulative exposure was associated with increased GDM risk (1.41 [1.17, 1.71] and 1.33 [1.06, 1.58], respectively). PFUnDA in early pregnancy was associated with lower GDM risk (0.79 [0.64, 0.98]), whereas mid-pregnancy levels were associated with higher risk (1.49 [1.18, 1.89]). PFHxS was associated with decreased GDM risk in early and mid-pregnancy (0.48 [0.38, 0.60] and 0.48 [0.37, 0.63], respectively) and with cumulative exposure (0.49 [0.38,0.63]). PFPeA was not associated with GDM. Similar conclusions were observed in BKMR models; however, overall associations in these models were not statistically significant. CONCLUSIONS: Higher risk of GDM was consistently observed in association with PFDA, PFNA, and PFOA exposure in both early and mid-pregnancy. Results should be corroborated in larger population-based cohorts and individuals of reproductive age should potentially avoid known sources of PFAS.

Reduced cardiovascular risks in women with endometriosis or polycystic ovary syndrome carrying a common functional IGF1R variant.

STUDY QUESTION: Is the increased future cardiovascular risk seen in women with endometriosis or polycystic ovary syndrome (PCOS) mitigated by functional insulin-like growth factor-1 receptor (IGF1R) single-nucleotide polymorphism (SNP) rs2016347 as previously shown in women with hypertensive disorders of pregnancy? SUMMARY ANSWER: This cohort study found that women with endometriosis or PCOS who carry a T allele of IGF1R SNP rs2016347 had a reduced future risk of developing cardiovascular disease (CVD) and associated risk factors, with risk reduction dependent on cohort era. WHAT IS KNOWN ALREADY: Women with endometriosis or PCOS have been shown to have an increased future risk of CVD and associated risk factors with limited predictive ability. STUDY DESIGN, SIZE, DURATION: This retrospective cohort study took place in the Nurses' Health Study 2 (NHS2), which enrolled 116 430 participants in 1989 who were followed through 2015. The study population was analyzed in its entirety, and subdivided into entry (pre-1989) and after entry (post-1989) exposure cohorts. All NHS2 participants were eligible for inclusion in the study, 9599 (8.2%) were excluded for missing covariates. PARTICIPANTS/MATERIALS, SETTING, METHODS: The NHS2 enrolled female registered nurses from 14 different states who ranged in age from 25 to 42 years at study entry. Data were collected from entry and biennial questionnaires, and analysis conducted from November 2020 to June 2021. Cox proportional hazard models were used to assess risk of CVD, hypertension (HTN), hypercholesterolemia (HC) and type 2 diabetes, both with and without genotyping for rs2016347. MAIN RESULTS AND THE ROLE OF CHANCE: While women without endometriosis or PCOS, as a whole, demonstrated no impact of genotype on risk in either cohort, women with endometriosis carrying a T allele had a lower risk of CVD (hazard ratio (HR), 0.48; 95% CI, 0.27-0.86, P = 0.02) and HTN (HR, 0.80; 95% CI, 0.66-0.97, P = 0.03) in the pre-1989 cohort, while those in the post-1989 cohort had a decrease in risk for HC (HR, 0.76; 95% CI, 0.62-0.94, P = 0.01). Women with PCOS in the post-1989 cohort showed a significant protective impact of the T allele on HTN (HR, 0.44; 95% CI, 0.27-0.73, P = 0.002) and HC (HR, 0.62; 95% CI, 0.40-0.95, P = 0.03). LIMITATIONS, REASONS FOR CAUTION: Data on specific endometriosis lesion locations or disease stage, as well as on PCOS phenotypes were lacking. In addition, data on systemic medical treatments beyond the use of oral contraceptives were missing, and these treatments may have confounded the results. WIDER IMPLICATIONS OF THE FINDINGS: These findings implicate systemic dysregulation of the insulin-like growth factor-1 axis in the development of HTN, HC and clinical CVD in endometriosis and PCOS, suggesting a common underlying pathogenetic mechanism. STUDY FUNDING/COMPETING INTEREST(S): The NHS2 infrastructure for questionnaire data collection was supported by National Institute of Health (NIH) grant U01CA176726. This work was also supported in part by NIH and National Cancer Institute grant U24CA210990; as well, research effort and publication costs were supported by the Elizabeth MA Stevens donor funds provided to the Buck Institute for Research on Aging. The authors declare they have no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Deep learning assessment of senescence-associated nuclear morphologies in mammary tissue from healthy female donors to predict future risk of breast cancer: a retrospective cohort study.

BACKGROUND: Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumour-promoting microenvironmental mechanism that secretes proinflammatory paracrine factors. With most work done in non-human models and the heterogeneous nature of senescence, the precise role of senescent cells in the development of cancer in humans is not well understood. Furthermore, more than 1 million non-malignant breast biopsies are taken every year that could be a major resource for risk stratification. We aimed to explore the clinical relevance for breast cancer development of markers of senescence in mammary tissue from healthy female donors. METHODS: In this retrospective cohort study, we applied single-cell deep learning senescence predictors, based on nuclear morphology, to histological images of haematoxylin and eosin-stained breast biopsy samples from healthy female donors at the Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (Indianapolis, IN, USA). All KTB participants (aged ≥18 years) who underwent core biopsies for research purposes between 2009 and 2019 were eligible for the study. Senescence was predicted in the epithelial (terminal duct lobular units [TDLUs] and non-TDLU epithelium), stromal, and adipose tissue compartments using validated models, previously trained on cells induced to senescence by ionising radiation (IR), replicative exhaustion (or replicative senescence; RS), or antimycin A, atazanavir-ritonavir, and doxorubicin (AAD) exposures. To benchmark our senescence-based cancer prediction results, we generated 5-year Gail scores-the current clinical gold standard for breast cancer risk prediction-for participants aged 35 years and older on the basis of characteristics at the time of tissue donation. The primary outcome was estimated odds of breast cancer via logistic modelling for each tissue compartment based on predicted senescence scores in cases (participants who had been diagnosed with breast cancer as of data cutoff, July 31, 2022) and controls (those who had not been diagnosed with breast cancer). FINDINGS: 4382 female donors (median age at donation 45 years [IQR 34-57]) were eligible for the study. As of data cutoff (median follow-up of 10 years [7-11]), 86 (2·0%) had developed breast cancer a mean of 4·8 years (SD 2·84) after date of donation and 4296 (98·0%) had not received a breast cancer diagnosis. Among the 86 cases, we found significant differences in adipose-specific IR and AAD senescence prediction scores compared with controls. Risk analysis showed that individuals in the upper half (above the median) of scores for the adipose tissue IR model had higher odds of developing breast cancer (odds ratio [OR] 1·71 [95% CI 1·10-2·68]; p=0·019), whereas the adipose AAD model revealed a reduced odds of developing breast cancer (OR 0·57 [0·36-0·88]; p=0·013). For the other tissue compartments and the RS model, no significant associations were found (except for stromal tissue via the IR model, had higher odds of developing breast cancer [OR 1·59, 1·03-2·49]). Individuals with both of the adipose risk factors had an OR of 3·32 (1·68-7·03; p=0·0009). Participants with 5-year Gail scores above the median had an OR for development of cancer of 2·33 (1·46-3·82; p=0·0012) compared with those with scores below the median. When combining Gail scores with our adipose AAD risk model, we found that individuals with both of these predictors had an OR of 4·70 (2·29-10·90; p<0·0001). When combining the Gail score with our adipose IR model, we found that individuals with both predictors had an OR of 3·45 (1·77-7·24; p=0·0002). INTERPRETATION: Assessment of senescence-associated nuclear morphologies with deep learning allows prediction of future cancer risk from normal breast biopsy samples. The combination of multiple models improved prediction of future breast cancer compared with the current clinical benchmark, the Gail model. Our results suggest an important role for microscope image-based deep learning models in predicting future cancer development. Such models could be incorporated into current breast cancer risk assessment and screening protocols. FUNDING: Novo Nordisk Foundation, Danish Cancer Society, and the US National Institutes of Health.

Breast cancer risk based on a deep learning predictor of senescent cells in normal tissue.

Background: The ability to predict future risk of cancer development in non-malignant biopsies is poor. Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumor-promoting microenvironmental mechanism that secretes pro-inflammatory paracrine factors. With most work done in non-human models and the heterogenous nature of senescence the precise role of senescent cells in the development of cancer in humans is not well understood. Further, more than one million non-malignant breast biopsies are taken every year that could be a major source of risk-stratification for women. Methods: We applied single cell deep learning senescence predictors based on nuclear morphology to histological images of 4,411 H&E-stained breast biopsies from healthy female donors. Senescence was predicted in the epithelial, stromal, and adipocyte compartments using predictor models trained on cells induced to senescence by ionizing radiation (IR), replicative exhaustion (RS), or antimycin A, Atv/R and doxorubicin (AAD) exposures. To benchmark our senescence-based prediction results we generated 5-year Gail scores, the current clinical gold standard for breast cancer risk prediction. Findings: We found significant differences in adipocyte-specific IR and AAD senescence prediction for the 86 out of 4,411 healthy women who developed breast cancer an average 4.8 years after study entry. Risk models demonstrated that individuals in the upper median of scores for the adipocyte IR model had a higher risk (OR=1.71 [1.10-2.68], p=0.019), while the adipocyte AAD model revealed a reduced risk (OR=0.57 [0.36-0.88], p=0.013). Individuals with both adipocyte risk factors had an OR of 3.32 ([1.68-7.03], p<0.001). Alone, 5-year Gail scores yielded an OR of 2.70 ([1.22-6.54], p=0.019). When combining Gail scores with our adipocyte AAD risk model, we found that individuals with both of these risk predictors had an OR of 4.70 ([2.29-10.90], p<0.001). Interpretation: Assessment of senescence with deep learning allows considerable prediction of future cancer risk from non-malignant breast biopsies, something that was previously impossible to do. Furthermore, our results suggest an important role for microscope image-based deep learning models in predicting future cancer development. Such models could be incorporated into current breast cancer risk assessment and screening protocols. Funding: This study was funded by the Novo Nordisk Foundation (#NNF17OC0027812), and by the National Institutes of Health (NIH) Common Fund SenNet program (U54AG075932).

Health inequities in SARS-CoV-2 infection, seroprevalence, and COVID-19 vaccination: Results from the East Bay COVID-19 study.

Comprehensive data on transmission mitigation behaviors and both SARS-CoV-2 infection and serostatus are needed from large, community-based cohorts to identify COVID-19 risk factors and the impact of public health measures. We conducted a longitudinal, population-based study in the East Bay Area of Northern California. From July 2020-March 2021, approximately 5,500 adults were recruited and followed over three data collection rounds to investigate the association between geographic and demographic characteristics and transmission mitigation behavior with SARS-CoV-2 prevalence. We estimated the populated-adjusted prevalence of antibodies from SARS-CoV-2 infection and COVID-19 vaccination, and self-reported COVID-19 test positivity. Population-adjusted SARS-CoV-2 seroprevalence was low, increasing from 1.03% (95% CI: 0.50-1.96) in Round 1 (July-September 2020), to 1.37% (95% CI: 0.75-2.39) in Round 2 (October-December 2020), to 2.18% (95% CI: 1.48-3.17) in Round 3 (February-March 2021). Population-adjusted seroprevalence of COVID-19 vaccination was 21.64% (95% CI: 19.20-24.34) in Round 3, with White individuals having 4.35% (95% CI: 0.35-8.32) higher COVID-19 vaccine seroprevalence than individuals identifying as African American or Black, American Indian or Alaskan Native, Asian, Hispanic, two or more races, or other. No evidence for an association between transmission mitigation behavior and seroprevalence was observed. Despite >99% of participants reporting wearing masks individuals identifying as African American or Black, American Indian or Alaskan Native, Asian, Hispanic, two or more races, or other, as well as those in lower-income households, and lower-educated individuals had the highest SARS-CoV-2 seroprevalence and lowest vaccination seroprevalence. Results demonstrate that more effective policies are needed to address these disparities and inequities.