Lifetime Exposure to Cigarette Smoke, B Cell Tumor Immune Infiltration, and Immunoglobulin Abundance in Ovarian Tumors.

BACKGROUND: Cigarette smoke exposure has been linked to systemic immune dysfunction, including for B cell and immunoglobulin (Ig) production, and poor outcomes in ovarian cancer patients. No study has evaluated the impact of smoke exposure across the lifecourse on B cell infiltration and Ig abundance in ovarian tumors. METHODS: We measured markers of B and plasma cells and Ig isotypes using multiplex immunofluorescence on 395 ovarian cancer tumors in the Nurses' Health Study (NHS)/NHSII. We conducted beta-binomial analyses evaluating odds ratios (OR) and 95% confidence intervals (CI) for positivity of immune markers by cigarette exposure among cases and Cox proportional hazards models to evaluate hazard ratios (HR) and 95%CI for developing tumors with low (

WITHDRAWN: Impact of spatial clustering of cytotoxic and tumor infiltrating lymphocytes on overall survival in women with high grade serous ovarian cancer.

The authors have withdrawn their manuscript owing to incorrect handling of multiple measures in the survival analyses. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

The Association of Kidney Function and Inflammatory Biomarkers with Epithelial Ovarian Cancer Risk.

BACKGROUND: One of the mechanisms of ovarian tumorigenesis is through inflammation. Kidney dysfunction is associated with increased inflammation; thus, we assessed its relationship with ovarian cancer risk. METHODS: In prospectively collected samples, we evaluated the association of kidney function markers and C-reactive protein (CRP) with ovarian cancer risk in the UK Biobank. We used multivariable-adjusted Cox proportional hazards models to evaluate quartiles of serum and urine markers with ovarian cancer risk overall and by histology. We assessed effect modification by CRP (≤3.0, >3.0 mg/L). RESULTS: Among 232,908 women (1,110 ovarian cancer cases diagnosed from 2006-2020), we observed no association between estimated glomerular filtration rate and ovarian cancer risk (Q4 vs. Q1: HR, 1.00; 95% confidence intervals, 0.83-1.22). Potassium was associated with endometrioid (Q4 vs. Q1: 0.33, 0.11-0.98) and clear cell (4.74, 1.39-16.16) tumors. Poor kidney function was associated with a nonsignificant increase in ovarian cancer risk among women with CRP>3.0 mg/L (e.g., uric acid Q4 vs. Q1; 1.23, 0.81-1.86), but not CRP≤3.0 mg/L (0.83, 0.66-1.05). Other associations did not vary across CRP categories. CONCLUSIONS: Kidney function was not clearly associated with ovarian cancer risk. Larger studies are needed to evaluate possible histology specific associations. Given the suggestive trend for increased ovarian cancer risk in women with poor kidney function and high CRP, future work is needed, particularly in populations with a high prevalence of inflammatory conditions. IMPACT: This study provided the first evaluation of markers of kidney function in relation to ovarian cancer risk.

Relationships among Inflammatory Biomarkers and Objectively Assessed Physical Activity and Sleep during and after Chemotherapy for Gynecologic Malignancies.

Little is known regarding associations between inflammatory biomarkers and objectively measured physical activity and sleep during and after chemotherapy for gynecologic cancer; thus, we conducted a longitudinal study to address this gap. Women with gynecologic cancer (patients) and non-cancer controls (controls) completed assessments before chemotherapy cycles 1, 3, and 6 (controls assessed contemporaneously), as well as at 6- and 12-month follow-ups. Physical activity and sleep were measured using wrist-worn actigraphs and sleep diaries, and blood was drawn to quantify circulating levels of inflammatory markers. Linear and quadratic random-effects mixed models and random-effects fluctuation mixed models were used to examine physical activity and sleep over time, as well as the associations with inflammatory biomarkers. On average, patients (n = 97) and controls (n = 104) were 62 and 58 years old, respectively. Compared to controls, patients were less active, more sedentary, had more time awake after sleep onset, and had lower sleep efficiency (p-values < 0.05). Across groups, higher levels of TNF-α were associated with more sedentary time and less efficient sleep (p-values ≤ 0.05). Higher levels of IL-1ß, TNF-α, and IL-6 were associated with lower levels of light physical activity (p-values < 0.05). Associations between inflammatory biomarkers, physical activity, and sleep did not differ between patients and controls. Given these results, we speculate that inflammation may contribute to less physical activity and more sleep problems that persist even 12 months after completing chemotherapy.

The relationship of lifetime history of depression on the ovarian tumor immune microenvironment.

BACKGROUND: Depression is associated with a higher ovarian cancer risk. Prior work suggests that depression can lead to systemic immune suppression, which could potentially alter the anti-tumor immune response. METHODS: We evaluated the association of pre-diagnosis depression with features of the anti-tumor immune response, including T and B cells and immunoglobulins, among women with ovarian tumor tissue collected in three studies, the Nurses' Health Study (NHS; n = 237), NHSII (n = 137) and New England Case-Control Study (NECC; n = 215). Women reporting depressive symptoms above a clinically relevant cut-point, antidepressant use, or physician diagnosis of depression at any time prior to diagnosis of ovarian cancer were considered to have pre-diagnosis depression. Multiplex immunofluorescence was performed on tumor tissue microarrays to measure immune cell infiltration. In pooled analyses, we estimated odds ratios (OR) and 95% confidence intervals (CI) for the positivity of tumor immune cells using a beta-binomial model comparing those with and without depression. We used Bonferroni corrections to adjust for multiple comparisons. RESULTS: We observed no statistically significant association between depression status and any immune markers at the Bonferroni corrected p-value of 0.0045; however, several immune markers were significant at a nominal p-value of 0.05. Specifically, there were increased odds of having recently activated cytotoxic (CD3+CD8+CD69+) and exhausted-like T cells (CD3+Lag3+) in tumors of women with vs. without depression (OR = 1.36, 95 %CI = 1.09-1.69 and OR = 1.24, 95 %CI = 1.01-1.53, respectively). Associations were comparable when considering high grade serous tumors only (comparable ORs = 1.33, 95 %CI = 1.05-1.69 and OR = 1.25, 95 %CI = 0.99-1.58, respectively). There were decreased odds of having tumor infiltrating plasma cells (CD138+) in women with vs. without depression (OR = 0.54, 95 %CI = 0.33-0.90), which was similar among high grade serous carcinomas, although not statistically significant. Depression was also related to decreased odds of having naïve and memory B cells (CD20+: OR = 0.54, 95 %CI = 0.30-0.98) and increased odds of IgG (OR = 1.22, 95 %CI = 0.97-1.53) in high grade serous carcinomas. CONCLUSION: Our results provide suggestive evidence that depression may influence ovarian cancer outcomes through changes in the tumor immune microenvironment, including increasing T cell activation and exhaustion and reducing antibody-producing B cells. Further studies with clinical measures of depression and larger samples are needed to confirm these results.

Relationships among Inflammatory Biomarkers and Self-Reported Treatment-Related Symptoms in Patients Treated with Chemotherapy for Gynecologic Cancer: A Controlled Comparison.

Previous research suggests that inflammation triggers cancer-treatment-related symptoms (i.e., fatigue, depression, and disruptions in sleep and physical activity), but evidence is mixed. This study examined relationships between inflammatory biomarkers and symptoms in patients with gynecologic cancer compared to age-matched women with no cancer history (i.e., controls). Patients (n = 121) completed assessments before chemotherapy cycles 1, 3, and 6, and 6 and 12 months later. Controls (n = 105) completed assessments at similar timepoints. Changes in inflammation and symptomatology were evaluated using random-effects mixed models, and cross-sectional differences between patients and controls in inflammatory biomarkers and symptoms were evaluated using least squares means. Associations among inflammatory biomarkers and symptoms were evaluated using random-effects fluctuation mixed models. The results indicated that compared to controls, patients typically have higher inflammatory biomarkers (i.e., TNF-alpha, TNFR1, TNFR2, CRP, IL-1ra) and worse fatigue, depression, and sleep (ps < 0.05). Patients reported lower levels of baseline physical activity (p = 0.02) that became more similar to controls over time. Significant associations were observed between CRP, depression, and physical activity (ps < 0.05), but not between inflammation and other symptoms. The results suggest that inflammation may not play a significant role in fatigue or sleep disturbance among gynecologic cancer patients but may contribute to depression and physical inactivity.

Measurement of Ovarian Tumor Immune Profiles by Multiplex Immunohistochemistry: Implications for Epidemiologic Studies.

BACKGROUND: Despite the immunogenic nature of many ovarian tumors, treatment with immune checkpoint therapies has not led to substantial improvements in ovarian cancer survival. To advance population-level research on the ovarian tumor immune microenvironment, it is critical to understand methodologic issues related to measurement of immune cells on tissue microarrays (TMA) using multiplex immunofluorescence (mIF) assays. METHODS: In two prospective cohorts, we collected formalin-fixed, paraffin-embedded ovarian tumors from 486 cases and created seven TMAs. We measured T cells, including several sub-populations, and immune checkpoint markers on the TMAs using two mIF panels. We used Spearman correlations, Fisher exact tests, and multivariable-adjusted beta-binomial models to evaluate factors related to immune cell measurements in TMA tumor cores. RESULTS: Between-core correlations of intratumoral immune markers ranged from 0.52 to 0.72, with more common markers (e.g., CD3+, CD3+CD8+) having higher correlations. Correlations of immune cell markers between the whole core, tumor area, and stromal area were high (range 0.69-0.97). In multivariable-adjusted models, odds of T-cell positivity were lower in clear cell and mucinous versus type II tumors (ORs, 0.13-0.48) and, for several sub-populations, were lower in older tissue (sample age > 30 versus ≤ 10 years; OR, 0.11-0.32). CONCLUSIONS: Overall, high correlations between cores for immune markers measured via mIF support the use of TMAs in studying ovarian tumor immune infiltration, although very old samples may have reduced antigenicity. IMPACT: Future epidemiologic studies should evaluate differences in the tumor immune response by histotype and identify modifiable factors that may alter the tumor immune microenvironment.

Predictors of residual disease after debulking surgery in advanced stage ovarian cancer.

Objective: Optimal debulking with no macroscopic residual disease strongly predicts ovarian cancer survival. The ability to predict likelihood of optimal debulking, which may be partially dependent on tumor biology, could inform clinical decision-making regarding use of neoadjuvant chemotherapy. Thus, we developed a prediction model including epidemiological factors and tumor markers of residual disease after primary debulking surgery. Methods: Univariate analyses examined associations of 11 pre-diagnosis epidemiologic factors (n=593) and 24 tumor markers (n=204) with debulking status among incident, high-stage, epithelial ovarian cancer cases from the Nurses' Health Studies and New England Case Control study. We used Bayesian model averaging (BMA) to develop prediction models of optimal debulking with 5x5-fold cross-validation and calculated the area under the curve (AUC). Results: Current aspirin use was associated with lower odds of optimal debulking compared to never use (OR=0.52, 95%CI=0.31-0.86) and two tissue markers, ADRB2 (OR=2.21, 95%CI=1.23-4.41) and FAP (OR=1.91, 95%CI=1.24-3.05) were associated with increased odds of optimal debulking. The BMA selected aspirin, parity, and menopausal status as the epidemiologic/clinical predictors with the posterior effect probability ≥20%. While the prediction model with epidemiologic/clinical predictors had low performance (average AUC=0.49), the model adding tissue biomarkers showed improved, but weak, performance (average AUC=0.62). Conclusions: Addition of ovarian tumor tissue markers to our multivariable prediction models based on epidemiologic/clinical data slightly improved the model performance, suggesting debulking status may be in part driven by tumor characteristics. Larger studies are warranted to identify those at high risk of poor surgical outcomes informing personalized treatment.

Lifetime Exposure to Cigarette Smoke and Risk of Ovarian Cancer by T-cell Tumor Immune Infiltration.

BACKGROUND: Exposure to cigarette smoke, particularly in early life, is modestly associated with ovarian cancer risk and may impact systemic immunity and the tumor immune response. However, no studies have evaluated whether cigarette smoke exposure impacts the ovarian tumor immune microenvironment. METHODS: Participants in the Nurses' Health Study (NHS) and NHSII reported on early life exposure to cigarette smoke and personal smoking history on questionnaires (n = 165,760). Multiplex immunofluorescence assays were used to measure markers of T cells and immune checkpoints in tumor tissue from 385 incident ovarian cancer cases. We used Cox proportional hazards models to evaluate HRs and 95% confidence intervals (CI) for developing ovarian tumors with a low (

Modification of the Association Between Frequent Aspirin Use and Ovarian Cancer Risk: A Meta-Analysis Using Individual-Level Data From Two Ovarian Cancer Consortia.

PURPOSE: Frequent aspirin use has been associated with reduced ovarian cancer risk, but no study has comprehensively assessed for effect modification. We leveraged harmonized, individual-level data from 17 studies to examine the association between frequent aspirin use and ovarian cancer risk, overall and across subgroups of women with other ovarian cancer risk factors. METHODS: Nine cohort studies from the Ovarian Cancer Cohort Consortium (n = 2,600 cases) and eight case-control studies from the Ovarian Cancer Association Consortium (n = 5,726 cases) were included. We used Cox regression and logistic regression to assess study-specific associations between frequent aspirin use (≥ 6 days/week) and ovarian cancer risk and combined study-specific estimates using random-effects meta-analysis. We conducted analyses within subgroups defined by individual ovarian cancer risk factors (endometriosis, obesity, family history of breast/ovarian cancer, nulliparity, oral contraceptive use, and tubal ligation) and by number of risk factors (0, 1, and ≥ 2). RESULTS: Overall, frequent aspirin use was associated with a 13% reduction in ovarian cancer risk (95% CI, 6 to 20), with no significant heterogeneity by study design (P = .48) or histotype (P = .60). Although no association was observed among women with endometriosis, consistent risk reductions were observed among all other subgroups defined by ovarian cancer risk factors (relative risks ranging from 0.79 to 0.93, all P-heterogeneity > .05), including women with ≥ 2 risk factors (relative risk, 0.81; 95% CI, 0.73 to 0.90). CONCLUSION: This study, the largest to-date on aspirin use and ovarian cancer, provides evidence that frequent aspirin use is associated with lower ovarian cancer risk regardless of the presence of most other ovarian cancer risk factors. Risk reductions were also observed among women with multiple risk factors, providing proof of principle that chemoprevention programs with frequent aspirin use could target higher-risk subgroups.

Pre-diagnosis and post-diagnosis dietary patterns and survival in women with ovarian cancer.

BACKGROUND: Evidence is limited on inflammation-related dietary patterns and mortality in ovarian cancer survivors. METHODS: We examined the associations between pre- and post-diagnosis dietary patterns, including change in diet from before to after diagnosis, and mortality among 1003 ovarian cancer survivors in two prospective cohort studies. Dietary pattern scores for empirical dietary inflammatory pattern (EDIP) and Alternative Healthy Eating Index (AHEI) were calculated based on food frequency questionnaires. We used Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for ovarian cancer-specific and all-cause mortality. RESULTS: Pre-diagnosis EDIP score and AHEI were not associated with mortality. Among non-high grade serous cases, a higher post-diagnosis EDIP score was associated with increased risk of all-cause mortality (HR5th vs 1st quintile = 1.95, 95% CI = 1.04-3.67, p-trend = 0.06). Compared to survivors consuming a low EDIP score diet before and after diagnosis, high post-diagnosis EDIP was associated with increased risk of ovarian cancer specific mortality (pre-to-post diagnosis low/high, HR = 1.38, 95% CI = 0.99-1.92; high/high HR = 1.58, 95% CI = 1.09-2.30) and all-cause mortality (low/high HR = 1.44, 95% CI = 1.06-1.95; high/high HR = 1.55, 95% CI = 1.10-2.19). CONCLUSION: Consuming a more inflammatory dietary pattern post-diagnosis was associated with increased mortality in ovarian cancer survivors, suggesting limiting the inflammatory potential of diet post-diagnosis could lead to enhanced survivorship.

Prospective Analyses of Sedentary Behavior in Relation to Risk of Ovarian Cancer.

We examined the association of sedentary behavior with risk of ovarian cancer overall, by tumor subtype, and by participant characteristics in the Nurses' Health Study (NHS) and Nurses' Health Study II (NHS II). A total of 69,558 NHS participants (1992-2016) and 104,130 NHS II participants (1991-2015) who reported on time spent sitting at home, at work, and while watching television were included in the analysis, which included 884 histologically confirmed ovarian cancer cases. Multivariable-adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for ovarian cancer by sitting time (no mutual adjustment for individual sitting types in primary analyses). We examined potential heterogeneity by tumor histological type (type I or II), body mass index (weight (kg)/height (m)2; < 25 or ≥25), and total physical activity (<15 or ≥15 metabolic equivalent of task-hours/week). We observed an increased risk of ovarian cancer for women who sat at work for 10-19 hours/week (HR = 1.25, 95% CI: 1.04, 1.51) and ≥20 hours/week (HR = 1.40, 95% CI: 1.14, 1.71) versus <5 hours/week. This association did not vary by body mass index, physical activity, or histotype (P for heterogeneity ≥ 0.43). No associations were observed for overall sitting, sitting while watching television, or other sitting at home. Longer sitting time at work was associated with elevated risk of ovarian cancer. Further investigations are required to confirm these findings and elucidate underlying mechanisms.

Antihypertensive medication use and ovarian cancer survival.

OBJECTIVE: Although experimental models suggest that use of beta-blockers, a common antihypertensive agent, may improve survival in ovarian cancer patients, results from clinical studies have been mixed. METHODS: We evaluated the associations of pre-diagnostic (n = 950) and post-diagnostic (n = 743) use of antihypertensive medications with survival among patients with invasive, epithelial ovarian cancer in the Nurses' Health Study (NHS; 1994-2016) and NHSII (2001-2017), with follow-up until 2018 and 2019, respectively. Cox proportional hazards models were used to estimate hazard ratios (HR) for ovarian cancer mortality according to antihypertensive medication use before and after diagnosis, considering multiple drug classes (beta-blockers, calcium-channel blockers, thiazide diuretics, angiotensin-converting enzyme [ACE] inhibitors). RESULTS: After adjusting for age, BMI, smoking status and tumor characteristics, pre-diagnostic use versus non-use of calcium-channel blockers was associated with higher ovarian cancer mortality (HR: 1.49; 95% CI: 1.13, 1.96), which was primarily due to polytherapy involving calcium-channel blockers (HR: 1.61; 95% CI: 1.15, 2.26). Pre-diagnostic use of beta-blockers, thiazide diuretics, or ACE inhibitors was not associated with ovarian cancer mortality. No association was observed for post-diagnostic antihypertensive medication use individually or in combination, except for lower mortality associated with polytherapy involving ACE inhibitors (HR: 0.53; 95% CI: 0.31, 0.91). CONCLUSION: Overall, we did not find clear relationships between antihypertensive medication use and ovarian cancer mortality. However, given the limitation of the data, we cannot determine whether the association may differ by type of beta-blockers. The reasons underlying the observed associations with pre-diagnostic calcium-channel blocker use and post-diagnostic ACE inhibitor use require further investigation.

Early life physical activity and risk of ovarian cancer in adulthood.

Emerging data suggest that exposures in early life may affect ovarian development and contribute to ovarian cancer risk. We evaluated the association between early life physical activity and risk of ovarian cancer in adulthood in two large prospective cohorts, the Nurses' Health Study (NHS) and NHSII. In total, analyses included 28 232 NHS participants (followed from 2004 to 2016) and 56 553 NHSII participants (followed from 1997 to 2017). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of ovarian cancer overall and by early life body mass index (BMI). Neither physical activity at ages 12-13, 14-17 or 18-22 years nor average physical activity across these three periods was associated with ovarian cancer risk overall (≥78 vs <24 MET-h/wk, HRs = 1.34, 1.21, 1.08 and 1.24, respectively), or by categories of early life BMI (Pheterogeneity ≥ .44). No association was observed with the risk of high-grade serous or poorly differentiated tumors or postmenopausal ovarian cancer. Overall, early life physical activity was not clearly related to ovarian cancer risk during adulthood.

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data.

Immune modulation is considered a hallmark of cancer initiation and progression. The recent development of immunotherapies has ushered in a new era of cancer treatment. These therapeutics have led to revolutionary breakthroughs; however, the efficacy of immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which cancer patients will benefit from immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or "zero-inflated" data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.

Prolactin and Risk of Epithelial Ovarian Cancer.

BACKGROUND: Prolactin is synthesized in the ovaries and may play a role in ovarian cancer etiology. One prior prospective study observed a suggestive positive association between prolactin levels and risk of ovarian cancer. METHODS: We conducted a pooled case-control study of 703 cases and 864 matched controls nested within five prospective cohorts. We used unconditional logistic regression to calculate adjusted odds ratios (OR) and 95% confidence intervals (CI) for the association between prolactin and ovarian cancer risk. We examined heterogeneity by menopausal status at blood collection, body mass index (BMI), age, and histotype. RESULTS: Among women with known menopausal status, we observed a positive trend in the association between prolactin and ovarian cancer risk (P trend = 0.045; OR, quartile 4 vs. 1 = 1.34; 95% CI = 0.97-1.85), but no significant association was observed for premenopausal or postmenopausal women individually (corresponding OR = 1.38; 95% CI = 0.74-2.58; P trend = 0.32 and OR = 1.41; 95% CI = 0.93-2.13; P trend = 0.08, respectively; P heterogeneity = 0.91). In stratified analyses, we observed a positive association between prolactin and risk for women with BMI ≥ 25 kg/m2, but not BMI < 25 kg/m2 (corresponding OR = 2.68; 95% CI = 1.56-4.59; P trend < 0.01 and OR = 0.90; 95% CI = 0.58-1.40; P trend = 0.98, respectively; P heterogeneity < 0.01). Associations did not vary by age, postmenopausal hormone therapy use, histotype, or time between blood draw and diagnosis. CONCLUSIONS: We found a trend between higher prolactin levels and increased ovarian cancer risk, especially among women with a BMI ≥ 25 kg/m2. IMPACT: This work supports a previous study linking higher prolactin with ovarian carcinogenesis in a high adiposity setting. Future work is needed to understand the mechanism underlying this association.

Prediagnosis and postdiagnosis leisure time physical activity and survival following diagnosis with ovarian cancer.

Little is known about the influence of prediagnosis and postdiagnosis physical activity on ovarian cancer survival. We investigated this association in two large cohorts, the Nurses' Health Study (NHS) and NHSII. Analyses included 1461 women with confirmed invasive, epithelial ovarian cancer and data on physical activity. Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for ovarian cancer-specific mortality. Ovarian cancer-specific mortality was not associated with physical activity reported 1-8 years before diagnosis overall (≥7.5 vs <1.5 MET-hours/week, HR = 0.96), for high-grade serous/ poorly differentiated tumors, or non-serous/ low-grade serous tumors (P-heterogeneity = .45). An inverse association was observed for activity 1-4 years after diagnosis (≥7.5 vs <1.5 MET-hours/week, HR = 0.67, 95%CI: 0.48-0.94), with similar results by histotype (P-heterogeneity = .53). Women who decreased their activity from ≥7.5 MET-hours/week 1-8 years before diagnosis to <7.5 MET-hours/week 1-4 years after diagnosis, compared to those with <7.5 MET-hours/week across periods, had a 49% increased risk of death (HR = 1.49, 95%CI: 1.07-2.08). Physical activity after, but not before, ovarian cancer diagnosis was associated with better prognosis.

The association of resistance training with risk of ovarian cancer.

BACKGROUND: Increasing evidence, including multiple putative inflammatory risk factors (e.g., c-reactive protein, and adiposity), supports that inflammation plays an important role in ovarian carcinogenesis. Resistance training (RT) is associated with lower levels of circulating inflammatory markers, independent of physical activity. METHODS: We evaluated the relationship between RT and risk of ovarian cancer accounting for other physical activity (e.g., walking) in two large prospective cohorts, the Nurses' Health Study (NHS) and NHSII. KEY RESULTS: In total, analyses included 42,005 NHS participants (2000-2016) and 67,289 NHSII participants (2001-2017) with RT assessed every 4 years. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of RT with ovarian cancer risk overall and by tumor subtype, adjusting for known and putative ovarian cancer risk factors. We identified a total of 609 cases over 1,748,884 person-years. No association was observed with overall ovarian cancer risk (RT ≥60 vs 0 min/wk, HR = 0.95, 95%CI: 0.74-1.22) or by histotype (comparable HR = 0.86 and 0.98 for type I and II tumors, respectively). Results did not differ by body mass index (Pinteraction = 0.97), or other physical activity (Pinteraction = 0.31). CONCLUSIONS & INFERENCES: We observed no evidence that moderate levels of RT were associated with risk of ovarian cancer. Further investigations are required to confirm these findings.

Prospective Analyses of Lifestyle Factors Related to Energy Balance and Ovarian Cancer Risk by Infiltration of Tumor-Associated Macrophages.

BACKGROUND: Lifestyle factors related to energy balance have been associated with ovarian cancer risk and influence the tumor immune microenvironment, including tumor-associated macrophages (TAM). However, no studies have assessed whether these factors differentially impact ovarian cancer risk by TAM densities. METHODS: We conducted a prospective analysis in the Nurses' Health Studies to examine the associations of physical activity, sitting time, and a food-based empirical dietary inflammatory pattern (EDIP) score with invasive epithelial ovarian cancer risk by TAM density assessed by immunohistochemistry. We considered density of CD68 (marker of total TAMs) and CD163 (marker of pro-carcinogenic M2-type TAMs), and their ratios. We used multivariable Cox proportional hazards regression to calculate hazard ratios (HR) and 95% confidence intervals (CI) of exposures with risk of ovarian tumors with high versus low TAMs, including analyses stratified by body mass index. RESULTS: Analyses included 312 incident ovarian cancer cases with TAM measurements. Physical activity, sitting time, and EDIP score were not differentially associated with ovarian cancer risk by TAM densities (P heterogeneity > 0.05). Among overweight and obese women, higher EDIP score was associated with increased risk of CD163 low-density tumors (HR comparing extreme tertiles, 1.57; 95% CI, 0.88-2.80; P trend = 0.01), but not CD163 high-density tumors (comparable HR, 1.16; 95% CI, 0.73-1.86; P trend = 0.24), though this difference was not statistically significant (P heterogeneity = 0.22). CONCLUSIONS: We did not observe differential associations between lifestyle factors and ovarian cancer risk by TAM densities. IMPACT: Future investigations examining the interplay between other ovarian cancer risk factors and the tumor immune microenvironment may help provide insight into ovarian cancer etiology.