Rural-urban disparities in psychosocial functioning in epithelial ovarian cancer patients.

OBJECTIVE: Although rural residence has been related to health disparities in cancer patients, little is known about how rural residence impacts mental health and quality of life (QOL) in ovarian cancer patients over time. This prospective longitudinal study investigated mental health and QOL of ovarian cancer patients in the first-year post-diagnosis. METHOD: Women with suspected ovarian cancer completed psychosocial surveys pre-surgery, at 6 months and one-year; clinical data were obtained from medical records. Histologically confirmed high grade epithelial ovarian cancer patients were eligible. Rural/urban residence was categorized from patient counties using the USDA Rural-Urban Continuum Codes. Linear mixed effects models examined differences in psychosocial measures over time, adjusting for covariates. RESULTS: Although disparities were not observed at study entry for any psychosocial variable (all p-values >0.22), urban patients showed greater improvement in total distress over the year following diagnosis than rural patients (p = 0.025) and were significantly less distressed at one year (p = 0.03). Urban patients had a more consistent QOL improvement than their rural counterparts (p = 0.006). There were no differences in the course of depressive symptoms over the year (p = 0.17). Social support of urban patients at 12 months was significantly higher than that of rural patients (p = 0.04). CONCLUSION: Rural patients reported less improvement in psychological functioning in the year following diagnosis than their urban counterparts. Clinicians should be aware of rurality as a potential risk factor for ongoing distress. Future studies should examine causes of these health disparities and potential long-term inequities and develop interventions to address these issues.

The biology of hope: Inflammatory and neuroendocrine profiles in ovarian cancer patients.

INTRODUCTION: Although the concept of hope is highly relevant for cancer patients, little is known about its association with cancer-relevant biomarkers. Here we examined how hope was related to diurnal cortisol and interleukin-6 (IL-6), a pro-inflammatory cytokine previously associated with tumor biology and survival in ovarian cancer. Secondly, we examined whether hope and hopelessness are distinctly associated with these biomarkers. METHOD: Participants were 292 high-grade ovarian cancer patients who completed surveys and provided saliva samples 4x/daily for 3 days pre-surgery to assess diurnal cortisol. Blood (pre-surgery) and ascites were assessed for IL-6. Hope and hopelessness were assessed using standardized survey items from established scales (Center for Epidemiological Studies Depression Scale; Profile of Mood States, Functional Assessment of Cancer Therapy). Two hopeless items were z-scored and combined into a composite for analysis. Regression models related these variables to nocturnal cortisol, cortisol slope, plasma and ascites IL-6, adjusting for cancer stage, BMI, age, and depression. RESULTS: Greater hope was significantly related to a steeper cortisol slope, ß = -0.193, p = 0.046, and lower night cortisol, ß = -0.227, p = 0.018, plasma IL-6, ß = -0.142, p = 0.033, and ascites IL-6, ß = -0.290, p = 0.002. Secondary analyses including both hope and hopelessness showed similar patterns, with distinct relationships of hope with significantly lower nocturnal cortisol ß = -0.233,p = 0.017 and ascites IL-6, ß = -0.282,p = 0.003, and between hopelessness and a flatter cortisol slope, ß = 0.211, p = 0.031. CONCLUSIONS: These data suggest a biological signature of hope associated with less inflammation and more normalized diurnal cortisol in ovarian cancer. These findings have potential clinical utility but need replication with more diverse samples and validated assessments of hope.

In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy.

In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients' tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets.

Well-being and stress vulnerability in ovarian cancer survivors during the COVID-19 pandemic.

OBJECTIVE: This study was designed to examine (1) whether ovarian cancer (OC) survivors would have greater well-being vs. elevated distress compared to community members during a universal health stressor (COVID-19) and (2) how resources and risk factors at diagnosis predicted vulnerability to a subsequent health-related stressor. METHODS: One hundred seventeen OC survivors were recruited from two academic medical centers and compared to a community-based sample on COVID-related distress and disruption. Latent class analysis identified differentially impacted groups of survivors. RESULTS: Survivors reported lower distress than community members. Predictors of higher distress included shorter-term survivorship, greater disruption, and poorer emotional well--being (EWB) at diagnosis. Survivors were divided into high- and low-COVID-19-impact subgroups; high-impact individuals endorsed higher perceived stress and lower EWB at diagnosis. CONCLUSION: Survivors reported lower COVID-related distress than community participants. While depression at diagnosis did not predict later distress, EWB was a strong predictor of response to a novel health-related stressor.

Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation.

There are strong correlations between the microbiome and human disease, including cancer. However, very little is known about potential mechanisms associated with malignant transformation in microbiome-associated gynecological cancer, except for HPV-induced cervical cancer. Our hypothesis is that differences in bacterial communities in upper genital tract epithelium may lead to selection of specific genomic variation at the cellular level of these tissues that may predispose to their malignant transformation. We first assessed differences in the taxonomic composition of microbial communities and genomic variation between gynecologic cancers and normal samples. Then, we performed a correlation analysis to assess whether differences in microbial communities selected for specific single nucleotide variation (SNV) between normal and gynecological cancers. We validated these results in independent datasets. This is a retrospective nested case-control study that used clinical and genomic information to perform all analyses. Our present study confirms a changing landscape in microbial communities as we progress into the upper genital tract, with more diversity in lower levels of the tract. Some of the different genomic variations between cancer and controls strongly correlated with the changing microbial communities. Pathway analyses including these correlated genes may help understand the basis for how changing bacterial landscapes may lead to these cancers. However, one of the most important implications of our findings is the possibility of cancer prevention in women at risk by detecting altered bacterial communities in the upper genital tract epithelium.

Carboplatin dosing in the treatment of ovarian cancer: An NRG oncology group study.

OBJECTIVE: To determine the effects of using National Comprehensive Cancer Network (NCCN) guidelines to estimate renal function on carboplatin dosing and explore adverse effects associated with a more accurate estimation of lower creatinine clearance (CrCl). METHODS: Retrospective data were obtained for 3830 of 4312 patients treated on GOG182 (NCT00011986)-a phase III trial of platinum-based chemotherapy for advanced-stage ovarian cancer. Carboplatin dose per patient on GOG182 was determined using the Jelliffe formula. We recalculated CrCl to determine dosing using Modification of Diet in Renal Disease (MDRD) and Cockcroft-Gault (with/without NCCN recommended modifications) formulas. Associations between baseline CrCl and toxicity were described using the area under the receiver operating characteristic curve (AUC). Sensitivity and positive predictive values described the model's ability to discriminate between subjects with/without the adverse event. RESULTS: AUC statistics (range, 0.52-0.64) showed log(CrClJelliffe) was not a good predictor of grade ≥3 adverse events (anemia, thrombocytopenia, febrile neutropenia, auditory, renal, metabolic, neurologic). Of 3830 patients, 628 (16%) had CrCl <60 mL/min. Positive predictive values for adverse events ranged from 1.8%-15%. Using the Cockcroft-Gault, Cockcroft-Gault with NCCN modifications, and MDRD (instead of Jelliffe) formulas to estimate renal function resulted in a >10% decrease in carboplatin dosing in 16%, 32%, and 5.2% of patients, respectively, and a >10% increase in carboplatin dosing in 41%, 9.6% and 12% of patients, respectively. CONCLUSION: The formula used to estimate CrCl affects carboplatin dosing. Estimated CrCl <60 mL/min (by Jelliffe) did not accurately predict adverse events. Efforts continue to better predict renal function. Endorsing National Cancer Institute initiatives to broaden study eligibility, our data do not support a minimum threshold CrCl <60 mL/min as an exclusion criterion from clinical trials.

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case−control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes.

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue. METHODS: In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform. RESULTS: The prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances. CONCLUSIONS: Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

Biobehavioral factors predict an exosome biomarker of ovarian carcinoma disease progression.

BACKGROUND: Biobehavioral factors such as social isolation and depression have been associated with disease progression in ovarian and other cancers. Here, the authors developed a noninvasive, exosomal RNA profile for predicting ovarian cancer disease progression and subsequently tested whether it increased in association with biobehavioral risk factors. METHODS: Exosomes were isolated from plasma samples from 100 women taken before primary surgical resection or neoadjuvant (NACT) treatment of ovarian carcinoma and 6 and 12 months later. Biobehavioral measures were sampled at all time points. Plasma from 76 patients was allocated to discovery analyses in which morning presurgical/NACT exosomal RNA profiles were analyzed by elastic net machine learning to identify a biomarker predicting rapid (≤6 months) versus more extended disease-free intervals following initial treatment. Samples from a second subgroup of 24 patients were analyzed by mixed-effects linear models to determine whether the progression-predictive biomarker varied longitudinally as a function of biobehavioral risk factors (social isolation and depressive symptoms). RESULTS: An RNA-based molecular signature was identified that discriminated between individuals who had disease progression in ≤6 months versus >6 months, independent of clinical variables (age, disease stage, and grade). In a second group of patients analyzed longitudinally, social isolation and depressive symptoms were associated with upregulated expression of the disease progression propensity biomarker, adjusting for covariates. CONCLUSION: These data identified a novel exosome-derived biomarker indicating propensity of ovarian cancer progression that is sensitive to biobehavioral variables. This derived biomarker may be potentially useful for risk assessment, intervention targeting, and treatment monitoring.

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer.

Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon's clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

Disparity of ovarian cancer survival between urban and rural settings.

OBJECTIVE: To determine if there is a difference in overall survival of patients with epithelial ovarian cancer in rural, urban, and metropolitan settings in the United States. METHODS: We performed a retrospective cohort study using 2004-2016 National Cancer Database (NCDB) data including high and low grade, stage I-IV disease. Bivariate analyses used Student's t-test for continuous variables and χ2 test for dichotomous variables. Kaplan-Meier curves estimated survival of patients based on location of residence, and univariate analyses using Cox proportional HR assessed survival based on baseline characteristics. Multivariate analysis was performed to account for significant covariates. Propensity score matching was used to validate the multivariate survival model. For all tests, p<0.05 was considered statistically significant. RESULTS: A total of 111 627 patients were included with a mean age of 62.5 years for metroolitan (range 18-90), 64.0 years for rural (range 19-90) and 63.2 years for urban areas (range 18-90). Of all patients included, 94 290 were in a metropolitan area (counties >1 million population or 50 000-999 999), 15 386 were in an urban area (population of 10 000-49 999), and 1951 were in a rural area (non-metropolitan/non-core population). Univariate Cox proportional hazards models showed clinically significant differences in survival in patients from metropolitan, urban, and rural areas. Multivariate Cox proportional hazards models showed a clinically significant increase in HRs for patients in rural settings (HR 1.17; 95% CI 1.06 to 1.29). Increasing age and stage, non-insured status, non-white race, and comorbidity were also significant for poorer survival. CONCLUSION: Patients with ovarian cancer who live in rural settings with small populations and greater distance to tertiary care centers have poorer survival. These differences hold after controlling for stage, age, and other significant risk factors related to poorer outcomes. To improve clinical outcomes, we need further studies to identify which of these factors are actionable.

Rural residence is related to shorter survival in epithelial ovarian cancer patients.

OBJECTIVE: Rural residence has been related to health disparities and greater mortality risk in cancer patients, including gynecologic cancer patients. Lower survival rates for rural cancer survivors have been attributed to limited access to specialized healthcare, including surgery. Here, we examined whether a rural/urban survival gap existed in ovarian cancer patients receiving surgery at tertiary-care facilities, and potential causes for this gap, including educational attainment. METHODS: Rural and urban patients with high grade invasive ovarian cancer (n = 342) seeking treatment at two midwestern tertiary-care university hospitals were recruited pre-surgery and followed until death or censoring date. Rural/urban residence was categorized using the USDA Rural-Urban Continuum Codes. Stratified Cox proportional hazards regression analyses, with clinical site as strata, adjusting for clinical and demographic covariates, were used to examine the effect of rurality on survival. RESULTS: Despite specialized surgical care, rural cancer survivors showed a higher likelihood of death compared to their urban counterparts, HR = 1.39 (95% CI: 1.04, 1.85) p = 0.026, adjusted for covariates. A rurality by education interaction was observed (p = 0.027), indicating significantly poorer survival in rural vs. urban patients among those with trade school/some college education, adjusted HR = 2.49 (95% CI: 1.44, 4.30), p = 0.001; there was no rurality survival disparity for the other 2 levels of education. CONCLUSIONS: Differences in ovarian cancer survival are impacted by rurality, which is moderated by educational attainment even in patients receiving initial care in tertiary settings. Clinicians should be aware of rurality and education as potential risk factors for adverse outcomes and develop approaches to address these possible risks.

Mitochondria in epithelial ovarian carcinoma exhibit abnormal phenotypes and blunted associations with biobehavioral factors.

Malignant tumor cells exhibit mitochondrial alterations and are also influenced by biobehavioral processes, but the intersection of biobehavioral factors and mitochondria in malignant tumors remains unexplored. Here we examined multiple biochemical and molecular markers of mitochondrial content and function in benign tissue and in high-grade epithelial ovarian carcinoma (EOC) in parallel with exploratory analyses of biobehavioral factors. First, analysis of a publicly-available database (n = 1435) showed that gene expression of specific mitochondrial proteins in EOC is associated with survival. Quantifying multiple biochemical and molecular markers of mitochondrial content and function in tissue from 51 patients with benign ovarian masses and 128 patients with high-grade EOC revealed that compared to benign tissue, EOCs exhibit 3.3-8.4-fold higher mitochondrial content and respiratory chain enzymatic activities (P < 0.001) but similar mitochondrial DNA (mtDNA) levels (- 3.1%), documenting abnormal mitochondrial phenotypes in EOC. Mitochondrial respiratory chain activity was also associated with interleukin-6 (IL-6) levels in ascites. In benign tissue, negative biobehavioral factors were inversely correlated with mitochondrial content and respiratory chain activities, whereas positive biobehavioral factors tended to be positively correlated with mitochondrial measures, although effect sizes were small to medium (r = - 0.43 to 0.47). In contrast, serous EOCs showed less pronounced biobehavioral-mitochondrial correlations. These results document abnormal mitochondrial functional phenotypes in EOC and warrant further research on the link between biobehavioral factors and mitochondria in cancer.

Identification of Novel Fusion Transcripts in High Grade Serous Ovarian Cancer.

Fusion genes are structural chromosomal rearrangements resulting in the exchange of DNA sequences between genes. This results in the formation of a new combined gene. They have been implicated in carcinogenesis in a number of different cancers, though they have been understudied in high grade serous ovarian cancer. This study used high throughput tools to compare the transcriptome of high grade serous ovarian cancer and normal fallopian tubes in the interest of identifying unique fusion transcripts within each group. Indeed, we found that there were significantly more fusion transcripts in the cancer samples relative to the normal fallopian tubes. Following this, the role of fusion transcripts in chemo-response and overall survival was investigated. This led to the identification of fusion transcripts significantly associated with overall survival. Validation was performed with different analytical platforms and different algorithms to find fusion transcripts.

Bacterial, Archaea, and Viral Transcripts (BAVT) Expression in Gynecological Cancers and Correlation with Regulatory Regions of the Genome.

Bacteria, archaea, and viruses are associated with numerous human cancers. To date, microbiome variations in transcription have not been evaluated relative to upper female genital tract cancer risk. Our aim was to assess differences in bacterial, archaea, and viral transcript (BAVT) expression between different gynecological cancers and normal fallopian tubes. In this case-control study we performed RNA sequencing on 12 normal tubes, 112 serous ovarian cancers (HGSC) and 62 endometrioid endometrial cancers (EEC). We used the centrifuge algorithm to classify resultant transcripts into four indexes: bacterial, archaea, viral, and human genomes. We then compared BAVT expression from normal samples, HGSC and EEC. T-test was used for univariate comparisons (correcting for multiple comparison) and lasso for multivariate modelling. For validation we performed DNA sequencing of normal tubes in comparison to HGSC and EEC BAVTs in the TCGA database. Pathway analyses were carried out to evaluate the function of significant BAVTs. Our results show that BAVT expression levels vary between different gynecological cancers. Finally, we mapped some of these BAVTs to the human genome. Numerous map locations were close to regulatory genes and long non-coding RNAs based on the pathway enrichment analysis. BAVTs may affect gynecological cancer risk and may be part of potential targets for cancer therapy.

Chronic difficulties are associated with poorer psychosocial functioning in the first year post-diagnosis in epithelial ovarian cancer patients.

OBJECTIVE: Ovarian cancer is characterized by poor prognosis, high levels of distress, disturbed sleep, and compromised quality of life (QOL). Although life stressors have been shown to significantly impact physical and psychological health in cancer populations, no studies have used a high-resolution stress assessment to differentiate effects of acute versus chronic stressors among women with ovarian cancer. We addressed this issue in the present prospective longitudinal study by examining how acute and chronic stress exposure in the year pre-diagnosis relate to depressive symptoms, sleep quality, and QOL over the first year post-diagnosis in women with ovarian cancer. METHODS: One hundred thirty-seven women completed the Life Events and Difficulties Schedule within a month of initial treatment for suspected ovarian cancer. Depressive symptoms, sleep, and QOL were measured pre-treatment, at six months, and one-year post-diagnosis. Mixed models were used to examine associations of acute and chronic stress pre-diagnosis with (a) change in psychosocial outcomes over the first year post-diagnosis and (b) levels of psychosocial outcomes across all time points. RESULTS: Both the number and severity of chronic difficulties (but not acute life events) were related to significantly greater depression, and poorer sleep quality and QOL, across all time-points. In contrast, these stress indices were unrelated to changes in psychosocial functioning over time. CONCLUSIONS: Chronic but not acute stress exposure predicted average levels of depression, sleep, and QOL in the first year post-diagnosis among women with ovarian cancer. Assessing stressors and designing interventions for reducing stress may thus be beneficial for ovarian cancer patients.

Creation and validation of models to predict response to primary treatment in serous ovarian cancer.

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case-control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival.

Long non-coding RNA's (lncRNA) are RNA sequences that do not encode proteins and are greater than 200 nucleotides in length. They regulate complex cellular mechanisms and have been associated with prognosis in various types of cancer. We aimed to identify lncRNA sequences that are associated with high grade serous ovarian cancer (HGSC) and assess their impact on overall survival. RNA was extracted from 112 HGSC patients and 12 normal fallopian tube samples from our Biobank tissue repository. RNA was sequenced and the Ultrafast and Comprehensive lncRNA detection and quantification pipeline (UClncR) was used for the identification of lncRNA sequences. Univariate logistic and multivariate lasso regression analyses identified lncRNA that was associated with HGSC. Univariate and multivariate Cox proportional hazard ratios were used to evaluate independent predictors of survival. 1943 of 16,325 investigated lncRNA's were differentially expressed in HGSC as compared to controls (p < 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA's associated with survival in HGSC. Three out of these four were found to be independently significant after accounting for all clinical covariates. Lastly, seven lncRNAs were independently associated with initial response to chemotherapy; four portended a worse response, while three were associated with improved response. More research is needed, but there is potential for these lncRNAs to be used as biomarkers of HGSC or predictors of treatment outcome in the future.

Epithelial-mesenchymal transition polarization in ovarian carcinomas from patients with high social isolation.

BACKGROUND: Social isolation has shown robust associations with clinical outcomes in the general population and in patients with cancer. In patients with ovarian cancer, social isolation has been found to be related to decreased survival and multiple biomarkers supporting tumor progression. However, to the authors' knowledge, little is known regarding the relationship between social isolation and the molecular characteristics of ovarian tumors. Herein, the authors have used genome-wide transcriptional profiling to quantify associations between social isolation and epithelial-mesenchymal transition (EMT) polarization in ovarian tumors and transcriptome-driven, promoter-based bioinformatics analyses to identify gene regulatory pathways that may potentially underlie these changes. METHODS: Tumor was sampled during primary surgical resection and immediately frozen in liquid nitrogen. After RNA extraction, microarray analysis of the transcriptome was performed and samples were analyzed to assess associations between EMT-related gene transcripts and social isolation (as indicated by a Social Provisions Scale Attachment subscale score <15). Convergent validation was provided by a promoter-based bioinformatic analysis of transcription factor activity. RESULTS: Primary analyses of 99 women demonstrated a lower average expression of gene transcripts previously associated with epithelial differentiation in women with high social isolation (-0.143 ± 0.048 log2 mRNA abundance; P = .004), but no difference in mesenchymal differentiation as a function of social isolation (+0.007 ± 0.0064 log2 mRNA abundance; P = .900). Upregulated activity was shown for 3 of the 4 targeted EMT-related transcription factors, including GATA4 (P = .014); SMAD2, SMAD3, and/or SMAD4 (P < .001); and TWIST1 (P < .001). Analyses of SNAIL2/SLUG activity indicated a directional trend toward increased activity that did not reach statistical significance (P = .123). CONCLUSIONS: The findings of the current study demonstrated differential EMT polarization and EMT-related transcription factor activity according to social isolation, a known socioenvironmental risk factor. LAY SUMMARY: Social isolation has shown robust associations with clinical outcomes in the general population and in patients with cancer. Herein, the authors examined the relationship between social isolation and the molecular characteristics of ovarian tumors. The authors investigated the epithelial-mesenchymal transition (EMT), a process whereby tumor cells lose epithelial characteristics and become more embryonic (mesenchymal), thereby enhancing invasiveness. Primary analyses demonstrated lower expression of genes previously associated with epithelial differentiation and increased activity of specific EMT-related transcription factors in individuals with high social isolation, indicating increased EMT polarization in these patients. These findings extend the understanding of how socioenvironmental factors may modulate tumor growth.