Autoantibodies, antigen-autoantibody complexes and antigens complement CA125 for early detection of ovarian cancer.

BACKGROUND: Multiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer. METHODS: Twenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone. RESULTS: Eight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased sensitivity for distinguishing early-stage ovarian cancers from benign pelvic masses by 25% (p = 0.0004) at 95% specificity. From 21 autoantibody candidates, 3 AAb (anti-p53, anti-CTAG1 and annt-Il-8) detected 22% of early stage ovarian cancers, potentially lengthening lead time prior to diagnosis. CONCLUSION: A four biomarker panel achieved greater sensitivity at the same specificity for early detection of ovarian cancer than CA125 alone.

Stacking Machine Learning Algorithms for Biomarker-Based Preoperative Diagnosis of a Pelvic Mass.

OBJECTIVE: To identify the most predictive parameters of ovarian malignancy and develop a machine learning (ML) based algorithm to preoperatively distinguish between a benign and malignant pelvic mass. METHODS: Retrospective study of 70 predictive parameters collected from 140 women with a pelvic mass. The women were split into a 3:1 "training" to "testing" dataset. Feature selection was performed using Gini impurity through an embedded random forest model and principal component analysis. Nine unique ML classifiers were assessed across a variety of model-specific hyperparameters using 25 bootstrap resamples of the training data. Model predictions were then combined into an ensemble stack by LASSO regression. The final ensemble stack and individual classifiers were then applied to the testing dataset to assess model performance. RESULTS: Feature selection identified HE4, CA125, and transferrin as three predictive parameters of malignancy. Assessment of the ensemble stack on the testing dataset outperformed all individual ML classifiers in predicting malignancy. The ensemble stack demonstrated an accuracy of 97.1%, a receiver operating characteristic (ROC) area under the curve (AUC) of 0.951, and a sensitivity of 93.3% with a specificity of 100%. CONCLUSIONS: Combining the measurement of three distinct biomarkers with the stacking of multiple ML classifiers into an ensemble can provide valuable preoperative diagnostic predictions for patients with a pelvic mass.

Osteopontin, Macrophage Migration Inhibitory Factor and Anti-Interleukin-8 Autoantibodies Complement CA125 for Detection of Early Stage Ovarian Cancer.

Early detection of ovarian cancer promises to reduce mortality. While serum CA125 can detect more than 60% of patients with early stage (I-II) disease, greater sensitivity might be observed with a panel of biomarkers. Ten protein antigens and 12 autoantibody biomarkers were measured in sera from 76 patients with early stage (I-II), 44 patients with late stage (III-IV) ovarian cancer and 200 healthy participants in the normal risk ovarian cancer screening study. A four-biomarker panel (CA125, osteopontin (OPN), macrophage inhibitory factor (MIF), and anti-IL-8 autoantibodies) detected 82% of early stage cancers compared to 65% with CA125 alone. In early stage subjects the area under the receiver operating characteristic curve (AUC) for the panel (0.985) was significantly greater (p < 0.001) than the AUC for CA125 alone (0.885). Assaying an independent validation set of sera from 71 early stage ovarian cancer patients, 45 late stage patients and 131 healthy women, AUC in early stage disease was improved from 0.947 with CA125 alone to 0.974 with the four-biomarker panel (p = 0.015). Consequently, OPN, MIF and IL-8 autoantibodies can be used in combination with CA125 to distinguish ovarian cancer patients from healthy controls with high sensitivity. Osteopontin appears to be a robust biomarker that deserves further evaluation in combination with CA125.

Melanoma-Induced Reprogramming of Schwann Cell Signaling Aids Tumor Growth.

The tumor microenvironment has been compared with a nonhealing wound involving a complex interaction between multiple cell types. Schwann cells, the key regulators of peripheral nerve repair, have recently been shown to directly affect nonneural wound healing. Their role in cancer progression, however, has been largely limited to neuropathic pain and perineural invasion. In this study, we showed that melanoma activated otherwise dormant functions of Schwann cells aimed at nerve regeneration and wound healing. Such reprogramming of Schwann cells into repair-like cells occurred during the destruction and displacement of neurons as the tumor expanded and via direct signaling from melanoma cells to Schwann cells, resulting in activation of the nerve injury response. Melanoma-activated Schwann cells significantly altered the microenvironment through their modulation of the immune system and the extracellular matrix in a way that promoted melanoma growth in vitro and in vivo. Local inhibition of Schwann cell activity following cutaneous sensory nerve transection in melanoma orthotopic models significantly decreased the rate of tumor growth. Tumor-associated Schwann cells, therefore, can have a significant protumorigenic effect and may present a novel target for cancer therapy. SIGNIFICANCE: These findings reveal a role of the nerve injury response, particularly through functions of activated Schwann cells, in promoting melanoma growth.

Multi-institutional Validation Study of Pancreatic Cyst Fluid Protein Analysis for Prediction of High-risk Intraductal Papillary Mucinous Neoplasms of the Pancreas.

OBJECTIVE: Preliminary work by our group suggested that proteins within the pancreatic cyst fluid (CF) may discriminate degree of IPMN dysplasia. We sought to externally validate these markers and determine whether their inclusion in a preoperative clinical nomogram could increase diagnostic accuracy. SUMMARY BACKGROUND DATA: IPMN is the most common radiographically identifiable precursor to pancreatic cancer; however, the timing and frequency of its malignant progression are unknown, and there are currently no reliable preoperative tests that can determine the grade of dysplasia in IPMN. METHODS: Clinical and radiographic data, as well as CF samples, were obtained from 149 patients who underwent resection for IPMN at 1 of 3 institutions. High-risk disease was defined as the presence of high-grade dysplasia or invasive carcinoma. Multianalyte bead array analysis (Luminex) of CF was performed for 4 protein markers that were previously associated with high-risk disease. Logistic regression models were fit on training data, with and without adjustment for a previously developed clinical nomogram and validated with an external testing set. The models incorporating clinical risk score were presented graphically as nomograms. RESULTS: Within the group of 149 resected patients, 89 (60%) had low-risk disease, and 60 (40%) had high-risk disease. All 4 CF markers (MMP9, CA72-4, sFASL, and IL-4) were overexpressed in patients with high-risk IPMN (P < 0.05). Two predictive models based on preselected combinations of CF markers had concordance indices of 0.76 (Model-1) and 0.80 (Model-2). Integration of each CF marker model into a previously described clinical nomogram leads to increased discrimination compared with either the CF models or nomogram alone (c-indices of 0.84 and 0.83, respectively). CONCLUSIONS: This multi-institutional study validated 2 CF protein marker models for preoperative identification of high-risk IPMN. When combined with a clinical nomogram, the ability to predict high-grade dysplasia was even stronger.

Changes in inflammatory endometrial cancer risk biomarkers in individuals undergoing surgical weight loss.

OBJECTIVE: Obesity has been strongly linked to endometrial cancer (EC) risk. A number of potential EC risk biomarkers have been proposed, including heightened pro-inflammatory cytokines and adipokines. To evaluate if bariatric surgery can serve as a means for altering levels of such EC risk biomarkers, we investigated changes in these biomarkers after weight loss. METHODS: Blood samples were collected pre-operatively and 6months post-operatively in 107 female bariatric surgery patients aged 18-72years. Wilcoxon signed-rank tests were used to compare biomarker levels (measured using xMAP immunoassays) pre- and post-surgery. Normative comparisons were implemented to contrast 6-month post-surgery biomarker levels to levels in a sample of 74 age-matched non-obese women. Linear regression was used to evaluate the relationship between biomarker expression at baseline and 6months post-surgery and the relationship between race and biomarker levels. RESULTS: On average, participants lost 30.15kg (SD: 12.26) after the bariatric intervention. Levels of C-peptide, insulin, CRP, leptin, IL-1Rα, and IL-6 significantly decreased, while levels of SHBG, IGFBP1, and adiponectin significantly increased with weight loss. Normative comparisons showed the levels of SHBG, C-peptide, insulin, IGFBP1, adiponectin, CRP, and TNFα after bariatric intervention approached the level of markers in comparison group. Multiple regression analyses revealed significant relationships between changes in BMI and changes in biomarker levels. The changes in IL-1Rα were significantly associated with race. CONCLUSIONS: Our findings demonstrate that normalization of EC risk biomarkers can be achieved with bariatric surgery. Improved understanding of biological mechanisms associated with weight loss may inform preventive strategies for EC.

Quantification of autoantibodies to plasminogen in plasma of patients with cancer.

BACKGROUND: The investigation of autoantibodies which may play a role in the processes of angiogenesis and tumorogenesis is important in the early diagnostis of cancer. OBJECTIVE: This study aimed to investigate the levels of autoantibodies to Glu-plasminogen (Pg) in plasma of patients with tumors. METHODS: Plasma samples from healthy volunteers were compared with samples from patients with prostate cancer using 2D electrophoresis and MALDI-TOF mass spectrometry. Plasma samples from 25 patients with prostate cancer, 15 patients with benign prostatic hyperplasia (BPH), 29 patients with breast cancer, and 43 healthy volunteers were tested using ELISA to anti-Pg IgG autoantibodies. Affinity chromatography on Pg-sepharoses was used to assess the quantity of anti-Pg IgG in control plasma and plasma of prostate cancer patients. ATTESTAT program was used for nonparametric analysis. RESULTS: Using 2D electrophoresis, marker spots below 50 kD were detected in prostate cancer samples. These spots were identified as fragments of Pg and IgG. Using affinity chromatography on Pg-sepharose, the quantity of IgG bound to Pg versus total IgG was determined to be 9% in control and 27% in prostate cancer samples. The frequency of occurence of elevated levels of anti-Pg IgG was 84% in prostate cancer samples, 69% in breast cancer samples, 40% in BPH samples, and 11% in healthy plasma. CONCLUSIONS: Autoantibodies to Pg may be involved in tumorogenesis and elevated levels of anti-Pg IgG antibodies may be a risk factor for tumor development.

Cross-species antibody microarray interrogation identifies a 3-protein panel of plasma biomarkers for early diagnosis of pancreas cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDA) is the fourth leading cause of cancer-related death in the United States, and its incidence is on the rise. Advanced disease is nearly uniformly lethal, emphasizing the need to identify PDA at its earliest stages. To discover early biomarkers of PDA, we evaluated the circulating proteome in murine preinvasive and invasive plasma samples and human prediagnostic and diagnostic samples. EXPERIMENTAL DESIGN: Using a customized antibody microarray platform containing >4,000 features, we interrogated plasma samples spanning preinvasive and invasive disease from a highly faithful mouse model of PDA. In parallel, we mined prediagnostic plasma from women in the Women's Health Initiative (WHI) who would later succumb to PDA together with matched, cancer-free control samples. Samples collected after an establishing diagnosis of PDA were also interrogated to further validate markers. RESULTS: We identified ERBB2 and TNC in our cross-species analyses, and multiple antibodies identified ESR1 in prediagnostic plasma from people that succumb to PDA. This 3-marker panel had an AUC of 0.86 (95% confidence interval [CI], 0.76-0.96) for the diagnostic cohort that increased to 0.97 (95% CI, 0.92-1.0) with CA19-9 included. The 3-marker panel also had an AUC of 0.68 (95% CI, 0.58-0.77) for the prediagnostic cohort. CONCLUSIONS: We identified potential disease detection markers in plasma up to 4 years before death from PDA with superior performance to CA19-9. These markers might be especially useful in high-risk cohorts to diagnose early, resectable disease, particularly in patients that do not produce CA19-9.

Urinary protein biomarkers in the early detection of lung cancer.

The early detection of lung cancer has the potential to greatly impact disease burden through the timely identification and treatment of affected individuals at a manageable stage of development. The insufficient specificity demonstrated by currently used screening and diagnostic techniques has led to intense investigation into biomarkers as diagnostic tools. Urine may represent a noninvasive alternative matrix for diagnostic biomarker development. We performed an analysis of 242 biomarkers in urines obtained from 83 patients with non-small cell lung carcinomas (NSCLC), 74 patients diagnosed with benign pulmonary conditions, and 77 healthy donors. A large number of significant alterations were observed between the NSCLC and control groups. A multivariate analysis identified a three-biomarker panel consisting of IGFBP-1, sIL-1Ra, CEACAM-1, which discriminated NSCLC from healthy controls with a sensitivity/specificity of 84/95 in an initial training set and 72/100 in an independent validation set. This panel performed well among multiple subtypes of NSCLC and early-stage disease but demonstrated only limited efficacy for the discrimination of NSCLC from benign controls and limited specificity for patients with several other cancers and tuberculosis. These findings demonstrate that urine biomarkers may provide screening and diagnostic properties which exceed those reported for serum biomarkers and approach a level necessary for further clinical development.

Circulating mediators of inflammation and immune activation in AIDS-related non-hodgkin lymphoma.

BACKGROUND: Non-Hodgkin lymphoma (NHL) is the most common AIDS-related malignancy in developed countries. An elevated risk of developing NHL persists among HIV-infected individuals in comparison to the general population despite the advent of effective antiretroviral therapy. The mechanisms underlying the development of AIDS-related NHL (A-NHL) are not fully understood, but likely involve persistent B-cell activation and inflammation. METHODS: This was a nested case-control study within the ongoing prospective Multicenter AIDS Cohort Study (MACS). Cases included 47 HIV-positive male subjects diagnosed with high-grade B-cell NHL. Controls were matched to each case from among participating HIV-positive males who did not develop any malignancy. Matching criteria included time HIV+ or since AIDS diagnosis, age, race and CD4+ cell count. Sera were tested for 161 serum biomarkers using multiplexed bead-based immunoassays. RESULTS: A subset of 17 biomarkers, including cytokines, chemokines, acute phase proteins, tissue remodeling agents and bone metabolic mediators was identified to be significantly altered in A-NHL cases in comparison to controls. Many of the biomarkers included in this subset were positively correlated with HIV viral load. A pathway analysis of our results revealed an extensive network of interactions between current and previously identified biomarkers. CONCLUSIONS: These findings support the current hypothesis that A-NHL develops in the context of persistent immune stimulation and inflammation. Further analysis of the biomarkers identified in this report should enhance our ability to diagnose, monitor and treat this disease.

Distinct age-matched serum biomarker profiles in patients with cutaneous T-cell lymphoma.

Immunological functions decline with age. Because MS/SzS predominately affects the elderly, it is important to distinguish age-related from cancer-specific changes. Also, MF and SzS are malignancies of CD4(+) T-lymphocytes, further compromising an immune state of the patients. The objectives of this study were to distinguish disease-specific immunological deterioration by performing comparative age-matched Luminex multiplex assessment of 34 serum biomarkers between patients with MF/SzS, HIV-infected individuals and normal controls. Controlling for age, expression level appears to significantly differ between patients with MF/SzS and controls for the following biomarkers: G-CSF, IL-5, MIP-1ß, TNF-α, VEGF, EOTAXIN, IL-8, IL-12, IL-2R, IP10, MCP-1, MIG, TNFR1 and TNFR2 (P < 0.05), while others showed normal age-related changes. Interestingly, cluster analysis placed MF/SzS profiles closer to HIV. This further underscores an immunologically compromised state of patients with MF/SzS and suggests its potential self-perpetuating role in disease progression.

Prediagnostic serum biomarkers as early detection tools for pancreatic cancer in a large prospective cohort study.

BACKGROUND: The clinical management of pancreatic cancer is severely hampered by the absence of effective screening tools. METHODS: Sixty-seven biomarkers were evaluated in prediagnostic sera obtained from cases of pancreatic cancer enrolled in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). RESULTS: The panel of CA 19-9, OPN, and OPG, identified in a prior retrospective study, was not effective. CA 19-9, CEA, NSE, bHCG, CEACAM1 and PRL were significantly altered in sera obtained from cases greater than 1 year prior to diagnosis. Levels of CA 19-9, CA 125, CEA, PRL, and IL-8 were negatively associated with time to diagnosis. A training/validation study using alternate halves of the PLCO set failed to identify a biomarker panel with significantly improved performance over CA 19-9 alone. When the entire PLCO set was used for training at a specificity (SP) of 95%, a panel of CA 19-9, CEA, and Cyfra 21-1 provided significantly elevated sensitivity (SN) levels of 32.4% and 29.7% in samples collected <1 and >1 year prior to diagnosis, respectively, compared to SN levels of 25.7% and 17.2% for CA 19-9 alone. CONCLUSIONS: Most biomarkers identified in previously conducted case/control studies are ineffective in prediagnostic samples, however several biomarkers were identified as significantly altered up to 35 months prior to diagnosis. Two newly derived biomarker combinations offered advantage over CA 19-9 alone in terms of SN, particularly in samples collected >1 year prior to diagnosis. However, the efficacy of biomarker-based tools remains limited at present. Several biomarkers demonstrated significant velocity related to time to diagnosis, an observation which may offer considerable potential for enhancements in early detection.

Biomarker testing for ovarian cancer: clinical utility of multiplex assays.

The improved detection of ovarian cancer at the earliest stages of development would confer a significant benefit in the therapeutic efficacy and overall survival associated with this devastating disease. The inadequate performance of currently used imaging modalities and the CA 125 biomarker test have precluded the establishment of screening programs and hindered the development of diagnostic tests for ovarian cancer. Two recently completed large clinical trials of ovarian cancer screening have reported findings of mixed impact, further clouding the issue. Considerable effort has been applied to the development of multiplexed biomarker-based tests and the most recent advances are discussed here. Within the clinical setting of pelvic mass differential diagnosis and triage, several significant advancements have been achieved recently, including the US Food and Drug Administration-approved Risk of Ovarian Malignancy Algorithm and OVA1 tests. The development and evaluation of those tests are described in this review. Thus while effective routine screening for ovarian cancer remains a lofty goal, advancement within the clinical management of pelvic mass diagnoses appears to be near at hand.

Circulating prolactin levels and risk of epithelial ovarian cancer.

PURPOSE: Indirect evidence from experimental and epidemiological studies suggests that prolactin may be involved in ovarian cancer development. However, the relationship between circulating prolactin levels and risk of ovarian cancer is unknown. METHODS: We conducted a nested case-control study of 230 cases and 432 individually matched controls within three prospective cohorts to evaluate whether pre-diagnostic circulating prolactin is associated with subsequent risk of ovarian cancer. We also assessed whether lifestyle and reproductive factors are associated with circulating prolactin among controls. RESULTS: Prolactin levels were significantly lower among post- versus pre-menopausal women, parous versus nulliparous women, and past versus never users of oral contraceptives in our cross-sectional analysis of controls. In our nested case-control study, we observed a non-significant positive association between circulating prolactin and ovarian cancer risk (OR(Q4vsQ1) 1.56, 95 % CI 0.94, 2.63, p trend 0.15). Our findings were similar in multivariate-adjusted models and in the subgroup of women who donated blood ≥5 years prior to diagnosis. We observed a significant positive association between prolactin and risk for the subgroup of women with BMI ≥25 kg/m(2) (OR(Q4vsQ1) 3.10, 95 % CI 1.39, 6.90), but not for women with BMI <25 kg/m(2) (OR(Q4vsQ1) 0.81, 95 % CI 0.40, 1.64). CONCLUSIONS: Our findings suggest that prolactin may be associated with increased risk of ovarian cancer, particularly in overweight/obese women. Factors associated with reduced risk of ovarian cancer, such as parity and use of oral contraceptives, were associated with lower prolactin levels, which suggests that modulation of prolactin may be a mechanism underlying their association with risk.

The quest for ovarian cancer screening biomarkers: are we on the right road?

Recent discoveries in the field of biomarkers for screening and early detection of ovarian cancer (OC) identified current needs for biomarkers capable of recognizing preclinical disease. The suggested approaches are (1) development of highly analytically sensitive CA 125 assays capable of detecting CA 125 released from small cancerous and preneoplastic lesions; (2) identification of biomarkers effective in CA 125-negative cases; and (3) performing biomarker discovery in samples obtained from patients with prophylactic bilateral salpingo-oophorectomy with pathologically confirmed preneoplastic lesions, or in appropriate animal models of ovarian cancer.

Multianalyte assay systems in the differential diagnosis of ovarian cancer.

INTRODUCTION: The efficient triage of women diagnosed with a pelvic mass presents a current area of unmet need. Unnecessary surgical intervention performed on patients at a decreased risk of malignancy represents a significant source of preventable morbidity, anxiety and cost. Likewise, delayed or overlooked referral of patients harboring malignant tumors is strongly associated with diminished outcomes. Current tools including imaging modalities and the CA 125 blood test are of insufficient accuracy to overcome these challenges. The use of multianalyte assays systems which include additional biomarkers capable of complementing the performance of CA 125 may offer the best hope of improvement. AREAS COVERED: Recent findings regarding the use of multianalyte biomarker panels for the differential diagnosis of a pelvic mass are reviewed and discussed. Particular attention is paid to to the FDA approved ROMA and OVA1 tests. The development, validation, recent evaluation and comparative performances of these two tests are reviewed in detail. EXPERT OPINION: The performances achieved by the ROMA and OVA1 diagnostic tests represent significant milestones in the application of multianalyte assay systems into standard clinical practice. The overall impact and cost-effectiveness of widespread clinical use of these tools remains to be evaluated.

A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-risk individuals found to be cancer-free by CT screening.

INTRODUCTION: Clinical decision making in the setting of computed tomography (CT) screening could benefit from accessible biomarkers that help predict the level of lung cancer risk in high-risk individuals with indeterminate pulmonary nodules. METHODS: To identify candidate serum biomarkers, we measured 70 cancer-related proteins by Luminex xMAP (Luminex Corporation) multiplexed immunoassays in a training set of sera from 56 patients with biopsy-proven primary non-small-cell lung cancer and 56 age-, sex-, and smoking-matched CT-screened controls. RESULTS: We identified a panel of 10 serum biomarkers-prolactin, transthyretin, thrombospondin-1, E-selectin, C-C motif chemokine 5, macrophage migration inhibitory factor, plasminogen activator inhibitor, receptor tyrosine-protein kinase, erbb-2, cytokeratin fragment 21.1, and serum amyloid A-that distinguished lung cancer patients from controls with an estimated balanced accuracy (average of sensitivity and specificity) of 76.0 ± 3.8% from 20-fold internal cross-validation. We then iteratively evaluated this model in an independent test and verification case/control studies confirming the initial classification performance of the panel. The classification performance of the 10-biomarker panel was also analytically validated using enzyme-linked immunosorbent assays in a second independent case/control population, further validating the robustness of the panel. CONCLUSIONS: The performance of this 10-biomarker panel-based model was 77.1% sensitivity/76.2% specificity in cross-validation in the expanded training set, 73.3% sensitivity/93.3% specificity (balanced accuracy 83.3%) in the blinded verification set with the best discriminative performance in stage I/II cases: 85% sensitivity (balanced accuracy 89.2%). Importantly, the rate of misclassification of CT-screened controls was not different in most control subgroups with or without airflow obstruction or emphysema or pulmonary nodules. These biomarkers have potential to aid in the early detection of lung cancer and more accurate interpretation of indeterminate pulmonary nodules detected by CT screening.

Protein biomarkers of ovarian cancer: the forest and the trees.

The goal of effective population-based screening for ovarian cancer remains elusive despite intense efforts aimed at improving upon biomarker and imaging modalities. While dozens of potential serum biomarkers for ovarian cancer have been identified in recent years, none have yet overcome the limitations that have hindered the clinical use of CA-125. Avenues of opportunity in biomarker development are emerging as investigators are beginning to appreciate the significance of remote, as well as local or regional, sources of biomarkers in the construction of diagnostic panels, as well as the importance of evaluating biomarkers in prediagnostic settings. As the list of candidate biomarkers of ovarian cancer continues to grow, refinements in the methods through which specific proteins are selected for further development as components of diagnostic panels are desperately sought. Such refinements must take into account both the bioinformatic and biological significance of each candidate. Approaches incorporating these considerations may potentially overcome the challenges to early detection posed by the histological heterogeneity of ovarian cancer. Here, we review the recent progress achieved in efforts to develop diagnostic biomarker panels for ovarian cancer and discuss the challenges that remain.

Factors associated with inflammation markers, a cross-sectional analysis.

Epidemiological studies have reported associations between circulating inflammation markers and risk of chronic diseases. It is of interest to examine whether risk factors for these diseases are associated with inflammation. We conducted a cross-sectional analysis to evaluate whether reproductive and lifestyle factors and circulating vitamin D were associated with inflammation markers, including C-reactive protein, cytokines (IL-1ß, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p40, IL-12p70, IL-13, TNFα), and cytokine modulators (IL-1RA, sIL-1RII, sIL-2Ra, sIL-4R, sIL-6R, sTNF-R1/R2), among 616 healthy women. We confirmed associations of several inflammation markers with age and BMI. We also observed significantly higher levels of certain inflammation markers in postmenopausal vs. premenopausal women (TNFα, sIL-1RII, sIL-2Ra), with increasing parity (IL-12p40), and with higher circulating 25(OH) vitamin D (IL-13) and lower levels among current users of non-steroidal anti-inflammatory drugs (NSAIDs) (IL-1ß, IL-2, IL-10, IL-12p70, and IL-12p40), current smokers (IL-4, IL-13, IL-12p40), and women with a family history of breast or ovarian cancer (IL-4, IL-10, IL-13). Our findings suggest that risk factors for chronic diseases (age, BMI, menopausal status, parity, NSAID use, family history of breast and ovarian cancer, and smoking) are associated with inflammation markers in healthy women.

-The advancement of biomarker-based diagnostic tools for ovarian, breast, and pancreatic cancer through the use of urine as an analytical biofluid.

Despite considerable advancements, the development of effective cancer screening tools based on serum biomarker measurements has thus far failed to achieve a meaningful clinical impact. The incremental progress observed over the course of serum biomarker development suggests that further refinements based on novel approaches may yet result in a breakthrough. The use of urine as an analytical biofluid for biomarker development may represent such an approach. The unique characteristics of urine including a high level of stability, ease of sampling, and an inactive and low-complexity testing matrix offer several potential advantages over the use of serum. A number of recent reports have demonstrated the utility of urine in the identification of novel cancer biomarkers and also the improved performance of biomarkers previously evaluated in serum. In this review, advancements related to the use of urine biomarkers within the settings of ovarian, breast, and pancreatic cancer are presented and discussed. Findings regarding the identification of specific urine biomarkers for each disease are highlighted along with comparative analyses of urine and serum biomarkers as diagnostic tools.