Novel risk models for early detection and screening of ovarian cancer

Purpose Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Results Model I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal. Materials and Methods This nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels. Conclusions These models have encouraging sensitivities for detecting pre-clinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.

[1]  I. Shih,et al.  The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. , 2016, The American journal of pathology.

[2]  Michael J. Walker,et al.  Protein Z: A putative novel biomarker for early detection of ovarian cancer , 2016, International journal of cancer.

[3]  Matthew Burnell,et al.  Risk Algorithm Using Serial Biomarker Measurements Doubles the Number of Screen-Detected Cancers Compared With a Single-Threshold Rule in the United Kingdom Collaborative Trial of Ovarian Cancer Screening , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  A. Chudecka-Głaz,et al.  ROMA, an algorithm for ovarian cancer. , 2015, Clinica chimica acta; international journal of clinical chemistry.

[5]  F. Vizoso,et al.  Expression and prognostic significance of fibronectin and matrix metalloproteases in breast cancer metastasis , 2014, Histopathology.

[6]  N. Yousif.  Fibronectin promotes migration and invasion of ovarian cancer cells through up‐regulation of FAK–PI3K/Akt pathway , 2014, Cell biology international.

[7]  W. Kisiel,et al.  Protein Z/protein Z-dependent protease inhibitor system in loco in human gastric cancer , 2013, Annals of Hematology.

[8]  Kathleen R. Cho,et al.  Type I to type II ovarian carcinoma progression: mutant Trp53 or Pik3ca confers a more aggressive tumor phenotype in a mouse model of ovarian cancer. , 2013, The American journal of pathology.

[9]  E. Fung,et al.  Ovarian malignancy risk stratification of the adnexal mass using a multivariate index assay. , 2013, Gynecologic oncology.

[10]  C. Berg,et al.  Longitudinal screening algorithm that incorporates change over time in CA125 levels identifies ovarian cancer earlier than a single-threshold rule. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  M. Coleman,et al.  Stage at diagnosis and ovarian cancer survival: evidence from the International Cancer Benchmarking Partnership. , 2012, Gynecologic oncology.

[12]  Ruedi Aebersold,et al.  Reproducible Quantification of Cancer-Associated Proteins in Body Fluids Using Targeted Proteomics , 2012, Science Translational Medicine.

[13]  Jean-Pierre Gillet,et al.  Multidrug Resistance–Linked Gene Signature Predicts Overall Survival of Patients with Primary Ovarian Serous Carcinoma , 2012, Clinical Cancer Research.

[14]  W. Kisiel,et al.  Co-localization of Protein Z, Protein Z-Dependent protease inhibitor and coagulation factor X in human colon cancer tissue: implications for coagulation regulation on tumor cells. , 2012, Thrombosis research.

[15]  L. Bołkun,et al.  Protein Z Concentrations in Patients With Acute Leukemia , 2012, Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis.

[16]  Brandon Whitcher,et al.  DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6 , 2011, British Journal of Cancer.

[17]  W. Kisiel,et al.  Protein Z is present in human breast cancer tissue , 2011, International journal of hematology.

[18]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[19]  Sudhir Srivastava,et al.  A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer , 2011, Cancer Prevention Research.

[20]  Sudhir Srivastava,et al.  Ovarian Cancer Biomarker Performance in Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Specimens , 2011, Cancer Prevention Research.

[21]  Ie-Ming Shih,et al.  The Origin and Pathogenesis of Epithelial Ovarian Cancer: A Proposed Unifying Theory , 2010, The American journal of surgical pathology.

[22]  G. Heinze,et al.  Serum C-reactive protein in the differential diagnosis of ovarian masses. , 2009, European journal of obstetrics, gynecology, and reproductive biology.

[23]  Matthew Burnell,et al.  Sensitivity and specificity of multimodal and ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) , 2009, Journal of Family Planning and Reproductive Health Care.

[24]  M. Parmar,et al.  Recruitment to multicentre trials—lessons from UKCTOCS: descriptive study , 2008, BMJ : British Medical Journal.

[25]  W. Kisiel,et al.  The role of hemostatic system inhibitors in malignancy. , 2007, Seminars in thrombosis and hemostasis.

[26]  M. West,et al.  Patterns of Gene Expression That Characterize Long-term Survival in Advanced Stage Serous Ovarian Cancers , 2005, Clinical Cancer Research.

[27]  I. Shih,et al.  Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. , 2004, The American journal of pathology.

[28]  K. Münstedt,et al.  Association between fibronectin expression and prognosis in ovarian carcinoma. , 2003, Anticancer research.

[29]  Mark B Pepys,et al.  C-reactive protein: a critical update. , 2003, The Journal of clinical investigation.

[30]  Kenneth M. Yamada,et al.  Fibronectin at a glance , 2002, Journal of Cell Science.

[31]  G. Broze.  Protein Z-Dependent Regulation of Coagulation , 2001, Thrombosis and Haemostasis.

[32]  B. Nolen,et al.  Protein biomarkers of ovarian cancer: the forest and the trees. , 2012, Future oncology.

[33]  Steven J Skates,et al.  A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. , 2009, Gynecologic oncology.

[34]  R. Bast,et al.  The CA 125 tumour-associated antigen: a review of the literature. , 1989, Human reproduction.