A B S T R A C T PurposeEarly detection of ovarian cancer has great promise to improve clinical outcome. Patients and MethodsNinety-six serum biomarkers were analyzed in sera from healthy women and from patients with ovarian cancer, benign pelvic tumors, and breast, colorectal, and lung cancers, using multiplex xMAP bead-based immunoassays. A Metropolis algorithm with Monte Carlo simulation (MMC) was used for analysis of the data. ResultsA training set, including sera from 139 patients with early-stage ovarian cancer, 149 patients with late-stage ovarian cancer, and 1,102 healthy women, was analyzed with MMC algorithm and cross validation to identify an optimal biomarker panel discriminating early-stage cancer from healthy controls. The four-biomarker panel providing the highest diagnostic power of 86% sensitivity (SN) for early-stage and 93% SN for late-stage ovarian cancer at 98% specificity (SP) was comprised of CA-125, HE4, CEA, and VCAM-1. This model was applied to an independent blinded validation set consisting of sera from 44 patients with early-stage ovarian cancer, 124 patients with late-stage ovarian cancer, and 929 healthy women, providing unbiased estimates of 86% SN for stage I and II and 95% SN for stage III and IV disease at 98% SP. This panel was selective for ovarian cancer showing SN of 33% for benign pelvic disease, SN of 6% for breast cancer, SN of 0% for colorectal cancer, and SN of 36% for lung cancer. ConclusionA panel of CA-125, HE4, CEA, and VCAM-1, after additional validation, could serve as an initial stage in a screening strategy for epithelial ovarian cancer.

Purpose: Serum-biomarker based screening for pancreatic cancer could greatly improve survival in appropriately targeted high-risk populations.Experimental Design: Eighty-three circulating proteins were analyzed in sera of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC, n ¼ 333), benign pancreatic conditions (n ¼ 144), and healthy control individuals (n ¼ 227). Samples from each group were split randomly into training and blinded validation sets prior to analysis. A Metropolis algorithm with Monte Carlo simulation (MMC) was used to identify discriminatory biomarker panels in the training set. Identified panels were evaluated in the validation set and in patients diagnosed with colon (n ¼ 33), lung (n ¼ 62), and breast (n ¼ 108) cancers.Results: Several robust profiles of protein alterations were present in sera of PDAC patients compared to the Healthy and Benign groups. In the training set (n ¼ 160 PDAC, 74 Benign, 107 Healthy), the panel of CA 19-9, ICAM-1, and OPG discriminated PDAC patients from Healthy controls with a sensitivity/specificity (SN/SP) of 88/90%, while the panel of CA 19-9, CEA, and TIMP-1 discriminated PDAC patients from Benign subjects with an SN/SP of 76/90%. In an independent validation set (n ¼ 173 PDAC, 70 Benign, 120 Healthy), the panel of CA 19-9, ICAM-1 and OPG demonstrated an SN/SP of 78/94% while the panel of CA19-9, CEA, and TIMP-1 demonstrated an SN/SP of 71/89%. The CA19-9, ICAM-1, OPG panel is selective for PDAC and does not recognize breast (SP ¼ 100%), lung (SP ¼ 97%), or colon (SP ¼ 97%) cancer.Conclusions: The PDAC-specific biomarker panels identified in this investigation warrant additional clinical validation to determine their role in screening targeted high-risk populations.