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Novel risk models for early detection and screening of ovarian cancer.

ABSTRACT: 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.

SUBMITTER: Russell MR

PROVIDER: S-EPMC5352196 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Novel risk models for early detection and screening of ovarian cancer.

Russell Matthew R MR D'Amato Alfonsina A Graham Ciaren C Crosbie Emma J EJ Gentry-Maharaj Aleksandra A Ryan Andy A Kalsi Jatinderpal K JK Fourkala Evangelia-Ourania EO Dive Caroline C Walker Michael M Whetton Anthony D AD Menon Usha U Jacobs Ian I Graham Robert L J RL

Oncotarget 20170101 1

<h4>Purpose</h4>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).<h4>Results</h4>Model I identifies cancers earlier than CA125 alone, with a potentia ...[more]

PMID: 27903971

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Project description:Purpose: Women at familial/genetic ovarian cancer risk often undergo screening despite unproven efficacy. Research suggests each woman has her own CA125 baseline; significant increases above this level may identify cancers earlier than standard 6- to 12-monthly CA125 > 35 U/mL.Experimental Design: Data from prospective Cancer Genetics Network and Gynecologic Oncology Group trials, which screened 3,692 women (13,080 woman-screening years) with a strong breast/ovarian cancer family history or BRCA1/2 mutations, were combined to assess a novel screening strategy. Specifically, serum CA125 q3 months, evaluated using a risk of ovarian cancer algorithm (ROCA), detected significant increases above each subject's baseline, which triggered transvaginal ultrasound. Specificity and positive predictive value (PPV) were compared with levels derived from general population screening (specificity 90%, PPV 10%), and stage-at-detection was compared with historical high-risk controls.Results: Specificity for ultrasound referral was 92% versus 90% (P = 0.0001), and PPV was 4.6% versus 10% (P > 0.10). Eighteen of 19 malignant ovarian neoplasms [prevalent = 4, incident = 6, risk-reducing salpingo-oophorectomy (RRSO) = 9] were detected via screening or RRSO. Among incident cases (which best reflect long-term screening performance), three of six invasive cancers were early-stage (I/II; 50% vs. 10% historical BRCA1 controls; P = 0.016). Six of nine RRSO-related cases were stage I. ROCA flagged three of six (50%) incident cases before CA125 exceeded 35 U/mL. Eight of nine patients with stages 0/I/II ovarian cancer were alive at last follow-up (median 6 years).Conclusions: For screened women at familial/genetic ovarian cancer risk, ROCA q3 months had better early-stage sensitivity at high specificity, and low yet possibly acceptable PPV compared with CA125 > 35 U/mL q6/q12 months, warranting further larger cohort evaluation. Clin Cancer Res; 23(14); 3628-37. ©2017 AACR.

Project description:IntroductionIdentification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately.MethodsProspective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided.ResultsDuring follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites.ConclusionThe PLCO lung cancer risk models demonstrate high discrimination and calibration.

Project description:High-grade serous ovarian cancer (HGSOC) represents the majority of ovarian cancers and accounts for the largest proportion of deaths from the disease. A timely detection of low volume HGSOC should be the goal of any screening studies. However, numerous transvaginal ultrasound (TVU) detection-based population studies aimed at detecting low-volume disease have not yielded reduced mortality rates. A quantitative invalidation of TVU as an effective HGSOC screening strategy is a necessary next step. Herein, we propose a mathematical model for a quantitative explanation on the reported failure of TVU-based screening to improve HGSOC low-volume detectability and overall survival.We develop a novel in silico mathematical assessment of the efficacy of a unimodal TVU monitoring regimen as a strategy aimed at detecting low-volume HGSOC in cancer-positive cases, defined as cases for which the inception of the first malignant cell has already occurred. Our findings show that the median window of opportunity interval length for TVU monitoring and HGSOC detection is approximately 1.76 years. This does not translate into reduced mortality levels or improved detection accuracy in an in silico cohort across multiple TVU monitoring frequencies or detection sensitivities. We demonstrate that even a semiannual, unimodal TVU monitoring protocol is expected to miss detectable HGSOC. Lastly, we find that circa 50% of the simulated HGSOC growth curves never reach the baseline detectability threshold, and that on average, 5-7 infrequent, rate-limiting stochastic changes in the growth parameters are associated with reaching HGSOC detectability and mortality thresholds respectively. Focusing on a malignancy poorly studied in the mathematical oncology community, our model captures the dynamic, temporal evolution of HGSOC progression. Our mathematical model is consistent with recent case reports and prospective TVU screening population studies, and provides support to the empirical recommendation against frequent HGSOC screening.

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Novel risk models for early detection and screening of ovarian cancer.

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Novel risk models for early detection and screening of ovarian cancer.

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