Risk Models for Breast Cancer and Their Validation.

Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer-Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132,139 women attending mammography screening in the State of Washington, USA

Use of the concordance index for predictors of censored survival data.

The concordance index is often used to measure how well a biomarker predicts the time to an event. Estimators of the concordance index for predictors of right-censored data are reviewed, including those based on censored pairs, inverse probability weighting and a proportional-hazards model. Predictive and prognostic biomarkers often lose strength with time, and in this case the aforementioned statistics depend on the length of follow up. A semi-parametric estimator of the concordance index is developed that accommodates converging hazards through a single parameter in a Pareto model. Concordance index estimators are assessed through simulations, which demonstrate substantial bias of classical censored-pairs and proportional-hazards model estimators. Prognostic biomarkers in a cohort of women diagnosed with breast cancer are evaluated using new and classical estimators of the concordance index.This work was funded by Cancer Research UK (grant number C569/A16891)

Mammographic density, endocrine therapy and breast cancer risk: A prognostic and predictive biomarker review

© 2018 The Cochrane Collaboration. This is a protocol for a Cochrane Review (Prognosis). The objectives are as follows: Endocrine therapy for breast cancer prevention has been shown to reduce risk, and for treatment of early stage oestrogen receptor-positive (ER-positive) breast cancer to reduce breast cancer mortality. The objective of the review is to synthesise available evidence on whether mammographic density reduction in these settings is (i) a prognostic biomarker and (ii) a predictive biomarker, as defined in the Introduction. We will explore sources of heterogeneity to identify the impact of differences in participants, measures of mammographic density, follow-up length and study design. Within the prognostic and predictive biomarker reviews, our analysis will consider prevention and treatment populations separately, and within these, selective oestrogen receptor modulators (SERMs) and aromatase inhibitors (AIs) separately

A DNA methylation classifier of cervical precancer based on human papillomavirus and human genes

Cancer Research UK. Grant Number: C569/A1040

Absolute quantitation of DNA methylation of 28 candidate genes in prostate cancer using pyrosequencing

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Aberrant DNA methylation plays a pivotal role in carcinogenesis and its mapping is likely to provide biomarkers for improved diagnostic and risk assessment in prostate cancer (PCa). We quantified and compared absolute methylation levels among 28 candidate genes in 48 PCa and 29 benign prostate hyperplasia (BPH) samples using the pyrosequencing (PSQ) method to identify genes with diagnostic and prognostic potential. RARB, HIN1, BCL2, GSTP1, CCND2, EGFR5, APC, RASSF1A, MDR1, NKX2-5, CDH13, DPYS, PTGS2, EDNRB, MAL, PDLIM4, HLAa, ESR1 and TIG1 were highly methylated in PCa compared to BPH (p < 0.001), while SERPINB5, CDH1, TWIST1, DAPK1, THRB, MCAM, SLIT2, CDKN2a and SFN were not. RARB methylation above 21% completely distinguished PCa from BPH. Separation based on methylation level of SFN, SLIT2 and SERPINB5 distinguished low and high Gleason score cancers, e.g. SFN and SERPINB5 together correctly classified 81% and 77% of high and low Gleason score cancers respectively. Several genes including CDH1 previously reported as methylation markers in PCa were not confirmed in our study. Increasing age was positively associated with gene methylation (p < 0.0001). Accurate quantitative measurement of gene methylation in PCa appears promising and further validation of genes like RARB, HIN1, BCL2, APC and GSTP1 is warranted for diagnostic potential and SFN, SLIT2 and SERPINB5 for prognostic potential

Long-term prediction by DNA methylation of high-grade cervical intraepithelial neoplasia: Results of the ARTISTIC cohort.

Methylation markers have shown potential for triaging high-risk HPV-positive (hrHPV+) women to identify those at increased risk of invasive cervical cancer (ICC). Our aim was to assess the performance of the S5 DNA methylation classifier for predicting incident high-grade cervical intraepithelial neoplasia (CIN) and ICC among hrHPV+ women in the ARTISTIC screening trial cohort. The S5 classifier, comprising target regions of tumour suppressor gene EPB41L3 and L1 and L2 regions of HPV16, HPV18, HPV31, and HPV33, was assayed by pyrosequencing in archived hrHPV+ liquid-based samples from 343 women with high-grade disease (139 CIN2, 186 CIN3, and 18 ICC) compared to 800 hrHPV+ controls. S5 DNA methylation correlated directly with increasing severity of disease and inversely with lead time to diagnosis. S5 could discriminate between hrHPV+ women who developed CIN3 or ICC and hrHPV+ controls (p <.0001) using samples taken on average 5 years before diagnosis. This relationship was independent of cytology at baseline. The S5 test showed much higher sensitivity than HPV16/18 genotyping for identifying prevalent CIN3 (93% vs. 61%, p = .01) but lower specificity (50% vs. 66%, p <.0001). The S5 classifier identified most women at high risk of developing precancer and missed very few prevalent advanced lesions thus appearing to be an objective test for triage of hrHPV+ women. The combination of methylation of host and HPV genes enables S5 to combine the predictive power of methylation with HPV genotyping to identify hrHPV-positive women who are at highest risk of developing CIN3 and ICC in the future