Lindegaardmatthews0941

BACKGROUND Emerging evidence supports a role of the receptor activator of nuclear factor κB (RANK) pathway in mammary gland development and breast carcinogenesis. Osteoprotegerin (OPG) is the endogenous decoy receptor for RANK-ligand (RANKL) that inhibits RANK-signaling. Whether OPG may be a biomarker of breast cancer risk remains unclear. METHODS We evaluated the association between plasma OPG and breast cancer risk in a case (n=297)-control (n=297) study nested within the Nurses' Health Study II. Cases were cancer-free and premenopausal at blood collection who developed invasive breast cancer. OPG was quantified using an enzyme-linked immunosorbent assay. Conditional logistic regression was used to estimate multivariable odds ratios (ORs) and 95% confidence intervals (CI) for the association between OPG levels and breast cancer risk adjusting for potential confounders. Unconditional logistic regression, additionally adjusting for matching factors, was used for the stratified analyses. RESULTS Overall, there was no substantial evidence for an association between plasma OPG levels and breast cancer risk, though the point estimate in the highest (vs. lowest) quartile was below one (OR = 0.78; 95%CI 0.46-1.33; P-trend= 0.30). There was no evidence of heterogeneity by various reproductive, hormonal, and tumor characteristics including hormone receptor status and grade (all P-heterogeneity ≥ 0.17). CONCLUSIONS Findings from this prospective study do not provide substantial evidence for an association between circulating OPG and breast cancer risk among premenopausal women; however, we were underpowered in stratified analyses. IMPACT Circulating OPG is likely not a biomarker of breast cancer risk among women at population-level risk. Copyright ©2020, American Association for Cancer Research.BACKGROUND The association between male pattern baldness and prostate cancer has been inconsistent. We prospectively investigated the association between baldness at age 45 and prostate cancer risk in the Health Professionals Follow-up Study (HPFS), focusing on clinical and molecular markers. METHODS Baldness was self-reported on the 1992 questionnaire using the modified Norwood-Hamilton scale prior to diagnosis. We estimated hazard ratios between baldness and prostate cancer risk among 36,760 men, with follow-up through 2014. We also investigated whether baldness was associated with prostate cancer defined by tumor protein expression of androgen receptor (AR) and the presence of the TMPRSS2ERG fusion. RESULTS During 22 years, 5,157 prostate cancer cases were identified. Fifty-six percent of the men had either frontal or vertex baldness. click here No significant associations were found between baldness and prostate cancer risk. Among men younger than 60 years, there was a statistically significant association between frontal and severe vertex baldness and overall prostate cancer (HR1.74, 95% CI1.23-2.48). Baldness was not significantly associated with expression of molecular subtypes defined by AR and TMPRSS2ERG immunohistochemistry of prostate tumors. CONCLUSION This study showed no association between baldness at age 45 and prostate cancer risk, overall or for clinical or molecular markers. The association between baldness and overall prostate cancer among younger men is intriguing, but caution is warranted when interpreting this finding. IMPACT The null findings from this large cohort study, together with previous literature's inconclusive findings across baldness patterns, suggest that baldness is not a consistent biomarker for prostate cancer risk or progression. Copyright ©2020, American Association for Cancer Research.Despite significant investment of funds and resources, few new cancer biomarkers have been introduced to the clinic in the last few decades. Even though many candidates produce promising results in the laboratory, deficiencies in sensitivity, specificity and predictive value make them less than desirable in a patient setting. This review will analyze these challenges in detail as well as discuss false discovery, problems with reproducibility and tumor heterogeneity. Circulating tumor DNA (ctDNA), an emerging cancer biomarker is also analyzed, particularly in the contexts of assay specificity, sensitivity, fragmentation, lead time, mutant allele fraction and clinical relevance. Emerging artificial intelligence technologies will likely be valuable tools in maximizing the clinical utility of ctDNA which is often found in very small quantities in patients with early stage tumors. Finally, the implications of challenging false discoveries are examined and some insights about improving cancer biomarker discovery are provided. Copyright ©2020, American Association for Cancer Research.BACKGROUND Independent validation of risk prediction models in prospective cohorts is required for risk-stratified cancer prevention. Such studies often have a two-phase design, where information on expensive biomarkers are ascertained in a nested sub-study of the original cohort. METHODS We propose a simple approach for evaluating model discrimination that accounts for incomplete follow-up and gains efficiency by using data from all individuals in the cohort irrespective of whether they were sampled in the sub-study. For evaluating the Area Under the Curve (AUC), we estimate probabilities of risk-scores for cases being larger than those in controls conditional on partial risk-scores, computed using partial covariate information. The proposed method was compared with an inverse probability weighted (IPW) approach that used information only from the subjects in the sub-study. We evaluated age-stratified AUC of a model including questionnaire-based risk factors and inflammation biomarkers to predict 10-year risk of lung cancer using data from Prostate, Lung, Colorectal and Ovarian Cancer (1993-2009) trial (30,297 ever-smokers, 1253 lung cancer patients). RESULTS For estimating age-stratified AUC of the combined lung cancer risk model, the proposed method was 3.8-5.3 times more efficient compared to the IPW approach across the different age groups. Extensive simulation studies also demonstrated substantial efficiency gain compared to the IPW approach. CONCLUSIONS Incorporating information from all individuals in a two-phase cohort study can substantially improve precision of discrimination measures of lung cancer risk models. IMPACT Novel, simple and practically useful methods are proposed for evaluating risk models, a critical step toward risk-stratified cancer prevention. Copyright ©2020, American Association for Cancer Research.