Evidence favoring a multidisciplinary team (MDT) approach in the treatment of lung cancer is scarce, especially in the United States. The purpose of the present investigation was to evaluate survival outcomes of lung cancer patients treated with an MDT compared with a traditional care model. The Stony Brook Cancer Center Registry was used to identify all lung cancer cases diagnosed between 2002 and 2016. We compared survival outcomes among 1956 lung cancer patients participating in our institution's Lung Cancer Evaluation Center's (LCEC) MDT program and 2315 lung cancer patients receiving traditional care. Log-ranks tests were used to evaluate differences in the 1-, 3-, 5-, and 10-year survival outcomes between the 2 groups. To address inherent biases, Cox proportional hazard models were used to estimate the effects on survival outcomes and adjust for possible confounders. Propensity matching was also performed to account for the effects of selection bias. The 5-year survival rates in the propensity-matched sample were one third greater among LCEC patients compared with those receiving a traditional care approach (33.6% vs. 23.0%; P < .001). After adjusting for potential confounders in the multivariable propensity-matched analyses, the LCEC model demonstrated a significant beneficial effect on 5-year survival outcomes compared with the standard treatment model (hazard ratio, 0.65; 95% confidence interval, 0.54-0.77). The results of the present investigation suggest an improved survival benefit from usage of an MDT model versus a traditional care model in the treatment of lung cancer. Despite the use of sophisticated statistical methods to mitigate bias in a nonrandomized study, additional research is needed to determine the extent to which an MDT approach for lung cancer influences patient outcomes.

Background: Genome-wide association studies have identified approximately 100 common genetic variants associated with breast cancer risk, the majority of which were discovered in women of European ancestry. Because of different patterns of linkage disequilibrium, many of these genetic markers may not represent signals in populations of African ancestry. Methods: We tested 74 breast cancer risk variants and conducted fine-mapping of these susceptibility regions in 6,522 breast cancer cases and 7,643 controls of African ancestry from three genetic consortia (AABC, AMBER, and ROOT). Results: Fifty-four of the 74 variants (73%) were found to have ORs that were directionally consistent with those previously reported, of which 12 were nominally statistically significant (P < 0.05). Through fine-mapping, in six regions (3p24, 12p11, 14q13, 16q12/FTO, 16q23, 19p13), we observed seven markers that better represent the underlying risk variant for overall breast cancer or breast cancer subtypes, whereas in another two regions (11q13, 16q12/TOX3), we identified suggestive evidence of signals that are independent of the reported index variant. Overlapping chromatin features and regulatory elements suggest that many of the risk alleles lie in regions with biological functionality. Conclusions: Through fine-mapping of known susceptibility regions, we have revealed alleles that better characterize breast cancer risk in women of African ancestry. Impact: The risk alleles identified represent genetic markers for modeling and stratifying breast cancer risk in women of African ancestry. Cancer Epidemiol Biomarkers Prev; 26(7); 1016–26. ©2017 AACR.

Background: The relationship between central adiposity and prostate cancer remains unclear.

Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27–1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th–60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63–2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.