No abstract

Background We applied machine learning to find a novel breast cancer predictor based on information in a mammogram. Methods Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case–control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index. Results Combining studies, almost all textural features were associated with case–control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10−6). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 × 10−5). Conclusions A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.

Several therapeutic strategies targeting Epstein-Barr virus (EBV)-associated tumors involve upregulation of viral lytic gene expression. Evidence has been presented that the unfolded protein response (UPR) leads to EBV lytic gene expression. Clofoctol, an antibacterial antibiotic, has been reported to upregulate the UPR in prostate cancer cell lines and to slow their growth. We investigated the effects of clofoctol on an EBV-positive Burkitt lymphoma cell line and confirmed the upregulation of all three branches of the UPR and activation of EBV lytic gene expression. While immediate early, early, and late EBV RNAs were all upregulated, immediate early and early viral proteins but not late viral proteins were expressed. Furthermore, infectious virions were not produced. The use of clofoctol in combination with a protein kinase R-like endoplasmic reticulum kinase inhibitor led to expression of late viral proteins. The effects of clofoctol on EBV lytic protein upregulation were not limited to lymphoid tumor cell lines but also occurred in naturally infected epithelial gastric cancer and nasopharyngeal cancer cell lines. An agent that upregulates lytic viral protein expression but that does not lead to the production of infectious virions may have particular value for lytic induction strategies in the clinical setting. IMPORTANCE Epstein-Barr virus is associated with many different cancers. In these cancers the viral genome is predominantly latent; i.e., most viral genes are not expressed, most viral proteins are not synthesized, and new virions are not produced. Some strategies for treating these cancers involve activation of lytic viral gene expression. We identify an antibacterial antibiotic, clofoctol, that is an activator of EBV lytic RNA and protein expression but that does not lead to virion production.