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Summary An individual’s sex has been long recognized as a key factor affecting cancer incidence, prognosis and treatment responses. However, the molecular basis for sex disparities in cancer remains poorly understood. We performed a comprehensive analysis of molecular differences between male and female patients in 13 cancer types of The Cancer Genome Atlas and revealed two sex-effect groups associated with distinct incidence and mortality profiles. One group contains a small number of sex-affected genes, whereas the other shows much more extensive sex-biased molecular signatures. Importantly, 53% of clinically actionable genes (60/114) show sex-biased signatures. Our study provides a systematic molecular-level understanding of sex effects in diverse cancers and suggests a pressing need to develop sex-specific therapeutic strategies in certain cancer types.

SUMMARY Ring NTPases are a class of ubiquitous molecular motors involved in basic biological partitioning processes. dsDNA viruses encode ring ATPases that translocate their genomes to near-crystalline densities within pre-assembled viral capsids. Here, X-ray crystallography, cryoEM, and biochemical analyses of the dsDNA packaging motor in bacteriophage phi29 show how individual subunits are arranged in a pentameric ATPase ring, and suggest how their activities are coordinated to translocate dsDNA. The resulting pseudo-atomic structure of the motor and accompanying functional analyses show how ATP is bound in the ATPase active site; identify two DNA contacts, including a potential DNA translocating loop; demonstrate that a trans-acting arginine finger is involved in coordinating hydrolysis around the ring; and suggest a functional coupling between the arginine finger and the DNA translocating loop. The ability to visualize the motor in action illuminates how the different motor components interact with each other and with their DNA substrate.

Inverse probability weighting can be used to estimate the average treatment effect in propensity score analysis. When there is lack of overlap in the propensity score distributions between the treatment groups under comparison, some weights may be excessively large, causing numerical instability and bias in point and variance estimation. We study a class of modified inverse probability weighting estimators that can be used to avoid this problem. These weights cause the estimand to deviate from the average treatment effect. We provide some justification for this deviation from the perspective of treatment effect discovery. We show that when lack of overlap occurs, the modified weights can achieve substantial gains in statistical power compared with inverse probability weighting and other propensity score methods. We develop analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness. Results from extensive simulations and a real data application support our conclusions. The proposed methodology is implemented in R package PSW.