Should multiple imputation be the method of choice for handling missing data in randomized trials?

The use of multiple imputation has increased markedly in recent years, and journal reviewers may expect to see multiple imputation used to handle missing data. However in randomized trials, where treatment group is always observed and independent of baseline covariates, other approaches may be preferable. Using data simulation we evaluated multiple imputation, performed both overall and separately by randomized group, across a range of commonly encountered scenarios. We considered both missing outcome and missing baseline data, with missing outcome data induced under missing at random mechanisms. Provided the analysis model was correctly specified, multiple imputation produced unbiased treatment effect estimates, but alternative unbiased approaches were often more efficient. When the analysis model overlooked an interaction effect involving randomized group, multiple imputation produced biased estimates of the average treatment effect when applied to missing outcome data, unless imputation was performed separately by randomized group. Based on these results, we conclude that multiple imputation should not be seen as the only acceptable way to handle missing data in randomized trials. In settings where multiple imputation is adopted, we recommend that imputation is carried out separately by randomized group.T Sullivan was supported by an Australian Postgraduate Award. I White was funded by the Medical Research Council (Unit Programme number U105260558). K Lee was supported by a National Health and Medical Research Council Career Development Fellowship (1053609)

An Integrated Approach to the Prediction of Chemotherapeutic Response in Patients with Breast Cancer

BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities

A point mutation in cpsE renders Streptococcus pneumoniae nonencapsulated and enhances its growth, adherence and competence.

BACKGROUND: The polysaccharide capsule is a major virulence factor of the important human pathogen Streptococcus pneumoniae. However, S. pneumoniae strains lacking capsule do occur. RESULTS: Here, we report a nasopharyngeal isolate of Streptococcus pneumoniae composed of a mixture of two phenotypes; one encapsulated (serotype 18C) and the other nonencapsulated, determined by serotyping, electron microscopy and fluorescence isothiocyanate dextran exclusion assay.By whole genome sequencing, we demonstrated that the phenotypes differ by a single nucleotide base pair in capsular gene cpsE (C to G change at gene position 1135) predicted to result in amino acid change from arginine to glycine at position 379, located in the cytoplasmic, enzymatically active, region of this transmembrane protein. This SNP is responsible for loss of capsule production as the phenotype is transferred with the capsule operon. The nonencapsulated variant is superior in growth in vitro and is also 117-fold more adherent to and more invasive into Detroit 562 human epithelial cells than the encapsulated variant.Expression of six competence pathway genes and one competence-associated gene was 11 to 34-fold higher in the nonencapsulated variant than the encapsulated and transformation frequency was 3.7-fold greater. CONCLUSIONS: We identified a new single point mutation in capsule gene cpsE of a clinical S. pneumoniae serotype 18C isolate sufficient to cause loss of capsule expression resulting in the co-existence of the encapsulated and nonencapsulated phenotype. The mutation caused phenotypic changes in growth, adherence to epithelial cells and transformability. Mutation in capsule gene cpsE may be a way for S. pneumoniae to lose its capsule and increase its colonization potential

A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury

The development of personalized medicine is a primary objective of the medical community and increasingly also of funding and registration agencies. Modeling is generally perceived as a key enabling tool to target this goal. Agent-Based Models (ABMs) have previously been used to simulate inflammation at various scales up to the whole-organism level. We extended this approach to the case of a novel, patient-specific ABM that we generated for vocal fold inflammation, with the ultimate goal of identifying individually optimized treatments. ABM simulations reproduced trajectories of inflammatory mediators in laryngeal secretions of individuals subjected to experimental phonotrauma up to 4 hrs post-injury, and predicted the levels of inflammatory mediators 24 hrs post-injury. Subject-specific simulations also predicted different outcomes from behavioral treatment regimens to which subjects had not been exposed. We propose that this translational application of computational modeling could be used to design patient-specific therapies for the larynx, and will serve as a paradigm for future extension to other clinical domains

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead