Citations

The American Society of Anesthesiologists (ASA) Annual Meeting is the primary venue for anesthesiologists to present research, share innovations, and build networks. Herein, we describe gender representation for physician speakers at the Annual Meeting relative to the specialty overall. Details of ASA Annual Meeting presentations for individuals and panels were abstracted from the ASA archives for 2011–2016. Observed speaker gender composition was compared to expected composition based on the gender distribution of members of the ASA. There were 5167 speaker slots across 2025 presentations and panels. Of the speaker slots, 3874 were assigned to men and 1293 to women. Speaker slot gender composition was relatively consistent between 2011 and 2016 (annual percentage 22.3%–27.7% women, trend test P = .062). ASA membership composition of women increased slightly over the study period (24%–28%). The overall observed number of women in speaker slots over the study period did not differ significantly from what would be expected based on the ASA membership composition (25.0% observed versus 25.9% expected; P = .153). However, the percentage of single speakers who were women was significantly less than would be expected based on the ASA gender distribution (20.2% observed versus 25.9% expected; P < .001). Interestingly, for panels that included 2–5 anesthesiologists, single-gender panels were more common than would be expected by chance, with all-male panels predominating (all P < .01). The gender composition of speakers at the ASA Annual Meeting largely reflected gender composition within the specialty, although women were not overrepresented at any meeting. The predominance of single-gender panels and underrepresentation of women as single speakers is a potential target to improve gender representation.

Purpose The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. Materials and Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. Conclusion PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.