Oncolytic Reovirus and Immune Checkpoint Inhibition as a Novel Immunotherapeutic Strategy for Breast Cancer.

As the current efficacy of oncolytic viruses (OVs) as monotherapy is limited, exploration of OVs as part of a broader immunotherapeutic treatment strategy for cancer is necessary. Here, we investigated the ability for immune checkpoint blockade to enhance the efficacy of oncolytic reovirus (RV) for the treatment of breast cancer (BrCa). In vitro, oncolysis and cytokine production were assessed in human and murine BrCa cell lines following RV exposure. Furthermore, RV-induced upregulation of tumor cell PD-L1 was evaluated. In vivo, the immunocompetent, syngeneic EMT6 murine model of BrCa was employed to determine therapeutic and tumor-specific immune responses following treatment with RV, anti-PD-1 antibodies or in combination. RV-mediated oncolysis and cytokine production were observed following BrCa cell infection and RV upregulated tumor cell expression of PD-L1. In vivo, RV monotherapy significantly reduced disease burden and enhanced survival in treated mice, and was further enhanced by PD-1 blockade. RV therapy increased the number of intratumoral regulatory T cells, which was reversed by the addition of PD-1 blockade. Finally, dual treatment led to the generation of a systemic adaptive anti-tumor immune response evidenced by an increase in tumor-specific IFN-γ producing CD8⁺ T cells, and immunity from tumor re-challenge. The combination of PD-1 blockade and RV appears to be an efficacious immunotherapeutic strategy for the treatment of BrCa, and warrants further investigation in early-phase clinical trials.

Scalable and accurate deep learning with electronic health records.

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart.

Scientific Overview on CSCI-CITAC Annual General Meeting and 2016 Young Investigators' Forum.

The 2016 Annual General Meeting of the Canadian Society of Clinician Investigators (CSCI) and Clinician Investigator Trainee Association of Canada/Association des Cliniciens-Chercheurs en Formation du Canada (CITAC/ACCFC) was a national conference held in Toronto November 21-23, 2016, in conjunction with The University of Toronto Clinician Investigator Program Research Day. The theme for this year's meeting was "Mapping Your Career as a Clinician-Scientist"; emphasizing essential skills for developing a fruitful career as clinician-scientist. The meeting featured an opening presentation by Dr. Alan Underhill, Dr. Nicola Jones and Alexandra Kuzyk. The keynote speakers were Dr. Nada Jabado (McGill University), who discussed the association between cancer and histones, Dr. Norman Rosenblum (University of Toronto), who addressed the career path and the "calling" of the Clinician Scientist, Dr. Martin Schmeing (McGill University), who was the 2016 Joe Doupe Award recipient, and Dr. Linda Rabeneck (Cancer Care Ontario and University of Toronto), who received the Friends of CIHR lectureship. The workshops, focusing on career development for clinician scientists, were hosted by Drs. Alan Underhill, Nicola Jones, Lynn Raymond, Michael Schlossmacher and Norman Rosenblum, as well as University of Toronto communication specialists, Caitlin Johannesson and Suzanne Gold. In addition, the Young Investigators' Forum included presentations from clinician investigator trainees from across the country. The research topics were diverse and comprehensive: from basic sciences to clinical practice; from epidemiology to medical engineering. All scientific abstracts are summarized in this review. Over 70 abstracts were showcased at this year's meeting during two poster sessions, with six outstanding abstracts selected for oral presentations during the President's Forum.

CheX in the three-phosphatase system of bacterial chemotaxis.

Bacterial chemotaxis involves the regulation of motility by a modified two-component signal transduction system. In Escherichia coli, CheZ is the phosphatase of the response regulator CheY but many other bacteria, including Bacillus subtilis, use members of the CheC-FliY-CheX family for this purpose. While Bacillus subtilis has only CheC and FliY, many systems also have CheX. The effect of this three-phosphatase system on chemotaxis has not been studied previously. CheX was shown to be a stronger CheY-P phosphatase than either CheC or FliY. In Bacillus subtilis, a cheC mutant strain was nearly complemented by heterologous cheX expression. CheX was shown to overcome the DeltacheC adaptational defect but also generally lowered the counterclockwise flagellar rotational bias. The effect on rotational bias suggests that CheX reduced the overall levels of CheY-P in the cell and did not truly replicate the adaptational effects of CheC. Thus, CheX is not functionally redundant to CheC and, as outlined in the discussion, may be more analogous to CheZ.