Triple Helix, Fall 2018

Table of Contents: Science Agenda: The Politics of Grant Writing / by Kavya Rajesh (p. 4) -- From the Experts / by Katherine Bruner (p. 5) -- 3D Printed Drugs: The Future of Pharmaceuticals / by Ethan Wang (p. 6) -- Computerized Markets: Wall Street Takeover / by James Kiraly (p. 10) -- The Evolution of Fear / by Alisha Ahmed (p. 14) -- ADDing Up / by Victor Liaw (p. 18) -- The Clone Wars / by Jina Zhou (p. 22) -- Physician-Assisted Suicide: Drawing the Line / by Haley Wolf (p. 26) -- Supervised Injection Sites / by Alex Gajewski (p. 30) -- On Emerging Medicalization and Health Care / by Patrick Lee (p. 33) -- The Future of Human Gene Modifications / by Elizabeth Robinson (p. 36)College of Natural SciencesUT LibrariesLiberal Art

Using an Anchor to Improve Linear Predictions with Application to Predicting Disease Progression

Linear models are some of the most straightforward and commonly used modelling approaches. Consider modelling approximately monotonic response data arising from a time-related process. If one has knowledge as to when the process began or ended, then one may be able to leverage additionalassumed data to reduce prediction error. This assumed data, referred to as the anchor, is treated as an additional data-point generated at either the beginning or end of the process. The response value of the anchor is equal to an intelligently selected value of the response (such as the upper bound, lower bound, or 99th percentile of the response, as appropriate). The anchor reduces the variance of prediction at the cost of a possible increase in prediction bias, resulting in a potentially reduced overall mean-square prediction error. This can be extremely eective when few individual data-points are available, allowing one to make linear predictions using as little as a single observed data-point. We develop the mathematics showing the conditions under which an anchor can improve predictions, and also demonstrate using this approach to reduce prediction error when modelling the disease progression of patients with amyotrophic lateral sclerosis.Modelos lineales son los modelos más fáciles de usar y comunes en modelamiento. Si se considera el modelamiento de una respuesta aprosimadamente monótona que surge de un proceso relacionado al tiempo y se sabe cuándo el proceso inició o terminó, es posible asumir datos adicionales como palanca para reducir el error de predicción. Estos datos adicionales son llamados de ``anclaje'' y son datos generados antes del inicion o después del final del proceso. El valor de respuesta del anclaje es igual a un valor de respuesta escogido de manera inteligente (como por ejemplo la cota superior, iferior o el percentil 99, según conveniencia). Este anclaje reduce la varianza de la predicción a costo de un posible sesgo en la misma, lo cual resulta en una reducción potencial del error medio de predicción. Lo anterior puede ser extremadamente efectivo cuando haypocos datos individuales, permitiendo hacer predicciones con muy pocos datos. En este trabajo presentamos en desarrollo matemático demostrando las condiciones bajo las cuales el anclaje puede mejorar predicciones y también demostramos una reducción del error de predicción aplicando el método a la modelación de progresión de enfermedad en pacientes con esclerosis lateral amiotrófica

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Using an onset-anchored Bayesian hierarchical model to improve predictions for amyotrophic lateral sclerosis disease progression

Abstract Background Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a rare disease with extreme between-subject variability, especially with respect to rate of disease progression. This makes modelling a subject’s disease progression, which is measured by the ALS Functional Rating Scale (ALSFRS), very difficult. Consider the problem of predicting a subject’s ALSFRS score at 9 or 12 months after a given time-point. Methods We obtained ALS subject data from the Pooled Resource Open-Access ALS Clinical Trials Database, a collection of data from various ALS clinical trials. Due to the typical linearity of the ALSFRS, we consider several Bayesian hierarchical linear models. These include a mixture model (to account for the two potential classes of “fast” and “slow” ALS progressors) as well as an onset-anchored model, in which an additional artificial data-point, using time of disease onset, is utilized to improve predictive performance. Results The onset-anchored model had a drastically reduced posterior predictive mean-square-error distributions, when compared to the Bayesian hierarchical linear model or the mixture model under a cross-validation approach. No covariates, other than time of disease onset, consistently improved predictive performance in either the Bayesian hierarchical linear model or the onset-anchored model. Conclusions Augmenting patient data with an additional artificial data-point, or onset anchor, can drastically improve predictive modelling in ALS by reducing the variability of estimated parameters at the cost of a slight increase in bias. This onset-anchored model is extremely useful if predictions are desired directly after a single baseline measure (such as at the first day of a clinical trial), a feat that would be very difficult without the onset-anchor. This approach could be useful in modelling other diseases that have bounded progression scales (e.g. Parkinson’s disease, Huntington’s disease, or inclusion-body myositis). It is our hope that this model can be used by clinicians and statisticians to improve the efficacy of clinical trials and aid in finding treatments for ALS

Using automated electronic medical record data extraction to model ALS survival and progression

Abstract Background To assess the feasibility of using automated capture of Electronic Medical Record (EMR) data to build predictive models for amyotrophic lateral sclerosis (ALS) outcomes. Methods We used an Informatics for Integrating Biology and the Bedside search discovery tool to identify and extract data from 354 ALS patients from the University of Kansas Medical Center EMR. The completeness and integrity of the data extraction were verified by manual chart review. A linear mixed model was used to model disease progression. Cox proportional hazards models were used to investigate the effects of BMI, gender, and age on survival. Results Data extracted from the EMR was sufficient to create simple models of disease progression and survival. Several key variables of interest were unavailable without including a manual chart review. The average ALS Functional Rating Scale – Revised (ALSFRS-R) baseline score at first clinical visit was 34.08, and average decline was − 0.64 per month. Median survival was 27 months after first visit. Higher baseline ALSFRS-R score and BMI were associated with improved survival, higher baseline age was associated with decreased survival. Conclusions This study serves to show that EMR-captured data can be extracted and used to track outcomes in an ALS clinic setting, potentially important for post-marketing research of new drugs, or as historical controls for future studies. However, as automated EMR-based data extraction becomes more widely used there will be a need to standardize ALS data elements and clinical forms for data capture so data can be pooled across academic centers

Quinolone-Resistant Uropathogenic Escherichia coli Strains from Phylogenetic Group B2 Have Fewer Virulence Factors than Their Susceptible Counterparts

The prevalence of 31 virulence factors was analyzed among nalidixic acid-susceptible and -resistant Escherichia coli strains from phylogenetic group B2. Hemolysin, cytotoxic necrotizing factor 1, and S and F1C fimbriae genes were less prevalent among nalidixic acid-resistant E. coli strains. Quinolone resistance may be associated with a decrease in the presence of some virulence factors