Early Detection Initiative: A randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5-1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen's design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n = 3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.

Corticosterone induced apoptosis of mouse oviduct epithelial cells independent of the TNF-α system.

It has been reported in recent studies that restraint stress on pregnant mice during the preimplantation stage elevated corticotrophin-releasing hormone (CRH) and glucocorticoid levels in the serum and oviducts; furthermore, CRH and corticosterone (CORT) impacted preimplantation embryos indirectly by triggering the apoptosis of oviductal epithelial cells (OECs) through activation of the Fas system. However, it remains unclear whether TNF-α signaling is involved in CRH- and/or glucocorticoid-induced apoptosis of OECs. In the present study, it was shown that culture with either CRH or CORT induced significant apoptosis of OECs. The culture of OECs with CRH augmented both FasL expression and TNF-α expression. However, culture with CORT increased FasL, but decreased TNF-α, expression significantly. Although knocking down/knocking out FasL expression in OECs significantly ameliorated the proapoptotic effects of both CRH and CORT, knocking down/knocking out TNF-α expression relieved only the proapoptotic effect of CRH but not that of CORT. Taken together, our results demonstrated that CRH-induced OEC apoptosis involved both Fas signaling and TNF-α signaling. Conversely, CORT-induced OEC apoptosis involved only the Fas, but not the TNF-α, signaling pathway. The data obtained are crucial for our understanding of the mechanisms by which various categories of stress imposed on pregnant females impair embryo development, as well as for the development of measures to protect the embryo from the adverse effects of stress.

Glucocorticoids impair oocyte competence and trigger apoptosis of ovarian cells via activating the TNF-α system.

Mechanisms by which female stress and particularly glucocorticoids impair oocyte competence are largely unclear. Although one study demonstrated that glucocorticoids triggered apoptosis in ovarian cells and oocytes by activating the FasL/Fas system, other studies suggested that they might induce apoptosis through activating other signaling pathways as well. In this study, both in vivo and in vitro experiments were conducted to test the hypothesis that glucocorticoids might trigger apoptosis in oocytes and ovarian cells through activating the TNF-α system. The results showed that cortisol injection of female mice (1.) impaired oocyte developmental potential and mitochondrial membrane potential with increased oxidative stress; (2.) induced apoptosis in mural granulosa cells (MGCs) with increased oxidative stress in the ovary; and (3.) activated the TNF-α system in both ovaries and oocytes. Culture with corticosterone induced apoptosis and activated the TNF-α system in MGCs. Knockdown or knockout of TNF-α significantly ameliorated the pro-apoptotic effects of glucocorticoids on oocytes and MGCs. However, culture with corticosterone downregulated TNF-α expression significantly in oviductal epithelial cells. Together, the results demonstrated that glucocorticoids impaired oocyte competence and triggered apoptosis in ovarian cells through activating the TNF-α system and that the effect of glucocorticoids on TNF-α expression might vary between cell types.

Designing precision medicine trials to yield a greater population impact.

Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.

Learning-based biomarker-assisted rules for optimized clinical benefit under a risk constraint.

Novel biomarkers, in combination with currently available clinical information, have been sought to improve clinical decision making in many branches of medicine, including screening, surveillance, and prognosis. Statistical methods are needed to integrate such diverse information to develop targeted interventions that balance benefit and harm. In the specific setting of disease detection, we propose novel approaches to construct a multiple-marker-based decision rule by directly optimizing a benefit function, while controlling harm at a maximally tolerable level. These new approaches include plug-in and direct-optimization-based algorithms, and they allow for the construction of both nonparametric and parametric rules. A study of asymptotic properties of the proposed estimators is provided. Simulation results demonstrate good clinical utilities for the resulting decision rules under various scenarios. The methods are applied to a biomarker study in prostate cancer surveillance.

Quantifying treatment effects using the personalized chance of longer survival.

The hazard ratio is widely used to measure or to summarize the magnitude of treatment effects, but it is justifiably difficult to interpret in a meaningful way to patients and perhaps for clinicians as well. In addition, it is most meaningful when the hazard functions are approximately proportional over time. We propose a new measure, termed personalized chance of longer survival. The measure, which quantifies the probability of living longer with one treatment over the another, accounts for individualized characteristics to directly address personalized treatment effects. Hence, the measure is patient focused, which can be used to evaluate subgroups easily. We believe it is intuitive to understand and clinically interpretable in the presence of nonproportionality. Furthermore, because it estimates the probability of living longer by some fixed amount of time, it encodes the probabilistic part of treatment effect estimation. We provide nonparametric estimation and inference procedures that can accommodate censored survival outcomes. We conduct extensive simulation studies, which characterize performance of the proposed method, and data from a large randomized Phase III clinical trial (SWOG S0819) are analyzed using the proposed method.

Greedy outcome weighted tree learning of optimal personalized treatment rules.

We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a high-dimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules. The method is further extended to right censored survival data by using the accelerated failure time model and introducing double weighting to the classification trees. The performance of the proposed method is demonstrated via simulation studies, as well as analyses of the Cancer Cell Line Encyclopedia (CCLE) data and the Tamoxifen breast cancer data.

Intravenous Followed by X-ray Fused with MRI-Guided Transendocardial Mesenchymal Stem Cell Injection Improves Contractility Reserve in a Swine Model of Myocardial Infarction.

The aim of this study is to determine the effects of early intravenous (IV) infusion later followed by transendocardial (TE) injection of allogeneic mesenchymal stem cells (MSCs) following myocardial infarction (MI). Twenty-four swine underwent balloon occlusion reperfusion MI and were randomized into 4 groups: IV MSC (or placebo) infusion (post-MI day 2) and TE MSC (or placebo) injection targeting the infarct border with 2D X-ray fluoroscopy fused to 3D magnetic resonance (XFM) co-registration (post-MI day 14). Continuous ECG recording, MRI, and invasive pressure-volume analyses were performed. IV MSC plus TE MSC treated group was superior to other groups for contractility reserve (p = 0.02) and freedom from VT (p = 0.03) but had more lymphocytic foci localized to the peri-infarct region (p = 0.002). No differences were observed in post-MI remodeling parameters. IV followed by XFM targeted TE MSC therapy improves contractility reserve and suppresses VT but does not affect post-MI remodeling and may cause an immune response.

New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.

Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation.

Discussion of combining biomarkers to optimize patient treatment recommendations.

Kang, Janes and Huang propose an interesting boosting method to combine biomarkers for treatment selection. The method requires modeling the treatment effects using markers. We discuss an alternative method, outcome weighted learning. This method sidesteps the need for modeling the outcomes, and thus can be more robust to model misspecification.

Menstrual cycle changes in women with inflammatory bowel disease: a study from the ocean state Crohn's and colitis area registry.

BACKGROUND: The effect of the inflammatory bowel diseases (IBD) on menstrual function is largely unknown. The aims of this study were to determine whether changes in menstrual function occur in the year before IBD diagnosis or in the initial years after diagnosis. METHODS: Women aged 18 years and older in the Ocean State Crohn's and Colitis Area Registry with at least 2 years of follow-up were eligible for this study. All patients were enrolled within 6 months of IBD diagnosis and followed prospectively. Menstrual cycle characteristics were retrospectively assessed. To assess for changes over time, general linear models for correlated data were used for continuous outcomes, and generalized estimating equations were used for discrete outcomes. RESULTS: One hundred twenty-one patients were studied. Twenty-five percent of patients experienced a change in cycle interval in the year before IBD diagnosis and 21% experienced a change in the duration of flow. Among women with dysmenorrhea, 40% experienced a change in the intensity of their menstrual pain and 31% experienced a change in its duration. Overall cycle regularity increased over time. Quality of life was significantly lower in women without regular cycles across all time points. CONCLUSIONS: Changes in menstrual function occur frequently in the year before IBD diagnosis; therefore, screening for menstrual irregularities should be considered in women with newly diagnosed IBD. Patients can be reassured that cycles typically become more regular over time.