Surrogate end points for survival in patients with advanced hepatocellular carcinoma treated with immune checkpoint inhibitors.

Aim: The authors investigated surrogacy of radiology-based end points for clinical trials investigating immune checkpoint inhibitors in advanced hepatocellular carcinoma. Methods: Data were collected from electronic databases reporting median overall survival (OS), median progression-free survival (PFS) and objective response rate (ORR). Weighted Pearson correlation coefficients and 95% confidence intervals (CIs) were calculated. Results: 26 clinical trials (41 treatment arms, 5144 patients) were included. ORR (coefficient: 0.71; 95% CI: 0.52-0.84) and PFS (coefficient: 0.63; 95% CI: 0.21-0.92) were positively correlated with OS. Sensitivity analyses suggested liver function, line of therapy and study phase did not greatly impact results. The COSMIC-312 study negatively impacted the overall weighted correlation. Conclusion: ORR and PFS are positively correlated with OS in patients with advanced hepatocellular carcinoma.

This study was conducted to determine how effective measurements of tumor size are at predicting how long patients with liver cancer survive when they receive a drug that blocks proteins involved in immune system regulation, which are made by some types of immune system cells in the body. The authors calculated how closely measurements of a patient's tumor were related to survival by examining data from 26 separate clinical trials that included more than 5000 patients. This study showed measurements of tumor size and the response (shrinkage, growth or no change in size) to checkpoint inhibitor drugs were likely to be related to the length of survival. This information is important for designing future clinical trials and may help speed the delivery of effective medicines to patients with liver cancer.

A Phase I Study of an IDO-1 Inhibitor (LY3381916) as Monotherapy and in Combination With an Anti-PD-L1 Antibody (LY3300054) in Patients With Advanced Cancer.

LY3381916 is an orally available, highly selective, potent inhibitor of indoleamine 2,3-dioxygenase 1. This study explored the safety, tolerability, pharmacokinetics, pharmacodynamics, and antitumor activity of LY3381916 monotherapy and in combination with a programmed death-ligand 1 (PD-L1) inhibitor (LY3300054) in patients with advanced solid tumors. During dose escalation, patients received escalating doses of LY3381916 at 60-600 mg once daily (qd) and 240 mg twice daily in monotherapy (n=21) and in combination with PD-L1 inhibitor at 700 mg every 2 weeks (n=21). A modified toxicity probability interval method was used to guide dose escalation. Dose-limiting toxicities occurred in 3 patients; 1 at LY3381916 240 mg twice daily (alanine aminotransferase/aspartate aminotransferase increase and systemic inflammatory response syndrome) and 2 at LY3381916 240 mg qd in combination with PD-L1 inhibitor (fatigue and immune-related hepatitis). LY3381916, at the recommended phase II dose, 240 mg qd, in combination with PD-L1 inhibitor, produced maximal inhibition of indoleamine 2,3-dioxygenase 1 activity in plasma and tumor tissue, and led to an increase of CD8 T cells in tumor tissue. In the combination dose expansion cohorts, 14 triple-negative breast cancer and 4 non-small cell lung cancer patients were enrolled. Treatment-related liver toxicity (grade ≥2 alanine aminotransferase/aspartate aminotransferase increase or immune-related hepatitis) was the most prominent adverse event in triple-negative breast cancer patients (n=5, 35.7%). Best response was stable disease. These preliminary data suggest an alternative dose level of LY3381916 is needed for the combination with PD-L1 inhibitor. The combination clinical activity was limited in this study.

A Geometric Variational Approach to Bayesian Inference.

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere S ∞ in L 2 , and the Fisher-Rao metric reduces to the standard L 2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the α-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback-Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Fréchet derivative operators motivated by the geometry of S ∞, and examine its properties. Through simulations and real data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models.

Skin Transcriptome of Middle-Aged Women Supplemented With Natural Herbo-mineral Shilajit Shows Induction of Microvascular and Extracellular Matrix Mechanisms.

Objective: Shilajit is a pale-brown to blackish-brown organic mineral substance available from Himalayan rocks. We demonstrated that in type I obese humans, shilajit supplementation significantly upregulated extracellular matrix (ECM)-related genes in the skeletal muscle. Such an effect was highly synergistic with exercise. The present study (clinicaltrials.gov NCT02762032) aimed to evaluate the effects of shilajit supplementation on skin gene expression profile and microperfusion in healthy adult females. Methods: The study design comprised six total study visits including a baseline visit (V1) and a final 14-week visit (V6) following oral shilajit supplementation (125 or 250 mg bid). A skin biopsy of the left inner upper arm of each subject was collected at visit 2 and visit 6 for gene expression profiling using Affymetrix Clariom™ D Assay. Skin perfusion was determined by MATLAB processing of dermascopic images. Transcriptome data were normalized and subjected to statistical analysis. The differentially regulated genes were subjected to Ingenuity Pathway Analysis (IPA®). The expression of the differentially regulated genes identified by IPA® were verified using real-time polymerase chain reaction (RT-PCR). Results: Supplementation with shilajit for 14 weeks was not associated with any reported adverse effect within this period. At a higher dose (250 mg bid), shilajit improved skin perfusion when compared to baseline or the placebo. Pathway analysis identified shilajit-inducible genes relevant to endothelial cell migration, growth of blood vessels, and ECM which were validated by quantitative real-time polymerase chain reaction (RT-PCR) analysis. Conclusions: This work provides maiden evidence demonstrating that oral shilajit supplementation in adult healthy women induced genes relevant to endothelial cell migration and growth of blood vessels. Shilajit supplementation improved skin microperfusion.

Geometric Sensitivity Measures for Bayesian Nonparametric Density Estimation Models.

We propose a geometric framework to assess global sensitivity in Bayesian nonparametric models for density estimation. We study sensitivity of nonparametric Bayesian models for density estimation, based on Dirichlet-type priors, to perturbations of either the precision parameter or the base probability measure. To quantify the different effects of the perturbations of the parameters and hyperparameters in these models on the posterior, we define three geometrically-motivated global sensitivity measures based on geodesic paths and distances computed under the nonparametric Fisher-Rao Riemannian metric on the space of densities, applied to posterior samples of densities: (1) the Fisher-Rao distance between density averages of posterior samples, (2) the log-ratio of Karcher variances of posterior samples, and (3) the norm of the difference of scaled cumulative eigenvalues of empirical covariance operators obtained from posterior samples. We validate our approach using multiple simulation studies, and consider the problem of sensitivity analysis for Bayesian density estimation models in the context of three real datasets that have previously been studied.

May Dietary Supplementation Augment Respiratory Burst in Wound-Site Inflammatory Cells?

Persistent infection contributes to wound chronicity. At the wound site, NADPH oxidase (NOX) activity in immune cells fights infection to enable the healing process. Fermented papaya preparation (FPP) is a carbohydrate-rich nutritional supplement that has demonstrated ability to bolster respiratory burst in experimental rodent systems. In FPP, glucose coexists with fructose and maltose in addition to multiple other sugar alcohols such as inositol. We have previously reported that FPP supplementation augments wound healing in diabetic mice via improvement of respiratory burst activity of wound innate immune cells. In this clinical study ( clinicaltrials.gov : NCT02332993), chronic wound patients were orally supplemented with FPP daily. Inducible production of reactive oxygen species was significantly higher in wound-site immune cells from patients supplemented with FPP and on standard of care (SoC) for wound management compared with those patients receiving SoC alone. Wound closure in FPP-supplemented patients showed improvement. Importantly, the consumption of this mixture of carbohydrates, including significant amounts of glucose, did not increase HbA1c. These observations warrant a full-length clinical trial testing the hypothesis that FPP improves wound closure by augmenting NOX activity in immune cells at the wound site. Antioxid. Redox Signal. 28, 401-405.

Nurses' Time Allocation and Multitasking of Nursing Activities: A Time Motion Study.

Nurses have been required to provide more patient-centered, efficient, and cost effective care. In order to do so, they need to work at the top of their license. We conducted a time motion study to document nursing activities on communication, hands-on tasks, and locations (where activities occurred), and compared differences between different time blocks (7am-11am, 11am-3pm, and 3pm-7pm). We found that nurses spent most of their time communicating with patients and in patient rooms. Nurses also spent most of their time charting and reviewing information in EHR, mostly at the nursing station. Nurses' work was not distributed equally across a 12-hour shift. We found that greater frequency and duration in hands-on tasks occurred between 7am-11am. In addition, nurses spent approximately 10% of their time on delegable and non-nursing activities, which could be used more effectively for patient care. The study results provide evidence to assist nursing leaders to develop strategies for transforming nursing practice through re-examination of nursing work and activities, and to promote nurses working at top of license for high quality care and best outcomes. Our research also presents a novel and quantifiable method to capture data on multidimensional levels of nursing activities.

Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma.

Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, "imaging habitats" were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions ("spatial imaging habitats") were derived, and those associated with overall survival (denoted "relevant" habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.

DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer.

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher-Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.