Bayesian predictive modeling for genomic based personalized treatment selection

Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly-parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this paper, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches

A Bayesian Nonparametric model for textural pattern heterogeneity

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumor heterogeneity through patterns of enhancement, texture, morphology, and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of Gray-Level Co-occurrence Matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero-inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine-learning approaches currently used in cancer radiomics with simulation studies.Comment: 45 pages, 7 figures, 1 Tabl

Studies of Diffuse Interstellar Bands. V. Pairwise Correlations of Eight Strong DIBs and Neutral Hydrogen, Molecular Hydrogen, and Color Excess

We establish correlations between equivalent widths of eight diffuse interstellar bands (DIBs), and examine their correlations with atomic hydrogen, molecular hydrogen, and EB-V . The DIBs are centered at \lambda\lambda 5780.5, 6204.5, 6283.8, 6196.0, 6613.6, 5705.1, 5797.1, and 5487.7, in decreasing order of Pearson\^as correlation coefficient with N(H) (here defined as the column density of neutral hydrogen), ranging from 0.96 to 0.82. We find the equivalent width of \lambda 5780.5 is better correlated with column densities of H than with E(B-V) or H2, confirming earlier results based on smaller datasets. We show the same is true for six of the seven other DIBs presented here. Despite this similarity, the eight strong DIBs chosen are not well enough correlated with each other to suggest they come from the same carrier. We further conclude that these eight DIBs are more likely to be associated with H than with H2, and hence are not preferentially located in the densest, most UV shielded parts of interstellar clouds. We suggest they arise from different molecules found in diffuse H regions with very little H (molecular fraction f<0.01). Of the 133 stars with available data in our study, there are three with significantly weaker \lambda 5780.5 than our mean H-5780.5 relationship, all of which are in regions of high radiation fields, as previously noted by Herbig. The correlations will be useful in deriving interstellar parameters when direct methods are not available. For instance, with care, the value of N(H) can be derived from W{\lambda}(5780.5).Comment: Accepted for publication in The Astrophysical Journal; 37 pages, 11 figures, 6 table

Landmark mediation survival analysis using longitudinal surrogate

Clinical cancer trials are designed to collect radiographic measurements of each patient’s baseline and residual tumor burden at regular intervals over the course of study. For solid tumors, the extent of reduction in tumor size following treatment is used as a measure of a drug’s antitumor activity. Statistical estimation of treatment efficacy routinely reduce the longitudinal assessment of tumor burden to a binary outcome describing the presence versus absence of an objective tumor response as defined by RECIST criteria. The objective response rate (ORR) is the predominate method for evaluating an experimental therapy in a single-arm trial. Additionally, ORR is routinely compared against a control therapy in phase III randomized controlled trials. The longitudinal assessments of tumor burden are seldom integrated into a formal statistical model, nor integrated into mediation analysis to characterize the relationships among treatment, residual tumor burden, and survival. This article presents a frameworkfor landmark mediation survival analyses devised to incorporate longitudinal assessment of tumor burden. R2 effect-size measures are developed to quantify the survival treatment mediation effects using longitudinal predictors. Analyses are demonstrated with applications to two colorectal cancer trials. Survival prediction is compared in the presence versus absence of longitudinal analysis. Simulation studies elucidate settings wherein patterns of tumor burden dynamics require longitudinal analysis