Protein Corona Sensor Array Nanosystem for Detection of Coronary Artery Disease.

Coronary artery disease (CAD) is the most common type of heart disease and represents the leading cause of death in both men and women worldwide. Early detection of CAD is crucial for decreasing mortality, prolonging survival, and improving patient quality of life. Herein, a non-invasive is described, nanoparticle-based diagnostic technology which takes advantages of proteomic changes in the nano-bio interface for CAD detection. Nanoparticles (NPs) exposed to biological fluids adsorb on their surface a layer of proteins, the "protein corona" (PC). Pathological changes that alter the plasma proteome can directly result in changes in the PC. By forming disease-specific PCs on six NPs with varying physicochemical properties, a PC-based sensor array is developed for detection of CAD using specific PC pattern recognition. While the PC of a single NP may not provide the required specificity, it is reasoned that multivariate PCs across NPs with different surface chemistries, can provide the desirable information to selectively discriminate the condition under investigation. The results suggest that such an approach can detect CAD with an accuracy of 92.84%, a sensitivity of 87.5%, and a specificity of 82.5%. These new findings demonstrate the potential of PC-based sensor array detection systems for clinical use.

Personalized treatment selection via product partition models with covariates.

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.

Bayesian graphical models for modern biological applications.

Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

Bayesian inference of networks across multiple sample groups and data types.

In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of chronic obstructive pulmonary disease. Bayesian inference; Chronic obstructive pulmonary disease (COPD); Data integration; Gaussian graphical model; Markov random field prior; Spike and slab prior.

Special issue on statistical analysis of networks: Preface by the guest editors.

The special issue on Statistical Analysis of Networks aspires to convey the breadth and depth of statistical learning with networks, ranging from networks that are observed to networks that are unobserved and learned from data. It includes ten select papers with methodological and theoretical advances, and demonstrates the usefulness of the network paradigm by applications to current problems.

Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease.

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity that may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.

Bayesian learning of multiple directed networks from observational data.

Graphical modeling represents an established methodology for identifying complex dependencies in biological networks, as exemplified in the study of co-expression, gene regulatory, and protein interaction networks. The available observations often exhibit an intrinsic heterogeneity, which impacts on the network structure through the modification of specific pathways for distinct groups, such as disease subtypes. We propose to infer the resulting multiple graphs jointly in order to benefit from potential similarities across groups; on the other hand our modeling framework is able to accommodate group idiosyncrasies. We consider directed acyclic graphs (DAGs) as network structures, and develop a Bayesian method for structural learning of multiple DAGs. We explicitly account for Markov equivalence of DAGs, and propose a suitable prior on the collection of graph spaces that induces selective borrowing strength across groups. The resulting inference allows in particular to compute the posterior probability of edge inclusion, a useful summary for representing flow directions within the network. Finally, we detail a simulation study addressing the comparative performance of our method, and present an analysis of two protein networks together with a substantive interpretation of our findings.

Calibration strategies for use of the nanoDot OSLD in CT applications.

Aluminum oxide based optically stimulated luminescent dosimeters (OSLD) have been recognized as a useful dosimeter for measuring CT dose, particularly for patient dose measurements. Despite the increasing use of this dosimeter, appropriate dosimeter calibration techniques have not been established in the literature; while the manufacturer offers a calibration procedure, it is known to have relatively large uncertainties. The purpose of this work was to evaluate two clinical approaches for calibrating these dosimeters for CT applications, and to determine the uncertainty associated with measurements using these techniques. Three unique calibration procedures were used to calculate dose for a range of CT conditions using a commercially available OSLD and reader. The three calibration procedures included calibration (a) using the vendor-provided method, (b) relative to a 120 kVp CT spectrum in air, and (c) relative to a megavoltage beam (implemented with 60 Co). The dose measured using each of these approaches was compared to dose measured using a calibrated farmer-type ion chamber. Finally, the uncertainty in the dose measured using each approach was determined. For the CT and megavoltage calibration methods, the dose measured using the OSLD nanoDot was within 5% of the dose measured using an ion chamber for a wide range of different CT scan parameters (80-140 kVp, and with measurements at a range of positions). When calibrated using the vendor-recommended protocol, the OSLD measured doses were on average 15.5% lower than ion chamber doses. Two clinical calibration techniques have been evaluated and are presented in this work as alternatives to the vendor-provided calibration approach. These techniques provide high precision for OSLD-based measurements in a CT environment.

Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants.

The evolution of "informatics" technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high-dimensionality of omics-type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.

Transcriptomic analysis reveals inflammatory and metabolic pathways that are regulated by renal perfusion pressure in the outer medulla of Dahl-S rats.

Studies exploring the development of hypertension have traditionally been unable to distinguish which of the observed changes are underlying causes from those that are a consequence of elevated blood pressure. In this study, a custom-designed servo-control system was utilized to precisely control renal perfusion pressure to the left kidney continuously during the development of hypertension in Dahl salt-sensitive rats. In this way, we maintained the left kidney at control blood pressure while the right kidney was exposed to hypertensive pressures. As each kidney was exposed to the same circulating factors, differences between them represent changes induced by pressure alone. RNA sequencing analysis identified 1,613 differently expressed genes affected by renal perfusion pressure. Three pathway analysis methods were applied, one a novel approach incorporating arterial pressure as an input variable allowing a more direct connection between the expression of genes and pressure. The statistical analysis proposed several novel pathways by which pressure affects renal physiology. We confirmed the effects of pressure on p-Jnk regulation, in which the hypertensive medullas show increased p-Jnk/Jnk ratios relative to the left (0.79 ± 0.11 vs. 0.53 ± 0.10, P < 0.01, n = 8). We also confirmed pathway predictions of mitochondrial function, in which the respiratory control ratio of hypertensive vs. control mitochondria are significantly reduced (7.9 ± 1.2 vs. 10.4 ± 1.8, P < 0.01, n = 6) and metabolomic profile, in which 14 metabolites differed significantly between hypertensive and control medullas ( P < 0.05, n = 5). These findings demonstrate that subtle differences in the transcriptome can be used to predict functional changes of the kidney as a consequence of pressure elevation.

Fludarabine, cyclophosphamide, and rituximab treatment achieves long-term disease-free survival in IGHV-mutated chronic lymphocytic leukemia.

Accurate identification of patients likely to achieve long-progression-free survival (PFS) after chemoimmunotherapy is essential given the availability of less toxic alternatives, such as ibrutinib. Fludarabine, cyclophosphamide, and rituximab (FCR) achieved a high response rate, but continued relapses were seen in initial reports. We reviewed the original 300 patient phase 2 FCR study to identify long-term disease-free survivors. Minimal residual disease (MRD) was assessed posttreatment by a polymerase chain reaction-based ligase chain reaction assay (sensitivity 0.01%). At the median follow-up of 12.8 years, PFS was 30.9% (median PFS, 6.4 years). The 12.8-year PFS was 53.9% for patients with mutated immunoglobulin heavy chain variable (IGHV) gene (IGHV-M) and 8.7% for patients with unmutated IGHV (IGHV-UM). 50.7% of patients with IGHV-M achieved MRD-negativity posttreatment; of these, PFS was 79.8% at 12.8 years. A plateau was seen on the PFS curve in patients with IGHV-M, with no relapses beyond 10.4 years in 42 patients (total follow-up 105.4 patient-years). On multivariable analysis, IGHV-UM (hazard ratio, 3.37 [2.18-5.21]; P < .001) and del(17p) by conventional karyotyping (hazard ratio, 7.96 [1.02-61.92]; P = .048) were significantly associated with inferior PFS. Fifteen patients with IGHV-M had 4-color MRD flow cytometry (sensitivity 0.01%) performed in peripheral blood, at a median of 12.8 years posttreatment (range, 9.5-14.7). All were MRD-negative. The high rate of very long-term PFS in patients with IGHV-M after FCR argues for the continued use of chemoimmunotherapy in this patient subgroup outside clinical trials; alternative strategies may be preferred in patients with IGHV-UM, to limit long-term toxicity.

A Bayesian screening approach for hepatocellular carcinoma using multiple longitudinal biomarkers.

Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor survival, therefore early detection is critical to improving the survival of patients with HCC. Current guidelines for high-risk patients include ultrasound screenings every six months, but ultrasounds are operator dependent and not sensitive for early HCC. Serum α-Fetoprotein (AFP) is a widely used diagnostic biomarker, but it has limited sensitivity and is not elevated in all HCC cases so, we incorporate a second blood-based biomarker, des'γ carboxy-prothrombin (DCP), that has shown potential as a screening marker for HCC. The data from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial is a valuable source of data to study biomarker screening for HCC. We assume the trajectories of AFP and DCP follow a joint hierarchical mixture model with random changepoints that allows for distinct changepoint times and subsequent trajectories of each biomarker. The changepoint indicators are jointly modeled with a Markov Random Field distribution to help detect borderline changepoints. Markov chain Monte Carlo methods are used to calculate posterior distributions, which are used in risk calculations among future patients and determine whether a patient has a positive screen. The screening algorithm was compared to alternatives in simulations studies under a range of possible scenarios and in the HALT-C Trial using cross-validation.

Long-term follow-up of patients receiving allogeneic stem cell transplant for chronic lymphocytic leukaemia: mixed T-cell chimerism is associated with high relapse risk and inferior survival.

There is limited information regarding the immunological predictors of post-allogeneic stem cell transplant (alloSCT) outcome in chronic lymphocytic leukaemia (CLL), such as mixed T-cell chimerism. We analysed 143 consecutive patients with relapsed/refractory CLL, transplanted between 2000 and 2012, to determine the prognostic relevance of mixed chimerism post-alloSCT and the ability of post-transplant immunomodulation to treat relapse. Mixed T-cell chimerism occurred in 50% of patients at 3 months and 43% at 6 months post-alloSCT; upon 3- and 6-month landmark analysis, this was associated with inferior progression-free survival (PFS) [Hazard ratio (HR) 1·93, P = 0·003 and HR 2·58, P < 0·001] and survival (HR 1·66, P = 0·05 and HR 2·17, P < 0·001), independent of baseline patient characteristics, and a lower rate of grade II-IV acute graft-versus-host disease (GHVD) (16% vs. 52%, P < 0·001). Thirty-three patients were treated with immunomodulation for relapse post-alloSCT (immunosuppression withdrawal, n = 6, donor lymphocyte infusion, n = 27); 17 achieved complete response (CR), which predicted superior PFS (53 months vs. 10 months, P < 0·001) and survival (117 months vs. 30 months, P = 0·006). Relapsed patients with mixed chimerism had inferior response to immunomodulation; conversion to full donor chimerism was highly correlated both with CR and with the development of severe acute GVHD, which was fatal in 3/8 patients. Novel therapeutic strategies are required for patients with mixed T-cell chimerism post-alloSCT for CLL.

A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study.

Integration of genomic data from multiple platforms has the capability to increase precision, accuracy, and statistical power in the identification of prognostic biomarkers. A fundamental problem faced in many multi-platform studies is unbalanced sample sizes due to the inability to obtain measurements from all the platforms for all the patients in the study. We have developed a novel Bayesian approach that integrates multi-regression models to identify a small set of biomarkers that can accurately predict time-to-event outcomes. This method fully exploits the amount of available information across platforms and does not exclude any of the subjects from the analysis. Through simulations, we demonstrate the utility of our method and compare its performance to that of methods that do not borrow information across regression models. Motivated by The Cancer Genome Atlas kidney renal cell carcinoma dataset, our methodology provides novel insights missed by non-integrative models.

Reproducibility of patient setup in the seated treatment position: A novel treatment chair design.

Radiotherapy in a seated position may be indicated for patients who are unable to lie on the treatment couch for the duration of treatment, in scenarios where a seated treatment position provides superior anatomical positioning and dose distributions, or for a low-cost system designed using a fixed treatment beam and rotating seated patient. In this study, we report a novel treatment chair that was constructed to allow for three-dimensional imaging and treatment delivery while ensuring robust immobilization, providing reproducibility equivalent to that in the traditional supine position. Five patients undergoing radiation treatment for head-and-neck cancers were enrolled and were setup in the chair, with immobilization devices created, and then imaged with orthogonal X-rays in a scenario that mimicked radiation treatments (without treatment delivery). Six subregions of the acquired images were rigidly registered to evaluate intra- and interfraction displacement and chair construction. Displacements under conditions of simulated image guidance were acquired by first registering one subregion; the residual displacement of other subregions was then measured. Additionally, we administered a patient questionnaire to gain patient feedback and assess comparison to the supine position. Average inter- and intrafraction displacements of all subregions in the seated position were less than 2 and 3 mm, respectively. When image guidance was simulated, L-R and A-P interfraction displacements were reduced by an average of 1 mm, providing setup of comparable quality to supine setups. The enrolled patients, who had no indication for a seated treatment position, reported no preference in the seated or the supine position. The novel chair design provides acceptable inter- and intrafraction displacement, with reproducibility equivalent to that reported for patients in the supine position. Patient feedback will be incorporated in the refinement of the chair, facilitating treatment of head-and-neck cancer in patients who are unable to lie for the duration of treatment or for use in an economical fixed-beam setup.

Outcomes of patients with chronic lymphocytic leukemia treated with first-line idelalisib plus rituximab after cessation of treatment for toxicity.

BACKGROUND: More active therapies are needed for older and unfit patients with chronic lymphocytic leukemia (CLL) who are not eligible for chemoimmunotherapy with fludarabine, cyclophosphamide, and rituximab. The phosphyotidylinositol-3-kinase δ inhibitor idelalisib is effective in patients with treatment-naive and relapsed/refractory CLL as monotherapy and in combination with rituximab, but it can be associated with treatment-limiting adverse events, particularly diarrhea/colitis. The outcomes for patients who cease treatment for adverse events have not been previously described. METHODS: The authors analyzed long-term follow-up data from 40 treatment-naïve patients aged ≥65 years who received treatment at The University of Texas MD Anderson Cancer Center on a phase 2 study of idelalisib plus rituximab for CLL. RESULTS: In patients who permanently ceased treatment because of toxicity, the time to subsequent disease progression was analyzed according to baseline characteristics. Fifteen patients permanently ceased therapy (PCT) because of toxicity (PCTTOX ), most commonly diarrhea/colitis (n = 7), at a median of 11 months after commencing treatment. PCTTOX was associated with a higher risk of subsequent disease progression (hazard ratio, 6.61; 95% confidence interval, 1.77-16.15) relative to that observed in patients who remained on therapy. Ten patients subsequently progressed, and 7 required salvage therapy; 5 patients remained progression-free at a median of 23.3 months (range, 8.5-28.6 months). Patients who were positive for ζ-associated protein-70 had more rapid disease progression after treatment cessation (P = .048). There were no CLL-related deaths. CONCLUSIONS: PCTTOX is the major determinant of PFS in patients who receive first-line idelalisib-based treatment. However, a subgroup of patients with favorable biologic characteristics has prolonged PFS, even after PCTTOX . The absence of CLL-related deaths indicates that salvage treatment is generally successful after PCTTOX . Cancer 2016;122:2505-11. © 2016 American Cancer Society.

Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set.

BACKGROUND: While clinical outcomes following immunotherapy have shown an association with tumor mutation load using whole exome sequencing (WES), its clinical applicability is currently limited by cost and bioinformatics requirements. METHODS: We developed a method to accurately derive the predicted total mutation load (PTML) within individual tumors from a small set of genes that can be used in clinical next generation sequencing (NGS) panels. PTML was derived from the actual total mutation load (ATML) of 575 distinct melanoma and lung cancer samples and validated using independent melanoma (n = 312) and lung cancer (n = 217) cohorts. The correlation of PTML status with clinical outcome, following distinct immunotherapies, was assessed using the Kaplan-Meier method. RESULTS: PTML (derived from 170 genes) was highly correlated with ATML in cutaneous melanoma and lung adenocarcinoma validation cohorts (R2 = 0.73 and R2 = 0.82, respectively). PTML was strongly associated with clinical outcome to ipilimumab (anti-CTLA-4, three cohorts) and adoptive T-cell therapy (1 cohort) clinical outcome in melanoma. Clinical benefit from pembrolizumab (anti-PD-1) in lung cancer was also shown to significantly correlate with PTML status (log rank P value < 0.05 in all cohorts). CONCLUSIONS: The approach of using small NGS gene panels, already applied to guide employment of targeted therapies, may have utility in the personalized use of immunotherapy in cancer.

Atypical chronic myeloid leukemia is clinically distinct from unclassifiable myelodysplastic/myeloproliferative neoplasms.

Atypical chronic myeloid leukemia (aCML) is a rare subtype of myelodysplastic/myeloproliferative neoplasm (MDS/MPN) largely defined morphologically. It is, unclear, however, whether aCML-associated features are distinctive enough to allow its separation from unclassifiable MDS/MPN (MDS/MPN-U). To study these 2 rare entities, 134 patient archives were collected from 7 large medical centers, of which 65 (49%) cases were further classified as aCML and the remaining 69 (51%) as MDS/MPN-U. Distinctively, aCML was associated with many adverse features and an inferior overall survival (12.4 vs 21.8 months, P = .004) and AML-free survival (11.2 vs 18.9 months, P = .003). The aCML defining features of leukocytosis and circulating myeloid precursors, but not dysgranulopoiesis, were independent negative predictors. Other factors, such as lactate dehydrogenase, circulating myeloblasts, platelets, and cytogenetics could further stratify MDS/MPN-U but not aCML patient risks. aCML appeared to have more mutated RAS (7/20 [35%] vs 4/29 [14%]) and less JAK2p.V617F (3/42 [7%] vs 10/52 [19%]), but was not statistically significant. Somatic CSF3R T618I (0/54) and CALR (0/30) mutations were not detected either in aCML or MDS/MPN-U. In conclusion, within MDS/MPN, the World Health Organization 2008 criteria for aCML identify a subgroup of patients with features clearly distinct from MDS/MPN-U. The MDS/MPN-U category is heterogeneous, and patient risk can be further stratified by a number of clinicopathological parameters.

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 article, 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.

Joint Bayesian variable and graph selection for regression models with network-structured predictors.

In this work, we develop a Bayesian approach to perform selection of predictors that are linked within a network. We achieve this by combining a sparse regression model relating the predictors to a response variable with a graphical model describing conditional dependencies among the predictors. The proposed method is well-suited for genomic applications because it allows the identification of pathways of functionally related genes or proteins that impact an outcome of interest. In contrast to previous approaches for network-guided variable selection, we infer the network among predictors using a Gaussian graphical model and do not assume that network information is available a priori. We demonstrate that our method outperforms existing methods in identifying network-structured predictors in simulation settings and illustrate our proposed model with an application to inference of proteins relevant to glioblastoma survival.