In this article, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where ...some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior, which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions.
With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted ...target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the precise way in which discoveries are counted, and the performance of FDR-controlling procedures is satisfactory only if there is a one-to-one correspondence between what scientists describe as unique discoveries and the number of rejected hypotheses. The presence of linkage disequilibrium between markers in genome-wide association studies (GWAS) often leads researchers to consider the signal associated to multiple neighboring SNPs as indicating the existence of a single genomic locus with possible influence on the phenotype. This a posteriori aggregation of rejected hypotheses results in inflation of the relevant FDR. We propose a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses. We show how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS. We demonstrate that our approach is versatile and useful when the data are analyzed using both tests based on single markers and multiple regression. We provide an R package that allows practitioners to apply our procedure on standard GWAS format data, and illustrate its performance on lipid traits in the North Finland Birth Cohort 66 cohort study.
Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we ...seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for compositional data, which have a fixed sum constraint. We use a hierarchical Bayesian model with latent layers and employ spike-and-slab priors for both edge and covariate selection. For posterior inference, we develop a novel variational inference scheme with an expectation-maximization step, to enable efficient estimation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of network recovery. We show the practical utility of our model via an application to microbiome data. The human microbiome has been shown to contribute too many of the functions of the human body, and also to be linked with a number of diseases. In our application, we seek to better understand the interaction between microbes and relevant covariates, as well as the interaction of microbes with each other. We call our algorithm simultaneous inference for networks and covariates and provide a Python implementation, which is available online.
A non-immunogenic tumor microenvironment (TME) is a significant barrier to immune checkpoint blockade (ICB) response. The impact of Polybromo-1 (PBRM1) on TME and response to ICB in renal cell ...carcinoma (RCC) remains to be resolved. Here we show that PBRM1/Pbrm1 deficiency reduces the binding of brahma-related gene 1 (BRG1) to the IFNγ receptor 2 (Ifngr2) promoter, decreasing STAT1 phosphorylation and the subsequent expression of IFNγ target genes. An analysis of 3 independent patient cohorts and of murine pre-clinical models reveals that PBRM1 loss is associated with a less immunogenic TME and upregulated angiogenesis. Pbrm1 deficient Renca subcutaneous tumors in mice are more resistance to ICB, and a retrospective analysis of the IMmotion150 RCC study also suggests that PBRM1 mutation reduces benefit from ICB. Our study sheds light on the influence of PBRM1 mutations on IFNγ-STAT1 signaling and TME, and can inform additional preclinical and clinical studies in RCC.
This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume ...expansions and thereby reducing toxicity events of intensity modulated radiation therapy for cervical cancer.
The cervix-uterus, vagina, parametrium, bladder, rectum, sigmoid, femoral heads, kidneys, spinal cord, and bowel bag were delineated on 408 computed tomography (CT) scans from patients treated at MD Anderson Cancer Center (n = 214), Polyclinique Bordeaux Nord Aquitaine (n = 30), and enrolled in a Medical Image Computing & Computer Assisted Intervention challenge (n = 3). The data were divided into 255 training, 61 validation, 62 internal test, and 30 external test CT scans. Two models were investigated: the 2-dimensional (2D) DeepLabV3+ (Google) and 3-dimensional (3D) Unet in RayStation (RaySearch Laboratories). Three intensity modulated radiation therapy plans were generated on each CT of the internal and external test sets using either the manual, 2D model, or 3D model segmentations. The dose constraints followed the External beam radiochemotherapy and MRI based adaptive BRAchytherapy in locally advanced CErvical cancer (EMBRACE) II protocol, with reduced margins of 5 and 3 mm for the target and nodal planning target volume. Geometric discrepancies between the manual and predicted contours were assessed using the Dice similarity coefficient (DSC), distance-to-agreement, and Hausdorff distance. Dosimetric discrepancies between the manual and model doses were assessed using clinical indices on the manual contours and the gamma index. Interobserver variability was assessed for the cervix-uterus, parametrium, and vagina for the definition of the primary clinical target volume (CTV
) on the external test set.
Average DSCs across all organs were 0.67 to 0.96, 0.71 to 0.97, and 0.42 to 0.92 for the 2D model and 0.66 to 0.96, 0.70 to 0.97, and 0.37 to 0.93 for the 3D model on the validation, internal, and external test sets. Average DSCs of the CTV
were 0.88 and 0.81 for the 2D model and 0.87 and 0.82 for the 3D model on the internal and external test sets. Interobserver variability of the CTV
corresponded to a mean (range) DSC of 0.85 (0.77-0.90) on the external test set. On the internal test set, the doses from the 2D and 3D model contours provided a CTV
V42.75 Gy >98% for 98% and 91% of the CT scans, respectively. On the external test set, these percentages were increased to 100% and 93% for the 2D and 3D models, respectively.
The investigated models provided auto-segmentation of the cervix anatomy with similar performances on 2 institutional data sets and reasonable dosimetric accuracies using small planning target volume margins, paving the way to automatic online dose optimization for advanced adaptive radiation therapy strategies.
Purpose
To evaluate the performance of an independent recalculation and compare it against current measurement‐based patient specific intensity‐modulated radiation therapy (IMRT) quality assurance ...(QA) in predicting unacceptable phantom results as measured by the Imaging and Radiation Oncology Core (IROC).
Methods
When institutions irradiate the IROC head and neck IMRT phantom, they are also asked to submit their internal IMRT QA results. Separately from this, IROC has previously created reference beam models on the Mobius3D platform to independently recalculate phantom results based on the institution's DICOM plan data. The ability of the institutions’ IMRT QA to predict the IROC phantom result was compared against the independent recalculation for 339 phantom results collected since 2012. This was done to determine the ability of these systems to detect failing phantom results (i.e., large errors) as well as poor phantom results (i.e., modest errors). Sensitivity and specificity were evaluated using common clinical thresholds, and receiver operator characteristic (ROC) curves were used to compare across different thresholds.
Results
Overall, based on common clinical criteria, the independent recalculation was 12 times more sensitive at detecting unacceptable (failing) IROC phantom results than clinical measurement‐based IMRT QA. The recalculation was superior, in head‐to‐head comparison, to the EPID, ArcCheck, and MapCheck devices. The superiority of the recalculation vs these array‐based measurements persisted under ROC analysis as the recalculation curve had a greater area under it and was always above that for these measurement devices. For detecting modest errors (poor phantom results rather than failing phantom results), neither the recalculation nor measurement‐based IMRT QA performed well.
Conclusions
A simple recalculation outperformed current measurement‐based IMRT QA methods at detecting unacceptable plans. These findings highlight the value of an independent recalculation, and raise further questions about the current standard of measurement‐based IMRT QA.
Abstract
Background
The majority of studies that provide insights into the influence of the microbiome on the health of hematologic malignancy patients have concentrated on the transplant setting. ...Here, we sought to assess the predictive capacity of the gastrointestinal microbiome and its relationship to infectious outcomes in patients with acute myeloid leukemia (AML).
Methods
16s rRNA-based analysis was performed on oral swabs and stool samples obtained biweekly from baseline until neutrophil recovery following induction chemotherapy (IC) in 97 AML patients. Microbiome characteristics were correlated with clinical outcomes both during and after IC completion.
Results
At the start of IC, higher stool Shannon diversity (hazard ratio HR, 0.36; 95% confidence interval CI, .18–.74) and higher relative abundance of Porphyromonadaceae (HR, 0.36; 95% CI, .18–.73) were associated with increased probability of remaining infection-free during neutropenia. A baseline stool Shannon diversity cutoff of <2 had optimal operating characteristics for predicting infectious complications during neutropenia. Although 56 patients received therapy >72 hours with a carbapenem, none of the patients had an infection with an extended spectrum β-lactamase–producing organism. Patients who received carbapenems for >72 hours had significantly lower α-diversity at neutrophil recovery (P = .001) and were approximately 4 times more likely to have infection in the 90 days following neutrophil recovery (HR, 4.55; 95% CI, 1.73–11.93).
Conclusions
Our results suggest that gut microbiome evaluation could assist with infectious risk stratification and that improved targeting of antibiotic administration during IC could decrease subsequent infectious complications in AML patients.
Baseline microbiome diversity is a strong independent predictor of infection during acute myeloid leukemia induction chemotherapy (IC) among clinical and microbiome covariates. Higher baseline levels of Porphyromonadaceae appear protective against infection, while carbapenem use is associated with consequences to the microbiome and infection susceptibility post-IC.
Abstract
Plasma and tumor caveolin-1 (Cav-1) are linked with disease progression in prostate cancer. Here we report that metabolomic profiling of longitudinal plasmas from a prospective cohort of 491 ...active surveillance (AS) participants indicates prominent elevations in plasma sphingolipids in AS progressors that, together with plasma Cav-1, yield a prognostic signature for disease progression. Mechanistic studies of the underlying tumor supportive onco-metabolism reveal coordinated activities through which Cav-1 enables rewiring of cancer cell lipid metabolism towards a program of 1) exogenous sphingolipid scavenging independent of cholesterol, 2) increased cancer cell catabolism of sphingomyelins to ceramide derivatives and 3) altered ceramide metabolism that results in increased glycosphingolipid synthesis and efflux of Cav-1-sphingolipid particles containing mitochondrial proteins and lipids. We also demonstrate, using a prostate cancer syngeneic RM-9 mouse model and established cell lines, that this Cav-1-sphingolipid program evidences a metabolic vulnerability that is targetable to induce lethal mitophagy as an anti-tumor therapy.
Most patients diagnosed with resected pancreatic adenocarcinoma (PDAC) survive less than 5 years, but a minor subset survives longer. Here, we dissect the role of the tumor microbiota and the immune ...system in influencing long-term survival. Using 16S rRNA gene sequencing, we analyzed the tumor microbiome composition in PDAC patients with short-term survival (STS) and long-term survival (LTS). We found higher alpha-diversity in the tumor microbiome of LTS patients and identified an intra-tumoral microbiome signature (Pseudoxanthomonas-Streptomyces-Saccharopolyspora-Bacillus clausii) highly predictive of long-term survivorship in both discovery and validation cohorts. Through human-into-mice fecal microbiota transplantation (FMT) experiments from STS, LTS, or control donors, we were able to differentially modulate the tumor microbiome and affect tumor growth as well as tumor immune infiltration. Our study demonstrates that PDAC microbiome composition, which cross-talks to the gut microbiome, influences the host immune response and natural history of the disease.
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•PDAC long-term survivors display high tumor microbial diversity and immunoactivation•A PDAC tumoral microbiome signature predicts PDAC long-term survival•The gut microbiome modulates the PDAC tumor microbiome landscape•Fecal microbial transplants can modulate tumors immunosuppression and growth
The distinct tumor microbiome from pancreatic cancer long-term survivors can be used to predict PDAC survival in humans, and transfer of long-term survivor gut microbiomes can alter the tumor microbiome and tumor growth in mouse models.
To evaluate the uncertainty of radiomics features from contrast-enhanced breath-hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic segmentation due to ...intra-observer, inter-observer, and inter-software reliability.
Three radiation oncologists manually delineated lung tumors twice from 10 CT scans using two software tools (3D-Slicer and MIM Maestro). Additionally, three observers without formal clinical training were instructed to use two semi-automatic segmentation tools, Lesion Sizing Toolkit (LSTK) and GrowCut, to delineate the same tumor volumes. The accuracy of the semi-automatic contours was assessed by comparison with physician manual contours using Dice similarity coefficients and Hausdorff distances. Eighty-three radiomics features were calculated for each delineated tumor contour. Informative features were identified based on their dynamic range and correlation to other features. Feature reliability was then evaluated using intra-class correlation coefficients (ICC). Feature range was used to evaluate the uncertainty of the segmentation methods.
From the initial set of 83 features, 40 radiomics features were found to be informative, and these 40 features were used in the subsequent analyses. For both intra-observer and inter-observer reliability, LSTK had higher reliability than GrowCut and the two manual segmentation tools. All observers achieved consistently high ICC values when using LSTK, but the ICC value varied greatly for each observer when using GrowCut and the manual segmentation tools. For inter-software reliability, features were not reproducible across the software tools for either manual or semi-automatic segmentation methods. Additionally, no feature category was found to be more reproducible than another feature category. Feature ranges of LSTK contours were smaller than those of manual contours for all features.
Radiomics features extracted from LSTK contours were highly reliable across and among observers. With semi-automatic segmentation tools, observers without formal clinical training were comparable to physicians in evaluating tumor segmentation.
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