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Purpose: We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated radiation treatment planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide. Methods: Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC radiation oncologist. Contours from a 10-patient subset were evaluated by five additional radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics. Results: Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram–based planning without edit. Upon clinical implementation, 50% of contours were not edited for use in treatment planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively. Conclusion: Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for treatment planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated radiation treatment planning algorithms. [ABSTRACT FROM AUTHOR]

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and other quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus. In the eQTL analysis, we utilize a recently proposed hierarchical multiple testing strategy which controls error rates regarding the discovery of functional variants. Our results elucidate the heritability and regulation of gene expression in this unique Latin American study population and identify a set of regulatory SNPs which may be relevant in future investigations of complex disease in this population. Since our subjects belong to extended families, we are able to compare traditional kinship-based estimates with those from more recent methods that depend only on genotype information. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Patients with rare solid tumors treated on early phase trials experience toxicities from their tumors and treatments. However, limited data exist to describe the detailed symptom burden suffered by these patients, particularly those with rare solid tumors treated with immunotherapy. We performed a prospective longitudinal study to capture patient-reported symptom burden. Patients completed the validated MD Anderson Symptom Inventory (MDASI)—Immunotherapy with 20 symptoms including 7 immunotherapy-specific items and 6 interference items at baseline and weekly thereafter for up to 9 weeks. Symptoms and interference were rated on 0–10 scales (0 = none or no interference, 10 = worst imaginable or complete interference). Group-based trajectory modelling determined higher and lower symptom groups. A total of 336 MDASI questionnaires were completed by 53 patients (mean age 55.4y, 53% male) with advanced rare cancers receiving pembrolizumab in a Phase II clinical trial. Symptoms reported as most severe over the course of the treatment over 9 weeks were fatigue [mean (M) = 3.8, SD = 2.3], pain (M = 3.7, SD = 2.9), disturbed sleep (M = 2.7, SD = 2.3), drowsiness (M = 2.6, SD = 2.0) and lack of appetite (M = 2.5, SD = 2.1). Pain in the abdomen (M = 2.2, SD = 2.4), rash (M = 1.1, SD = 1.8) and diarrhea (M = 0.9, SD = 1.5) were less severe. Interference with walking was rated the highest (M = 3.4, SD = 2.8) and relations with others was rated the lowest (M = 2.1, SD = 2.6). Using a composite score based on the five most severe symptoms (fatigue, pain, lack of appetite, feeling drowsy and sleep disturbance), 43% were classified into the high symptom burden group. Using a score based on immunotherapy-specific symptoms (e.g., rash, diarrhea) 33% of patients were included in the high symptom group. Symptom burden stayed relatively stable in the high- and low-symptom burden patient groups from baseline through 9 weeks. Some patients with rare malignancies experienced high symptom burden even at baseline. In patients with rare cancers, symptom trajectories stayed relatively stable over nine weeks of treatment with pembrolizumab. Trial registration: ClinicalTrials.gov identifier: NCT02721732. [ABSTRACT FROM AUTHOR]

Cytotoxic T lymphocyte (CTL)-based cancer immunotherapies have shown great promise for inducing clinical regressions by targeting tumor-associated antigens (TAA). To expand the TAA landscape of pancreatic ductal adenocarcinoma (PDAC), we performed tandem mass spectrometry analysis of HLA class I-bound peptides from 35 PDAC patient tumors. This identified a shared HLA-A*0101 restricted peptide derived from co-transcriptional activator Vestigial-like 1 (VGLL1) as a putative TAA demonstrating overexpression in multiple tumor types and low or absent expression in essential normal tissues. Here we show that VGLL1-specific CTLs expanded from the blood of a PDAC patient could recognize and kill in an antigen-specific manner a majority of HLA-A*0101 allogeneic tumor cell lines derived not only from PDAC, but also bladder, ovarian, gastric, lung, and basal-like breast cancers. Gene expression profiling reveals VGLL1 as a member of a unique group of cancer-placenta antigens (CPA) that may constitute immunotherapeutic targets for patients with multiple cancer types. Cytotoxic T lymphocyte (CTL)-based immunotherapies can induce tumor regressions by targeting HLA class I-bound tumor-associated peptides. Here, the authors identified a peptide derived from Vestigial-like 1 (VGLL1) as a shared, potentially therapeutic CTL target expressed by multiple cancer types. [ABSTRACT FROM AUTHOR]

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. The mechanisms associated with Caveolin-1 (Cav-1) mediated metabolic changes in prostate cancer are unclear. Here, the authors show that Cav-1 promotes rewiring of cancer cell lipid metabolism towards a program of exogenous lipid scavenging and vesicle biogenesis that intersects with mitochondrial dynamics in prostate tumors. [ABSTRACT FROM AUTHOR]

Background: Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast‐enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams. Purpose: The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast‐enhanced abdomen CT examinations. Methods: Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast‐enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations. Results: The best classifier was found to be a random forest, with a precision of 0.892 in the held‐out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU. Conclusions: The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time‐consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast‐enhanced CT examinations. [ABSTRACT FROM AUTHOR]

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality. [ABSTRACT FROM AUTHOR]

Purpose: As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam. Methods: Eight materials were tested for radiological material‐equivalence in a carbon ion beam. The materials were computed tomography (CT)‐scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread‐out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU‐RLSP relationship. Results: Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV). Conclusions: Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples. [ABSTRACT FROM AUTHOR]

Objective. This study characterized optically-stimulated luminescent dosimeter (OSLD) nanoDots for use in a therapeutic carbon beam using the Imaging and Radiation Oncology Core (IROC) framework for remote output verification. Approach. The absorbed dose correction factors for OSLD (fading, linearity, beam quality, angularity, and depletion), as defined by AAPM TG 191, were characterized for carbon beams. For the various correction factors, the effect of linear energy transfer (LET) was examined by characterizing in both a low and high LET setting. Main results. Fading was not statistically different between reference photons and carbon, nor between low and high LET beams; thus, the standard IROC-defined exponential function could be used to characterize fading. Dose linearity was characterized with a linear fit; while low and high LET carbon linearity was different, these differences were small and could be rolled into the uncertainty budget if using a single linearity correction. A linear fit between beam quality and dose-averaged LET was determined. The OSLD response at various angles of incidence was not statistically different, thus a correction factor need not be applied. There was a difference in depletion between low and high LET irradiations in a primary carbon beam, but this difference was small over the standard five readings. The largest uncertainty associated with the use of OSLDs in carbon was because of the k Q correction factor, with an uncertainty of 6.0%. The overall uncertainty budget was 6.3% for standard irradiation conditions. Significance. OSLD nanoDot response was characterized in a therapeutic carbon beam. The uncertainty was larger than for traditional photon applications. These findings enable the use of OSLDs for carbon absorbed dose measurements, but with less accuracy than conventional OSLD audit programs. [ABSTRACT FROM AUTHOR]

Motivation Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. Results We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. Availability and implementation The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package. [ABSTRACT FROM AUTHOR]

Purpose: Immune checkpoint inhibitors (ICI) have become standard of care for some types of lung cancer. Along with expanding usage comes the emergence of immune-related adverse events (irAEs), including ICI-related pneumonitis (ICI-P). Treatment guidelines for managing irAEs have been developed; however, how clinicians manage irAEs in the real-world setting is less well known. We aimed to describe the outcomes and care patterns of grade ≥ 3 ICI-P in an onco-hospitalist service. Patients and methods: We included patients with lung cancer treated with ICI who were admitted to an oncology hospitalist service with a suspicion of ICI-P. We described the hospitalization characteristics, treatment patterns, discharge practices, and clinical outcomes of patients with confirmed ICI-P. The primary outcome was time to start treatment for ICI-P. Results: Among 49 patients admitted with a suspicion of ICI-P, 31 patients were confirmed to have ICI-P and subsequently received ICI-P directed treatment. Pulmonology was consulted in 97% of patients. Median time to start treatment for ICI-P was 1 day (IQR 0–3.5 days). All 31 patients received corticosteroids. Inpatient mortality was 32%. Majority of patients discharged with steroids were prescribed prophylaxis for gastritis and opportunistic infections. Thirty-eight percent of patients were seen by pulmonology and 86% were seen by the oncology team post-discharge. Conclusion: Our study confirms prior findings of high mortality among patients with high-grade ICI-P. Early diagnosis and treatment are key to improving clinical outcomes. Understanding the care patterns and adherence to treatment guidelines of clinicians caring for this patient population may help identify ways to further standardize management practices and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: This study investigated the influence of oral microbial features on the trajectory of oral mucositis (OM) in patients with squamous cell carcinoma of the head and neck. Methods: OM severity was assessed and buccal swabs were collected at baseline, at the initiation of cancer treatment, weekly during cancer treatment, at the termination of cancer treatment, and after cancer treatment termination. The oral microbiome was characterized via the 16S ribosomal RNA V4 region with the Illumina platform. Latent class mixed‐model analysis was used to group individuals with similar trajectories of OM severity. Locally estimated scatterplot smoothing was used to fit an average trend within each group and to assess the association between the longitudinal OM scores and longitudinal microbial abundances. Results: Four latent groups (LGs) with differing patterns of OM severity were identified for 142 subjects. LG1 has an early onset of high OM scores. LGs 2 and 3 begin with relatively low OM scores until the eighth and 11th week, respectively. LG4 has generally flat OM scores. These LGs did not vary by treatment or clinical or demographic variables. Correlation analysis showed that the abundances of Bacteroidota, Proteobacteria, Bacteroidia, Gammaproteobacteria, Enterobacterales, Bacteroidales, Aerococcaceae, Prevotellaceae, Abiotrophia, and Prevotella_7 were positively correlated with OM severity across the four LGs. Negative correlation was observed with OM severity for a few microbial features: Abiotrophia and Aerococcaceae for LGs 2 and 3; Gammaproteobacteria and Proteobacteria for LGs 2, 3, and 4; and Enterobacterales for LGs 2 and 4. Conclusions: These findings suggest the potential to personalize treatment for OM. Plain Language Summary: Oral mucositis (OM) is a common and debilitating after effect for patients treated for squamous cell carcinoma of the head and neck.Trends in the abundance of specific microbial features may be associated with patterns of OM severity over time.Our findings suggest the potential to personalize treatment plans for OM via tailored microbiome interventions. Trends in the abundance of specific microbial features were observed to be associated with patterns of oral mucositis (OM) severity over time. These findings suggest the potential to personalize treatment plans for OM via tailored microbiome interventions. [ABSTRACT FROM AUTHOR]

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1%${D}_{1{\mathrm{\% }}}$ and D98%${D}_{98{\mathrm{\% }}}$ were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization. [ABSTRACT FROM AUTHOR]

• Low pitch CT scans reduce helical artifacts produced at bowel interfaces. • While controlling other settings to prevent increased radiation dose, consideration should be made for low helical pitch scanning to improve image quality, particularly when bowel or peritoneal pathology is suspected. • Modern CT scanners still allow fast scanning even at low pitch. High helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image quality. This study aims to determine the severity and incidence of helical artifacts in abdominal CT imaging and their relation to the helical pitch scan parameter. To obtain a dataset with varying pitch values, we used CT exam data both internal and external to our center. A cohort of 59 consecutive adult patients receiving an abdomen CT examination at our center with an accompanying prior examination from an external center was selected for retrospective review. Two expert observers performed a blinded rating of helical artifact in each examination using a five-point Likert scale. The incidence of artifacts with respect to the helical pitch was assessed. A generalized linear mixed-effects regression (GLMER) model, with study arm (Internal or External to our center) and helical pitch as the fixed-effect predictor variables, was fit to the artifact ratings, and significance of the predictor variables was tested. For a pitch of <0.75, the proportion of exams with mild or worse helical artifacts (Likert scores of 1–3) was <1%. The proportion increased to 16% for exams with pitch between 0.75 and 1.2, and further increased to 78% for exams with a pitch greater than 1.2. Pitch was significantly associated with helical artifact in the GLMER model (p = 2.8 × 10−9), while study arm was not a significant factor (p = 0.76). The incidence and severity of helical artifact increased with helical pitch. This difference persisted even after accounting for the potential confounding factor of the center where the study was performed. [ABSTRACT FROM AUTHOR]

Previous analysis of 13 overlapping subcongenic strains led to the identification of a 1.37 Mbp region on chromosome 13 (positions 80.92 to 82.29 Mbp in the Rn5 genome assembly) that influenced the mean arterial blood pressure of the Dahl salt-sensitive (SS) rat on a high-salt diet by more than 20 mmHg. The goal of the present study was to identify biological pathways that could mediate the blood pressure effect of this genomic region. RNA-seq analysis was performed for the renal outer medulla tissue in five selected subcongenic strains, the SS, and a congenic strain from which the subcongenic strains were derived. Rats were fed a 0.4% salt diet or switched to a high-salt diet for 7 days. Affymetrix GeneChip data for SS and three additional congenic or consomic strains were obtained from a previous study. The RNA-seq and microarray data were merged using a cross-platform normalization method to generate a transcriptome dataset containing 90 observations for each gene. A Bayesian model analysis was performed for 243 biological pathways to assess their likelihood to discriminate blood pressure levels across experimental groups. Seven pathways showed posterior probabilities greater than 0.4. These pathways involved neuroactive ligand-receptor interaction, phenylalanine, tyrosine and tryptophan biosynthesis, and protein degradation. A Bayesian approach was used to estimate undirected graphical models among the three known genes located in the 1.37 Mbp region (Astn1, Fam5b, and Rfwd2) and genes in each of the 7 pathways identified above and 11 additional pathways known to be involved in blood pressure regulation. The analysis identified several previously unknown relationships between the three candidate genes and genes in pathways that could regulate blood pressure. The study demonstrated a new, unbiased approach for identifying biological pathways mediating the effect of a candidate genomic region on hypertension. [ABSTRACT FROM AUTHOR]

Introduction: Antibiotic use is a risk factor for Clostridioides difficile infection (CDI). Few studies have correlated use of prior antibiotic classes with CDI, microbiome composition, and disease severity in patients with cancer. We hypothesized that previous antibiotic exposure and fecal microbiome composition at time of presentation are risk factors for severe CDI in patients with cancer. Methods: This non-interventional, prospective, cohort study examined 200 patients with cancer who had their first episode or first recurrence of CDI. C. difficile was identified using nucleic acid amplification testing. Univariate analysis was used to determine significant risk factors for severe CDI. Fecal microbiome composition was determined by sequencing the V3/V4 region of 16 s rDNA encoding gene. Differential abundance analyses were used to single out significant microbial features which differed across severity levels. Results: On univariate analysis, factors associated with severe CDI included the presence of toxin A/B in stools (odds ratio [OR] 2.14 [1.05–4.36] p = 0.04 and prior 90-day metronidazole use (OR 2.66 [1.09–6.50] p = 0.03). Although alpha and beta diversity was similar between disease severity groups and toxin A/B in stools, increased abundance of Bacteroides uniformis, Ruminococcaceae, and Citrobacter koseri were associated with protection from severe CDI (p < 0.05) and depletion of anaerobes was higher in patients with prior metronidazole exposure. Conclusion: Use of metronidazole for non-CDI indications within 90 days prior to diagnosis and presence of toxin A/B in stools were associated with severe CDI. Findings provide valuable insights into risk factors for severe CDI in an underserved population with cancer that warrants further exploration. [ABSTRACT FROM AUTHOR]

Purpose: Complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with incorrect level treatments of the spine. The purpose of this work was to mitigate such challenges by fully automating the treatment planning process for diagnostic and simulation computed tomography (CT) scans.Methods and Materials: Vertebral bodies are labeled on CT scans of any length using 2 intendent deep-learning models-mirroring 2 different experts labeling the spine. Then, a U-Net++ architecture was trained, validated, and tested to contour each vertebra (n = 220 CT scans). Features from the CT and auto-contours were input into a random forest classifier to predict whether vertebrae were correctly labeled. This classifier was trained using auto-contours from cone beam computed tomography, positron emission tomography/CT, simulation CT, and diagnostic CT images (n = 56 CT scans, 751 contours). Auto-plans were generated via scripting. Each model was combined into a framework to make a fully automated clinical tool. A retrospective planning study was conducted in which 3 radiation oncologists scored auto-plan quality on an unseen patient cohort (n = 60) on a 5-point scale. CT scans varied in scan length, presence of surgical implants, imaging protocol, and metastatic burden.Results: The results showed that the uniquely designed convolutional neural networks accurately labeled and segmented vertebral bodies C1-L5 regardless of imaging protocol or metastatic burden. Mean dice-similarity coefficient was 85.0% (cervical), 90.3% (thoracic), and 93.7% (lumbar). The random forest classifier predicted mislabeling across various CT scan types with an area under the curve of 0.82. All contouring and labeling errors within treatment regions (11 of 11), including errors from patient plans with atypical anatomy (eg, T13, L6) were detected. Radiation oncologists scored 98% of simulation CT-based plans and 92% of diagnostic CT-based plans as clinically acceptable or needing minor edits for patients with typical anatomy. On average, end-to-end treatment planning time of the clinical tool was less than 8 minutes.Conclusions: This novel method to automatically verify, contour, and plan palliative spine treatments is efficient and effective across various CT scan types. Furthermore, it is the first to create a clinical tool that can automatically verify vertebral level in CT images. [ABSTRACT FROM AUTHOR]

• Developed a colorectal model that includes anatomical variations of 103 individuals. • Colorectal model is age-scalable (0.1–18 years) across the pediatric age range. • Integrated colorectal model into the age-scalable in-house phantom. • The colorectal model can be integrated into any computational phantom. • The colorectal model will be used for dose reconstruction in late effects studies. The purposes of this study were to develop and integrate a colorectal model that incorporates anatomical variations of pediatric patients into the age-scalable MD Anderson Late Effects (MDA-LE) computational phantom, and validate the model for pediatric radiation therapy (RT) dose reconstructions. Colorectal contours were manually derived from whole-body non-contrast computed tomography (CT) scans of 114 pediatric patients (age range: 2.1–21.6 years, 74 males, 40 females). One contour was used for an anatomical template, 103 for training and 10 for testing. Training contours were used to create a colorectal principal component analysis (PCA)-based statistical shape model (SSM) to extract the population's dominant deformations. The SSM was integrated into the MDA-LE phantom. Geometric accuracy was assessed between patient-specific and SSM contours using several overlap metrics. Two alternative colorectal shapes were generated using the first 17 dominant modes of the PCA-based SSM. Dosimetric accuracy was assessed by comparing colorectal doses from test patients' CT-based RT plans (ground truth) with reconstructed doses for the mean and two alternative models in age-matched MDA-LE phantoms. When using all 103 PCA modes, the mean (min–max) Dice similarity coefficient, distance-to-agreement and Hausdorff distance between the patient-specific and reconstructed contours for the test patients were 0.89 (0.85–0.91), 2.1 mm (1.7–3.0), and 8.6 mm (5.7–14.3), respectively. The average percent difference between reconstructed and ground truth mean and maximum colorectal doses for the mean (alternative 1, 2) model were 6.3% (8.1%, 6.1%) and 4.4% (4.3%, 4.7%), respectively. We developed, validated and integrated a colorectal PCA-based SSM into the MDA-LE phantom and demonstrated its dosimetric performance for accurate pediatric RT dose reconstruction. [ABSTRACT FROM AUTHOR]

Background: In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing the area under the receiver operating characteristic curve. For ordinal responses, the optimal predictor combination can similarly be obtained by maximization of the hypervolume under the manifold (HUM). Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases. Results: We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem, which we refer to as Spherically Constrained Optimization Routine (SCOR). Through extensive simulation studies, we demonstrate that the proposed method achieves better performance than existing methods including the step-down algorithm. Finally, we illustrate the proposed method to predict the severity of swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck. Conclusions: Our proposed method addresses an important challenge in combining multiple biomarkers to predict an ordinal outcome. This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various stages of progression or a toxicity with multiple grades of severity. We provide the implementation of our proposed SCOR method as an R package, available online at https://CRAN.R-project.org/package=SCOR. [ABSTRACT FROM AUTHOR]

Background: Identification of features is a critical task in microbiome studies that is complicated by the fact that microbial data are high dimensional and heterogeneous. Masked by the complexity of the data, the problem of separating signals (differential features between groups) from noise (features that are not differential between groups) becomes challenging and troublesome. For instance, when performing differential abundance tests, multiple testing adjustments tend to be overconservative, as the probability of a type I error (false positive) increases dramatically with the large numbers of hypotheses. Moreover, the grouping effect of interest can be obscured by heterogeneity. These factors can incorrectly lead to the conclusion that there are no differences in the microbiome compositions. Results: We translate and represent the problem of identifying differential features, which are differential in two-group comparisons (e.g., treatment versus control), as a dynamic layout of separating the signal from its random background. More specifically, we progressively permute the grouping factor labels of the microbiome samples and perform multiple differential abundance tests in each scenario. We then compare the signal strength of the most differential features from the original data with their performance in permutations, and will observe a visually apparent decreasing trend if these features are true positives identified from the data. Simulations and applications on real data show that the proposed method creates a U-curve when plotting the number of significant features versus the proportion of mixing. The shape of the U-Curve can convey the strength of the overall association between the microbiome and the grouping factor. We also define a fragility index to measure the robustness of the discoveries. Finally, we recommend the identified features by comparing p-values in the observed data with p-values in the fully mixed data. Conclusions: We have developed this into a user-friendly and efficient R-shiny tool with visualizations. By default, we use the Wilcoxon rank sum test to compute the p-values, since it is a robust nonparametric test. Our proposed method can also utilize p-values obtained from other testing methods, such as DESeq. This demonstrates the potential of the progressive permutation method to be extended to new settings. [ABSTRACT FROM AUTHOR]

Purpose: 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.Methods and Materials: 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 (CTVT) on the external test set.Results: 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 CTVT 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 CTVT 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 CTVT 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.Conclusions: 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. [ABSTRACT FROM AUTHOR]

Fatigue is the most prevalent symptom of cancer and its treatments. Changes in the intestinal microbiome have been identified in chronic fatigue syndrome and other neuropsychiatric disorders, and cancer patients. However, the association between intestinal microbiome and fatigue in patients with advanced cancers has not been evaluated. Understanding the connection between intestinal microbiome and fatigue will provide interventional and therapeutic opportunities to manipulate the microbiome to improve fatigue and other patients' reported outcomes. In this project, we aimed to identify associations between microbiome composition and fatigue in advanced cancer patients. In this cross-sectional observational study at a tertiary cancer care center, we included 88 patients with advanced, metastatic, unresectable cancers who were in a washout period from chemotherapy. We measured fatigue using the MD Anderson Symptom Inventory—Immunotherapy fatigue score, and used 16srRNA to analyze intestinal microbiome. Using correlation analysis we found that Eubacterium hallii was negatively associated with fatigue severity scores (r = − 0.30, p = 0.005), whereas Cosenzaea was positively associated with fatigue scores (r = 0.33, p = 0.0002). We identified microbial species that exhibit distinct composition between high-fatigued and low-fatigued cancer patients. Further studies are warranted to investigate whether modulating the microbiome reduces cancer patients' fatigue severity and improves their quality of life. [ABSTRACT FROM AUTHOR]

Background: The estimation of microbial networks can provide important insight into the ecological relationships among the organisms that comprise the microbiome. However, there are a number of critical statistical challenges in the inference of such networks from high-throughput data. Since the abundances in each sample are constrained to have a fixed sum and there is incomplete overlap in microbial populations across subjects, the data are both compositional and zero-inflated. Results: We propose the COmpositional Zero-Inflated Network Estimation (COZINE) method for inference of microbial networks which addresses these critical aspects of the data while maintaining computational scalability. COZINE relies on the multivariate Hurdle model to infer a sparse set of conditional dependencies which reflect not only relationships among the continuous values, but also among binary indicators of presence or absence and between the binary and continuous representations of the data. Our simulation results show that the proposed method is better able to capture various types of microbial relationships than existing approaches. We demonstrate the utility of the method with an application to understanding the oral microbiome network in a cohort of leukemic patients. Conclusions: Our proposed method addresses important challenges in microbiome network estimation, and can be effectively applied to discover various types of dependence relationships in microbial communities. The procedure we have developed, which we refer to as COZINE, is available online at https://github.com/MinJinHa/COZINE. [ABSTRACT FROM AUTHOR]

Purpose: In this study, we investigated registration methods for estimating the large interfractional sigmoid deformations that occur between external beam radiation therapy (EBRT) and brachytherapy (BT) for cervical cancer.Methods and Materials: Sixty-three patients were retrospectively analyzed. The sigmoid colon was delineated on 2 computed tomography images acquired during EBRT (without applicator) and BT (with applicator) for each patient. Five registration approaches were compared to propagate the contour of the sigmoid from BT to EBRT anatomies: rigid registration, commercial hybrid (ANAtomically CONstrained Deformation Algorithm), controlling ROI surface projection of RayStation, and the classical and constrained symmetrical thin-plate spline robust point matching (sTPS-RPM) methods. Deformation of the sigmoid due to insertion of the BT applicator was reported. Registration performance was compared by using the Dice similarity coefficient (DSC), distance to agreement, and Hausdorff distance. The 2 sTPS-RPM methods were compared by using surface triangle quality criteria between deformed surfaces. Using the deformable approaches, the BT dose of the sigmoid was deformed toward the EBRT anatomy. The displacement and discrepancy between the deformable methods to propagate the planned D1cm3 and D2cm3 of the sigmoid from BT to EBRT anatomies were reported for 55 patients.Results: Large and complex deformations of the sigmoid were observed for each patient. Rigid registration resulted in poor sigmoid alignment with a mean DSC of 0.26. Using the contour to drive the deformation, ANAtomically CONstrained Deformation Algorithm was able to slightly improve the alignment of the sigmoid with a mean DSC of 0.57. Using only the sigmoid surface as controlling ROI, the mean DSC was improved to 0.79. The classical and constrained sTPS-RPM methods provided mean DSCs of 0.95 and 0.96, respectively, with an average inverse consistency error <1 mm. The constrained sTPS-RPM provided more realistic deformations and better surface topology of the deformed sigmoids. The planned mean (range) D1cm3 and D2cm3 of the sigmoid were 13.4 Gy (1-24.1) and 12.2 Gy (1-21.5) on the BT anatomy, respectively. Using the constrained sTPS-RPM to deform the sigmoid from BT to EBRT anatomies, these hotspots had a mean (range) displacement of 27.1 mm (6.8-81).Conclusions: Large deformations of the sigmoid were observed between the EBRT and BT anatomies, suggesting that the D1cm3 and D2cm3 of the sigmoid would unlikely to be at the same position throughout treatment. The proposed constrained sTPS-RPM seems to be the preferred approach to manage the large deformation due to BT applicator insertion. Such an approach could be used to map the EBRT dose to the BT anatomy for personalized BT planning optimization. [ABSTRACT FROM AUTHOR]

• The overall pass rate has remained unchanged over the data's time period. • The complexity of treatment plans has increased. • Complexity metrics show good predictive power in determining output parameters. • One complexity metric is insufficient to monitor treatment plans. To elucidate the important factors and their interplay that drive performance on IMRT phantoms from the Imaging and Radiation Oncology Core (IROC). IROC's IMRT head and neck phantom contains two targets and an organ at risk. Point and 2D dose are measured by TLDs and film, respectively. 1,542 irradiations between 2012–2020 were retrospectively analyzed based on output parameters, complexity metrics, and treatment parameters. Univariate analysis compared parameters based on pass/fail, and random forest modeling was used to predict output parameters and determine the underlying importance of the variables. The average phantom pass rate was 92% and has not significantly improved over time. The step-and-shoot irradiation technique had significantly lower pass rates that significantly affected other treatment parameters' pass rates. The complexity of plans has significantly increased with time, and all aperture-based complexity metrics (except MCS) were associated with the probability of failure. Random forest-based prediction of failure had an accuracy of 98% on held-out test data not used in model training. While complexity metrics were the most important contributors, the specific metric depended on the set of treatment parameters used during the irradiation. With the prevalence of errors in radiotherapy, understanding which parameters affect treatment delivery is vital to improve patient treatment. Complexity metrics were strongly predictive of irradiation failure; however, they are dependent on the specific treatment parameters. In addition, the use of one complexity metric is insufficient to monitor all aspects of the treatment plan. [ABSTRACT FROM AUTHOR]

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes. In contrast, metagenomic and whole genome shotgun (WGS) sequencing involve sequencing from random fragments of the entire gene, making identification of consistent marker genes challenging owing to the vast diversity of genomic regions, resulting in a scarcity of robust tools for constructing phylogenetic trees. Although bacterial sequence tree construction tools exist for upstream bioinformatics, many downstream researchers-those integrating these trees into statistical models or machine learning-are either unaware of these tools or find them difficult to use due to the steep learning curve of processing raw sequences. This is compounded by the fact that public datasets often lack phylogenetic trees, providing only abundance tables and taxonomic classifications. To address this, we present a comprehensive review of phylogenetic tree construction techniques for microbiome data (16S rRNA or whole-genome shotgun sequencing). We outline the strengths and limitations of current methods, offering expert insights and step-by-step guidance to make these tools more accessible and widely applicable in quantitative microbiome data analysis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genomics or imaging studies. We distinguish between the predictors, which are the features utilized in the outcome prediction model, and the subject-level covariates, which modulate the effects of the predictors on the outcome. We construct a varying coefficients modeling framework where we infer a network among the predictor variables and utilize this network information to encourage the selection of related predictors. We employ variable selection spike-and-slab priors that enable the selection of both network-linked predictor variables and covariates that modify the predictor effects. We demonstrate through simulation studies that our method outperforms existing alternative methods in terms of both feature selection and predictive accuracy. We illustrate VERGE with an application to characterizing the influence of gut microbiome features on obesity, where we identify a set of microbial taxa and their ecological dependence relations. We allow subject-level covariates, including sex and dietary intake variables to modify the coefficients of the microbiome predictors, providing additional insight into the interplay between these factors., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Purpose: The Imaging Radiation Oncology Core (IROC) head and neck (H&N) phantom is used to credential institutions for intensity modulated radiation therapy delivery for all anatomic sites where delivery of modulated therapy is a primary challenge. This study evaluated how appropriate the use of this phantom is for varied clinical anatomy by evaluating how closely the IROC H&N phantom described clinical dose errors from beam modeling compared with various anatomic sites., Methods and Materials: The multileaf collimator (MLC) offset, transmission, percent depth dose, and 7 additional beam modeling parameters for a Varian accelerator were modified in RayStation to match community data at the 2.5th, 25th, 50th, 75th, and 97.5th percentile levels. Modifications were evaluated on 25 H&N phantom cases and 25 clinical cases (H&N, prostate, lung, mesothelioma, and brain), generating 2000 plan perturbations. Differences in mean dose delivered to clinical target volumes and maximum dose to organs at risk were compared between phantom and clinical plans to assess the relationship between dose deviations in phantom versus clinical target volumes and as a function of 18 different complexity metrics., Results: Perturbations to MLC offset and transmission parameters demonstrated the greatest impact on dose accuracy for phantom and clinical plans (for all anatomic sites). The phantom demonstrated equivalent or greater sensitivity to these parameter perturbations compared with clinical sites, largely aligning with treatment complexity. The mean MLC gap best described the impact of errors in treatment planning system beam modeling parameters in phantom plans and clinical plans from various anatomic sites., Conclusions: When compared across various anatomic sites, the IROC H&N credentialing phantom exhibited similar or greater sensitivity to errors in the treatment planning system. As such, it is a suitable surrogate device for assessing institutional performance across various anatomic sites. If an institution successfully irradiates the phantom, that result confers confidence that intensity modulated radiation therapy to a wide range of anatomic sites can be successfully delivered by the institution., (Copyright © 2024. Published by Elsevier Inc.)

Acute lower gastrointestinal GVHD (aLGI-GVHD) is a serious complication of allogeneic hematopoietic stem cell transplantation. Although the intestinal microbiota is associated with the incidence of aLGI-GVHD, how the intestinal microbiota impacts treatment responses in aLGI-GVHD has not been thoroughly studied. In a cohort of patients with aLGI-GVHD (n = 37), we found that non-response to standard therapy with corticosteroids was associated with prior treatment with carbapenem antibiotics and a disrupted fecal microbiome characterized by reduced abundances of Bacteroides ovatus. In a murine GVHD model aggravated by carbapenem antibiotics, introducing B. ovatus reduced GVHD severity and improved survival. These beneficial effects of Bacteroides ovatus were linked to its ability to metabolize dietary polysaccharides into monosaccharides, which suppressed the mucus-degrading capabilities of colonic mucus degraders such as Bacteroides thetaiotaomicron and Akkermansia muciniphila, thus reducing GVHD-related mortality. Collectively, these findings reveal the importance of microbiota in aLGI-GVHD and therapeutic potential of B. ovatus., Competing Interests: Declaration of interests R.R.J. has served as a consultant or advisory board member for Postbiotics Plus, Merck, Microbiome DX, Karius, MaaT Pharma, LISCure, Seres, Kaleido, and Prolacta and has received patent license fee or stock options from Seres, Kaleido, and Postbiotics Plus. E.J.S. has served as a consultant or advisory board member for Adaptimmune, Axio, Navan, Fibroblasts, and FibroBiologics, NY Blood Center, and Celaid Therapeutics and has received patent license fee from Takeda and Affimed. J.U.P. reports research funding, intellectual property fees, and travel reimbursement from Seres Therapeutics, and consulting fees from DaVolterra, CSL Behring, Crestone Inc, and from MaaT Pharma. J.U.P. serves on an advisory board of and holds equity in Postbiotics Plus Research. J.U.P. has filed intellectual property applications related to the microbiome (reference numbers #62/843,849, #62/977,908, and #15/756,845). Memorial Sloan Kettering Cancer Center (MSK) has financial interests relative to Seres Therapeutics. E.H., M.A.J., J.L.K., and R.R.J. are inventors on a patent application by The University of Texas MD Anderson Cancer Center supported by results of the current study entitled, “Methods and Compositions for Treating Cancer therapy-induced Neutropenic Fever and/or GVHD.”, (Copyright © 2024 Elsevier Inc. All rights reserved.)

Summary: Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks., Availability and Implementation: We provide a Shiny app at https://biostatistics.mdanderson.org/shinyapps/survivalContour/ and an R package available at https://github.com/YushuShi/survivalContour as implementations of this tool., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Gut-microbiota modulation shows promise in improving immune-checkpoint blockade (ICB) response; however, precision biomarker-driven, placebo-controlled trials are lacking. We performed a multicenter, randomized placebo-controlled, biomarker-stratified phase I trial in patients with ICB-naïve metastatic melanoma using SER-401, an orally delivered Firmicutesenriched spore formulation. Fecal microbiota signatures were characterized at baseline; patients were stratified by high versus low Ruminococcaceae abundance prior to randomization to the SER-401 arm (oral vancomycin-preconditioning/SER-401 alone/nivolumab + SER-401), versus the placebo arm [placebo antibiotic/placebo microbiome modulation (PMM)/nivolumab + PMM (NCT03817125)]. Analysis of 14 accrued patients demonstrated that treatment with SER-401 + nivolumab was safe, with an overall response rate of 25% in the SER-401 arm and 67% in the placebo arm (though the study was underpowered related to poor accrual during the COVID-19 pandemic). Translational analyses demonstrated that vancomycin preconditioning was associated with the disruption of the gut microbiota and impaired immunity, with incomplete recovery at ICB administration (particularly in patients with high baseline Ruminococcaceae). These results have important implications for future microbiome modulation trials. Significance: This first-of-its-kind, placebo-controlled, randomized biomarker-driven microbiome modulation trial demonstrated that vancomycin + SER-401 and anti-PD-1 are safe in melanoma patients. Although limited by poor accrual during the pandemic, important insights were gained via translational analyses, suggesting that antibiotic preconditioning and interventional drug dosing regimens should be carefully considered when designing such trials., (©2024 The Authors; Published by the American Association for Cancer Research.)

Tools for genome-wide rapid identification of peptide-major histocompatibility complex targets of T-cell receptors (TCR) are not yet universally available. We present a new antigen screening method, the T-synapse (Tsyn) reporter system, which includes antigen-presenting cells (APC) with a Fas-inducible NF-κB reporter and T cells with a nuclear factor of activated T cells (NFAT) reporter. To functionally screen for target antigens from a cDNA library, productively interacting T cell-APC aggregates were detected by dual-reporter activity and enriched by flow sorting followed by antigen identification quantified by deep sequencing (Tsyn-seq). When applied to a previously characterized TCR specific for the E7 antigen derived from human papillomavirus type 16 (HPV16), Tsyn-seq successfully enriched the correct cognate antigen from a cDNA library derived from an HPV16-positive cervical cancer cell line. Tsyn-seq provides a method for rapidly identifying antigens recognized by TCRs of interest from a tumor cDNA library. See related Spotlight by Makani and Joglekar, p. 515., (©2024 American Association for Cancer Research.)