Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors.

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

The Impact of an Intergenerational Service Learning Program on Psychological Distress for Homebound Older Adults.

This study examined the benefits of an intergenerational home-based service learning program to reduce psychological distress for homebound older adults. Multivariate regression analyses were conducted with a sample of 182 to examine the association of length of service from the program and presence of caregivers with psychological distress. Findings indicated length of service (ß = -0.15, p < .05) and having a child as a caregiver (ß = -0.14, p < .05) were associated with a reduction in psychological distress. Policies and practice can support a pipeline of geriatric health professionals through innovative service learning models to benefit older adults, caregivers, and students.

Dual Stromal Targeting Sensitizes Pancreatic Adenocarcinoma for Anti-Programmed Cell Death Protein 1 Therapy.

BACKGROUND & AIMS: The stroma in pancreatic ductal adenocarcinoma (PDAC) contributes to its immunosuppressive nature and therapeutic resistance. Herein we sought to modify signaling and enhance immunotherapy efficacy by targeting multiple stromal components through both intracellular and extracellular mechanisms. METHODS: A murine liver metastasis syngeneic model of PDAC was treated with focal adhesion kinase inhibitor (FAKi), anti-programmed cell death protein 1 (PD-1) antibody, and stromal hyaluronan (HA) degradation by PEGylated recombinant human hyaluronidase (PEGPH20) to assess immune and stromal modulating effects of these agents and their combinations. RESULTS: The results showed that HA degradation by PEGPH20 and reduction in phosphorylated FAK expression by FAKi leads to improved survival in PDAC-bearing mice treated with anti-PD-1 antibody. HA degradation in combination with FAKi and anti-PD-1 antibody increases T-cell infiltration and alters T-cell phenotype toward effector memory T cells. FAKi alters the expression of T-cell modulating cytokines and leads to changes in T-cell metabolism and increases in effector T-cell signatures. HA degradation in combination with anti-PD-1 antibody and FAKi treatments reduces granulocytes, including granulocytic- myeloid-derived suppressor cells and decreases C-X-C chemokine receptor type 4 (CXCR4)-expressing myeloid cells, particularly the CXCR4-expressing granulocytes. Anti-CXCR4 antibody combined with FAKi and anti-PD-1 antibody significantly decreases metastatic rates in the PDAC liver metastasis model. CONCLUSIONS: This represents the first preclinical study to identify synergistic effects of targeting both intracellular and extracellular components within the PDAC stroma and supports testing anti-CXCR4 antibody in combination with FAKi as a PDAC treatment strategy.

Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning.

MOTIVATION: Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed under the assumption that similar microRNAs tend to associate with similar diseases. Since such an assumption is not always valid, these methods may not always be applicable to all kinds of MDAs. Considering that the relationship between long noncoding RNA (lncRNA) and different diseases and the co-regulation relationships between the biological functions of lncRNA and microRNA have been established, we propose here a multiview multitask method to make use of the known lncRNA-microRNA interaction to predict MDAs on a large scale. The investigation is performed in the absence of complete information of microRNAs and any similarity measurement for it and to the best knowledge, the work represents the first ever attempt to discover MDAs based on lncRNA-microRNA interactions. RESULTS: In this paper, we propose to develop a deep learning model called MVMTMDA that can create a multiview representation of microRNAs. The model is trained based on an end-to-end multitasking approach to machine learning so that, based on it, missing data in the side information can be determined automatically. Experimental results show that the proposed model yields an average area under ROC curve of 0.8410+/-0.018, 0.8512+/-0.012 and 0.8521+/-0.008 when k is set to 2, 5 and 10, respectively. In addition, we also propose here a statistical approach to predicting lncRNA-disease associations based on these associations and the MDA discovered using MVMTMDA. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/yahuang1991polyu/MVMTMDA/.

A Comparison of Alternative Network Meta-Analysis Methods in the Presence of Nonproportional Hazards: A Case Study in First-Line Advanced or Metastatic Renal Cell Carcinoma.

OBJECTIVES: Network meta-analysis (NMA) of time-to-event outcomes based on constant hazard ratios can result in biased findings when the proportional hazards (PHs) assumption does not hold in a subset of trials. We aimed to summarize the published non-PH NMA methods for time-to-event outcomes, demonstrate their application, and compare their results. METHODS: The following non-PH NMA methods were compared through an illustrative case study in oncology of 4 randomized controlled trials in terms of progression-free survival and overall survival: (1) 1-step or (2) 2-step multivariate NMAs based on traditional survival distributions or fractional polynomials, (3) NMAs with restricted cubic splines for baseline hazard, and (4) restricted mean survival NMA. RESULTS: For progression-free survival, the PH assumption did not hold across trials and non-PH NMA methods better reflected the relative treatment effects over time. The most flexible models (fractional polynomials and restricted cubic splines) fit better to the data than the other approaches. Estimated hazard ratios obtained with different non-PH NMA methods were similar at 5 years of follow-up but differed thereafter in the extrapolations. Although there was no strong evidence of PH violation for overall survival, non-PH NMA methods captured this uncertainty in the relative treatment effects over time. CONCLUSIONS: When the PH assumption is questionable in a subset of the randomized controlled trials, we recommend assessing alternative non-PH NMA methods to estimate relative treatment effects for time-to-event outcomes. We propose a transparent and explicit stepwise model selection process considering model fit, external constraints, and clinical validity. Given inherent uncertainty, sensitivity analyses are suggested.

The Association of Opioid Misuse and Suicidality among People with Disabilities.

Background/Purpose: Past research has linked non-medical prescription opioid use (NMPOU) with suicide, though less focus has been placed among people with disabilities impacted by the opioid epidemic. This study examined the relationship of NMPOU and suicidality among people with and without disabilities while controlling for sociodemographic and other variables. Method: Using the 2019 National Survey on Drug Use and Health, weighted logistic regression analyses were conducted on a cross-sectional sample of 38,088 respondents 18 and older to examine the effect of opioid misuse and disability on serious thoughts of suicide, having a suicide plan, and making a suicide attempt. Results: Findings indicated opioid misuse was associated with 37% higher odds for having a suicide plan in the past year (OR = 1.37, p < .05). The main results indicated the people with disabilities had 30% higher odds for having a suicide plan (OR = 1.30, p < .05) and 73% higher odds for a suicide attempt in the past year (OR = 1.73, p < .001). Interaction analysis found that opioid misuse was associated with higher odds for having a suicide plan (OR = 1.89, p < .01), and having a suicide attempt among those with disabilities (OR = 2.57, p < .01). Conclusion: Results indicated that opioid misuse is a risk factor for suicide, and people with disabilities were at greater risk. Health workers can serve as a nexus point in effectively engaging at-risk people with disabilities in substance use and mental health prevention and recovery services.

Teaching Note-Supporting Homebound Older Adults and Caregivers Through Integrative Service Learning.

The Supporting Older Adults & Caregivers: Integrative Service Learning (SOCIAL) partnership trains undergraduate social work students to provide practical home-based support for older adults with chronic illness and their family caregivers, serving as a pipeline for future leaders in older adult care. More than 2 million older adults are homebound, and 5 million need help leaving their homes due to physical limitations from chronic conditions or cognitive impairments. Family members often assume daily caregiving tasks to assist their loved ones, navigate health care systems, and provide much needed emotional support. The challenges of caregiving are further compounded by the problems associated with insufficient health care professionals who are trained to work with older adults. Integrative service learning models can provide home-based support to older adults while offering valuable, hands-on learning experiences for students. In this teaching note, we acknowledge a need for developing an educational pipeline that can provide training opportunities for students to work with older adults and their caregivers at home. We provide an example of an integrative service-learning model which offers valuable pedagogical experiences to baccalaureate students along with strategies for curriculum building, community engagement, research and evaluation, and program sustainability.

NASW's Supervisory Leaders in Aging: One-year practice change outcomes of an innovative training program for social work supervisors.

Social work supervision addresses professional development, staff support, and management of direct service workers. It is important in aging-services settings because of the impacts of complex and evolving biopsychosocial forces in clients' lives. This article presents findings of the Supervisory Leaders in Aging (SLA) study based on data available one-year post completion. SLA is a 30-hour certificate program for supervisors from aging-services settings addressing best practices in supervision of gerontological practice. The study compares participants' self-assessment of use of supervisory best practices before attending a 3-month workshop series and at two times following graduation. This article reports findings from the analysis of data provided by 114 out of 129 supervisors who completed the program. Participants increased the frequency of use of best practices at both three and 12 months after graduation. These increases were conceptually meaningful and statistically significant among participants who were low users of best practices prior to the program. SLA has led to significant adoption and maintenance of supervisory best practices among participating social work supervisors and especially among those who have not previously adopted routine use of best practices. The interactive small-group learning activities of SLA's educational model should be promoted and the curriculum of best practices should be further refined and tested as SLA is implemented in other communities.

Cost-Effectiveness of Cemiplimab Versus Standard of Care in the United States for First-Line Treatment of Advanced Non-small Cell Lung Cancer With Programmed Death-Ligand 1 Expression ≥50.

OBJECTIVES: This study aimed to evaluate the cost-effectiveness, from a US commercial payer perspective, of cemiplimab versus other first-line treatments for advanced non-small cell lung cancer with programmed death-ligand 1 expression ≥50%. METHODS: A 30-year "partitioned survival" model was constructed. Overall survival and progression-free survival were estimated by applying time-varying hazard ratios from a network meta-analysis of randomized clinical trials. Overall survival and progression-free survival were estimated from EMPOWER-Lung 1 (cemiplimab monotherapy vs chemotherapy) and KEYNOTE-024 and KEYNOTE-042 (pembrolizumab monotherapy vs chemotherapy). Drug acquisition costs were based on published 2020 US list prices. A 3% discount rate was applied to life-years, quality-adjusted life-years (QALYs), and costs. A deterministic analysis was performed on the base case; 1-way sensitivity and probabilistic sensitivity analyses assessed model and parameter uncertainties. RESULTS: Cemiplimab was associated with increased time in the "preprogression" (13.08 vs 7.90 and 6.08 months) and "postprogression" (47.30 vs 29.49 and 14.78 months) health states versus pembrolizumab and chemotherapy, respectively. Compared with pembrolizumab and chemotherapy, cemiplimab generated 1.00 (95% CI -0.266 to 2.440) and 1.78 (95% CI 0.607-3.20) incremental QALYs, respectively, with incremental cost-effectiveness ratios of $68 254 and $89 219 per QALY for cemiplimab versus pembrolizumab and cemiplimab versus chemotherapy, respectively. The probability of cemiplimab being cost-effective at a willingness-to-pay threshold of $100 000 to $150 000 per QALY was 62% to 76% versus pembrolizumab and 56% to 84% versus chemotherapy. CONCLUSIONS: Findings suggest that cemiplimab, versus pembrolizumab or versus chemotherapy, is a cost-effective first-line treatment option for advanced non-small cell lung cancer with programmed death-ligand 1 expression ≥50%.

Graph convolution for predicting associations between miRNA and drug resistance.

MOTIVATION: MicroRNA (miRNA) therapeutics is becoming increasingly important. However, aberrant expression of miRNAs is known to cause drug resistance and can become an obstacle for miRNA-based therapeutics. At present, little is known about associations between miRNA and drug resistance and there is no computational tool available for predicting such association relationship. Since it is known that miRNAs can regulate genes that encode specific proteins that are keys for drug efficacy, we propose here a computational approach, called GCMDR, for finding a three-layer latent factor model that can be used to predict miRNA-drug resistance associations. RESULTS: In this paper, we discuss how the problem of predicting such associations can be formulated as a link prediction problem involving a bipartite attributed graph. GCMDR makes use of the technique of graph convolution to build a latent factor model, which can effectively utilize information of high-dimensional attributes of miRNA/drug in an end-to-end learning scheme. In addition, GCMDR also learns graph embedding features for miRNAs and drugs. We leveraged the data from multiple databases storing miRNA expression profile, drug substructure fingerprints, gene ontology and disease ontology. The test for performance shows that the GCMDR prediction model can achieve AUCs of 0.9301 ± 0.0005, 0.9359 ± 0.0006 and 0.9369 ± 0.0003 based on 2-fold, 5-fold and 10-fold cross validation, respectively. Using this model, we show that the associations between miRNA and drug resistance can be reliably predicted by properly introducing useful side information like miRNA expression profile and drug structure fingerprints. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/yahuang1991polyu/GCMDR/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convolutional neural network.

MOTIVATION: Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. However, detection of circRNA-disease associations by biological experiments alone is often blind, limited to small scale, high cost and time consuming. Therefore, there is an urgent need for reliable computational methods to rapidly infer the potential circRNA-disease associations on a large scale and to provide the most promising candidates for biological experiments. RESULTS: In this article, we propose an efficient computational method based on multi-source information combined with deep convolutional neural network (CNN) to predict circRNA-disease associations. The method first fuses multi-source information including disease semantic similarity, disease Gaussian interaction profile kernel similarity and circRNA Gaussian interaction profile kernel similarity, and then extracts its hidden deep feature through the CNN and finally sends them to the extreme learning machine classifier for prediction. The 5-fold cross-validation results show that the proposed method achieves 87.21% prediction accuracy with 88.50% sensitivity at the area under the curve of 86.67% on the CIRCR2Disease dataset. In comparison with the state-of-the-art SVM classifier and other feature extraction methods on the same dataset, the proposed model achieves the best results. In addition, we also obtained experimental support for prediction results by searching published literature. As a result, 7 of the top 15 circRNA-disease pairs with the highest scores were confirmed by literature. These results demonstrate that the proposed model is a suitable method for predicting circRNA-disease associations and can provide reliable candidates for biological experiments. AVAILABILITY AND IMPLEMENTATION: The source code and datasets explored in this work are available at https://github.com/look0012/circRNA-Disease-association. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative efficacy and safety of adjuvant nivolumab versus other treatments in adults with resected melanoma: a systematic literature review and network meta-analysis.

BACKGROUND: Immune checkpoint inhibitors and targeted therapies are approved for adjuvant treatment of patients with resected melanoma; however, they have not been compared in randomized controlled trials (RCTs). We compared the efficacy and safety of adjuvant nivolumab with other approved treatments using available evidence from RCTs in a Bayesian network meta-analysis (NMA). METHODS: A systematic literature review was conducted through May 2019 to identify relevant RCTs evaluating approved adjuvant treatments. Outcomes of interest included recurrence-free survival (RFS)/disease-free survival (DFS), distant metastasis-free survival (DMFS), all-cause grade 3/4 adverse events (AEs), discontinuations, and discontinuations due to AEs. Time-to-event outcomes (RFS/DFS and DMFS) were analyzed both assuming that hazard ratios (HRs) are constant over time and that they vary. RESULTS: Of 26 identified RCTs, 19 were included in the NMA following a feasibility assessment. Based on HRs for RFS/DFS, the risk of recurrence with nivolumab was similar to that of pembrolizumab and lower than that of ipilimumab 3 mg/kg, ipilimumab 10 mg/kg, or interferon. Risk of recurrence with nivolumab was similar to that of dabrafenib plus trametinib at 12 months, however, was lower beyond 12 months (HR [95% credible interval] at 24 months, 0.46 [0.27-0.78]; at 36 months, 0.28 [0.14-0.59]). Based on HRs for DMFS, the risk of developing distant metastases was lower with nivolumab than with ipilimumab 10 mg/kg or interferon and was similar to dabrafenib plus trametinib. CONCLUSION: Adjuvant therapy with nivolumab provides an effective treatment option with a promising risk-benefit profile.

Blocking PGE2-induced tumour repopulation abrogates bladder cancer chemoresistance.

Cytotoxic chemotherapy is effective in debulking tumour masses initially; however, in some patients tumours become progressively unresponsive after multiple treatment cycles. Previous studies have demonstrated that cancer stem cells (CSCs) are selectively enriched after chemotherapy through enhanced survival. Here we reveal a new mechanism by which bladder CSCs actively contribute to therapeutic resistance via an unexpected proliferative response to repopulate residual tumours between chemotherapy cycles, using human bladder cancer xenografts. Further analyses demonstrate the recruitment of a quiescent label-retaining pool of CSCs into cell division in response to chemotherapy-induced damages, similar to mobilization of normal stem cells during wound repair. While chemotherapy effectively induces apoptosis, associated prostaglandin E2 (PGE2) release paradoxically promotes neighbouring CSC repopulation. This repopulation can be abrogated by a PGE2-neutralizing antibody and celecoxib drug-mediated blockade of PGE2 signalling. In vivo administration of the cyclooxygenase-2 (COX2) inhibitor celecoxib effectively abolishes a PGE2- and COX2-mediated wound response gene signature, and attenuates progressive manifestation of chemoresistance in xenograft tumours, including primary xenografts derived from a patient who was resistant to chemotherapy. Collectively, these findings uncover a new underlying mechanism that models the progressive development of clinical chemoresistance, and implicate an adjunctive therapy to enhance chemotherapeutic response of bladder urothelial carcinomas by abrogating early tumour repopulation.

Comparing Efficacy, Safety, and Preinfusion Period of Axicabtagene Ciloleucel versus Tisagenlecleucel in Relapsed/Refractory Large B Cell Lymphoma.

Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are autologous anti-CD19 chimeric antigen receptor T (CAR T) cell therapies for the treatment of patients with relapsed/refractory large B cell lymphoma (RR-LBCL). Both can induce durable responses; however, cross-trial comparisons are difficult due to differences in study design. In this study, the registration trials of axi-cel and tisa-cel were compared using a matching adjusted indirect comparison (MAIC). A MAIC was performed to adjust for differences in patient characteristics between trials. The estimates for the ZUMA-1 (axi-cel) trial were adjusted using patient-level data to match the study population in JULIET (tisa-cel) for key variables: International Prognostic Index), Eastern Cooperative Oncology Group score, stage, refractoriness or relapsed disease, double/triple hit status, cell of origin, and number of prior lines of therapy. The endpoints analyzed were response, overall survival (OS), and adverse events. After adjusting for differences in patient characteristics between trials, axi-cel was associated with a greater objective response rate (relative risk [RR]=1.61; 95% confidence interval [CI], 1.29 to 2.01) and complete response (RR = 1.62; 95% CI, 1.16 to 2.27) than tisa-cel among patients who underwent infusion. The OS from infusion onward comparing axi-cel to tisa-cel had a hazard ratio of 0.51 (95% CI, 0.31 to 0.83). The indirect comparison showed a higher rate of grade 1 to 2 cytokine release syndrome (CRS) in ZUMA-1 compared with JULIET (RR = 2.03; 95% CI, 1.55 to 2.65) and similar rates of grade ≥3 CRS and neurologic events. In the absence of a direct head-to-head study, the MAIC statistical technique suggests axi-cel may have superior efficacy but a greater risk of grade 1 to 2 CRS. Future real-world studies can further inform the relative efficacy and safety of CAR T therapies in RR-LBCL.

Plasminogen activator inhibitor-2 (PAI-2) overexpression supports bladder cancer development in PAI-1 knockout mice in N-butyl-N- (4-hydroxybutyl)-nitrosamine- induced bladder cancer mouse model.

BACKGROUND: Accumulating evidence suggests that plasminogen activator inhibitor-1 (PAI-1) plays an important role in bladder tumorigenesis by regulating cell cycle. However, it remains unclear whether and how inhibition of PAI-1 suppresses bladder tumorigenesis. METHODS: To elucidate the therapeutic effect of PAI-1 inhibition, we tested its tumorigenicity in PAI-1 knockout (KO) mice exposed to a known bladder carcinogen. RESULTS: PAI-1 deficiency did not inhibit carcinogen-induced bladder cancer in mice although carcinogen-exposed wild type mice significantly increased PAI-1 levels in bladder tissue, plasma and urine. We found that PAI-1 KO mice exposed to carcinogen tended to upregulate protein C inhibitor (PAI-3), urokinase-type plasminogen activator (uPA) and tissue-type PA (tPA), and significantly increased PAI-2, suggesting a potential compensatory function of these molecules when PAI-1 is abrogated. Subsequent studies employing gene expression microarray using mouse bladder tissues followed by post hoc bioinformatics analysis and validation experiments by qPCR and IHC demonstrated that SERPING1 is further downregulated in PAI-1 KO mice exposed to BBN, suggesting that SERPING1 as a potential missing factor that regulate PAI-2 overexpression (compensation pathway). CONCLUSIONS: These results indicate that serpin compensation pathway, specifically PAI-2 overexpression in this model, supports bladder cancer development when oncoprotein PAI-1 is deleted. Further investigations into PAI-1 are necessary in order to identify true potential targets for bladder cancer therapy.

Chest computed tomography for staging renal tumours: validation and simplification of a risk prediction model from a large contemporary retrospective cohort.

OBJECTIVES: To externally validate a nomogram recently proposed by Larcher et al. (BJU Int. 2017; 120: 490) and to develop a simplified model with comparable accuracy to guide on the need for staging chest computed tomography (CT) for patients with new renal masses. PATIENTS AND METHODS: We analysed the data of 1082 consecutive patients with unilateral enhancing renal masses referred to urology multidisciplinary team meetings at two centres between 2011 and 2017. All patients underwent a staging chest CT at diagnosis. We fitted multivariable logistic regression models and tested the Larcher model performance using area under the receiver-operating curve (AUC), calibration and decision curve analysis. RESULTS: Forty-two patients (3.9%) had a positive chest CT. The Larcher nomogram had an AUC of 83.8% (95% confidence interval [CI] 77.1-90.6), but was only moderately well calibrated (calibration-in-the-large = -0.61, slope = 0.82). Specifically, the nomogram overestimated the risk of positive chest CT, and the magnitude of miscalibration increased with increasing predicted risks. Using a stepwise backward approach, a new model was developed including tumour size, nodal stage and systemic symptoms. Compared with the Larcher model, the new model had a similar AUC (82.7% [95% CI 75.5-90.0]), but improved calibration and clinical net benefit. The predicted risk of positive chest CT was <1% in the low-risk group and 1.9-79.9% in the high-risk group. CONCLUSION: The Larcher nomogram is an accurate prediction tool that was moderately well calibrated with our dataset. However, our simplified model has similar accuracy and uses more objective variables available from referral, so may be easier to incorporate into clinical practice. The low-risk group from our model (tumour size ≤4 cm and no systemic symptoms) had a risk of positive chest CT <1%, suggesting these patients may forego chest CT.

Shaving Is an Epiphenomenon of Type I and II Anti-CD20-Mediated Phagocytosis, whereas Antigenic Modulation Limits Type I Monoclonal Antibody Efficacy.

Rituximab is an anti-CD20 mAb used in the treatment of B cell malignancies. Loss of surface CD20 Ag from the surface of target cells is thought to be one mechanism governing resistance to rituximab, but how this occurs is not completely understood. Two explanations for this have been proposed: antigenic modulation whereby mAb:CD20 complexes are internalized in a B cell intrinsic process and shaving, in which mAb:CD20 complexes undergo trogocytic removal by effector cells, such as macrophages. However, there is conflicting evidence as to which predominates in clinical scenarios and hence the best strategies to overcome resistance. In this study, we investigated the relative importance of modulation and shaving in the downregulation of surface mAb:CD20. We used both murine and human systems and treated ex vivo macrophages with varying concentrations of non-FcγR-interacting beads to achieve differential macrophage saturation states, hence controllably suppressing further phagocytosis of target cells. We then monitored the level and localization of mAb:CD20 using a quenching assay. Suppression of phagocytosis with bead treatment decreased shaving and increased modulation, suggesting that the two compete for surface rituximab:CD20. Under all conditions tested, modulation predominated in rituximab loss, whereas shaving represented an epiphenomenon to phagocytosis. We also demonstrate that the nonmodulating, glycoengineered, type II mAb obinutuzumab caused a modest but significant increase in shaving compared with type II BHH2 human IgG1 wild-type mAb. Therefore, shaving may represent an important mechanism of resistance when modulation is curtailed, and glycoengineering mAb to increase affinity for FcγR may enhance resistance because of shaving.

Age Differences in Non-Medical Prescription Opioid Use and Psychological Distress.

Background/Purpose: Prescription opioid use has been recognized as an epidemic in the United States and globally. More research is needed to understand the association of opioids and mental health for older adults. This study examined age differences in the association of non-medical prescription opioid use (NMPOU) and psychological distress, with a focus on older adult populations. Methods: This study used the 2016 National Survey on Drug Use and Health (NSDUH), and included 37,842 adults aged 18 and older. Weighted multiple regression and logistic regression analyses were used to examine the association of NMPOU and psychological distress, measured by the Kessler Psychological Distress Scale (K6). Results: NMPOU was associated with higher psychological distress (b = 0.48, SE = 0.16, p < .01). For those 50 and older, NMPOU was associated with 224% increased odds of meeting the clinical threshold for having a serious mental illness (SMI; OR = 2.24, p < .01, 95% CI: 1.23, 4.09). Conclusions: Although the prevalence of NMPOU and psychological distress trended downward throughout the lifespan, the association of NMPOU on SMI was highest among the youngest and oldest adults. These findings highlight the need for services and supports that are tailored for older adult populations. Future research is needed to investigate vulnerabilities from life stage stressors specific to older adults, which may account for the disproportionate odds of opioid use on mental health pathology. Interprofessional collaboration is needed among geriatric professionals to provide effective mental health treatment for this at-risk population.

A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features.

Constructing prediction models from expression profiles for large scale lncRNA-miRNA interaction profiling.

Motivation: The interaction of miRNA and lncRNA is known to be important for gene regulations. However, not many computational approaches have been developed to analyze known interactions and predict the unknown ones. Given that there are now more evidences that suggest that lncRNA-miRNA interactions are closely related to their relative expression levels in the form of a titration mechanism, we analyzed the patterns in large-scale expression profiles of known lncRNA-miRNA interactions. From these uncovered patterns, we noticed that lncRNAs tend to interact collaboratively with miRNAs of similar expression profiles, and vice versa. Results: By representing known interaction between lncRNA and miRNA as a bipartite graph, we propose here a technique, called EPLMI, to construct a prediction model from such a graph. EPLMI performs its tasks based on the assumption that lncRNAs that are highly similar to each other tend to have similar interaction or non-interaction patterns with miRNAs and vice versa. The effectiveness of the prediction model so constructed has been evaluated using the latest dataset of lncRNA-miRNA interactions. The results show that the prediction model can achieve AUCs of 0.8522 and 0.8447 ± 0.0017 based on leave-one-out cross validation and 5-fold cross validation. Using this model, we show that lncRNA-miRNA interactions can be reliably predicted. We also show that we can use it to select the most likely lncRNA targets that specific miRNAs would interact with. We believe that the prediction models discovered by EPLMI can yield great insights for further research on ceRNA regulation network. To the best of our knowledge, EPLMI is the first technique that is developed for large-scale lncRNA-miRNA interaction profiling. Availability and implementation: Matlab codes and dataset are available at https://github.com/yahuang1991polyu/EPLMI/. Contact: yu-an.huang@connect.polyu.hk or zhuhongyou@ms.xjb.ac.cn. Supplementary information: Supplementary data are available at Bioinformatics online.