Citations

Diffuse B-cell lymphomas are the most prevalent canine hematologic malignancies, and their clinical presentation resembles that of human B-cell non-Hodgkin lymphomas (NHLs). However, a lack of a clear subtyping framework perpetuates imprecise treatment approaches that fail to address mechanisms of therapy resistance. Here, we employed deep phenotyping via serial sampling and longitudinal transcriptomics to evaluate intratumoral composition and therapy response in a 6-year-old neutered male goldendoodle with stage 5A marginal zone lymphoma (MZL). Following sequential treatment consisting of a single IV dose of oncolytic vesicular stomatitis virotherapy (VSV) and standard CHOP chemotherapy 30 days later, we performed scRNAseq on seven serial lymph node biopsies and spatial sequencing on a resection taken 10 days post-VSV treatment. B cells (>90%) comprised three transcriptionally distinct and temporally stable subpopulations, with specific copy number profiles and robust spatial organization despite disrupted lymph node architecture. Analysis across subpopulations revealed downregulation of the quiescence program including KLF2, suggesting similarity to KLF2-deficient human MZL. While VSV successfully reached target B cells, downstream transcriptional responses were predominantly localized to the T-cell compartment. T cells (≤6%) showed increased proliferation and upregulation of cytotoxicity markers post-VSV administration, enriching for leukocyte-mediated cytotoxicity pathways consistent with an anti-viral immune response. Ultimately, these results provide an in vivo proof-of-concept for the treatment paradigm in canine lymphoma, while emphasizing the need for improved subtyping frameworks and multidimensional treatment strategies targeting intratumoral heterogeneity.

ABSTRACT Most fracture risk assessment tools use clinical risk factors combined with bone mineral density (BMD) to improve assessment of osteoporosis; however, stratifying fracture risk remains challenging. This study developed a fracture risk assessment tool that uses information about volumetric bone density and three‐dimensional structure, obtained using high‐resolution peripheral quantitative compute tomography (HR‐pQCT), to provide an alternative approach for patient‐specific assessment of fracture risk. Using an international prospective cohort of older adults ( n = 6802) we developed a tool to predict osteoporotic fracture risk, called μFRAC. The model was constructed using random survival forests, and input predictors included HR‐pQCT parameters summarizing BMD and microarchitecture alongside clinical risk factors (sex, age, height, weight, and prior adulthood fracture) and femoral neck areal BMD (FN aBMD). The performance of μFRAC was compared to the Fracture Risk Assessment Tool (FRAX) and a reference model built using FN aBMD and clinical covariates. μFRAC was predictive of osteoporotic fracture (c‐index = 0.673, p < 0.001), modestly outperforming FRAX and FN aBMD models (c‐index = 0.617 and 0.636, respectively). Removal of FN aBMD and all clinical risk factors, except age, from μFRAC did not significantly impact its performance when estimating 5‐year and 10‐year fracture risk. The performance of μFRAC improved when only major osteoporotic fractures were considered (c‐index = 0.733, p < 0.001). We developed a personalized fracture risk assessment tool based on HR‐pQCT that may provide an alternative approach to current clinical methods by leveraging direct measures of bone density and structure. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

ABSTRACT Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p &lt; 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).