Reid, Kelly

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.

Introduction: Progression-free survival (PFS), a common endpoint used in multiple myeloma (MM) trials, is defined as the time to the earliest occurrence of disease progression or death. Disease progression in MM is determined by the International Myeloma Working Group (IMWG) Uniform Response Criteria based on imaging and biomarkers from blood, urine, and bone marrow biopsy testing. In clinical trials, IMWG-recommended assessments are protocol-based. However, in routine care, they are often performed and recorded differently based on a multitude of factors. Algorithms to ascertain progression events using routinely collected biomarker data are important to classify disease progression and align with trial-defined endpoints. The aim of this study was to construct and evaluate an optimized real-world Progression (rwP) algorithm using real-world data (RWD) and demonstrate how biomarker data are captured and used to derive endpoints in routine practice. Method: A cohort of 270 transplant ineligible newly diagnosed MM (NDMM) patients starting on lenalidomide and dexamethasone (Rd) and meeting select MAIA (NCT02252172) trial eligibility criteria were included from the nationwide Flatiron Health electronic health record-derived deidentified database from 2015 to 2023. A rwP algorithm was developed using key IMWG-recommended biomarkers, including serum protein electrophoresis (SPEP), urine protein electrophoresis (UPEP), and serum free light chains (FLCs). The initial baseline value for each biomarker was identified, if available, during either the pre-treatment period (45 days prior through 7 days after first line treatment (1L) start) or the on-treatment period (8 days after 1L start through 1L end). Available biomarkers were followed for disease assessment. If any subsequent lab value during 1L treatment was less than the initial baseline value, the subsequent “on-treatment baseline” value was adopted as the new lowest value; otherwise, the initial value was used as the “on-treatment baseline” value for assessing progression events. Three hierarchies, which describe the order of utilizing key IMWG-recommended biomarkers to define rwP events, were explored, including: i) strict hierarchy (SH) [SPEP > UPEP > FLCs], ii) partial hierarchy (PH) [SPEP = UPEP > FLCs], and iii) no hierarchy (NH) [SPEP = UPEP = FLCs]. The PH option closely resembles the IMWG criteria, with FLCs only used for rwP assessments when no measurable SPEP/UPEP are identified. The NH approach considers all available biomarkers equally and resembles how they are utilized in real-world clinical practice. The feasibility of requiring confirmatory events following the first suggestion of progression was also explored. Assays following the progression event up to the end of 1L were considered as confirmatory progression events if they met IMWG criteria. Results: We applied the rwP algorithm with each hierarchy to all eligible NDMM patients (N = 270). The SH, PH, and NH approaches respectively identified 106 (39.3%), 106 (39.3%), and 123 (45.6%) patients with progression events. Results from the SH and PH approach are consistent likely due to the rarity of UPEP usage in clinical practice. The NH approach captured more progression events because FLCs are used in the presence of measurable SPEP or UPEP, resulting in 73 progression events captured by FLCs in comparison to 32 from other approaches, potentially increasing false-positive capture due to the volatility of FLC results. Among the 106 PH-identified patients who progressed, 84 (79.2%) had IMWG-recommended tests following their progression event, and 74 (69.8%) had their progression confirmed before the end of 1L, with 35 (33.0%) receiving their confirmatory reading within 2 months after progression. These results suggest that requiring confirmatory testing as part of the rwP is feasible, though it may introduce false negatives for patients receiving clinical progression confirmation instead of getting confirmatory labs in routine clinical practice. Conclusion: We present a comprehensive assessment of important design choices for an rwP algorithm for NDMM, including a hierarchy of key assays and the inclusion of confirmatory testing. Further validation of the algorithm with health care provider assessments and the inclusion of bone marrow biopsy and imaging data is important for evaluating the performance of the algorithm in RWD.