ORCID Profile
0000-0001-8027-5415
Current Organisations
Lakeshore Animal Health Partners
,
Sunnybrook Research Institute
,
University of Guelph
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Publications
Evaluation of PD-1 and PD-L1 expression in canine urothelial carcinoma cell lines
Publisher: Elsevier BV
Date: 2022
DOI: 10.1016/J.VETIMM.2021.110367
Abstract: Urothelial carcinoma (UC) is the most common urinary tumor in dogs and despite combinational therapies, only modest improvements in survival have been achieved in recent years. Given the utility of monoclonal antibodies against PD-1 and PD-L1 in human UC, we evaluated the protein and mRNA expression in three established canine urothelial carcinoma cell lines. Flow cytometry and western blot analysis confirmed cell line expression of both molecules in varying degrees. Reverse transcription PCR (RT-PCR) documented mRNA expression in all three cell lines for both PD-1 and PD-L1. Fluorescence microscopy was consistent with strong PD-1 and PD-L1 expression in the canine cell lines and was in line with previous human literature. Importantly, the flow cytometry work described in this study revealed higher cell intrinsic PD-1 expression in these cell lines which may have implications for tumor behavior and potential treatment opportunities in the future. Further work is necessary to determine the expression patterns in canine UC and potential for benefit with immunotherapy directed against PD-1 and PD-L1.
A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization
Publisher: MDPI AG
Date: 22-03-2023
DOI: 10.3390/SU15065575
Abstract: In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P& O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for ex le, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.
Comparative Evaluation of Tumor-Infiltrating Lymphocytes in Companion Animals: Immuno-Oncology as a Relevant Translational Model for Cancer Therapy
Publisher: MDPI AG
Date: 13-10-2022
Abstract: Despite the important role of preclinical experiments to characterize tumor biology and molecular pathways, there are ongoing challenges to model the tumor microenvironment, specifically the dynamic interactions between tumor cells and immune infiltrates. Comprehensive models of host-tumor immune interactions will enhance the development of emerging treatment strategies, such as immunotherapies. Although in vitro and murine models are important for the early modelling of cancer and treatment-response mechanisms, comparative research studies involving veterinary oncology may bridge the translational pathway to human studies. The natural progression of several malignancies in animals exhibits similar pathogenesis to human cancers, and previous studies have shown a relevant and evaluable immune system. Veterinary oncologists working alongside oncologists and cancer researchers have the potential to advance discovery. Understanding the host-tumor-immune interactions can accelerate drug and biomarker discovery in a clinically relevant setting. This review presents discoveries in comparative immuno-oncology and implications to cancer therapy.
Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Publisher: Wiley
Date: 08-2023
DOI: 10.1002/PATH.6155
Abstract: The clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Publisher: Wiley
Date: 08-2023
DOI: 10.1002/PATH.6165
Abstract: Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.
Evaluation of lymphocyte‐specific programmed cell death protein 1 receptor expression and cytokines in blood and urine in canine urothelial carcinoma patients
Publisher: Wiley
Date: 12-2021
DOI: 10.1111/VCO.12788
Abstract: Urothelial carcinoma (UC) is the most common urinary tumour in dogs. Despite a range of treatment options, prognosis remains poor in dogs. In people, breakthroughs with checkpoint inhibitors have established new standards of care for muscle‐invasive bladder cancer patients and elevated levels of programmed cell death protein 1 (PD‐1) suggest immune checkpoint blockade may be a novel target for therapy. The goal of this study was to determine if canine UC patients express elevated levels of lymphocyte‐specific PD‐1 and/or urinary cytokine biomarkers compared to healthy dogs. Paired blood and urine were evaluated in 10 canine UC patients, five cystitis patients and 10 control dogs for lymphocyte‐specific PD‐1 expression via flow cytometry and relative cytokine expression. In UC patients, PD‐1 expression was significantly elevated on CD8 + lymphocytes in urine s les. UC patients had a higher CD4:CD8 ratio in their urine compared to healthy dogs, however, there was no significant variation in the CD8:Treg ratio between any group. Cystitis patients had significantly elevated levels of CD4 + T cells, CD8 + T cells and Tregs in their blood s les compared to UC patients and healthy dogs. Cytokine analysis demonstrated significant elevations in urinary cytokines (granulocyte‐macrophage colony‐stimulating factor, interferon‐gamma [IFN‐γ], interleukin (IL)‐2, IL‐6 IL‐7, IL‐8 and IL‐15, IP‐10, KC‐like, IL‐18, monocyte chemoattractant protein‐1 and tumour necrosis factor‐alpha). Several of these cytokines have been previously correlated with both lymphocyte‐specific PD‐1 expression (IFN‐γ, IL‐2, IL‐7 and IL‐15) in muscle‐invasive urothelial carcinoma in humans. Our results provide evidence of urinary lymphocyte PD‐1 expression and future studies could elucidate whether veterinary UC patients will respond favourably to anti‐PD‐1 immune checkpoint inhibitor therapy.
Related Organisations
VCA Toronto Veterinary Emergency & Referral Hospital
Location: Canada
Related Funding Activities
No related grants have been discovered for Dr. Varun Kumar.