ORCID Profile
0000-0001-6822-2900
Current Organisation
Peter MacCallum Cancer Centre
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Publisher: Cold Spring Harbor Laboratory
Date: 19-07-2023
DOI: 10.1101/2023.07.18.549549
Abstract: Dementia is a largely untreatable syndrome that is epidemiologically associated with metabolic diseases such as type 2 diabetes (T2D) and obesity. Drugs used to treat T2D such as metformin are inexpensive, safely given to millions of people, and have also been reported to slow neurodegeneration. We hypothesised that the neuroprotective benefits of metformin might extend to metabolically healthy in iduals and tested this hypothesis in a mouse prion model that recapitulates key features of human neurodegenerative disease, including synaptic loss and motor impairment. These features and the time course of this model (24 weeks) allows the effects of metabolic risk factors and metformin to be tested and potentially generalised to other forms of neurodegenerative disease. Mice fed a high fat diet (HFD) developed high adiposity with impaired glucose and insulin homeostasis, similar to the effects of chronic obesity seen in humans whereas mice on matched control diet (CD) remain metabolically healthy. Chronic treatment with metformin in HFD-fed mice significantly increased survival and health span relative to vehicle-treated mice. Mice fed a HFD also had a modestly extended health span relative to mice fed CD, as measured by development of motor signs of prion disease. Metformin also significantly extended health span in metabolically healthy CD-fed mice. Using targeted mass spectrometry, we found that metformin reached deep brain structures at likely functional concentrations, raising the intriguing possibility that it may exert its neuroprotective effects directly on the brain. Together, these data broadly support the premise of repurposing metformin for neuroprotection, even in metabolically healthy in iduals.
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.
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2019
No related grants have been discovered for Rashindrie Perera.