Karz, A. | Search bioRxiv

bioRxiv2024

Unveiling common transcriptomic features between melanoma brain metastases and neurodegenerative diseases

Melanoma represents a critical clinical challenge due to its high incidence rates and unfavorable clinical outcomes. This type of skin cancer presents unique adaptability to the brain microenvironment, but its underlying molecular mechanisms are poorly understood. To further characterize its tumor neurobiology, we explore the relation between the transcriptional profiles of melanoma brain metastasis (MBM) and the neurodegenerative diseases Alzheimers disease, Parkinsons disease, and multiple sclerosis. Through an in silico approach, we unveiled the neurodegenerative signature of MBM when compared to melanoma non-brain metastasis (53 dysregulated genes enriched in 11 functional terms) and to non tumor-bearing brain controls (195 dysregulated genes, mostly involved in development and cell differentiation, chromatin remodeling and nucleosome organization, and translation). Two genes, ITGA10 and DNAJC6, emerged as key potential markers, as they are dysregulated in both scenarios. Lastly, we developed a user-friendly web tool (https://bioinfo.cipf.es/metafun-mbm/) as an open source, so that any user can interactively delve into the results.

cancer biology↗

bioRxiv2023

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

Diagnosis of most diseases relies on expert histopathological evaluation of tissue sections by an experienced pathologist. By using standardized staining techniques and an expanding repertoire of markers, a trained eye is able to recognize disease-specific patterns with high accuracy and determine a diagnosis. As efforts to study mechanisms of metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate effects on tumor burden resulting from specific interventions. However, current methods of quantifying tumor burden are low in either resolution or throughput. Artificial neural networks, which can perform in-depth image analyses of tissue sections, provide an opportunity for automated recognition of consistent histopathological patterns. In order to increase the outflow of data collection from preclinical studies, we trained a deep neural network for quantitative analysis of melanoma tumor content on histopathological sections of murine models. This AI-based algorithm, made freely available to academic labs through a web-interface called MetFinder, promises to become an asset for researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

cancer biology↗