Year: 2024
Type: article
Abstract: Dear Editor, Our investigation into the mitochondrial proteome dynamics in metastatic melanoma reveals profound metabolic reprogramming that is central to the progression and treatment resistance of BRAF V600E-mutated tumours.These findings highlight significant alterations in mitochondrial function and immune response, opening new avenues for precision medicine by identifying targeted treatments that could disrupt these adaptive pathways and lead to more effective therapeutic strategies.Melanoma, characterized by its aggressive behaviour and high metastatic capacity, faces further challenges with the BRAF V600E mutation, which significantly drives cancer progression and impacts treatment responses.1 Building upon our previous insights into melanoma's molecular dynamics, this study delves deeper into mitochondrial proteome alterations associated with the BRAF V600E mutation.2,3 Through comprehensive analysis of proteomic data from a total of 127 melanoma lymph node metastases, sourced from the Human Melanoma Proteome Atlas project, we demonstrate how specific mitochondrial disruptions enhance melanoma proliferation and facilitate progression towards distant metastasis (Figure 1A) .4 By investigating the specific role of mitochondrial dysfunction in the progression and immune evasion of BRAF V600E-mutated metastatic melanoma, we aim to elucidate the molecular mechanisms that drive aggressive melanoma behaviour and identify potential targets for precision medicine.This approach highlights the critical interplay between mitochondrial function and immune response, paving the way for innovative therapeutic strategies to combat this challenging form of cancer.In this study, we utilized proteomic data from 127 melanoma metastasis samples.Out of these, 78 samples included information about the BRAF mutation status, with 45 harbouring the BRAF V600E mutation and 33 displaying the wild-type (WT) variant.The WT group does not
DOI: 10.1002/ctm2.1773
Authors: Natália Pinto de Almeida, Ágnes Judit Jánosi, Runyu Hong, Ahmad Rajeh, Fábio Nogueira, Leticia Szadai, Beata Szeitz, Indira Pla Parada, Viktória Doma, Nicole Woldmar, Jéssica Guedes, Zsuzsanna Újfaludi, Aron Bartha, Yonghyo Kim, Charlotte Welinder, Bo Baldetorp, Lajos Vince Kemény, Zoltan Pahi, Guihong Wan, Nga Nguyen, Tibor Pankotai, Balázs Győrffy, Krzysztof Pawłowski, Peter Horvatovich, Attila Marcell Szasz, Aniel Sanchez, Magdalena Kuras, Jimmy Rodriguez Murillo, Lazaro Betancourt, Gilberto B. Domont, Yevgeniy R. Semenov, Kun‐Hsing Yu, Ho Jeong Kwon, István Balázs Németh, David Fenyő, Elisabet Wieslander, György Marko‐Varga, Jeovanis Gil
Institutions: Universidade Federal do Rio de Janeiro, Lund University, University of Szeged, New York University, Harvard University, Massachusetts General Hospital, Semmelweis University, Korea Research Institute of Chemical Technology, University of Pecs, HUN-REN Research Centre for Natural Sciences, Warsaw University of Life Sciences, The University of Texas Southwestern Medical Center, University of Groningen, Yonsei University, Tokyo Medical University
Language: English
Topic: Melanoma and MAPK Pathways
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,470
Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, IngaBritt och Arne Lundbergs Forskningsstiftelse, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Fru Berta Kamprads Stiftelse, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of General Medical Sciences, DOD Peer Reviewed Cancer Research Program, Apotekarsocieteten
Awards: G1018740717, G2191576040, G3833354119, G5574549518, G775120557, G8300810847, G8846374798, Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence Approach, Explainable Artificial Intelligence for Melanoma Recurrence Prediction via Integrative Modeling of Multiplexed Imaging with Transcriptomics and Histopathology, Robust, Generalizable, and Fair Machine Learning Models for Biomedicine
OpenAlex ID: W4400856540
