Prostate cancer is the second leading cause of cancer-related death in men. Despite having a relatively lower tumor mutational burden than most tumor types, multiple gene fusions such as have been characterized and linked to more aggressive disease. Individual tumor samples have been found to contain multiple fusions, and it remains unknown whether these fusions increase tumor immunogenicity. Here, we investigated the role of fusion burden on the prevalence and expression of key molecular and immune effectors in prostate cancer tissue specimens that represented the different stages of disease progression and androgen sensitivity, including hormone-sensitive and castration-resistant prostate cancer. We found that tumor fusion burden was inversely correlated with tumor mutational burden and not associated with disease stage. High fusion burden correlated with high immune infiltration, PD-L1 expression on immune cells, and immune signatures, representing activation of T cells and M1 macrophages. High fusion burden inversely correlated with immune-suppressive signatures. Our findings suggest that high tumor fusion burden may be a more appropriate biomarker than tumor mutational burden in prostate cancer, as it more closely associates with immunogenicity, and suggests that tumors with high fusion burden could be potential candidates for immunotherapeutic agents.
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http://dx.doi.org/10.1158/2326-6066.CIR-19-0568 | DOI Listing |
Neural Netw
January 2025
Medical Big Data Lab, Shenzhen Research Institute of Big Data, Shenzhen, 518172, China. Electronic address:
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal performance due to the intricate pathophysiology of the ICH.
View Article and Find Full Text PDFMol Diagn Ther
January 2025
Istituto Europeo di Oncologia, IRCCS, Via Adamello 16, 20139, Milan, Italy.
Background: Predicting response to targeted cancer therapies increasingly relies on both simple and complex genetic biomarkers. Comprehensive genomic profiling using high-throughput assays must be evaluated for reproducibility and accuracy compared with existing methods.
Methods: This study is a multicenter evaluation of the Oncomine™ Comprehensive Assay Plus (OCA Plus) Pan-Cancer Research Panel for comprehensive genomic profiling of solid tumors.
Sensors (Basel)
January 2025
State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 401133, China.
With the advancement of federated learning (FL), there is a growing demand for schemes that support multi-task learning on multi-modal data while ensuring robust privacy protection, especially in applications like intelligent connected vehicles. Traditional FL schemes often struggle with the complexities introduced by multi-modal data and diverse task requirements, such as increased communication overhead and computational burdens. In this paper, we propose a novel privacy-preserving scheme for multi-task federated split learning across multi-modal data (MTFSLaMM).
View Article and Find Full Text PDFCancers (Basel)
January 2025
Division of Hematology Oncology, Akron Children's Hospital, One Perkins Square, Akron, OH 44308, USA.
Inflammation plays a crucial role in wound healing and the host immune response following pathogenic invasion. However, unresolved chronic inflammation can result in tissue fibrosis and genetic alterations that contribute to the pathogenesis of human diseases such as cancer. Recent scientific advancements exploring the underlying mechanisms of malignant cellular transformations and cancer progression have exposed significant disparities between pediatric and adult-onset cancers.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
GITA Lab., Faculty of Engineering, University of Antioquia, Medellín 050010, Colombia.
Background/objectives: Parkinson's disease (PD) affects more than 6 million people worldwide. Its accurate diagnosis and monitoring are key factors to reduce its economic burden. Typical approaches consider either speech signals or video recordings of the face to automatically model abnormal patterns in PD patients.
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