Cancer is a major burden of disease worldwide with considerable impact on society. The tide of immunotherapy has finally changed after decades of disappointing results and has become a clinically validated treatment for many cancers. Immunotherapy takes many forms in cancer treatment, including the adoptive transfer of ex vivo activated T cells, oncolytic viruses, natural killer cells, cancer vaccines and administration of antibodies or recombinant proteins that either costimulate cells or block the so-called immune checkpoint pathways. Recently, cancer immunotherapy has received a high degree of attention, which mainly contains the treatments for programmed death ligand 1 (PD-L1), programmed death 1 (PD-1), chimeric antigen receptors (CARs) and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4). Here, this paper reviewed the current understandings of the main strategies in cancer immunotherapy (adoptive cellular immunotherapy, immune checkpoint blockade, oncolytic viruses and cancer vaccines) and discuss the progress in the synergistic design of immune-targeting combination therapies.
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http://dx.doi.org/10.3923/pjbs.2018.135.150 | DOI Listing |
Clin Exp Med
January 2025
Department of Clinical Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Krakow Branch, Poland.
Immune checkpoint inhibitors have improved the treatment of metastatic renal cell carcinoma (RCC), with the combination of nivolumab (NIVO) and ipilimumab (IPI) showing promising results. However, not all patients benefit from these therapies, emphasizing the need for reliable, easily assessable biomarkers. This multicenter study involved 116 advanced RCC patients treated with NIVO + IPI across nine oncology centers in Poland.
View Article and Find Full Text PDFSupport Care Cancer
January 2025
Department of Nursing, Nanfang Hosptial of Southern Medical University, Guangzhou, 510515, People's Republic of China.
Purpose: Our study aim was to understand the (human and organizational) factors influencing fall risk among people with hematological malignancies using the Reason model as a framework, providing insights that can inform the development of safe and effective fall management strategies.
Methods: Purposive sampling was employed to conduct semi-structured interviews with 13 people with hematological malignancies and 12 nurses from the hematology department of a tertiary grade A hospital in Guangzhou from December 2023 to February 2024. The topic analysis method was utilized to analyze the interview data.
J Cancer Educ
January 2025
Department of Pharmacy, Al Rafidain University College, 10001, Baghdad, Iraq.
Chemotherapy-drug interactions (CDIs) pose significant challenges in oncology, affecting treatment efficacy and patient safety. Despite their importance, there is a lack of validated tools to assess oncologists' knowledge of CDIs. This study aimed to develop and validate a comprehensive questionnaire to address this gap and ensure the reliability and validity of the instrument.
View Article and Find Full Text PDFLasers Med Sci
January 2025
Departamento de Biofísica e Biometria Instituto de Biologia Roberto Alcântara Gomes, Universidade do Estado do Rio de Janeiro, Avenida 28 de Setembro, 87, fundos, Vila Isabel, Rio de Janeiro, 20551030, Brazil.
In this article, we aim to evaluate the effects of photobiomodulation on mitochondria quantity, biogenesis, and mitophagy-associated genes in breast cancer (BC) cells. Both models were irradiated with a low-power infrared laser (880 nm, 150 mW) and amber LED (617 nm, 1500 mW), alone or simultaneously. We evaluated the mRNA expression of PINK1 and PGC-1α genes, and the mitochondrial number was assessed based on the ratio of mitochondrial DNA/genomic DNA (mtDNA/gDNA).
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
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