The beneficial effects of electrochemotherapy (ECT) for superficial tumours and, more recently, deep-seated malignancies in terms of local control and quality of life are widely accepted. However, the variability in responses across histotypes needs to be explored. Currently, patient selection for ECT is based on clinical factors (tumour size, histotype, and exposure to previous oncological treatments), whereas there are no biomarkers to predict the response to treatment. In this field, two major areas of investigation can be identified, i.e., tumour cell characteristics and the tumour microenvironment (vasculature, extracellular matrix, and immune infiltrate). For each of these areas, we describe the current knowledge and discuss how to foster further investigation. This review aims to provide a summary of the currently used guiding clinical factors and delineates a research roadmap for future studies to identify putative biomarkers of response to ECT. These biomarkers may allow researchers to improve ECT practice by customising treatment parameters, manipulating the tumour and its microenvironment, and exploring novel therapeutic combinations.
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http://dx.doi.org/10.1016/j.ejso.2021.03.229 | DOI Listing |
JAMA Netw Open
January 2025
Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, the Netherlands.
Importance: Patients with achalasia face a higher risk of developing esophageal cancer (EC), but the surveillance strategies for these patients remain controversial due to the long disease duration and the lack of identified risk factors.
Objective: To investigate the prevalence of esophageal Candida infection among patients with achalasia and to assess the association of Candida infection with EC risk within this population.
Design, Setting, And Participants: This retrospective cohort study included patients with achalasia diagnosed at or referred for treatment and monitoring to the Erasmus University Medical Center in Rotterdam, the Netherlands, between January 1, 1980, and May 31, 2024.
Breast Cancer
January 2025
Department of Pathology and Histotechnology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Exosome markers, CD63 and CD81, belong to the tetraspanin family and are expressed in solid tumors. It has been reported that these tetraspanin family members are prognostic factors in some cancers. However, the expression of CD63 and CD81 in pathological breast cancer specimens has not been reported.
View Article and Find Full Text PDFDiscov Oncol
January 2025
The Department of Experimental Medicine, Meishan City People's Hospital, No. 288, South Fourth Section, Dongpo Avenue, Meishan, 620000, Sichuan, China.
Background: Thyroid carcinoma (THCA) is the most common cancer of the endocrine system. Natural killer (NK) cell play an important role in tumor immune surveillance. The aim of this study was to explore the possible molecular mechanisms involved in NK cell in THCA to help the management and treatment of the disease.
View Article and Find Full Text PDFNaunyn Schmiedebergs Arch Pharmacol
January 2025
Solid Tumor Research Center, Cellular and Molecular Medicine Research Institute, Urmia University of Medical Sciences, Urmia, Iran.
Chemotherapy remains the cornerstone of cancer treatment; however, its efficacy is frequently compromised by the development of chemoresistance. Multidrug resistance (MDR), characterized by the refractoriness of cancer cells to a wide array of chemotherapeutic agents, presents a significant barrier to achieving successful and sustained cancer remission. One critical factor contributing to this chemoresistance is the overexpression of ATP-binding cassette (ABC) transporters.
View Article and Find Full Text PDFSupport Care Cancer
January 2025
Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
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