Background: New combinations based on standard therapeutic modalities and immunotherapy require understanding the immunomodulatory properties of traditional treatments. The objective was to evaluate the impact of brachytherapy (BT) on the immune system of cervical cancer and to identify the best modality, High-dose-rate brachytherapy (HDR-BT) vs. Pulsed-dose-rate (PDR-BT), to target it.
Methods: Nineteen patients enrolled in a prospective study received chemoradiation (CRT) and subsequently HDR-BT or PDR-BT. Peripheral blood samples were obtained for immunophenotyping analysis by flow-cytometry before CRT, BT, and two and four weeks after BT. The Friedman one-way ANOVA, Conover post hoc test, and the Wilcoxon signed-rank test were used to compare changes in cell populations at different periods, perform multiple pairwise comparisons and assess differences between treatment groups (PDR and HDR).
Results: Natural killer cells (NKs) were the best target for BT. Patients receiving HDR-BT achieved significantly higher values and longer time of the CD56dimCD16 + NK cells with greater cytotoxic capacity than the PDR-BT group, which presented their highest elevation of CD56-CD16 + NK cells. Furthermore, both BT modalities were associated with an increase in myeloid-derived suppressor cells (MDSCs), related to a worse clinical prognosis. However, there was a decrease in the percentage of CD4 + CD25 + Foxp3 + CD45RA + regulatory T cells (Tregs) in patients receiving HDR-BT, although there were no significant differences between BT.
Conclusions: Immune biomarkers are important predictive determinants in cervical cancer. Higher cytotoxic NK cells and a trend toward lower values of Tregs might support the use of HDR-BT to the detriment of PDR-BT and help develop effective combinations with immunotherapy.
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http://dx.doi.org/10.1186/s12865-023-00559-y | DOI Listing |
Int J Clin Oncol
January 2025
Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka-shi, Saitama, 350-1298, Japan.
Cervical cancer is the third most common malignant tumor in women worldwide in terms of both incidence and mortality. The field of cervical cancer treatment is rapidly evolving, and various combination therapies are being explored to enhance the efficacy of immune checkpoint inhibitors (ICI) and provide new treatment options for patients at different disease stages. Clinical trials involving immune checkpoint inhibitors are now being conducted following a phase 3 trial with cemiplimab, an ICI, which demonstrated a significant improvement in prognosis in advanced or metastatic cervical cancer patients.
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February 2025
Department of Genetics & Biotechnology, University College of Science, Osmania University, Hyderabad, Telangana, India.
Drug resistance and cancer recurrence are major cause of Cervical cancer (CC) patient mortality. Cisplatin (CDDP) is the major drug that has been extremely used in all stages in treating CC, although relapse and malignant instances have been observed as a result of cisplatin resistance in CC. In the present study, we established Cisplatin resistant CC HeLa cell line model and the cytotoxic effects of Andro as a single agent or in combination with CDDP were investigated to assess its potential as a chemotherapeutic agent in cisplatin-resistant HeLa (CisR-HeLa) cells.
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February 2025
Department of Acupuncture, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.
The incidence of cervical cancer is high among women globally. The potential therapeutic efficacy of luteolin in the treatment of cervical cancer has been identified. Therefore, we aim to elucidate the mechanism of action of luteolin in the treatment of cervical cancer through a comprehensive approach that integrates metabolomics with bioinformatics.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, California, USA.
Background: Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement learning (DRL) has recently been introduced to the planning processes of intensity-modulated radiation therapy (IMRT) and brachytherapy for prostate, lung, and cervical cancers. However, existing DRL planning models are built upon the Q-learning framework and rely on weighted linear combinations of clinical metrics for reward calculation.
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