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A better understanding of the microenvironment in relation to lymph node metastasis is essential for the development of effective immunotherapeutic strategies against cervical cancer. In the present study, we investigated the microenvironment of tumor-draining lymph nodes of patients with cervical cancer by comprehensive flow cytometry-based phenotyping and enumeration of immune-cell subsets in tumor-negative (LN(-), n = 20) versus tumor-positive lymph nodes (LN(+), n = 8), and by the study of cytokine release profiles (n = 4 for both LN(-) and LN(+)). We found significantly lower CD4(+) and higher CD8(+) T-cell frequencies in LN(+) samples, accompanied by increased surface levels of activation markers (HLA-DR; ICOS; PD-1; CTLA-4) and the memory marker CD45RO. Furthermore, in LN(+), we found increased rates of a potentially regulatory antigen-presenting cell (APC) subset (CD11c(hi)CD14(+)PD-L1(+)) and of myeloid-derived suppressor cell subsets; the LN(+) APC subset correlated with significantly elevated frequencies of FoxP3(+) regulatory T cells (Treg). After in vitro stimulation with different Toll-like receptor (TLR) ligands (PGN; Poly-IC; R848), we observed higher production levels of IL6, IL10, and TNFα but lower levels of IFNγ in LN(+) samples. We conclude that, despite increased T-cell differentiation and activation, a switch to a profound immune-suppressive microenvironment in LN(+) of patients with cervical cancer will enable immune escape. Our data indicate that the CD14(+)PD-L1(+) APC/Treg axis is a particularly attractive and relevant therapeutic target to specifically tackle microenvironmental immune suppression and thus enhances the efficacy of immunotherapy in patients with metastasized cervical cancer.
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http://dx.doi.org/10.1158/2326-6066.CIR-14-0149 | DOI Listing |
Cancer Causes Control
September 2025
College of Public Health, Iowa Cancer Registry, Epidemiology Department, University of Iowa, Iowa City, IA, USA.
Purpose: Human papillomavirus (HPV) causes oral and anogenital cancers, the incidence of which is increasing. Late-stage diagnosis is associated with increased mortality. Neighborhood-level characteristics and distance to place of diagnosis may impact timely diagnosis.
View Article and Find Full Text PDFJpn J Clin Oncol
September 2025
International Health Program, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei City 112, Taipei, Taiwan.
Objectives: Treatment delay can adversely affect cancer prognosis and public health. However, previous studies have not examined the association between cancer treatment delay and 5-year mortality risk for various cancer types in a single study population.
Methods: We used retrospective cohort data from 21 740 patients diagnosed with common cancers between 2000 and 2017, with mortality follow-up to 2022, from the Philippines' Department of Health-Rizal Cancer Registry to understand how treatment delay of <30, 30-90, or >90 days was associated with 5-year all-cause mortality risk, by cancer type and stage at diagnosis.
Int J Gen Med
September 2025
Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China.
Purpose: The fourth most common cause of cancer-related deaths in women is cervical cancer. Though treatment of early-stage cervical cancer is often effective, middle and advanced stage cervical cancer is hard to treat and prone to recurrence. We sought to explore the mechanism underlying cervical cancer progression to identify new therapeutic approaches.
View Article and Find Full Text PDFFront Immunol
September 2025
Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden.
Background: Metabolic reprogramming is an important hallmark of cervical cancer (CC), and extensive studies have provided important information for translational and clinical oncology. Here we sought to determine metabolic association with molecular aberrations, telomere maintenance and outcomes in CC.
Methods: RNA sequencing data from TCGA cohort of CC was analyzed for their metabolic gene expression profile and consensus clustering was then performed to classify tumors into different groups/subtypes.