Purpose: Cancer patients are at risk of severe COVID-19 infection, and vaccination is recommended. Nevertheless, we observe a failure of COVID-19 vaccines in this vulnerable population. We hypothesize that senescent peripheral T-cells alter COVID-19 vaccine-induced immunity.
Methods: We performed a monocentric prospective study and enrolled cancer patients and healthy donors before the COVID-19 vaccination. The primary objective was to assess the association of peripheral senescent T-cells (CD28CD57KLRG1) with COVID-19 vaccine-induced immunity.
Results: Eighty cancer patients have been included, with serological and specific T-cell responses evaluated before and at 3 months post-vaccination. Age ≥ 70 years was the principal clinical factor negatively influencing the serological (p=0.035) and specific SARS-CoV-2 T-cell responses (p=0.047). The presence of senescent T-cells was correlated to lower serological (p=0.049) and specific T-cell responses (p=0.009). Our results sustained the definition of a specific cut-off for senescence immune phenotype (SIP) (≥ 5% of CD4 and ≥ 39.5% of CD8 T-cells), which was correlated to a lower serological response induced by COVID-19 vaccination for CD4 and CD8 SIP (p=0.039 and p=0.049 respectively). While CD4 SIP level had no impact on COVID-19 vaccine efficacy in elderly patients, our results unraveled a possible predictive role for CD4 SIP T-cell levels in younger cancer patients.
Conclusions: Elderly cancer patients have a poor serological response to vaccination; specific strategies are needed in this population. Also, the presence of a CD4 SIP affects the serological response in younger patients and seems to be a potential biomarker of no vaccinal response.
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http://dx.doi.org/10.3389/fimmu.2023.1160664 | 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 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.
View Article and Find Full Text PDFAnn Surg Oncol
January 2025
Department of Gastrointestinal Surgery, Stavanger University Hospital, Stavanger, Norway.
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