Article ; Online: Cancer associated macrophage-like cells in metastatic renal cell carcinoma predicts for poor prognosis and tracks treatment response in real time.
2023 Volume 13, Issue 1, Page(s) 10544
Abstract: Renal Cell Carcinoma (RCC) is a fatal urological cancer, with one third of patients diagnosed with metastasis, resulting in a 5-year survival of only 12%. Recent advancements in therapies have increased survival in mRCC, but lack efficacy in subtypes, ... ...
| Abstract | Renal Cell Carcinoma (RCC) is a fatal urological cancer, with one third of patients diagnosed with metastasis, resulting in a 5-year survival of only 12%. Recent advancements in therapies have increased survival in mRCC, but lack efficacy in subtypes, due to treatment resistance and toxic side effects. Currently, white blood cells, hemoglobin, and platelets are limitedly used as blood based biomarkers to help determine RCC prognosis. Cancer associated macrophage-like cells (CAMLs) are a potential mRCC biomarker which have been identified in peripheral blood of patients with malignant tumors and have been shown to predict poor clinical patient outcomes based on their number and size. In this study, blood samples from 40 RCC patients were obtained to evaluate the clinical utility of CAMLs. CAML changes were monitored during treatment regimens to evaluate their ability to predict treatment efficacy. It was observed that patients with smaller CAMLs had better progression free survival (HR = 2.84, 95% CI 1.22-6.60, p = 0.0273) and overall survival (HR = 3.95, 95% CI 1.45-10.78, p = 0.0154) versus patients with larger CAMLs. These findings suggest that CAMLs can be used as a diagnostic, prognostic, and predictive biomarker for patients with RCC which may help improve management of advanced RCC. |
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| MeSH term(s) | Humans ; Carcinoma, Renal Cell ; Kidney Neoplasms ; Blood Platelets ; Drug-Related Side Effects and Adverse Reactions ; Macrophages |
| Language | English |
| Publishing date | 2023-06-29 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. |
| ZDB-ID | 2615211-3 |
| ISSN | 2045-2322 ; 2045-2322 |
| ISSN (online) | 2045-2322 |
| ISSN | 2045-2322 |
| DOI | 10.1038/s41598-023-37671-3 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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