Journal
INTERNATIONAL JOURNAL OF CANCER
Volume 148, Issue 4, Pages 921-931Publisher
WILEY
DOI: 10.1002/ijc.33362
Keywords
biomarkers; circulating tumor DNA; metastatic relapsed cervical cancer; prognostic prediction; tumor monitoring
Categories
Ask authors/readers for more resources
The study showed that mutant genes identified in plasma DNA samples from MRCC patients influence treatment outcomes, with a decrease in mutations after chemotherapy associated with better response. Longitudinal monitoring with ctDNA in liquid biopsy samples provides predictive and prognostic information during treatment.
Limited and inefficient treatment options exist for metastatic relapsed cervical cancer (MRCC), and there are currently no reliable indicators to guide therapeutic selection. We performed deep sequencing analyses targeting 322 cancer-related genes in plasma cell-free DNA and matched white blood cells in 173 serial blood samples from 82 locally advanced CC (LACC) or MRCC patients and when possible during treatment. We identified five notable nonsynonymous mutant genes (PIK3CA, BRAF, GNA11, FBXW7 and CDH1) in the MRCC samples as the metastatic relapse significantly mutated (MSG) genes and found that MRCC patients with any detectable MSG mutations had significantly shorter progression-free survival (PFS) (P = .005) and overall survival (OS) (P = .007) times than those without detectable MSG mutations. Additionally, analyses of matched prechemotherapy and postchemotherapy plasma revealed that a reduction in the number of MSG mutations after chemotherapy was significantly associated with partial remission (PR) and stable disease (SD) (P = .007). Among the patients included in the longitudinal tracking ctDNA analysis, an increase in MSG mutations was observed earlier in response to disease progression than radiological imaging. Our results outline the mutation profiles of MRCC. We show how longitudinal monitoring with ctDNA in liquid biopsy samples provides both predictive and prognostic information during treatment.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available