Cell-Free DNA Variant Sequencing Using Plasma and AR-V7 Testing of Circulating Tumor Cells in Prostate Cancer Patients
CELLS(2021)
Friedrich Alexander Univ Erlangen Nurnberg
Abstract
Prostate cancer (PCa) is the second most common malignant cancer and is a major cause of morbidity and mortality among men worldwide. There is still an urgent need for biomarkers applicable for diagnosis, prognosis, therapy prediction, or therapy monitoring in PCa. Liquid biopsies, including cell-free DNA (cfDNA) and circulating tumor cells (CTCs), are a valuable source for studying such biomarkers and are minimally invasive. In our study, we investigated the cfDNA of 34 progressive PCa patients, via targeted sequencing, for sequence variants and for the occurrence of CTCs, with a focus on androgen receptor splice variant 7 (AR-V7)-positive CTCs. The cfDNA content was associated with overall survival (OS; p = 0.014), disease-specific survival (DSS; p = 0.004), and time to treatment change (TTC; p = 0.001). Moreover, when considering all sequence variants grouped by their functional impact and allele frequency, a significant association with TTC (p = 0.017) was observed. When investigating only pathogenic or likely pathogenic gene variants, variants of the BRCA1 gene (p = 0.029) and the AR ligand-binding domain (p = 0.050) were associated with a shorter TTC. Likewise, the presence of CTCs was associated with a shorter TTC (p = 0.031). The presence of AR-V7-positive CTCs was associated with TTC (p < 0.001) in Kaplan–Meier analysis. Interestingly, all patients with AR-V7-positive CTCs also carried TP53 point mutations. Altogether, analysis of cfDNA and CTCs can provide complementary information that may support temporal and targeted treatment decisions and may elucidate the optimal choice within the variety of therapy options for advanced PCa patients.
MoreTranslated text
Key words
prostate cancer,cfDNA,CTCs,prognosis,sequence variants
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined