Basic Science with Preclinical Models to Investigate and Develop Liquid Biopsy: What Are the Available Data and Is It a Fruitful Approach?
The molecular analysis of circulating analytes (circulating tumor-DNA (ctDNA), -cells (CTCs) and -RNA (ctRNA)/exosomes) deriving from solid tumors and detected in the bloodstream—referred as liquid biopsy—has emerged as one of the most promising concepts in cancer management. Compelling data have ev...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/23/10/5343 |
| Summary: | The molecular analysis of circulating analytes (circulating tumor-DNA (ctDNA), -cells (CTCs) and -RNA (ctRNA)/exosomes) deriving from solid tumors and detected in the bloodstream—referred as liquid biopsy—has emerged as one of the most promising concepts in cancer management. Compelling data have evidenced its pivotal contribution and unique polyvalence through multiple applications. These data essentially derived from translational research. Therewith, data on liquid biopsy in basic research with preclinical models are scarce, a concerning lack that has been widely acknowledged in the field. This report aimed to comprehensively review the available data on the topic, for each analyte. Only 17, 17 and 2 studies in basic research investigated ctDNA, CTCs and ctRNA/exosomes, respectively. Albeit rare, these studies displayed noteworthy relevance, demonstrating the capacity to investigate questions related to the biology underlying analytes release that could not be explored via translational research with human samples. Translational, clinical and technological sectors of liquid biopsy may benefit from basic research and should take note of some important findings generated by these studies. Overall, results underscored the need to intensify the efforts to conduct future studies on liquid biopsy in basic research with new preclinical models. |
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| ISSN: | 1661-6596 1422-0067 |