Liquid biopsy for cancer detection: Clinical and epidemiological considerations

2021 ◽  
pp. clincanres.2426.2021
Author(s):  
Nicolas Wentzensen ◽  
Megan A Clarke
Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 507
Author(s):  
Bernd Timo Hermann ◽  
Sebastian Pfeil ◽  
Nicole Groenke ◽  
Samuel Schaible ◽  
Robert Kunze ◽  
...  

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%–0.5%) to determine DEEPGENTM’s performance and minimal input requirements. Our findings confirm DEEPGENTM’s effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.


Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 638
Author(s):  
Linjing Liu ◽  
Xingjian Chen ◽  
Olutomilayo Olayemi Petinrin ◽  
Weitong Zhang ◽  
Saifur Rahaman ◽  
...  

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.


2018 ◽  
Vol 25 (3) ◽  
pp. 915-923 ◽  
Author(s):  
Barbara Kinga Barták ◽  
Alexandra Kalmár ◽  
Orsolya Galamb ◽  
Barnabás Wichmann ◽  
Zsófia Brigitta Nagy ◽  
...  

2019 ◽  
Author(s):  
Hyunji Kim ◽  
Fehmi Civitci ◽  
Josiah Wagner ◽  
Pavana Anur ◽  
Matthew Rames ◽  
...  

2019 ◽  
Vol 13 (12) ◽  
pp. 991-994 ◽  
Author(s):  
Lynn Sorbara ◽  
Sudhir Srivastava

2017 ◽  
Vol 8 (1to3) ◽  
pp. 1
Author(s):  
Spinder Kour ◽  
Sanjeet Singh ◽  
Nishant Singh ◽  
Paramjit Singh ◽  
Kanika Sharma

2017 ◽  
Vol 28 ◽  
pp. x9
Author(s):  
K. Kato ◽  
K. Ohkawa ◽  
R. Takada ◽  
H. Uehara ◽  
Y. Kukita ◽  
...  

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