scholarly journals Elucidating Prostate Cancer Behaviour During Treatment via Low-pass Whole-genome Sequencing of Circulating Tumour DNA

2021 ◽  
Author(s):  
Semini Sumanasuriya ◽  
George Seed ◽  
Harry Parr ◽  
Rossitza Christova ◽  
Lorna Pope ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Low Pass ◽  
Circulating Tumour Dna

scholarly journals Comparison of Circulating Tumour DNA and Extracellular Vesicle DNA by Low-Pass Whole-Genome Sequencing Reveals Molecular Drivers of Disease in a Breast Cancer Patient

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Olivia Ruhen ◽  
Bob Mirzai ◽  
Michael E. Clark ◽  
Bella Nguyen ◽  
Carlos Salomon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number Variants ◽  
Extracellular Vesicle ◽  
Whole Genome ◽  
Breast Cancer Patients ◽  
Low Pass ◽  
Serial Samples ◽  
Circulating Tumour Dna

There is increasing recognition of circulating tumour DNA (ctDNA) as a non-invasive alternative to tumour tissue for the molecular characterisation and monitoring of disease. Recent evidence suggests that cancer-associated changes can also be detected in the DNA contained within extracellular vesicles (EVs). As yet, there has been limited investigation into the relationship between EV DNA and ctDNA, and no studies have examined the EV DNA of breast cancer patients. The aim of this study was to use low-pass whole-genome sequencing to identify copy number variants (CNVs) in serial samples of both ctDNA and EV DNA from a patient with breast cancer. Of the 52 CNVs identified in tumour DNA, 36 (69%) were detected in at least one ctDNA sample and 13 (25%) in at least one EV DNA sample. The number of detectable variants in ctDNA and EV DNA increased over the natural history of the patient’s disease, which was associated with progression to cerebral metastases. This case study demonstrates that, while CNVs are detectable in patient EV DNA, ctDNA has greater sensitivity than EV DNA for serial monitoring of breast cancer.

scholarly journals Low Abundance of Circulating Tumor DNA in Localized Prostate Cancer

2019 ◽  
pp. 1-13 ◽  
Author(s):  
S. Thomas Hennigan ◽  
Shana Y. Trostel ◽  
Nicholas T. Terrigino ◽  
Olga S. Voznesensky ◽  
Rachel J. Schaefer ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Metastatic Prostate Cancer ◽  
Circulating Tumor Dna ◽  
Localized Prostate Cancer ◽  
Whole Genome ◽  
Aggressive Disease ◽  
Low Pass ◽  
Tumor Dna

PURPOSE Despite decreased screening-based detection of clinically insignificant tumors, most diagnosed prostate cancers are still indolent, indicating a need for better strategies for detection of clinically significant disease before treatment. We hypothesized that patients with detectable circulating tumor DNA (ctDNA) were more likely to harbor aggressive disease. METHODS We applied ultra-low-pass whole-genome sequencing to profile cell-free DNA from 112 patients diagnosed with localized prostate cancer and performed targeted resequencing of plasma DNA for somatic mutations previously identified in matched solid tumor in nine cases. We also performed similar analyses of data from patients with metastatic prostate cancer. RESULTS In all cases of localized prostate cancer, even in clinically high-risk patients who subsequently had recurrent disease, ultra-low-pass whole-genome sequencing and targeted resequencing did not detect ctDNA in plasma acquired before surgery or before recurrence. In contrast, using both approaches, ctDNA was detected in patients with metastatic prostate cancer. CONCLUSION Our findings demonstrate clear differences between localized and advanced prostate cancer with respect to the dissemination and detectability of ctDNA. Because allele-specific alterations in ctDNA are below the threshold for detection in localized prostate cancer, other approaches to identify cell-free nucleic acids of tumor origin may demonstrate better specificity for aggressive disease.

Copy‐Number Variants Detection by Low‐Pass Whole‐Genome Sequencing

2017 ◽  
Vol 94 (1) ◽  
Author(s):  
Zirui Dong ◽  
Weiwei Xie ◽  
Haixiao Chen ◽  
Jinjin Xu ◽  
Huilin Wang ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Copy Number Variants ◽  
Whole Genome ◽  
Low Pass

iStopCancer: A database of 6,016 low pass whole genome sequencing of minimal invasive samples from 21 cancer types of Chinese population.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 7062-7062
Author(s):  
Min Yuan ◽  
Qian Ziliang ◽  
Juemin Fang ◽  
Zhongzheng Zhu ◽  
Jianguo Wu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Patients ◽  
Genome Sequencing ◽  
Minimal Invasive ◽  
The Body ◽  
Whole Genome Sequencing Data ◽  
Low Grade ◽  
Whole Genome ◽  
Low Pass ◽  
Low Coverage

7062 Background: Cancer is a group of genetic diseases that result from changes in the genome of cells in the body, leading them to grow uncontrollably. Recent researches suggest Chromosome instability (CIN), which is defined as an increased rate of chromosome gains and losses, manifests as cell-to-cell karyotypic heterogeneity and drives cancer initiation and evolution. Methods: In the past two years, we initiated iStopCancer project, and characterized 4515 ‘best available’ minimal-invasive samples from cancer patients and 1501 plasma samples from non-tumor diseases by using low-pass whole genome sequencing. DNA from ‘best available’ minimal-invasive samples, including peripheral plasma, urines, pancreatic juice, bile and effusions were analyzed by low coverage whole genome sequencing followed by the UCAD Bioinformatics workflow to characterize the CINs. In total, 32T bp nucleotide (coverage =1.7X for each sample) were collected. All the data can be visualized on website: http://www.istopcancer.net/pgweb/cn/istopcancer.jsp . Results: 3748(83%) of tumors present detectable CIN (CIN score>1000) in minimal-invasive samples. The missed cancer patients were majorly from patients with either tumor size less than 2cm or less-aggressive cancers, including thyroid cancer, low-grade urothelial carcinoma, lung cancer in-situ, et al. Of the 1501 non-tumor individuals, 30(2.0%) present detectable CIN (|Z|>=3) at the time of sample collection, 24(80.0%) was diagnosed as tumor patient in 3-6 months follow-up. There were 9 (0.59%) of non-cancer individuals without detectable CIN were also reported as tumor patients during 6-month following up. In summary, the positive and negative prediction value is 80.0% and 99.4% respectively. The false alarms were majorly from patients with EBV activations, which indicates virus may interference chromosome stability and drove virus-associated carcinogenesis. For the patient with repeated detections, plasma cfDNA CIN dynamics predicted clinical responses and disease recurrences. Quick clearance of plasma cfDNA CIN in 2-3 weeks was found in 153 (83.6%) patients. Meanwhile, no quick clearance was found in majority of SDs/PDs (73/88=83.0%). Furthermore, cfDNA CIN predicts clinical response 2-8 weeks ahead of traditional biomarkers (CEA, CA15-3, CA199, AFP et al). Conclusions: Large-scale low coverage whole genome sequencing data provides useful information for cancer detection and managements.

scholarly journals A comprehensive benchmarking of WGS-based structural variant callers

2020 ◽  
Author(s):  
Varuni Sarwal ◽  
Sebastian Niehus ◽  
Ram Ayyala ◽  
Sei Chang ◽  
Angela Lu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Gold Standard ◽  
Detection Methods ◽  
Whole Genome ◽  
Sequencing Data ◽  
Structural Variant ◽  
Gold Standard Dataset ◽  
Low Pass ◽  
Standard Set

AbstractAdvances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low and ultra-low pass sequencing data.

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