Evaluation of Low Coverage Whole Genome Sequencing as a New Method for Detecting Malignant Ovarian Mass

2021 ◽  
Author(s):  
Chen Ming ◽  
Zhong Pengqiang ◽  
Hong Mengzhi ◽  
Tan Jinfeng ◽  
Yu Xuegao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Whole Genome Sequencing ◽  
Tumor Markers ◽  
Genome Sequencing ◽  
Pelvic Mass ◽  
Diagnostic Value ◽  
Whole Genome ◽  
Ovarian Malignancy ◽  
Z Scores ◽  
Low Coverage

Abstract To evaluate whether low coverage whole genome sequencing is suitable for the detection of malignant pelvic mass and compare its diagnostic value with traditional tumor markers. We enrolled 63 patients with a pelvic mass suspicious for ovarian malignancy. Each patient underwent low coverage whole genome sequencing (LCWGS) and traditional tumor markers test. The pelvic masses were finally confirmed via pathological examination. The copy number variants (CNVs) of whole genome were detected and the Stouffers Z-scores for each CNV was extracted. The risk of malignancy (RM) of each suspicious sample was calculated based on the CNV counts and Z-scores, which was subsequently compared with ovarian cancer markers CA125 and HE4, and the risk of ovarian malignancy algorithm (ROMA). Receiver Operating Characteristic Curve (ROC) were used to access the diagnostic value of variables. As confirmed by pathological diagnosis, 44 (70%) patients with malignancy and 19 patients with benign mass were identified. Our results showed that CA125 and HE4, the CNV, the mean of Z-scores (Zmean), the max of Z-scores (Zmax), the RM and the ROMA were significantly different between patients with malignant and benign masses. The area under curve ( AUC) of CA125, HE4, CNV, Zmax, and Zmean was 0.775, 0.866, 0.786, 0.685 and 0.725 respectively. ROMA and RM showed similar AUC (0.876 and 0.837), but differed in sensitivity and specificity. After all, we develop a LCWGS based method for the identification of pelvic mass of suspicious ovarian cancer. LCWGS shows accurate result and could be complementary with the existing diagnostic methods.

scholarly journals Applying low coverage whole genome sequencing to detect malignant ovarian mass

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Ming Chen ◽  
Pengqiang Zhong ◽  
Mengzhi Hong ◽  
Jinfeng Tan ◽  
Xuegao Yu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Whole Genome Sequencing ◽  
Tumor Markers ◽  
Genome Sequencing ◽  
Pelvic Mass ◽  
Diagnostic Value ◽  
Whole Genome ◽  
Ovarian Malignancy ◽  
Z Scores ◽  
Low Coverage

AbstractTo evaluate whether low coverage whole genome sequencing is suitable for the detection of malignant pelvic mass and compare its diagnostic value with traditional tumor markers. We enrolled 63 patients with a pelvic mass suspicious for ovarian malignancy. Each patient underwent low coverage whole genome sequencing (LCWGS) and traditional tumor markers test. The pelvic masses were finally confirmed via pathological examination. The copy number variants (CNVs) of whole genome were detected and the Stouffers Z-scores for each CNV was extracted. The risk of malignancy (RM) of each suspicious sample was calculated based on the CNV counts and Z-scores, which was subsequently compared with ovarian cancer markers CA125 and HE4, and the risk of ovarian malignancy algorithm (ROMA). Receiver Operating Characteristic Curve (ROC) were used to access the diagnostic value of variables. As confirmed by pathological diagnosis, 44 (70%) patients with malignancy and 19 patients with benign mass were identified. Our results showed that CA125 and HE4, the CNV, the mean of Z-scores (Zmean), the max of Z-scores (Zmax), the RM and the ROMA were significantly different between patients with malignant and benign masses. The area under curve (AUC) of CA125, HE4, CNV, Zmax, and Zmean was 0.775, 0.866, 0.786, 0.685 and 0.725 respectively. ROMA and RM showed similar AUC (0.876 and 0.837), but differed in sensitivity and specificity. In the validation cohort, the AUC of RM was higher than traditional serum markers. In conclusion, we develop a LCWGS based method for the identification of pelvic mass of suspicious ovarian cancer. LCWGS shows accurate result and could be complementary with the existing diagnostic methods.

scholarly journals 353 ASAS-EAAP Talk: Low-coverage whole-genome sequencing in local livestock breeds

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 81-82
Author(s):  
Joaquim Casellas ◽  
Melani Martín de Hijas-Villalba ◽  
Marta Vázquez-Gómez ◽  
Samir Id Lahoucine
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Error Rate ◽  
Allele Frequencies ◽  
Paternity Testing ◽  
Sequencing Error ◽  
Whole Genome ◽  
Genomic Evaluation ◽  
Sequencing Error Rate ◽  
Low Coverage

Abstract Current European regulations for autochthonous livestock breeds put a special emphasis on pedigree completeness, which requires laboratory paternity testing by genetic markers in most cases. This entails significant economic expenditure for breed societies and precludes other investments in breeding programs, such as genomic evaluation. Within this context, we developed paternity testing through low-coverage whole-genome data in order to reuse these data for genomic evaluation at no cost. Simulations relied on diploid genomes composed by 30 chromosomes (100 cM each) with 3,000,000 SNP per chromosome. Each population evolved during 1,000 non-overlapping generations with effective size 100, mutation rate 10–4, and recombination by Kosambi’s function. Only those populations with 1,000,000 ± 10% polymorphic SNP per chromosome in generation 1,000 were retained for further analyses, and expanded to the required number of parents and offspring. Individuals were sequenced at 0.01, 0.05, 0.1, 0.5 and 1X depth, with 100, 500, 1,000 or 10,000 base-pair reads and by assuming a random sequencing error rate per SNP between 10–2 and 10–5. Assuming known allele frequencies in the population and sequencing error rate, 0.05X depth sufficed to corroborate the true father (85,0%) and to discard other candidates (96,3%). Those percentages increased up to 99,6% and 99,9% with 0,1X depth, respectively (read length = 10,000 bp; smaller read lengths slightly improved the results because they increase the number of sequenced SNP). Results were highly sensitive to biases in allele frequencies and robust to inaccuracies regarding sequencing error rate. Low-coverage whole-genome sequencing data could be subsequently integrated into genomic BLUP equations by appropriately constructing the genomic relationship matrix. This approach increased the correlation between simulated and predicted breeding values by 1.21% (h2 = 0.25; 100 parents and 900 offspring; 0.1X depth by 10,000 bp reads). Although small, this increase opens the door to genomic evaluation in local livestock breeds.

scholarly journals Diagnostic value of exome and whole genome sequencing in craniosynostosis

2016 ◽  
Vol 54 (4) ◽  
pp. 260-268 ◽  
Author(s):  
Kerry A Miller ◽  
Stephen R F Twigg ◽  
Simon J McGowan ◽  
Julie M Phipps ◽  
Aimée L Fenwick ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Diagnostic Value ◽  
Whole Genome

scholarly journals Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders: a cost-efficient approach

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Yanjun Zan ◽  
Thibaut Payen ◽  
Mette Lillie ◽  
Christa F. Honaker ◽  
Paul B. Siegel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Efficient Approach ◽  
Cost Efficient ◽  
Low Coverage

scholarly journals Phylogenomics from low‐coverage whole‐genome sequencing

2019 ◽  
Vol 10 (4) ◽  
pp. 507-517 ◽  
Author(s):  
Feng Zhang ◽  
Yinhuan Ding ◽  
Chao‐Dong Zhu ◽  
Xin Zhou ◽  
Michael C. Orr ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Low Coverage

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 Extremely low-coverage whole genome sequencing in South Asians captures population genomics information

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Navin Rustagi ◽  
Anbo Zhou ◽  
W. Scott Watkins ◽  
Erika Gedvilaite ◽  
Shuoguo Wang ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Population Genomics ◽  
South Asians ◽  
Whole Genome ◽  
Low Coverage
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