scholarly journals Non-invasive detection of urothelial carcinoma by cost-effective low-coverage whole genome sequencing from urine exfoliated cells DNA

2020 ◽  
Vol 19 ◽  
pp. e1004
Author(s):  
Z. Zeng ◽  
Q. Ziliang ◽  
Y. Ying ◽  
B. Wang ◽  
J. Ji ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Cost Effective ◽  
Whole Genome ◽  
Non Invasive ◽  
Exfoliated Cells ◽  
Low Coverage

Non-invasive detection of urothelial carcinoma (UC) by cost-effective low-coverage whole genome sequencing from urine exfoliated cells DNA.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1552-1552
Author(s):  
Shuxiong Zeng ◽  
Dongxing Nai ◽  
Yingdie Ye ◽  
Jiatao Ji ◽  
Zunlin Zhou ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Whole Genome Sequencing ◽  
Cancer Patients ◽  
Genome Sequencing ◽  
Chromosomal Aberrations ◽  
Cost Effective ◽  
Urine Samples ◽  
Control Group ◽  
Whole Genome ◽  
Exfoliated Cells

1552 Background: Urothelial carcinoma (UC) is a malignancy with frequent chromosomal aberrations. The FISH assays were more sensitive as compared to cytology tests. Here we investigated cost-effective whole genome sequencing technology, which is able to detect all chromosomal aberrations for UC diagnoses. Methods: UC patients and control group are prospectively recruited in trial NCT03998371. First-morning-voided urine were freshly collected before TURBT or cystectomy. Urine Exfoliated Cells DNA was analyzed by illumina HiSeq X10, followed by genotyping by bioinformatics workflow UCAD. Results: 195 individuals were prospectively recruited. 121 UC patients and 67 non-tumor diseases were included in this study. 7 other malignancies as confirmed by pathological testing were excluded. Frequent chromosome copy number changes were found in cancer patients as compared non-tumor controls, including chromosome 3 gain, 17 gain, 7 gain and 9p loss used in FISH assays were found. In addition to that, chr9q loss, 8q gain, 5q loss, 17p loss, 11p loss, 1q gain, 8p loss, 10q loss, 6q loss, 4q loss and 11q loss were also frequent in cancer patients (AUC > 0.65). Metacentric chromosomes showed better AUC compared to acrocentric and telocentric chromosomes (P = 1.7e-03). A novel diagnosis model UCAD was built by incorporating all the chromosomal changes. The model reached performance of AUC = 0.933. At the optimal cutoff |Z| > = 3.16, the sensitivity, specificity and accuracy were 84.7%, 97.9% and 89.0% respectively. The prediction positivity was found correlated with urine microscopy visible epithelial cells (P = 0.00069), tumor invasiveness (Ta/Tis vs the other, P = 0.0048) and tumor grade (P = 0.0030), but not microscopy RBC/WBC findings, urine culture findings, smoke and drinking history. The UCAD model outperformed cytology tests by predicting all 16-cytology positive and 12 cytology negative tumors with comparable specificity. The model found 75.0% more tumors. And UCAD identified more upper urinary tract cancer (P = 0.012) and smaller tumors ( < 3cm, P = 5.9e-04). The adding of cytology to UCAD did not improve diagnosing sensitivity and specificity. UCAD reproduce the diagnoses among morning - void urine, morning, afternoon urine samples with correlation coefficient R2> 0.98. All the urine samples showed high concordances with matched tumor samples (R2> 0.85). Conclusions: UCAD could be a high specific, robust UC diagnoses method with improved sensitivity as compared to cytology tests. Clinical trial information: NCT03998371.

scholarly journals Noninvasive Detection of Urothelial Carcinoma by Cost-effective Low-coverage Whole-genome Sequencing from Urine-Exfoliated Cell DNA

2020 ◽  
Vol 26 (21) ◽  
pp. 5646-5654
Author(s):  
Shuxiong Zeng ◽  
Yidie Ying ◽  
Naidong Xing ◽  
Baiyun Wang ◽  
Ziliang Qian ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Cost Effective ◽  
Noninvasive Detection ◽  
Whole Genome ◽  
Low Coverage

scholarly journals A beginner's guide to low-coverage whole genome sequencing for population genomics

2021 ◽  
Author(s):  
Runyang Nicolas Lou ◽  
Arne Jacobs ◽  
Aryn Wilder ◽  
Nina Overgaard Therkildsen
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Population Genomics ◽  
Cost Effective ◽  
Whole Genome ◽  
Model Species ◽  
Population Genomic ◽  
Genomic Studies ◽  
Lower Depth ◽  
Low Coverage

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and non-model species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analyzed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency and genetic diversity estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in non-model species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

scholarly journals Low-Coverage Whole Genome Sequencing Diagnoses Endometrial Carcinoma from Tampon DNA of Uterine Bleeding Patients

2021 ◽  
Author(s):  
haifeng qiu ◽  
lei zhang ◽  
jing li ◽  
tingting cao ◽  
yun feng ◽  
...  
Keyword(s):  
Endometrial Carcinoma ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Chromosome Aberrations ◽  
Uterine Bleeding ◽  
Serous Carcinoma ◽  
Whole Genome ◽  
Invasive Approach ◽  
Exfoliated Cells ◽  
Low Coverage

Abstract Background Endometrial carcinoma (EC) is a disease predominantly affecting postmenopausal women. It accounts for about 5% of abnormal uterine bleeding. It is still challenging to diagnose cancers from uterine bleeding patients. Previously, chromosome aberrations were found to be frequent in EC. Here we employed a low coverage whole genome sequencing technology to investigate chromosome aberrations in tampon-collected DNA of patients with suspicious EC. Methods Thirty ECs and 14 benign cases with abnormal bleeding are prospectively recruited. Tampons were used to collect exfoliated cells and DNA extracted from these exfoliated cells was analyzed by a customized bioinformatics workflow, uterine exfoliated cell chromosomal aneuploidy detector (UterCAD). Results As our data shown, frequent chromosome copy number variations (CNV) were found in EC patients as compared to non-tumor controls, especially the chromosome 8q gain and 10q gain. Using UterCAD, CNVs were detected in tampon-derived DNA from 83.3% (25/30) EC, which were 80.8% (21/26) EECs and 100% (4/4) USCs. In EEC group, CNVs were found in 81.3% (13/16), 85.7% (6/7), and 66.7% (2/3) patients of stage IA, IB, and II/III, respectively. Moreover, all the 4 USC patients presented significant CNVs. Conclusions UterCAD could be a highly specific, robust uterine cancer diagnosis method, with an especially high sensitivity for the more aggressive subtype - serous carcinoma. It may be used as a non-invasive approach for diagnosis and active surveillance in endometrial cancer prior to the use of biopsy, thereby largely reducing the treatment burden on patients.

scholarly journals A beginner's guide to low-coverage whole genome sequencing for population genomics

2021 ◽  
Author(s):  
Runyang Nicolas Lou ◽  
Arne Jacobs ◽  
Aryn Wilder ◽  
Nina Overgaard Therkildsen
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Population Genomics ◽  
Cost Effective ◽  
Whole Genome ◽  
Model Species ◽  
Population Genomic ◽  
Genomic Studies ◽  
Lower Depth ◽  
Low Coverage

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and non-model species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analyzed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency and genetic diversity estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in non-model species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

scholarly journals A beginner's guide to low-coverage whole genome sequencing for population genomics

2020 ◽  
Author(s):  
Runyang Nicolas Lou ◽  
Arne Jacobs ◽  
Aryn Wilder ◽  
Nina Overgaard Therkildsen
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Population Genomics ◽  
Frequency Estimation ◽  
Cost Effective ◽  
Whole Genome ◽  
Model Species ◽  
Population Genomic ◽  
Lower Depth ◽  
Low Coverage

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and non-model species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data and how the distribution of sequencing effort between the number of samples analyzed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate that the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference compared to sequencing fewer samples each at higher depth. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in non-model species, and discuss current limitations and future perspectives for lcWGS-based analysis. With this overview, we hope to make lcWGS more approachable and stimulate broader adoption.

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