scholarly journals Whole‐genome sequencing of cell‐free DNA yields genome‐wide read distribution patterns to track tissue of origin in cancer patients

2020 ◽  
Vol 10 (6) ◽  
Author(s):  
Han Liang ◽  
Fuqiang Li ◽  
Sitan Qiao ◽  
Xinlan Zhou ◽  
Guoyun Xie ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Patients ◽  
Genome Sequencing ◽  
Distribution Patterns ◽  
Whole Genome ◽  
Cell Free Dna ◽  
Free Dna ◽  
Genome Wide ◽  
Tissue Of Origin ◽  
Read Distribution

scholarly journals Whole genome sequencing of cell-free DNA yields genome-wide read distribution patterns to track tissue of origin in cancer patients

2019 ◽  
Author(s):  
Han Liang ◽  
Fuqiang Li ◽  
Sitan Qiao ◽  
Xinlan Zhou ◽  
Guoyun Xie ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Patients ◽  
Genome Sequencing ◽  
Somatic Mosaicism ◽  
Distribution Patterns ◽  
Whole Genome ◽  
Cell Free Dna ◽  
Free Dna ◽  
Tissue Of Origin ◽  
Read Distribution

AbstractSomatic mosaicism is widespread among tissues and could indicate distinct tissue origins of circulating cell-free DNA (cfDNA), DNA fragments released by lytic cells into the blood. By investigating the alignment patterns of whole genome sequencing reads with the genomic DNA of different tissues, we found that the read distributions formed type-specific patterns in some regions as a result of somatic mosaicism. We then utilized this information to construct a tissue-of-origin mapping model and evaluated its predictive performance on whole genome sequencing data from tissue and cfDNA samples. In total, 1,545 tissue samples associated with 13 cancer types were included, and identification of the tissue of origin achieved a specificity of 82% and a sensitivity of 80%. Furthermore, a total of 30 cfDNA samples from lung cancer and liver cancer patients and healthy controls were analyzed to predict their tissues of origin with a specificity of 87% and a sensitivity of 87%. Our results show that read distribution patterns from whole genome sequencing could be used to identify cfDNA tissues of origin with high accuracy, suggesting the potential application of our model to early cancer detection and diagnosis.

scholarly journals Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Nathan Wan ◽  
David Weinberg ◽  
Tzu-Yu Liu ◽  
Katherine Niehaus ◽  
Eric A. Ariazi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Plasma Cell ◽  
Early Stage ◽  
Whole Genome ◽  
Cell Free Dna ◽  
Free Dna

scholarly journals Shallow Whole Genome Sequencing on Circulating Cell-Free DNA Allows Reliable Noninvasive Copy-Number Profiling in Neuroblastoma Patients

2017 ◽  
Vol 23 (20) ◽  
pp. 6305-6314 ◽  
Author(s):  
Nadine Van Roy ◽  
Malaïka Van Der Linden ◽  
Björn Menten ◽  
Annelies Dheedene ◽  
Charlotte Vandeputte ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome ◽  
Cell Free Dna ◽  
Free Dna ◽  
Circulating Cell Free Dna

scholarly journals Detection of MDM2 amplification by shallow whole genome sequencing of cell-free DNA of patients with dedifferentiated liposarcoma

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262272
Author(s):  
Joanna Przybyl ◽  
Lien Spans ◽  
Kristen Ganjoo ◽  
Nam Bui ◽  
David Mohler ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Soft Tissue ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Soft Tissue Tumors ◽  
Whole Genome ◽  
Plasma Samples ◽  
Cell Free Dna ◽  
Free Dna ◽  
Retroperitoneal Tumors

High-level amplification of MDM2 and other genes in the 12q13–15 locus is a hallmark genetic feature of well-differentiated and dedifferentiated liposarcomas (WDLPS and DDLPS, respectively). Detection of this genomic aberration in plasma cell-free DNA may be a clinically useful assay for non-invasive distinction between these liposarcomas and other retroperitoneal tumors in differential diagnosis, and might be useful for the early detection of disease recurrence. In this study, we performed shallow whole genome sequencing of cell-free DNA extracted from 10 plasma samples from 3 patients with DDLPS and 1 patient with WDLPS. In addition, we studied 31 plasma samples from 11 patients with other types of soft tissue tumors. We detected MDM2 amplification in cell-free DNA of 2 of 3 patients with DDLPS. By applying a genome-wide approach to the analysis of cell-free DNA, we also detected amplification of other genes that are known to be recurrently affected in DDLPS. Based on the analysis of one patient with DDLPS with longitudinal plasma samples available, we show that tracking MDM2 amplification in cell-free DNA may be potentially useful for evaluation of response to treatment. The patient with WDLPS and patients with other soft tissue tumors in differential diagnosis were negative for the MDM2 amplification in cell-free DNA. In summary, we demonstrate the feasibility of detecting amplification of MDM2 and other DDLPS-associated genes in plasma cell-free DNA using technology that is already routinely applied for other clinical indications. Our results may have clinical implications for improved diagnosis and surveillance of patients with retroperitoneal tumors.

scholarly journals Cancer detection using whole-genome sequencing of cell free DNA

Oncotarget ◽  
2013 ◽  
Vol 4 (8) ◽  
pp. 1119-1120 ◽  
Author(s):  
Rebecca J. Leary ◽  
Mark Sausen ◽  
Luis A. Diaz ◽  
Victor E. Velculescu
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Cancer Detection ◽  
Whole Genome ◽  
Cell Free Dna ◽  
Free Dna

Su1658 – Machine Learning Enables Detection of Early-Stage Colorectal Cancer by Whole-Genome Sequencing of Plasma Cell-Free Dna

2019 ◽  
Vol 156 (6) ◽  
pp. S-600-S-601 ◽  
Author(s):  
Nathan Wan ◽  
David Weinberg ◽  
Tzu-yu Liu ◽  
Katherine Niehaus ◽  
Daniel Delubac ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Plasma Cell ◽  
Early Stage ◽  
Whole Genome ◽  
Cell Free Dna ◽  
Free Dna

scholarly journals Multimodal analysis of cell-free DNA whole-genome sequencing for pediatric cancers with low mutational burden

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peter Peneder ◽  
Adrian M. Stütz ◽  
Didier Surdez ◽  
Manuela Krumbholz ◽  
Sabine Semper ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Ewing Sarcoma ◽  
Liquid Biopsy ◽  
Circulating Tumor Dna ◽  
Whole Genome ◽  
Cell Free Dna ◽  
Genetic Aberrations ◽  
Free Dna ◽  
Tumor Dna

AbstractSequencing of cell-free DNA in the blood of cancer patients (liquid biopsy) provides attractive opportunities for early diagnosis, assessment of treatment response, and minimally invasive disease monitoring. To unlock liquid biopsy analysis for pediatric tumors with few genetic aberrations, we introduce an integrated genetic/epigenetic analysis method and demonstrate its utility on 241 deep whole-genome sequencing profiles of 95 patients with Ewing sarcoma and 31 patients with other pediatric sarcomas. Our method achieves sensitive detection and classification of circulating tumor DNA in peripheral blood independent of any genetic alterations. Moreover, we benchmark different metrics for cell-free DNA fragmentation analysis, and we introduce the LIQUORICE algorithm for detecting circulating tumor DNA based on cancer-specific chromatin signatures. Finally, we combine several fragmentation-based metrics into an integrated machine learning classifier for liquid biopsy analysis that exploits widespread epigenetic deregulation and is tailored to cancers with low mutation rates. Clinical associations highlight the potential value of cfDNA fragmentation patterns as prognostic biomarkers in Ewing sarcoma. In summary, our study provides a comprehensive analysis of circulating tumor DNA beyond recurrent genetic aberrations, and it renders the benefits of liquid biopsy more readily accessible for childhood cancers.

Early cancer detection using low-coverage whole-genome sequencing of cell-free DNA fragments.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e22510-e22510
Author(s):  
Shunli Yang ◽  
Pei Zhihua ◽  
Jianing Yu ◽  
Xiuyu Zhao ◽  
Yiqian Liu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Patients ◽  
Genome Sequencing ◽  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Whole Genome ◽  
Healthy Individuals ◽  
Free Dna ◽  
Low Coverage

e22510 Background: Recent advances in circulating cell-free DNA (cfDNA) of plasma have shown that tumor diagnosis based on tumor-specific genetic and epigenetic changes (e.g., somatic mutations, copy number variations, and DNA methylation) is a promising non-invasive method. However, the number of tumor-specific genomic variants identified by whole-genome sequencing (WGS) in early cancer patients is very limited. Moreover, the mutations generated by clonal hematopoiesis in cfDNA can further confound the detection of cancer-specific mutations. It has been shown that ctDNA and cfDNA fragments have differences in length distribution. Compared with a limited number of genomic mutations, cfDNA fragment size index (FSI) is more abundant and easier to be detected. Methods: We designed a novel method for fragment detection of plasma cfDNA based on low-coverage WGS. The fragment length differences between healthy individuals and tumor patients were systematically analyzed. The training dataset includes 50 healthy individuals and 354 patients from eight different cancers. After the data preprocessing, we calculated the weight of fragmental bins and built a model for distinguishing healthy individuals from cancer patients. An independent dataset involving 22 healthy controls and 340 cancer patients was used to validate the model. The performance of our method was measured by the area under the curve (AUC) using the one-versus-all approach. Results: In our analysis, a total of 504 markers were selected from the dataset for model construction. Our model performed well for all cancer types on both training (AUC = 0.804) and validation (AUC = 0.837) datasets. Conclusions: The good performance of our model in large-scale plasma samples demonstrates the potential clinical application of cfDNA fragment analysis in early cancer detection based on low-coverage WGS.

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