A new method towards calculating the cancer cell fraction in cell-free DNA.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13053-e13053
Author(s):  
Tiancheng Han ◽  
Jianing Yu ◽  
Xiaojing Lin ◽  
Hongyu Xie ◽  
Xue Song ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Cancer Cell ◽  
Circulating Tumor Dna ◽  
Accurate Estimation ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Target Sequencing ◽  
Paired Samples ◽  
Free Dna ◽  
Cell Fractions

e13053 Background: Circulating tumor DNA (ctDNA) has been applied and showed potential in cancer early/late-stage detection, tumor genotyping and post-operation recurrence monitoring. The fraction of ctDNA in cell-free DNA (noted as ccf hereby), in addition to standard SNV/INDEL/CNV analysis, has also been showed to associate with the tumor progression and prognosis. In theory, accurate ccf can further be useful in correcting and improving given SNV/INDEL/CNV results. Existing tools capable for calculating ccf (PureCN, FACETS, Sequenza, etc.) use coverage data in targeted regions and SNP allele frequency to calculate the tumor fraction, which fail to give accurate estimation at relatively low ctDNA concentrations. Methods: A Maximum Likelihood model was built to estimate ccf. We first select informative SNPs with significantly different VAF in the case and paired-control samples. The mutation type of an informative SNP is determined by the variant allele frequency (VAF) in the paired samples and the copy number of the case sample. Likelihood of each SNP given a specific ccf was then calculated. After clustering SNPs into clones, the ccf of each clone was estimated using a global likelihood. Results: Performance of the method was validated by ctDNA dilution series analysis. 6 cfDNA from cancer patient was diluted (concentrations: 1/3 - 1/81). Detection limit of the method is ~2%, and correlation between estimated and expected ccf ranged from 0.93 to 0.98. Conclusions: We have developed a novel method to better estimate cancer cell fractions in cell-free DNA. Results showed our method is able to calculate ccf at lower ctDNA concentrations with higher accuracy and stability than benchmarked tools. We describe here a method for target-sequencing data that is more sensible, accurate and stable than currently available tools.

scholarly journals COMP-10. THE MUTATIONAL PROFILE OF GLIOBLASTOMA-DERIVED CELL-FREE DNA IN PLASMA REPRESENTS A DISTINCT SUBSET OF THE SOMATIC MUTATIONS PRESENT IN GLIOBLASTOMA SOLID TUMOR DNA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi63-vi63
Author(s):  
Hunter Underhill ◽  
David Nix ◽  
Christian Davidson ◽  
Sabine Hellwig ◽  
Carrie Fuertes ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Solid Tumor ◽  
Somatic Mutations ◽  
Low Frequency ◽  
Clonal Diversity ◽  
Circulating Tumor Dna ◽  
Cell Free Dna ◽  
Free Dna ◽  
Frequency Threshold ◽  
Tumor Dna

Abstract Glioblastoma’s mutational landscape varies widely in the same tumor. Using conventional criteria to identify mutations from a focal tissue specimen (e.g., variant allele frequency > 5%) undersamples glioblastoma’s broad clonal diversity, which may limit detection of glioblastoma-derived circulating cell-free DNA in plasma (i.e., circulating tumor DNA; ctDNA). Here, we sought to enhance somatic variant identification in solid tumor DNA to improve detection and characterize glioblastoma-derived ctDNA. Tumor DNA and plasma cell-free DNA (collected < 24 hours prior to the surgical procedure) were isolated from eight glioblastoma patients. DNA was capture-enriched using a custom-designed, glioblastoma-targeted, next-generation sequencing panel (124 genes, 118 kb) followed by paired-end sequencing. Samples were prepared in duplicate with molecular barcodes to enable detection of very-low frequency variants. Somatic mutations in tumor DNA were identified using variable allele frequency thresholds and were considered positive in ctDNA if present in both duplicate samples. Using a lower allele frequency threshold to identify mutations in tumor DNA significantly increased detection of ctDNA (F(1.04,7.29)=14.81, P=0.006). At a solid tumor allele frequency threshold of ≥ 5%, only a single patient (12.5%) had tumor mutations detected in ctDNA. However, at a threshold ≥ 1%, all patients (100%) had at least one tumor mutation detected in ctDNA. Moreover, at a threshold ≥ 0.5%, 7 out of 8 patients (87.5%) had > 12 tumor mutations present in ctDNA. The increased detection of ctDNA enabled the subsequent discovery that somatic mutations in APC, KIT, MSH6, and NF1 were more likely to be present in ctDNA compared to somatic mutations in ATRX, LZTR1, SLC26A3, and TERT which were absent in ctDNA (χ 2=8.0, P=0.005). Thus, stronger sampling of glioblastoma’s genetic heterogeneity in tumor DNA improves detection of ctDNA allowing comparisons between mutational profiles that may lead to the identification of similarities and differences with key biologic and/or clinical implications.

A dual target sequencing solution to assess genomic and epigenomic alterations in cell-free DNA with no sample splitting.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3043-3043
Author(s):  
Grace Q. Zhao ◽  
Yun Bao ◽  
Heng Wang ◽  
Wanping Hu ◽  
John Coller ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Dna Analysis ◽  
Residual Disease ◽  
Early Stage ◽  
Cancer Cell Line ◽  
Assay Sensitivity ◽  
Cell Free Dna ◽  
Target Sequencing ◽  
Free Dna ◽  
Dual Analysis

3043 Background: Assessing the genomic and epigenomic changes on plasma cell-free DNA (cfDNA) using next-generation sequencing (NGS) has become increasingly important for cancer detection and treatment selection guidance. However, two major hurdles of existing targeted NGS methods make them impractical for the clinical setting. First, there is no comprehensive, end to end, kit solution available for targeted methylation sequencing (TMS), let alone one that analyzes both mutation and methylation information in one assay. Second, the low yield of cfDNA from clinical blood samples presents a major challenge for conducting multi-omic analysis. Thus, an assay that is capable of both genomic and epigenomic analysis would be advantageous for clinical research and future diagnostic assays. Methods: Here, we report the performance of Point-n-SeqTM dual analysis, a kit solution that can provide in-depth DNA analysis with highly flexible and customizable focused panels to enable both genomic and epigenomic analysis without sample splitting. With custom panels of tens to thousands of markers designed with > 99% first-pass success rate, we conducted both performance validation and multi-center, multi-operator, reproducibility studies. Using spike-in titration of cancer cell-line gDNA with known mutation and methylation profiles, Point-n-Seq assay achieved a reliable detection level down to 0.003% of tumor DNA with a linear relationship between the measured and expected fractions. Benchmarked with conventional targeted sequencing and methylation sequencing, Point-n-Seq solution also demonstrated improved performance, speed and shortened hands-on time. Results: In a pilot clinical study, a colorectal cancer (CRC) TMS panel covering 560 methylation markers and a mutation panel with > 350 hotspot mutations in 22 genes were used in the dual assay. Using 1ml of plasma from late-stage CRC patients, cancer-specific methylation signals were detected in all samples tested, and oncogenic mutations. In an early-stage cohort (33 stage I/II CRC patient ), comparison of the analysis between tumor-informed, personalized-mutation panels (̃100 private SNVs) for each patient and the tumor-independent CRC methylation panels were conducted. The initial results showed that tumor-independent TMS assay achieved a comparable detection compared to the personalized tumor-informed approach. Moreover, cfDNA size information (fragmentome) is also integrated into the analysis of the same Point-n-Seq workflow to improve the assay sensitivity. Conclusions: Point-n-Seq dual analysis is poised to advance both research and clinical applications of early cancer detection, minimal residual disease (MRD), and monitoring.

scholarly journals DISMIR: a deep learning-based cancer-detection method by integrating DNA sequence and methylation information of individual cell-free DNA reads

2021 ◽  
Author(s):  
Jiaqi Li ◽  
Lei Wei ◽  
Xianglin Zhang ◽  
Wei Zhang ◽  
Haochen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Cancer Detection ◽  
High Throughput Sequencing ◽  
Detection Method ◽  
Low Cost ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Free Dna ◽  
Novel Approach

ABSTRACTDetecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel non-invasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise prediction with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as “switching region” to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state, and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultra-low sequencing depths. Analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites’ methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

scholarly journals A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing

2021 ◽  
Vol 8 ◽  
Author(s):  
Bo-Wei Han ◽  
Xu Yang ◽  
Shou-Fang Qu ◽  
Zhi-Wei Guo ◽  
Li-Min Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Nucleosome Occupancy ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Transcription Start Sites ◽  
Free Dna ◽  
Coverage Accuracy ◽  
Moderate Cost ◽  
Coverage Profile ◽  
High Depth

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.

scholarly journals Detection of Circulating Tumor DNA in Patients With Leiomyosarcoma With Progressive Disease

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew L. Hemming ◽  
Kelly Klega ◽  
Justin Rhoades ◽  
Gavin Ha ◽  
Kate E. Acker ◽  
...  
Keyword(s):  
Disease Progression ◽  
Tumor Size ◽  
Disease Burden ◽  
Progressive Disease ◽  
Circulating Tumor Dna ◽  
Tumor Burden ◽  
Blood Draw ◽  
Cell Free Dna ◽  
Free Dna ◽  
Tumor Dna

Purpose Leiomyosarcoma (LMS) is a soft-tissue sarcoma characterized by multiple copy number alterations (CNAs) and without common recurrent single-nucleotide variants. We evaluated the feasibility of detecting circulating tumor DNA (ctDNA) with next-generation sequencing in a cohort of patients with LMS whose tumor burden ranged from no evidence of disease to metastatic progressive disease. Patients and Methods We evaluated cell-free DNA in plasma samples and paired genomic DNA from resected tumors from patients with LMS by ultra-low passage whole-genome sequencing. Sequencing reads were aligned to the human genome and CNAs that were identified in cell-free DNA and tumor DNA by ichorCNA software to determine the presence of ctDNA. Clinical data were reviewed to assess disease burden and clinicopathologic features. Results We identified LMS ctDNA in 11 (69%) of 16 patients with disease progression and total tumor burden greater than 5 cm. Sixteen patients with stable disease or low disease burden at the time of blood draw were found to have no detectable ctDNA. Higher ctDNA fraction of total cell-free DNA was associated with increasing tumor size and disease progression. Conserved CNAs were found between primary tumors and ctDNA in each case, and recurrent CNAs were found across LMS samples. ctDNA levels declined after resection of progressive disease in one case and became detectable upon disease relapse in another individual patient. Conclusion These results suggest that ctDNA, assayed by a widely available sequencing approach, may be useful as a biomarker for a subset of patients with uterine and extrauterine LMS. Higher levels of ctDNA correlate with tumor size and disease progression. Liquid biopsies may assist in guiding treatment decisions, monitoring response to systemic therapy, surveying for disease recurrence, and differentiating benign and malignant smooth muscle tumors.

scholarly journals Urothelial Carcinoma Detection Based on Copy Number Profiles of Urinary Cell-Free DNA by Shallow Whole-Genome Sequencing

2019 ◽  
Vol 66 (1) ◽  
pp. 188-198 ◽  
Author(s):  
Guangzhe Ge ◽  
Ding Peng ◽  
Bao Guan ◽  
Yuanyuan Zhou ◽  
Yanqing Gong ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Genome Sequencing ◽  
Copy Number ◽  
Support Vector ◽  
Sequencing Data ◽  
Validation Data ◽  
Data Set ◽  
Cell Free Dna ◽  
Clinical Sensitivity ◽  
Free Dna

Abstract BACKGROUND Current noninvasive assays for urothelial carcinoma (UC) lack clinical sensitivity and specificity. Given the utility of plasma cell-free DNA (cfDNA) biomarkers, the development of urinary cfDNA biomarkers may improve the diagnostic sensitivity. METHODS We assessed copy number alterations (CNAs) by shallow genome-wide sequencing of urinary cfDNA in 95 cancer-free individuals and 65 patients with UC, 58 with kidney cancer, and 45 with prostate cancer. We used a support vector machine to develop a diagnostic classifier based on CNA profiles to detect UC (UCdetector). The model was further validated in an independent cohort (52 patients). Genome sequencing data of tumor specimens from 90 upper tract urothelial cancers (UTUCs) and CNA data for 410 urothelial carcinomas of bladder (UCBs) from The Cancer Genome Atlas were used to validate the classifier. Genome sequencing data for urine sediment from 32 patients with UC were compared with cfDNA. To monitor the treatment efficacy, we collected cfDNA from 7 posttreatment patients. RESULTS Urinary cfDNA was a more sensitive alternative to urinary sediment. The UCdetector could detect UC at a median clinical sensitivity of 86.5% and specificity of 94.7%. UCdetector performed well in an independent validation data set. Notably, the CNA features selected by UCdetector were specific markers for both UTUC and UCB. Moreover, CNA changes in cfDNA were consistent with the treatment effects. Meanwhile, the same strategy could localize genitourinary cancers to tissue of origin in 70.1% of patients. CONCLUSIONS Our findings underscore the potential utility of urinary cfDNA CNA profiles as a basis for noninvasive UC detection and surveillance.

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