CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA

2021 ◽  
Author(s):  
Saifur Rahaman ◽  
Xiangtao Li ◽  
Jun Yu ◽  
Ka-Chun Wong
Keyword(s):  
Test Data ◽  
Cancer Detection ◽  
Blood Test ◽  
Class Imbalance ◽  
Cancer Type ◽  
Protein Biomarkers ◽  
Class Imbalance Problem ◽  
Multiple Cancer ◽  
Cell Free Dna ◽  
Free Dna

Abstract Motivation The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. Methods A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from the blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Results Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK published in Science, 2018) for cancer detection. The results reveal that our proposed method (i.e., CancerEMC) can achieve the best performance result for both binary cancer classification with 99.1748% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.1214% accuracy (AUC = 0.938). For addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.4966% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. Availability https://github.com/saifurcubd/Cancer-Detection

scholarly journals Cancer Detection and Classification by CpG Island Hypermethylation Signatures in Plasma Cell-Free DNA

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5611
Author(s):  
Jinyong Huang ◽  
Alex C. Soupir ◽  
Brian D. Schlick ◽  
Mingxiang Teng ◽  
Ibrahim H. Sahin ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cancer Detection ◽  
Cpg Island ◽  
Cpg Islands ◽  
Response Monitoring ◽  
Aberrant Methylation ◽  
Tumor Type ◽  
Multiple Cancer ◽  
Cell Free Dna ◽  
Free Dna

Cell-free DNA (cfDNA) methylation has emerged as a promising biomarker for early cancer detection, tumor type classification, and treatment response monitoring. Enrichment-based cfDNA methylation profiling methods such as cfMeDIP-seq have shown high accuracy in the classification of multiple cancer types. We have previously optimized another enrichment-based approach for ultra-low input cfDNA methylome profiling, termed cfMBD-seq. We reported that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of high-CpG-density regions, such as CpG islands. However, the clinical feasibility of cfMBD-seq is unknown. In this study, we applied cfMBD-seq to profiling the cfDNA methylome using plasma samples from cancer patients and non-cancer controls. We identified 1759, 1783, and 1548 differentially hypermethylated CpG islands (DMCGIs) in lung, colorectal, and pancreatic cancer patients, respectively. Interestingly, the vast majority of DMCGIs were overlapped with aberrant methylation changes in corresponding tumor tissues, indicating that DMCGIs detected by cfMBD-seq were mainly driven by tumor-specific DNA methylation patterns. From the overlapping DMCGIs, we carried out machine learning analyses and identified a set of discriminating methylation signatures that had robust performance in cancer detection and classification. Overall, our study demonstrates that cfMBD-seq is a powerful tool for sensitive detection of tumor-derived epigenomic signals in cfDNA.

scholarly journals A multiparametric extraction method for Vn96-isolated plasma extracellular vesicles and cell-free DNA that enables multi-omic profiling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeremy W. Roy ◽  
Catherine A. Taylor ◽  
Annie P. Beauregard ◽  
Surendar R. Dhadi ◽  
D. Craig Ayre ◽  
...  
Keyword(s):  
Extracellular Vesicles ◽  
Liquid Biopsy ◽  
Small Rna Sequencing ◽  
Isolation Method ◽  
Protein Biomarkers ◽  
Cell Free Dna ◽  
Extraction Protocol ◽  
Free Dna ◽  
Rich Material ◽  
Short Timeframe

AbstractExtracellular vesicles (EVs) have been recognized as a rich material for the analysis of DNA, RNA, and protein biomarkers. A remaining challenge for the deployment of EV-based diagnostic and prognostic assays in liquid biopsy testing is the development of an EV isolation method that is amenable to a clinical diagnostic lab setting and is compatible with multiple types of biomarker analyses. We have previously designed a synthetic peptide, known as Vn96 (ME kit), which efficiently isolates EVs from multiple biofluids in a short timeframe without the use of specialized lab equipment. Moreover, it has recently been shown that Vn96 also facilitates the co-isolation of cell-free DNA (cfDNA) along with EVs. Herein we describe an optimized method for Vn96 affinity-based EV and cfDNA isolation from plasma samples and have developed a multiparametric extraction protocol for the sequential isolation of DNA, RNA, and protein from the same plasma EV and cfDNA sample. We are able to isolate sufficient material by the multiparametric extraction protocol for use in downstream analyses, including ddPCR (DNA) and ‘omic profiling by both small RNA sequencing (RNA) and mass spectrometry (protein), from a minimum volume (4 mL) of plasma. This multiparametric extraction protocol should improve the ability to analyse multiple biomarker materials (DNA, RNA and protein) from the same limited starting material, which may improve the sensitivity and specificity of liquid biopsy tests that exploit EV-based and cfDNA biomarkers for disease detection and monitoring.

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.

Prognostic significance of blood-based multi-cancer detection in plasma cell-free DNA

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Oscar D. Pons-Belda ◽  
Amaia Fernandez-Uriarte ◽  
Annie Ren ◽  
Eleftherios P. Diamandis
Keyword(s):  
Plasma Cell ◽  
Cancer Detection ◽  
Prognostic Significance ◽  
Cell Free Dna ◽  
Free Dna

Abstract LB058: Performance of a cell-free DNA-based multi-cancer detection test as a tool for diagnostic resolution of symptomatic cancers

2021 ◽  
Author(s):  
Alan H. Bryce ◽  
Minetta C. Liu ◽  
Michael V. Seiden ◽  
David D. Thiel ◽  
Donald Richards ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Cell Free Dna ◽  
Free Dna ◽  
A Cell ◽  
Diagnostic Resolution

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.

Blood Collection and Cell-Free DNA Isolation Methods Influence the Sensitivity of Liquid Biopsy Analysis for Colorectal Cancer Detection

2018 ◽  
Vol 25 (3) ◽  
pp. 915-923 ◽  
Author(s):  
Barbara Kinga Barták ◽  
Alexandra Kalmár ◽  
Orsolya Galamb ◽  
Barnabás Wichmann ◽  
Zsófia Brigitta Nagy ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Detection ◽  
Liquid Biopsy ◽  
Dna Isolation ◽  
Blood Collection ◽  
Cell Free Dna ◽  
Free Dna ◽  
Isolation Methods

scholarly journals Tissue and Cell-Free DNA-Based Epigenomic Approaches for Cancer Detection

2019 ◽  
Vol 66 (1) ◽  
pp. 105-116 ◽  
Author(s):  
Alessandro Leal ◽  
David Sidransky ◽  
Mariana Brait
Keyword(s):  
Cancer Detection ◽  
Cancer Diagnosis ◽  
Malignant Tumors ◽  
Unmet Need ◽  
Clinical Routine ◽  
Commercial Development ◽  
Cell Free Dna ◽  
Cancer Management ◽  
Diagnosis And Prognosis ◽  
Free Dna

Abstract BACKGROUND Over 9 million people die of cancer each year worldwide, reflecting the unmet need for effective biomarkers for both cancer diagnosis and prognosis. Cancer diagnosis is complex because the majority of malignant tumors present with long periods of latency and lack of clinical presentation at early stages. During carcinogenesis, premalignant cells experience changes in their epigenetic landscapes, such as differential DNA methylation, histone modifications, nucleosome positioning, and higher orders of chromatin changes that confer growth advantage and contribute to determining the biologic phenotype of human cancers. CONTENT Recent progress in microarray platforms and next-generation sequencing approaches has allowed the characterization of abnormal epigenetic patterns genome wide in a large number of cancer cases. The sizable amount of processed data also comes with challenges regarding data management and assessment for effective biomarker exploration to be further applied in prospective clinical trials. Epigenetics-based single or panel tests of genes are being explored for clinical management to fulfill unmet needs in oncology. The advance of these tests to the clinical routine will depend on rigorous, extensive, and independent validation in well-annotated cohort of patients and commercial development of clinical routine–friendly and adequate procedures. SUMMARY In this review we discuss the analytic validation of tissue and cell-free DNA-based epigenomic approaches for early cancer detection, diagnosis, and treatment monitoring and the clinical utility of candidate epigenetic alterations applied to colorectal, glioblastoma, breast, prostate, bladder, and lung cancer management.

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