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.

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 A novel decision tree model based on chromosome imbalances in cell-free DNA and CA-125 in the differential diagnosis of ovarian cancer

2021 ◽  
pp. 172460082199235
Author(s):  
Weina Zhang ◽  
Yu-min Zhang ◽  
Yuan Gao ◽  
Shengmiao Zhang ◽  
Weixin Chu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Differential Diagnosis ◽  
Decision Tree ◽  
Copy Number ◽  
Malignant Tumors ◽  
Copy Number Variations ◽  
Ca 125 ◽  
Benign Tumors ◽  
Cell Free Dna ◽  
Free Dna

Objective: CA-125 is widely used as biomarker of ovarian cancer. However, CA-125 suffers low accuracy. We developed a hybrid analytical model, the Ovarian Cancer Decision Tree (OCDT), employing a two-layer decision tree, which considers genetic alteration information from cell-free DNA along with CA-125 value to distinguish malignant tumors from benign tumors. Methods: We consider major copy number alterations at whole chromosome and chromosome-arm level as the main feature of our detection model. Fifty-eight patients diagnosed with malignant tumors, 66 with borderline tumors, and 10 with benign tumors were enrolled. Results: Genetic analysis revealed significant arm-level imbalances in most malignant tumors, especially in high-grade serous cancers in which 12 chromosome arms with significant aneuploidy ( P<0.01) were identified, including 7 arms with significant gains and 5 with significant losses. The area under receiver operating characteristic curve (AUC) was 0.8985 for copy number variations analysis, compared to 0.8751 of CA125. The OCDT was generated with a cancerous score (CScore) threshold of 5.18 for the first level, and a CA-125 value of 103.1 for the second level. Our most optimized OCDT model achieved an AUC of 0.975. Conclusions: The results suggested that genetic variations extracted from cfDNA can be combined with CA-125, and together improved the differential diagnosis of malignant from benign ovarian tumors. The model would aid in the pre-operative assessment of women with adnexal masses. Future clinical trials need to be conducted to further evaluate the value of CScore in clinical settings and search for the optimal threshold for malignancy detection.

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.

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