A novel biophysical based marker with multilevel, multiparameter expression for early stage cancer detection.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20673-e20673
Author(s):  
Junjie Wu ◽  
Gengxi Jiang ◽  
Jiansong Ji ◽  
Xuedong Du ◽  
Yue Lin ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Early Stage ◽  
Physical Property ◽  
Stage I ◽  
Potential Candidate ◽  
Healthy Individuals ◽  
Blind Test ◽  
Early Stage Cancer ◽  
Multi Level

e20673 Background: Early stage cancer detection remains to be elusive despite of many years of efforts. In this work, a bio-physical based marker (named Cancer Differentiation Analysis (CDA)) with multi-level and multi-parameter expression features has been developed which has shown a number of clear advantages over the traditional approaches such as bio-chemistry based marker, circulating tumor cell (CTC) and circuiting DNA (ct-DNA). In stage I non-small cell lung cancer (NSCLC), sensitivity and specificity reached a record high of 85.2% and 93.0%, respectively. Methods: In this study, 832 NSCLC cancer samples with pathological information and 642 samples from healthy individuals were measured in a single blind test. Peripheral blood of each individual was drawn in EDTA tubes. One class of bio-physical property in blood samples was utilized for CDA tests. The CDA data were first processed using an algorithm built from data base and subsequently analyzed using SPSS. The results were shown in the table. Results: The results indicated that CDA technology has a very good sensitivity and specificity even at stage I (85% and 93%, respectively), which is much better than those previously reported results by other methods. Conclusions: Initial results showed that CDA technology could effectively screen NSCLC patients from healthy individuals. As a novel bio-physical based cancer detection approach with multi-level and multi-parameter expressions, CDA technology could be a potential candidate for early stage cancer screening. [Table: see text]

Cancer differentiation analysis technology as a novel technology for cerebral cancer screening.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 2040-2040
Author(s):  
Hongmei Tao ◽  
Xing Tang ◽  
Yue Lin ◽  
Chris Chang Yu ◽  
Xuedong Du
Keyword(s):  
Cancer Screening ◽  
Sensitivity And Specificity ◽  
Physical Property ◽  
Screening Methods ◽  
Potential Candidate ◽  
Healthy Individuals ◽  
Level Information ◽  
Area Under Roc Curve ◽  
Multi Level ◽  
Initial Results

2040 Background: While the current cancer screening methods mostly failed to detect cerebral cancer, a novel, promising technology named cancer differentiation analysis (CDA) technology has been developed to measure novel bio-physical properties to obtain valuable multi-level and multi-parameter information including protein, cellular and molecular level information. Initial results showed that CDA technology is capable of detecting cerebral cancer with a high degree of sensitivity and specificity. Methods: In this study, samples from 78 cerebral cancer patients and 321 healthy individuals were measured. Peripheral blood of each individual was drawn in EDTA tubes. One class of bio-physical property in blood samples was utilized for CDA tests. CDA data were conducted using SPSS, and the results were shown in table. Results: The average CDA values of cerebral cancer and control groups were 52.30 and 33.38 (rel. units) respectively. The results indicated that cerebral cancer could be significantly distinguished from the control (p < 0.001). Area under ROC curve (AUC) was 0.980, and sensitivity and specificity was 92.3% and 96.6% respectively. Conclusions: Initial results showed that CDA technology could effectively distinguish cerebral cancer from healthy individuals. As a novel bio-physical based cancer detection approach with multi-level and multi-parameter expressions, CDA could be a potential candidate for cerebral cancer screening. Results from Statistical Analysis of CDA. [Table: see text]

Deep features using convolutional neural network for Early stage cancer detection

2018 ◽  
Author(s):  
Sawon Pratiher ◽  
Sabyasachi Mukhopadhyay ◽  
Prasanta K. Panigrahi ◽  
Nirmalya Ghosh ◽  
Shubhobrata Bhattacharya ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Early Stage ◽  
Early Stage Cancer

scholarly journals Protein corona-enabled serological tests for early stage cancer detection

2020 ◽  
Vol 1 ◽  
pp. 100025 ◽  
Author(s):  
Erica Quagliarini ◽  
Riccardo Di Santo ◽  
Daniela Pozzi ◽  
Giulio Caracciolo
Keyword(s):  
Cancer Detection ◽  
Early Stage ◽  
Protein Corona ◽  
Serological Tests ◽  
Early Stage Cancer

Genome-wide cell-free DNA (cfDNA) methylation signatures and effect on tissue of origin (TOO) performance.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3049-3049 ◽  
Author(s):  
Minetta C. Liu ◽  
Arash Jamshidi ◽  
Oliver Venn ◽  
Alexander P. Fields ◽  
M. Cyrus Maher ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Early Stage ◽  
Stage Iv ◽  
Stage I ◽  
Multiple Cancer ◽  
Isolated Cells ◽  
Genome Wide ◽  
Ffpe Tumor ◽  
Formalin Fixed Paraffin Embedded ◽  
Cancer Types

3049 Background: For multi-cancer detection using cfDNA, TOO determination is critical to enable safe and efficient diagnostic follow-up. Previous array-based studies captured < 2% of genomic CpGs. Here, we report genome-wide fragment-level methylation patterns across 811 cancer cell methylomes representing 21 tumor types (97% of SEER cancer incidence), and define effects of this methylation database on TOO prediction within a machine learning framework. Methods: Genomic DNA from 655 formalin-fixed paraffin-embedded (FFPE) tumor tissues and 156 isolated cells from tumors was subjected to a prototype 30x whole-genome bisulfite sequencing (WGBS) assay, as previously reported in the Circulating Cell-free Genome Atlas (CCGA) study (NCT02889978). Two independent TOO models, one with and one without the methylation database, were fitted on training samples; each was used to predict on the test set. A WGBS classifier was used to detect cancer at 98% specificity; reported TOO results reflect percent agreement between predicted and true TOO among those detected cancers (166 cases: 81 stage I-III, 69 stage IV, 16 non-informative). Results: Genome-wide methylation data generated from this database allowed fragment-level analysis and coverage of ~30 million CpGs across the genome (~60-fold greater than array-based approaches). Incorrect TOO assignments decreased by 35% (20% to 13%) after incorporating methylation database information into TOO classification. Improvement was observed across all cancer types and was consistent in early-stage cancers (stage I-III). Respective performances in breast cancer (n = 23) were 87% vs 96%; in lung cancer (n = 32) were 85% vs 88%; in hepatobiliary (n = 10) were 70% vs 90%; and in pancreatic cancer (n = 17) were 94% vs 100%. Results using an optimized approach informed by these results in a large cohort of CCGA participants will be reported. Conclusions: Incorporating data from a large methylation database improved TOO performance in multiple cancer types. This supports feasibility of this methylation-based approach as an early cancer detection test across cancer types. Clinical trial information: NCT02889978.

The development and validation of a novel targeted methylation sequencing technology detecting 0.003 percent tumor signal in early-stage cancer plasma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 4092-4092
Author(s):  
Grace Q. Zhao ◽  
Yun Bao ◽  
Heng Wang ◽  
Jianmin Wang ◽  
Shengrong Lin
Keyword(s):  
Early Stage ◽  
Methylation Status ◽  
Gc Content ◽  
Stage I ◽  
Healthy Population ◽  
Current Sensitivity ◽  
High Background ◽  
Early Stage Cancer ◽  
Depth Analysis ◽  
Low Efficiency

4092 Background: Methylation analysis in cell-free DNA holds great potential for early cancer detection. In the plasma of early stage cancer patient, the tumor content is estimated to be less than 0.1%, therefore demands a highly sensitive assay. Targeted Methylation Sequencing (TMS) is the most promising approach; however, the current sensitivity and specificity are compromised by low efficiency and low recovery of target enrichment, and further hampered by background noise associated with large panels. The ideal solution would be an in-depth analysis using a focused small cancer-specific methylation biomarker panel, but is not supported by existing technologies. Methods: Here we present a new technology designed for TMS analysis in cfDNA: Point-n-Seq, featuring an enrichment of target molecules directly from cfDNA before bisulfite conversion and amplification. Particularly, this technology enables small focused panel that interrogates the methylation status of 1 to ~1000 markers. We designed a CRC panel covering 100 methylation markers in 3 steps: identify ~1000 CRC-specific markers from public databases; eliminate makers with high background signal in baseline cfDNA of healthy population; finalize the list with the most differentiating markers between patient and healthy cfDNA. Results: The capture of Point-n-Seq CRC panel is highly efficient resulting in high uniformity (94% > 0.5X) and on-target rate ( > 80%). For 20 ng cfDNA input, more than 1000 deduped informative reads were obtained for each marker on average, despite the high GC content ( > 80%). The output of informative reads was linear to the cfDNA input ranging from 1 ng to 40 ng. In titration studies, 0.6 pg (0.2X genome equivalent) methylated DNA in 20 ng cfDNA (0.003%) was reliably detected over cfDNA background. Using plasma samples from patients with CRC - early stage (I, n = 7; II, n = 7), late stage (III, n = 11; IV, n = 3), and control individuals (n = 105), the average fractions of methylated signal are 0.0034%, 0.013%, 0.09%, 0.17%, 0.29% for control, stage I, II, III, IV accordingly. With a simple cut-off using methylation fraction, Point-n Seq CRC panel achieved a sensitivity of 86% for stage I, 100% for stage (II-IV) at a specificity of 91%, with AUC = 0.96. Conclusions: Point-n-Seq TMS is the first hybridization based NGS technology enables the small focused methylation panel (e.g. 100 markers) sequencing using cfDNA, and it will greatly facilitate the development of practical and cost-effective methylation assays for clinical use.

scholarly journals Multispectral fluorescence imaging of human ovarian and Fallopian tube tissue for early stage cancer detection

2015 ◽  
Author(s):  
Tyler Tate ◽  
Brenda Baggett ◽  
Photini Rice ◽  
Jennifer Watson ◽  
Gabe Orsinger ◽  
...  
Keyword(s):  
Fluorescence Imaging ◽  
Fallopian Tube ◽  
Cancer Detection ◽  
Early Stage ◽  
Early Stage Cancer ◽  
Multispectral Fluorescence Imaging
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