Quantitative and qualitative characterization of plasma DNA identifies primary and recurrent colorectal cancer

In colorectal cancer, CD133+ cells from fresh biopsies proved to be more tumorigenic than their CD133– counterparts. Nevertheless, the function of CD133 protein in tumorigenic cells seems only marginal. Moreover, CD133 expression alone is insufficient to isolate true cancer stem cells, since only 1 out of 262 CD133+ cells actually displays stem-cell capacity. Thus, new markers for colorectal cancer stem cells are needed. Here, we show the extensive characterization of CD133+ cells in 5 different colon carcinoma continuous cell lines (HT29, HCT116, Caco2, GEO and LS174T), each representing a different maturation level of colorectal cancer cells. Markers associated with stemness, tumorigenesis and metastatic potential were selected. We identified 6 molecules consistently present on CD133+ cells: CD9, CD29, CD49b, CD59, CD151, and CD326. By contrast, CD24, CD26, CD54, CD66c, CD81, CD90, CD99, CD112, CD164, CD166, and CD200 showed a discontinuous behavior, which led us to identify cell type-specific surface antigen mosaics. Finally, some antigens, e.g. CD227, indicated the possibility of classifying the CD133+ cells into 2 subsets likely exhibiting specific features. This study reports, for the first time, an extended characterization of the CD133+ cells in colon carcinoma cell lines and provides a “dictionary” of antigens to be used in colorectal cancer research.

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CSMD: a computational subtraction-based microbiome discovery pipeline for species-level characterization of clinical metagenomic samples

Bioinformatics ◽

10.1093/bioinformatics/btz790 ◽

2019 ◽

Author(s):

Yu Liu ◽

Paul W Bible ◽

Bin Zou ◽

Qiaoxing Liang ◽

Cong Dong ◽

...

Keyword(s):

Species Level ◽

Supplementary Information ◽

Clinical Samples ◽

Plasma Dna ◽

Bioinformatics Pipeline ◽

Microbial Identification ◽

Reference Databases ◽

Microbial Dna ◽

Higher Sensitivity

Abstract Motivation Microbiome analyses of clinical samples with low microbial biomass are challenging because of the very small quantities of microbial DNA relative to the human host, ubiquitous contaminating DNA in sequencing experiments and the large and rapidly growing microbial reference databases. Results We present computational subtraction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate species-level microbiome profiles for clinical samples with low microbial loads. CSMD applies strategies for the maximal elimination of host sequences with minimal loss of microbial signal and effectively detects microorganisms present in the sample with minimal false positives using a stepwise convergent solution. CSMD was benchmarked in a comparative evaluation with other classic tools on previously published well-characterized datasets. It showed higher sensitivity and specificity in host sequence removal and higher specificity in microbial identification, which led to more accurate abundance estimation. All these features are integrated into a free and easy-to-use tool. Additionally, CSMD applied to cell-free plasma DNA showed that microbial diversity within these samples is substantially broader than previously believed. Availability and implementation CSMD is freely available at https://github.com/liuyu8721/csmd. Supplementary information Supplementary data are available at Bioinformatics online.

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Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

Journal of Clinical Medicine ◽

10.3390/jcm9103313 ◽

2020 ◽

Vol 9 (10) ◽

pp. 3313 ◽

Cited By ~ 2

Author(s):

Hemant Goyal ◽

Rupinder Mann ◽

Zainab Gandhi ◽

Abhilash Perisetti ◽

Aman Ali ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Cancer Screening ◽

Colorectal Cancer Screening ◽

Narrow Band Imaging ◽

Screening Methods ◽

Polyp Detection ◽

Mortality And Morbidity ◽

Magnifying Chromoendoscopy

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

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