Cancer marker-free enrichment and direct mutation detection in rare cancer cells by combining multi-property isolation and microfluidic concentration

Lab on a Chip ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 757-766 ◽  
Author(s):  
Soo Hyeon Kim ◽  
Hiroshi Ito ◽  
Masahiro Kozuka ◽  
Hidenori Takagi ◽  
Mitsuharu Hirai ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Negative Selection ◽  
Mutation Detection ◽  
Direct Detection ◽  
Rare Cancer ◽  
Enrichment Method ◽  
Cancer Marker ◽  
Direct Mutation ◽  
Marker Free ◽  
Free Enrichment

We present a novel cancer marker-free CTC enrichment method by size-based filtration and immunomagnetic negative selection followed by dielectrophoretic concentration for direct detection of genetic mutations in rare cancer cells suspended in whole blood.

Do rare cancer cells have a tale to tell?

2007 ◽  
Author(s):  
Carmen Phillips
Keyword(s):  
Cancer Cells ◽  
Rare Cancer

Abstract 188: Deep learning enables label-free profiling of the tumor microenvironment and enrichment of rare cancer cells

2021 ◽  
Author(s):  
Mahyar Salek ◽  
Hou-pu Chou ◽  
Prashast Khandelwal ◽  
Krishna P. Pant ◽  
Thomas J. Musci ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tumor Microenvironment ◽  
Cancer Cells ◽  
Rare Cancer ◽  
Label Free

A Microfluidic Platform for On-Chip Analysis of Circulating Tumor Cells

2021 ◽  
Author(s):  
Jeff Darabi ◽  
Joseph Schober
Keyword(s):  
Cancer Cells ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Whole Blood ◽  
Peripheral Blood ◽  
Automated Image Analysis ◽  
Rare Cancer ◽  
On Chip ◽  
Chip Analysis ◽  
Selection Of

Abstract Studies have shown that primary tumor sites begin shedding cancerous cells into peripheral blood at early stages of cancer, and the presence and frequency of circulating tumor cells (CTCs) in blood is directly proportional to disease progression. The challenge is that the concentration of the CTCs in peripheral blood may be extremely low. In the past few years, several microfluidic-based concepts have been investigated to isolate CTCs from whole blood. However, these devices are generally hampered by complex fabrication processes and very low volumetric throughputs, which may not be practical for rapid clinical applications. This paper presents a high-performance yet simple magnetophoretic microfluidic chip for the enrichment and on-chip analysis of rare CTCs from blood. Microscopic and flow cytometric assays developed for selection of cancer cell lines, selection of monoclonal antibodies, and optimization of bead coupling are discussed. Additionally, on-chip characterization of rare cancer cells using high resolution immunofluorescence microscopy and modeling results for prediction of CTC capture length are presented. The device has the ability to interface directly with on-chip pre and post processing modules such as mixing, incubation, and automated image analysis systems. These features will enable us to isolate rare cancer cells from whole blood and detect them on the chip with subcellular resolution.

scholarly journals Construction of a Robust Entropy-Driven DNA Nanomachine for Single-Molecule Detection of Rare Cancer Cells

2019 ◽  
Vol 91 (12) ◽  
pp. 7505-7509 ◽  
Author(s):  
Fei Ma ◽  
Shu-hua Wei ◽  
Chun-yang Zhang
Keyword(s):  
Cancer Cells ◽  
Single Molecule ◽  
Single Molecule Detection ◽  
Rare Cancer ◽  
Dna Nanomachine

Capture and detection of rare cancer cells in blood by intrinsic fluorescence of a novel functionalized diatom

2020 ◽  
Vol 30 ◽  
pp. 101753 ◽  
Author(s):  
Javid Esfandyari ◽  
Behnaz Shojaedin-Givi ◽  
Hadi Hashemzadeh ◽  
Mohammad Mozafari-Nia ◽  
Zahra Vaezi ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Intrinsic Fluorescence ◽  
Rare Cancer

scholarly journals Differentiation potential to neural-like cells in human adrenocortical carcinoma SW-13 cells

2019 ◽  
Author(s):  
Eriko Shimada ◽  
Yusuke Tsuruwaka
Keyword(s):  
Early Diagnosis ◽  
Adrenal Gland ◽  
Cancer Cells ◽  
Adrenocortical Carcinoma ◽  
Neural Differentiation ◽  
Neural Induction ◽  
Carcinoma Cells ◽  
Rare Cancer ◽  
Differentiation Potential ◽  
Aggressive Tumor

Various cancer cells are known to show neural differentiation. Adrenocortical carcinoma (ACC) is a rare and frequently aggressive tumor originating in the cortex of the adrenal gland. Early diagnosis of ACC is challenging due to a lot of unknown aspects such as cell characteristics in a rare cancer. In the present study, morphological features were examined in the adrenal cortex carcinoma cells SW-13 as an initial candidate, which were exposed to neural differentiation condition. SW-13 cells treated with the neural induction supplement showed neural-like differentiation with elongated filaments. It was suggested that SW-13 cells had neural differentiation potential and could be a research tool to elucidate the cell characteristics in future ACC studies.

High-purity Isolation for Genotyping Rare Cancer Cells from Blood Using a Microfluidic Chip Cell Sorter

2021 ◽  
Vol 42 (1) ◽  
pp. 407-417
Author(s):  
MIO IKEDA ◽  
YASUHIRO KOH ◽  
JUN OYANAGI ◽  
SHUNSUKE TERAOKA ◽  
MASAYUKI ISHIGE ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Microfluidic Chip ◽  
High Purity ◽  
Rare Cancer ◽  
Cell Sorter

scholarly journals Quantifying the influence of mutation detection on tumour subclonal reconstruction

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Lydia Y. Liu ◽  
Vinayak Bhandari ◽  
Adriana Salcedo ◽  
Shadrielle M. G. Espiritu ◽  
Quaid D. Morris ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Cells ◽  
Clinical Outcomes ◽  
Cancer Cell ◽  
Genome Sequencing ◽  
Somatic Mutations ◽  
Mutation Detection ◽  
Cell Populations ◽  
Whole Genome ◽  
Prostate Cancers

AbstractWhole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

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