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Self-digitization chip for single-cell genotyping of cancer-related mutations
Cancer is a heterogeneous disease, and patient-level genetic assessments can guide therapy choice and impact prognosis. However, little is known about the impact of genetic variability within a tumor, intratumoral heterogeneity (ITH), on disease progression or outcome. Current approaches using bulk...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5931502/ https://www.ncbi.nlm.nih.gov/pubmed/29718986 http://dx.doi.org/10.1371/journal.pone.0196801 |
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author | Thompson, Alison M. Smith, Jordan L. Monroe, Luke D. Kreutz, Jason E. Schneider, Thomas Fujimoto, Bryant S. Chiu, Daniel T. Radich, Jerald P. Paguirigan, Amy L. |
author_facet | Thompson, Alison M. Smith, Jordan L. Monroe, Luke D. Kreutz, Jason E. Schneider, Thomas Fujimoto, Bryant S. Chiu, Daniel T. Radich, Jerald P. Paguirigan, Amy L. |
author_sort | Thompson, Alison M. |
collection | PubMed |
description | Cancer is a heterogeneous disease, and patient-level genetic assessments can guide therapy choice and impact prognosis. However, little is known about the impact of genetic variability within a tumor, intratumoral heterogeneity (ITH), on disease progression or outcome. Current approaches using bulk tumor specimens can suggest the presence of ITH, but only single-cell genetic methods have the resolution to describe the underlying clonal structures themselves. Current techniques tend to be labor and resource intensive and challenging to characterize with respect to sources of biological and technical variability. We have developed a platform using a microfluidic self-digitization chip to partition cells in stationary volumes for cell imaging and allele-specific PCR. Genotyping data from only confirmed single-cell volumes is obtained and subject to a variety of relevant quality control assessments such as allele dropout, false positive, and false negative rates. We demonstrate single-cell genotyping of the NPM1 type A mutation, an important prognostic indicator in acute myeloid leukemia, on single cells of the cell line OCI-AML3, describing a more complex zygosity distribution than would be predicted via bulk analysis. |
format | Online Article Text |
id | pubmed-5931502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59315022018-05-11 Self-digitization chip for single-cell genotyping of cancer-related mutations Thompson, Alison M. Smith, Jordan L. Monroe, Luke D. Kreutz, Jason E. Schneider, Thomas Fujimoto, Bryant S. Chiu, Daniel T. Radich, Jerald P. Paguirigan, Amy L. PLoS One Research Article Cancer is a heterogeneous disease, and patient-level genetic assessments can guide therapy choice and impact prognosis. However, little is known about the impact of genetic variability within a tumor, intratumoral heterogeneity (ITH), on disease progression or outcome. Current approaches using bulk tumor specimens can suggest the presence of ITH, but only single-cell genetic methods have the resolution to describe the underlying clonal structures themselves. Current techniques tend to be labor and resource intensive and challenging to characterize with respect to sources of biological and technical variability. We have developed a platform using a microfluidic self-digitization chip to partition cells in stationary volumes for cell imaging and allele-specific PCR. Genotyping data from only confirmed single-cell volumes is obtained and subject to a variety of relevant quality control assessments such as allele dropout, false positive, and false negative rates. We demonstrate single-cell genotyping of the NPM1 type A mutation, an important prognostic indicator in acute myeloid leukemia, on single cells of the cell line OCI-AML3, describing a more complex zygosity distribution than would be predicted via bulk analysis. Public Library of Science 2018-05-02 /pmc/articles/PMC5931502/ /pubmed/29718986 http://dx.doi.org/10.1371/journal.pone.0196801 Text en © 2018 Thompson et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Thompson, Alison M. Smith, Jordan L. Monroe, Luke D. Kreutz, Jason E. Schneider, Thomas Fujimoto, Bryant S. Chiu, Daniel T. Radich, Jerald P. Paguirigan, Amy L. Self-digitization chip for single-cell genotyping of cancer-related mutations |
title | Self-digitization chip for single-cell genotyping of cancer-related mutations |
title_full | Self-digitization chip for single-cell genotyping of cancer-related mutations |
title_fullStr | Self-digitization chip for single-cell genotyping of cancer-related mutations |
title_full_unstemmed | Self-digitization chip for single-cell genotyping of cancer-related mutations |
title_short | Self-digitization chip for single-cell genotyping of cancer-related mutations |
title_sort | self-digitization chip for single-cell genotyping of cancer-related mutations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5931502/ https://www.ncbi.nlm.nih.gov/pubmed/29718986 http://dx.doi.org/10.1371/journal.pone.0196801 |
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