Resource-efficient pooled sequencing expands translational impact in solid tumors Article Swipe
YOU?
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· 2021
· Open Access
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· DOI: https://doi.org/10.1101/2021.06.06.447265
Intratumoral genetic heterogeneity (ITH) poses a significant challenge to utilizing sequencing for decision making in the management of cancer. Although sequencing of multiple tumor regions can address the pitfalls of ITH, it does so at a significant increase in cost and resource utilization. We propose a pooled multiregional sequencing strategy, whereby DNA aliquots from multiple tumor regions are mixed prior to sequencing, as a cost-effective strategy to boost translational value by addressing ITH while preserving valuable residual tissue for secondary analysis. Focusing on kidney cancer, we demonstrate that DNA pooling from as few as two regions significantly increases mutation detection while reducing clonality misattribution. This leads to an increased fraction of patients identified with therapeutically actionable mutations, improved patient risk stratification, and improved inference of evolutionary trajectories with an accuracy comparable to bona fide multiregional sequencing. The same approach applied to non-small-cell lung cancer data substantially improves tumor mutational burden (TMB) detection. Our findings demonstrate that pooled DNA sequencing strategies are a cost-effective alternative to address intrinsic genetic heterogeneity in clinical settings.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.06.06.447265
- https://www.biorxiv.org/content/biorxiv/early/2021/06/07/2021.06.06.447265.full.pdf
- OA Status
- green
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3167032502
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3167032502Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.06.06.447265Digital Object Identifier
- Title
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Resource-efficient pooled sequencing expands translational impact in solid tumorsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-07Full publication date if available
- Authors
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Renzo G. DiNatale, Roy Mano, Vladimir Makarov, Nicole Rusk, Esther Drill, Andrew Winer, Alexander Sankin, Angela Yoo, Benjamin Freeman, James J. Hsieh, Ying‐Bei Chen, Jonathan Coleman, Michael Berger, Irina Ostrovnaya, Timothy A. Chan, Paul Russo, Ed Reznik, A. Ari HakimiList of authors in order
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https://doi.org/10.1101/2021.06.06.447265Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2021/06/07/2021.06.06.447265.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2021/06/07/2021.06.06.447265.full.pdfDirect OA link when available
- Concepts
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DNA sequencing, Pooling, Computational biology, Biology, Exome sequencing, Deep sequencing, Mutation, Computer science, Genetics, Genome, DNA, Gene, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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