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Precision oncology using a limited number of cells: optimization of whole genome amplification products for sequencing applications

Overview of attention for article published in BMC Cancer, July 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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8 X users
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1 patent

Citations

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23 Dimensions

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52 Mendeley
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Title
Precision oncology using a limited number of cells: optimization of whole genome amplification products for sequencing applications
Published in
BMC Cancer, July 2017
DOI 10.1186/s12885-017-3447-6
Pubmed ID
Authors

Shonan Sho, Colin M. Court, Paul Winograd, Sangjun Lee, Shuang Hou, Thomas G. Graeber, Hsian-Rong Tseng, James S. Tomlinson

Abstract

Sequencing analysis of circulating tumor cells (CTCs) enables "liquid biopsy" to guide precision oncology strategies. However, this requires low-template whole genome amplification (WGA) that is prone to errors and biases from uneven amplifications. Currently, quality control (QC) methods for WGA products, as well as the number of CTCs needed for reliable downstream sequencing, remain poorly defined. We sought to define strategies for selecting and generating optimal WGA products from low-template input as it relates to their potential applications in precision oncology strategies. Single pancreatic cancer cells (HPAF-II) were isolated using laser microdissection. WGA was performed using multiple displacement amplification (MDA), multiple annealing and looping based amplification (MALBAC) and PicoPLEX. Quality of amplified DNA products were assessed using a multiplex/RT-qPCR based method that evaluates for 8-cancer related genes and QC-scores were assigned. We utilized this scoring system to assess the impact of de novo modifications to the WGA protocol. WGA products were subjected to Sanger sequencing, array comparative genomic hybridization (aCGH) and next generation sequencing (NGS) to evaluate their performances in respective downstream analyses providing validation of the QC-score. Single-cell WGA products exhibited a significant sample-to-sample variability in amplified DNA quality as assessed by our 8-gene QC assay. Single-cell WGA products that passed the pre-analysis QC had lower amplification bias and improved aCGH/NGS performance metrics when compared to single-cell WGA products that failed the QC. Increasing the number of cellular input resulted in improved QC-scores overall, but a resultant WGA product that consistently passed the QC step required a starting cellular input of at least 20-cells. Our modified-WGA protocol effectively reduced this number, achieving reproducible high-quality WGA products from ≥5-cells as a starting template. A starting cellular input of 5 to 10-cells amplified using the modified-WGA achieved aCGH and NGS results that closely matched that of unamplified, batch genomic DNA. The modified-WGA protocol coupled with the 8-gene QC serve as an effective strategy to enhance the quality of low-template WGA reactions. Furthermore, a threshold number of 5-10 cells are likely needed for a reliable WGA reaction and product with high fidelity to the original starting template.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 25%
Student > Ph. D. Student 11 21%
Student > Master 6 12%
Student > Postgraduate 3 6%
Student > Bachelor 2 4%
Other 8 15%
Unknown 9 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 27%
Medicine and Dentistry 10 19%
Agricultural and Biological Sciences 8 15%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Engineering 2 4%
Other 7 13%
Unknown 9 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 June 2021.
All research outputs
#5,505,434
of 22,985,065 outputs
Outputs from BMC Cancer
#1,341
of 8,351 outputs
Outputs of similar age
#86,191
of 314,066 outputs
Outputs of similar age from BMC Cancer
#25
of 131 outputs
Altmetric has tracked 22,985,065 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,351 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 314,066 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.