MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches

Joseph Markowitz, Zachary Abrams, Naduparambil K. Jacob, Xiaoli Zhang, John N. Hassani, Nicholas Latchana, Lai Wei, Kelly E. Regan, Taylor R. Brooks, Sarvani R. Uppati, Kala M. Levine, Tanios Bekaii-Saab, Kari L. Kendra, Gregory B. Lesinski, J. Harrison Howard, Thomas Olencki, Philip R. Payne, William E. Carson

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background: MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We hypothesized that miRNA array-based technologies work best for miRNA profiling of patient-derived plasma samples when the techniques and patient populations are precisely defined. Methods: Plasma samples were obtained from five sources: melanoma clinical trial of interferon and bortezomib (12), purchased normal donor plasma samples (four), gastrointestinal tumor bank (nine), melanoma tumor bank (ten), or aged-matched normal donors (eight) for the tumor bank samples. Plasma samples were purified for miRNAs and quantified using NanoString® arrays or by the company Exiqon. Standard biostatistical array approaches were utilized for data analysis and compared to a rank-based analytical approach. Results: With the prospectively collected samples, fewer plasma samples demonstrated visible hemolysis due to increased attention to eliminating factors, such as increased pressure during phlebotomy, small gauge needles, and multiple punctures. Cancer patients enrolled in a melanoma clinical study exhibited the clearest pattern of miRNA expression as compared to normal donors in both the rank-based analytical method and standard biostatistical array approaches. For the patients from the tumor banks, fewer miRNAs (<5) were found to be differentially expressed and the false positive rate was relatively high. Conclusion: In order to obtain consistent results for NanoString miRNA arrays, it is imperative that patient cohorts have similar clinical characteristics with a uniform sample preparation procedure. A clinical workflow has been optimized to collect patient samples to study plasma miRNAs.

Original languageEnglish (US)
Pages (from-to)5931-5941
Number of pages11
JournalOncoTargets and Therapy
Volume9
DOIs
StatePublished - Sep 29 2016
Externally publishedYes

Fingerprint

Computational Biology
MicroRNAs
Clinical Trials
Melanoma
Neoplasms
Tissue Donors
Small Untranslated RNA
Phlebotomy
Workflow
Protein Biosynthesis
Hemolysis
Punctures
Interferons
Needles
Technology
Pressure
Population

Keywords

  • Melanoma
  • miRNA
  • Profiling
  • Rank-based statistic

ASJC Scopus subject areas

  • Oncology
  • Pharmacology (medical)

Cite this

Markowitz, J., Abrams, Z., Jacob, N. K., Zhang, X., Hassani, J. N., Latchana, N., ... Carson, W. E. (2016). MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches. OncoTargets and Therapy, 9, 5931-5941. https://doi.org/10.2147/OTT.S106288

MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches. / Markowitz, Joseph; Abrams, Zachary; Jacob, Naduparambil K.; Zhang, Xiaoli; Hassani, John N.; Latchana, Nicholas; Wei, Lai; Regan, Kelly E.; Brooks, Taylor R.; Uppati, Sarvani R.; Levine, Kala M.; Bekaii-Saab, Tanios; Kendra, Kari L.; Lesinski, Gregory B.; Howard, J. Harrison; Olencki, Thomas; Payne, Philip R.; Carson, William E.

In: OncoTargets and Therapy, Vol. 9, 29.09.2016, p. 5931-5941.

Research output: Contribution to journalArticle

Markowitz, J, Abrams, Z, Jacob, NK, Zhang, X, Hassani, JN, Latchana, N, Wei, L, Regan, KE, Brooks, TR, Uppati, SR, Levine, KM, Bekaii-Saab, T, Kendra, KL, Lesinski, GB, Howard, JH, Olencki, T, Payne, PR & Carson, WE 2016, 'MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches', OncoTargets and Therapy, vol. 9, pp. 5931-5941. https://doi.org/10.2147/OTT.S106288
Markowitz, Joseph ; Abrams, Zachary ; Jacob, Naduparambil K. ; Zhang, Xiaoli ; Hassani, John N. ; Latchana, Nicholas ; Wei, Lai ; Regan, Kelly E. ; Brooks, Taylor R. ; Uppati, Sarvani R. ; Levine, Kala M. ; Bekaii-Saab, Tanios ; Kendra, Kari L. ; Lesinski, Gregory B. ; Howard, J. Harrison ; Olencki, Thomas ; Payne, Philip R. ; Carson, William E. / MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches. In: OncoTargets and Therapy. 2016 ; Vol. 9. pp. 5931-5941.
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abstract = "Background: MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We hypothesized that miRNA array-based technologies work best for miRNA profiling of patient-derived plasma samples when the techniques and patient populations are precisely defined. Methods: Plasma samples were obtained from five sources: melanoma clinical trial of interferon and bortezomib (12), purchased normal donor plasma samples (four), gastrointestinal tumor bank (nine), melanoma tumor bank (ten), or aged-matched normal donors (eight) for the tumor bank samples. Plasma samples were purified for miRNAs and quantified using NanoString{\circledR} arrays or by the company Exiqon. Standard biostatistical array approaches were utilized for data analysis and compared to a rank-based analytical approach. Results: With the prospectively collected samples, fewer plasma samples demonstrated visible hemolysis due to increased attention to eliminating factors, such as increased pressure during phlebotomy, small gauge needles, and multiple punctures. Cancer patients enrolled in a melanoma clinical study exhibited the clearest pattern of miRNA expression as compared to normal donors in both the rank-based analytical method and standard biostatistical array approaches. For the patients from the tumor banks, fewer miRNAs (<5) were found to be differentially expressed and the false positive rate was relatively high. Conclusion: In order to obtain consistent results for NanoString miRNA arrays, it is imperative that patient cohorts have similar clinical characteristics with a uniform sample preparation procedure. A clinical workflow has been optimized to collect patient samples to study plasma miRNAs.",
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AU - Abrams, Zachary

AU - Jacob, Naduparambil K.

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AU - Hassani, John N.

AU - Latchana, Nicholas

AU - Wei, Lai

AU - Regan, Kelly E.

AU - Brooks, Taylor R.

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AU - Levine, Kala M.

AU - Bekaii-Saab, Tanios

AU - Kendra, Kari L.

AU - Lesinski, Gregory B.

AU - Howard, J. Harrison

AU - Olencki, Thomas

AU - Payne, Philip R.

AU - Carson, William E.

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N2 - Background: MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We hypothesized that miRNA array-based technologies work best for miRNA profiling of patient-derived plasma samples when the techniques and patient populations are precisely defined. Methods: Plasma samples were obtained from five sources: melanoma clinical trial of interferon and bortezomib (12), purchased normal donor plasma samples (four), gastrointestinal tumor bank (nine), melanoma tumor bank (ten), or aged-matched normal donors (eight) for the tumor bank samples. Plasma samples were purified for miRNAs and quantified using NanoString® arrays or by the company Exiqon. Standard biostatistical array approaches were utilized for data analysis and compared to a rank-based analytical approach. Results: With the prospectively collected samples, fewer plasma samples demonstrated visible hemolysis due to increased attention to eliminating factors, such as increased pressure during phlebotomy, small gauge needles, and multiple punctures. Cancer patients enrolled in a melanoma clinical study exhibited the clearest pattern of miRNA expression as compared to normal donors in both the rank-based analytical method and standard biostatistical array approaches. For the patients from the tumor banks, fewer miRNAs (<5) were found to be differentially expressed and the false positive rate was relatively high. Conclusion: In order to obtain consistent results for NanoString miRNA arrays, it is imperative that patient cohorts have similar clinical characteristics with a uniform sample preparation procedure. A clinical workflow has been optimized to collect patient samples to study plasma miRNAs.

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