MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches
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 popula...
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Published in: | OncoTargets and therapy Vol. 9; pp. 5931 - 5941 |
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Abstract | 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.
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.
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.
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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AbstractList | 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. BACKGROUNDMicroRNAs (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. METHODSPlasma 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. RESULTSWith 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. CONCLUSIONIn 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. 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. 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. 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. 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. |
Audience | Academic |
Author | Wei, Lai Uppati, Sarvani R Bekaii-Saab, Tanios Howard, J Harrison Levine, Kala M Zhang, Xiaoli Regan, Kelly E Hassani, John N Olencki, Thomas Lesinski, Gregory B Markowitz, Joseph Abrams, Zachary Brooks, Taylor R Kendra, Kari L Carson, 3rd, William E Jacob, Naduparambil K Latchana, Nicholas Payne, Philip R |
AuthorAffiliation | 2 Comprehensive Cancer Center, The Ohio State University, Columbus, OH 5 Department of Biomedical Informatics 3 Department of Oncologic Sciences, USF Morsani School of Medicine, Tampa, FL 4 Division of Medical Oncology, The Ohio State University Wexner Medical Center 6 Department of Radiation Oncology 7 Center for Biostatistics 1 Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL 8 Department of Surgery, The Ohio State University, Columbus, OH, USA |
AuthorAffiliation_xml | – name: 4 Division of Medical Oncology, The Ohio State University Wexner Medical Center – name: 6 Department of Radiation Oncology – name: 7 Center for Biostatistics – name: 3 Department of Oncologic Sciences, USF Morsani School of Medicine, Tampa, FL – name: 8 Department of Surgery, The Ohio State University, Columbus, OH, USA – name: 1 Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL – name: 5 Department of Biomedical Informatics – name: 2 Comprehensive Cancer Center, The Ohio State University, Columbus, OH |
Author_xml | – sequence: 1 givenname: Joseph surname: Markowitz fullname: Markowitz, Joseph organization: Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL; Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Department of Oncologic Sciences, USF Morsani School of Medicine, Tampa, FL; Division of Medical Oncology, The Ohio State University Wexner Medical Center – sequence: 2 givenname: Zachary surname: Abrams fullname: Abrams, Zachary organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Department of Biomedical Informatics – sequence: 3 givenname: Naduparambil K surname: Jacob fullname: Jacob, Naduparambil K organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Department of Radiation Oncology – sequence: 4 givenname: Xiaoli surname: Zhang fullname: Zhang, Xiaoli organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Center for Biostatistics – sequence: 5 givenname: John N surname: Hassani fullname: Hassani, John N organization: Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL – sequence: 6 givenname: Nicholas surname: Latchana fullname: Latchana, Nicholas organization: Department of Surgery, The Ohio State University, Columbus, OH, USA – sequence: 7 givenname: Lai surname: Wei fullname: Wei, Lai organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Center for Biostatistics – sequence: 8 givenname: Kelly E surname: Regan fullname: Regan, Kelly E organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Department of Biomedical Informatics – sequence: 9 givenname: Taylor R surname: Brooks fullname: Brooks, Taylor R organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH – sequence: 10 givenname: Sarvani R surname: Uppati fullname: Uppati, Sarvani R organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH – sequence: 11 givenname: Kala M surname: Levine fullname: Levine, Kala M organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH – sequence: 12 givenname: Tanios surname: Bekaii-Saab fullname: Bekaii-Saab, Tanios organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Division of Medical Oncology, The Ohio State University Wexner Medical Center – sequence: 13 givenname: Kari L surname: Kendra fullname: Kendra, Kari L organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Division of Medical Oncology, The Ohio State University Wexner Medical Center – sequence: 14 givenname: Gregory B surname: Lesinski fullname: Lesinski, Gregory B organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Division of Medical Oncology, The Ohio State University Wexner Medical Center – sequence: 15 givenname: J Harrison surname: Howard fullname: Howard, J Harrison organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Department of Surgery, The Ohio State University, Columbus, OH, USA – sequence: 16 givenname: Thomas surname: Olencki fullname: Olencki, Thomas organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Division of Medical Oncology, The Ohio State University Wexner Medical Center – sequence: 17 givenname: Philip R surname: Payne fullname: Payne, Philip R organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Department of Biomedical Informatics – sequence: 18 givenname: William E surname: Carson, 3rd fullname: Carson, 3rd, William E organization: Comprehensive Cancer Center, The Ohio State University, Columbus, OH; Department of Surgery, The Ohio State University, Columbus, OH, USA |
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Keywords | melanoma miRNA profiling rank-based statistic |
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Snippet | MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We... Background: MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer.... BACKGROUNDMicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We... |
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SubjectTerms | Analysis Bioinformatics Biomarkers Biometry Blood Blood plasma Cancer therapies Cell cycle Clinical trials Computational biology Cyclin-dependent kinases Disease DNA microarrays Health aspects Informatics Innovations Kinases Medical prognosis Melanoma Metastasis MicroRNA MicroRNAs Oncology Original Research Patients Phlebotomy Principal components analysis Skin cancer |
Title | MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches |
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