Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival

Persistent Link:
http://hdl.handle.net/10150/603628
Title:
Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival
Author:
Schissler, Grant A.; Li, Qike; Gardeux, Vincent; Achour, Ikbel; Li, Haiquan; Piegorsch, Walter W.; Lussier, Yves A.
Affiliation:
GIDP Statistics
Issue Date:
2016-02-24
Rights:
Copyright © is held by the author.
Collection Information:
This item is part of the GPSC Student Showcase collection. For more information about the Student Showcase, please email the GPSC (Graduate and Professional Student Council) at gpsc@email.arizona.edu.
Abstract:
Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P<0.05, n¼80 invasive car- cinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpret- ability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome).
Description:
Poster exhibited at GPSC Student Showcase, February 24th, 2016, University of Arizona.
Keywords:
Precision medicine; Mahalanobis distance; RNA-seq; breast cancer

Full metadata record

DC FieldValue Language
dc.contributor.authorSchissler, Grant A.en
dc.contributor.authorLi, Qikeen
dc.contributor.authorGardeux, Vincenten
dc.contributor.authorAchour, Ikbelen
dc.contributor.authorLi, Haiquanen
dc.contributor.authorPiegorsch, Walter W.en
dc.contributor.authorLussier, Yves A.en
dc.date.accessioned2016-03-24T20:55:27Zen
dc.date.available2016-03-24T20:55:27Zen
dc.date.issued2016-02-24en
dc.identifier.urihttp://hdl.handle.net/10150/603628en
dc.descriptionPoster exhibited at GPSC Student Showcase, February 24th, 2016, University of Arizona.en
dc.description.abstractMotivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P<0.05, n¼80 invasive car- cinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpret- ability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome).en
dc.language.isoen_USen
dc.rightsCopyright © is held by the author.en_US
dc.subjectPrecision medicineen
dc.subjectMahalanobis distanceen
dc.subjectRNA-seqen
dc.subjectbreast canceren
dc.titleDynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survivalen_US
dc.contributor.departmentGIDP Statisticsen
dc.description.collectioninformationThis item is part of the GPSC Student Showcase collection. For more information about the Student Showcase, please email the GPSC (Graduate and Professional Student Council) at gpsc@email.arizona.edu.en_US
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