عنوان

A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment

Number

TL4bh5416t

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment

General Material Designation

[Thesis]

First Statement of Responsibility

Katrib, Amal

Subsequent Statement of Responsibility

Xing, Yi

Name of Publisher, Distributor, etc.

UCLA

Date of Publication, Distribution, etc.

2018

Body granting the degree

UCLA

Text preceding or following the note

2018

Text of Note

Researchers continue to struggle in deciphering the underlying molecular machinery of complex, multifactorial, and comorbid medical disorders. Integrating multiple layers of data -from genomic to exposomic - and evaluating their combinatorial effect on the phenome can mitigate limitations of simple differential analyses and ultimately help uncover causal factors.In my dissertation work, I specifically focus on the integration of transcriptomic data with other data types that have a high clinical translatability such as phenomic and radiomic characteristics. I apply a multi-layered transcriptome-phenome-radiome integrative framework to two use case scenarios to demonstrate its benefits and drawbacks. For use case scenario 1, I perform a multi-level analysis of RNA sequencing collected from in-house human placental decidual samples of various modes of parturition in late-stage pregnancy. I highlight differences in gene expression, co-expression, and alternative splicing and identify tissue- and labor-specific enrichment. I then incorporate dense prognostic and maternal and fetal phenomic information to derive genes and biological processes associated with premature and ceased labor. I demonstrate how an integrative framework successfully allows us to extract biologically relevant information that would have otherwise been missed through hypothesis-driven or monolayer differential analysis. For use case scenario. 2, I generate isoform-level information from RNA sequencing collected from The Cancer Genome Atlas (TCGA) GBM tumors. Using additional layers of the transcriptome, I filter for tumor-enriched genes to subtract microenvironment effects. I then incorporate 2 forms of quantitative morphologic radiomic features to extract exon inclusion-radiophenotype correlates. Through functional annotation, I highlight the underlying biological differences between tumor phenotypes. I demonstrate how an integrative framework provides exploratory insights into the biology of a GBM tumor yet fails to reveal significant associations due to data quality and analytical limitations. The potential applications of a multi-layered and clinically-informed integration of the transcriptome, phenome, and radiome extend far beyond the immediate rejoice of joining systems biology efforts in the integration of "big data". Through a synergistic coupling of functional molecular indexes, phenotypic characterization, and dense prognostic traits, it enables an in-depth and comprehensive investigation of multifactorial disorders. In the process, it uses a converged data- and hypothesis-mediated approach to balance the benefit of a comprehensive analysis approach and an elaborate mechanistic depiction of etiology. By incorporating individual-level information (from phenomic and radiomic traits) into population-level findings (from transcriptomic analyses), it poses as a promising contributor to the personalized and precision medicine initiatives of modern medicine.

Katrib, Amal

Xing, Yi

UCLA

Electronic name

p

[Thesis]

276903

a

Y