Matching Cancer Genomes to Established Cell Lines for Personalized Oncology

Dudley, Joel T.; Chen, Rong; Butte, Atul J.

doi:10.1142/9789814335058_0026

Cited by 6 publications

(8 citation statements)

References 22 publications

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“…The potential of using bioinformatic tools to integrate public data with clinical gene profiling results and identify drug targets or candidate biomarkers is still largely untested. However, two recent studies from Stanford used a similar approach to identify drug targets for cancer, one of which they validated, or search for transplant biomarkers [50,51]. …”

Section: Resultsmentioning

confidence: 99%

Genomic meta-analysis of growth factor and integrin pathways in chronic kidney transplant injury

et al. 2013

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BackgroundChronic Allograft Nephropathy (CAN) is a clinical entity of progressive kidney transplant injury. The defining histology is tubular atrophy with interstitial fibrosis (IFTA). Using a meta-analysis of microarrays from 84 kidney transplant biopsies, we revealed growth factor and integrin adhesion molecule pathways differentially expressed and correlated with histological progression. A bioinformatics approach mining independent datasets leverages new and existing data to identify correlative changes in integrin and growth factor signaling pathways.ResultsAnalysis of CAN/IFTA Banff grades showed that hepatocyte growth factor (HGF), and epidermal growth factor (EGF) pathways are significantly differentially expressed in all classes of CAN/IFTA. MAPK-dependent pathways were also significant. However, the TGFβ pathways, albeit present, failed to differentiate CAN/IFTA progression. The integrin subunits β8, αv, αμ and β5 are differentially expressed, but β1, β6 and α6 specifically correlate with progression of chronic injury. Results were validated using our published proteomic profiling of CAN/IFTA.ConclusionsCAN/IFTA with chronic kidney injury is characterized by expression of distinct growth factors and specific integrin adhesion molecules as well as their canonical signaling pathways. Drug target mapping suggests several novel candidates for the next generation of therapeutics to prevent or treat progressive transplant dysfunction with interstitial fibrosis.

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Section: Resultsmentioning

confidence: 99%

Genomic meta-analysis of growth factor and integrin pathways in chronic kidney transplant injury

et al. 2013

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“…The catalog of phenome-wide associations, which evaluate phenomic correlations of genotypes, is rapidly growing and currently being leveraged for drug development and drug repositioning ( Denny et al , 2010 ; Hall et al , 2014 ; Namjou et al , 2014 ). We recently used EMR-wide phenomic information to identify: shared genetic architectures of various diseases ( Glicksberg et al , 2015 ; Li et al , 2014 ; Suthram et al , 2010 ), sub-types of type-2 diabetes ( Li et al , 2015a ), drug repurposing for various indications ( Dudley et al , 2011a ; Shameer et al , 2015 ), disease progression patterns through data stream visualization ( Badgeley et al , 2016 ; Shameer et al , 2016 ), disease risk estimations ( Nead et al , 2016 ), and genomics-informed, personalized therapy ( Dudley et al , 2011b , 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Glicksberg

Badgeley

et al. 2016

Bioinformatics

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Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL).Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes.Contacts: rong.chen@mssm.edu or joel.dudley@mssm.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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“…Several previous studies have compared the genomic and transcriptomic differences between tumors and cell lines, but their sample size has been very limited [ 5 - 8 ]. Integrating disease tissue gene expression and drug gene expression profiled in cancer cell lines for therapeutic discovery has been extensively applied by our lab and others [ 9 - 13 ]. Rapid advances in the field of genomics have led to the generation of a high volume of molecular data across various tumors and cell lines.…”

Section: Introductionmentioning

confidence: 99%

Relating hepatocellular carcinoma tumor samples and cell lines using gene expression data in translational research

et al. 2015

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Cancer cell lines are used extensively to study cancer biology and to test hypotheses in translational research. The relevance of cell lines is dependent on how closely they resemble the tumors being studied. Relating tumors and cell lines, and recognizing their similarities and differences are thus very important for translational research. Rapid advances in genomics have led to the generation of large volumes of genomic and transcriptomic data for a diverse set of primary cancer samples, normal tissue samples and cancer cell lines. Hepatocellular Carcinoma (HCC) is one of the most common tumors worldwide, with high occurrence in Asia and sub-Saharan regions. The current effective treatments of HCC remain limited. In this work, we compared the gene expression measurements of 200 HCC tumor samples from The Cancer Genome Atlas and over 1000 cancer cell lines including 25 HCC cancer cell lines from Cancer Cell Line Encyclopedia. We showed that the HCC tumor samples correlate closely with HCC cell lines in comparison to cell lines derived from other tumor types. We further demonstrated that the most commonly used HCC cell lines resemble HCC tumors, while we identified nearly half of the cell lines that do not resemble primary tumors. Interestingly, a substantial number of genes that are critical for disease development or drug response are either expressed at low levels or absent among highly correlated cell lines; additional attention should be paid to these genes in translational research. Our study will be used to guide the selection of HCC cell lines and pinpoint the specific genes that are differentially expressed in either tumors or cell lines.

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Matching Cancer Genomes to Established Cell Lines for Personalized Oncology

Cited by 6 publications

References 22 publications

Genomic meta-analysis of growth factor and integrin pathways in chronic kidney transplant injury

Genomic meta-analysis of growth factor and integrin pathways in chronic kidney transplant injury

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Relating hepatocellular carcinoma tumor samples and cell lines using gene expression data in translational research

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