Chi Zhang PhD Center for Computational Biology and Bioinformatics Department of Medical and Molecular Genetics 09282016 112 Center for Computational Biology and Bioinformatics Research Interests ID: 538152
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Slide1
Computational modeling of cancer micro-environment by using large scale data analysis
Chi Zhang, Ph.D
.
Center for Computational Biology and Bioinformatics
Department of Medical and Molecular Genetics09/28/2016
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Center for Computational Biology and BioinformaticsSlide2
Research Interests
Computational modeling of the:Tissue level characteristicsCellular and biochemical level changes (metabolism)Genomics alterationsby integrative analysis of multiple omics data types, to Identify key biological mechanisms related to cancer initiation, progression and metastasis.Predict biomarkers for diagnosis and selection of therapiesGoogle: Chi Zhang, Indiana UniversityWebpage: csbl.bmb.uga.edu/~zhangchi/
2/12Slide3
Cell line and animal models:
i) Cellular characteristicsii) Responses to certain treatmentiii) Responses under certain conditions…Micro-environment of real cancer tissue vs. experimental conditionsHighly unstable micro-environmental factors Immune response Oxygen level Acidity level
Study cancer micro-environment through omics data modeling
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Immune cells
Extracellular matrix (ECM)
Cancer micro-environment
Oxygen level
Acidity
H Douglas, and R Weinberg. Cell (2000)Slide4
Interactions
among the cancer cells, immune cells and stromal cells, play critical roles in the progression of cancer.
4/12
Decipher
the
cell
components in tissue samples by transcriptomics data
Lisa M. Coussens and Zena Werb, Nature, (2002)
It is critical to decipher the signals from different cell components in the tissue samplesSlide5
Tissue based omics data
By a regression based cell deconvolution analysis, we can solve (estimate) the relative proportion of each cell type in each tissue sample.
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Tissue
gene expression
Cancer
cell
T cell
Macrophage
g1
g2
g3
g4
g5
……Slide6
Applications in colon cancer
and ImplicationsA subgroup of colon cancer samplesElevated CD4+ T cells, tumor associated macrophages, neutrophilsDecreased CD8+ T cellsHighly mutated genomeHigh level oxidative stress (may cause DNA damage)
6
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Over expressed
NADPH oxidase
are highly
correlated
with
the
oxidative stress
responsive genes and the predicted
immune cell proportions
.Slide7
Linking the micro-environmental alterations to genomic mutations
7/12
Gain or loss of functions led by a certain mutation
Example:
Collective effect of multiple mutations
It is critical to comprehensively infer the functions of each mutation and their collective effect
A bi-clustering based data integration approach:
Genomics, Transcriptomics and Clinical Data
Beta-Catenin-binding region
CtBP
-binding region
Cell Migration
Cell Adhesion
Control of Proliferation
Chromosomal segregation
APC geneSlide8
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Example: APC mutation in colon cancer
A
possible functional change due to APC mutations in the 14th
and
15th exons.
SamplesIn the bi-cluster
Other samples with APC mutation
Exon and nucleotide positions
APC mutation profile in colon cancer samplesSlide9
Functional changes and prognosis
of the concurrent mutations
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Gene Functions:
Innate immune response
Tumor associated macrophage
T cell activation
Interferon gamma signaling
Steroid hormone metabolismSlide10
10/12
Applications on more
cancer
types
We have studied:
21 well studied cancer associated genes.20 other frequently mutated genes.18 cancer types.
~40,000 significant bi-clusters have been identified
Acute Myeloid
Leukemia
Dr
. Reuben
Kapur
Colorectal cancer
Outcome of chemotherapySlide11
Cell line data:
Mutation Gene expressionDrug responsePredict for possible drugsFuture directions
Mutation:
Druggable
target on protein tertiary structure
Dr.
Samy
Meroueh
Mutations on a certain exon:
Alternative splicing
A
dysfunctioned
isoform
Dr.
Yunlong
Liu
Dr.
Lijun
Chen
Dr. Lang Li
Linking the results to cancer micro-environment:
Elucidate how certain mutations are selected
More data types
Dr. Chi Zhang
Center for Computational Biology and Bioinformatics
Dr. Yong
Zang
Clinical Trial
Outcome with respect to certain clinical features
Dr. Ning Xia
Development of new algorithm,
Machine Learning
11/12Slide12
Thank you!
You are welcomed to do rotation in my lab!Chi Zhangczhang87@iu.eduSuit 5000 (Room 5021), HITS Buildingcsbl.bmb.uga.edu/~zhangchi
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