cancer’bioinformaticsdatascience.sehir.edu.tr/static/pdf/dsai2018/cancer bioinformatics.pdf ·...
TRANSCRIPT
Cancer BioinformaticsMehmet Baysan
Istanbul Sehir University5/8/2018
My Research • Algorithms-‐Optimization
• Wireless Networks• M. Baysan, R. Chandrasekaran, K. Sarac, “A Polynomial Time Solution to Minimum Forwarding Set Problem for Disk Graphs,” Ad Hoc Networks, vol. 10, no. 7, pp. 1253-‐1266,
2012• R. Chandrasekaran and M. Dwande, M. Baysan “Graph Labelings and Applications: Analysis of the Covering Problem,” Discrete Applied Mathematics, vol. 159, no. 8, pp.
746-‐759, 2011• M. Baysan, K. Sarac, S. Bereg and R. Chandrasekaran, “A Polynomial Time Solution to Minimum Forwarding Set Problem in Wireless Ad Hoc Networks,” IEEE Transactions on
Parallel and Distributed Systems, vol. 20, no. 7, pp. 913-‐924, 2009 (Selected as Featured Article of this issue)• A. Chiganmi, M. Baysan, K. Sarac and R. Prakash, “Variable Power Broadcast using Local Information in Ad Hoc Networks,” Ad Hoc Networks,vol. 6, no. 5, pp. 675-‐695, 2008
• Scheduling• I. Averbakh, M. Baysan*, “Approximation algorithm for the on-‐line multi-‐customer two-‐level supply chain scheduling problem,” Operations Research Letters,vol. 41, no. 6,
pp. 710-‐714, 2013• I. Averbakh, M. Baysan*, “Batching and Delivery in Semi-‐online Distribution Systems,” Discrete Applied Mathematics, vol. 161, no. 1-‐2, pp. 28-‐42, 2013• I. Averbakh, M. Baysan*, “Semi-‐online Two-‐level Supply Chain Scheduling Problems,” Journal of Scheduling, vol. 15, no.3, pp. 381-‐390, 2012
• Graph Labeling• R. Chandrasekaran and M. Dwande, M. Baysan “Graph Labelings and Applications: Analysis of the Covering Problem,” Discrete Applied Mathematics, vol. 159, no. 8, pp.
746-‐759, 2011
• Bioinformatics• Cancer Bioinformatics
• M. Baysan, K. Woolard, M. Cam, W. Zhang, H. Song, S. Kotliarova, D. Balamatsias, A. Linkous, S. Ahn, J. Walling, G. Belova, H.A. Fine, "Detailed Longitudinal Sampling of Glioma Stem Cells In Situ Reveals Chr7 Gain and Chr10 Loss As Repeated Events in Primary Tumor Formation and Recurrence," International Journal of Cancer, vol. 141, issue 10, pp. 2002-‐2013, 2017
• S. Bozdag, A. Li, M. Baysan, H. A. Fine, “Master Regulators, Regulatory Networks and Pathways of Glioblastoma Subtypes,” Cancer Informatics, vol. 13, Suppl. 3, pp. 33-‐44, 2014
• M. Baysan, K. Woolard, S. Bozdag, G. Riddick, S. Kotliarova, M. Cam, G. I. Belova, S. Ahn, W. Zhang, H. Song, J. Walling, H. Stevenson, P. Meltzer, H. A. Fine, “Micro-‐Environment Causes Significant, Reproducible and Reversible Changes in DNA Methylation and mRNA Expression Profiles in Patient-‐Derived Glioma Stem Cells,” PLoS ONE, vol. 9, no. 4, pp. e94045, 2014
• C. Ene, L. Edwards, G. Riddick, M. Baysan, K. Woolard, S. Kotliarova, C. Lai, G. Belova, M. Cam, J. Walling, M. Zhou, H. Stevenson, H. S. Kim, K. Killian, T. Veenstra, R. Bailey, H. Song, W. Zhang, H. A. Fine, "Histone demethylase Jumonji D3 (JMJD3) functions as a tumor suppressor by regulating p53 activity through lysine demethylation," PLoSONE, vol. 7, no. 12, pp. e51407, 2012
• M. Baysan, S. Bozdag, M. Cam, S. Kotliarova, S. Ahn, J. Walling, J. K. Killian, H. Stevenson, P. Meltzer, H. A. Fine, “G-‐CIMP Status Prediction of Glioblastoma Samples Using mRNA Expression Data,” PLoS ONE, vol. 7, no. 11, pp. e47839, 2012
http://www.amazon.com/Human-‐Genome-‐Poster-‐Blueprint-‐24x36in/dp/B004RZB90E
Cost Decrease and Data Accumulation
• 1.000$ genome mapping DONE• Analysis, Interpretation?• Data is getting cheaper, information?
Extremetech.com
Mendelian Diseases and Sequencing
Bioinformatics Arise With Availability of High-‐Throughput Data• Analyses of High-‐Throughput Bio-‐Medical Data Requires;• Bio-‐Medicine
• Limitations and Strength of Data• Right Questions• Interpretation of Results
• Programming • Data Manipulation• Visualization
• Statistics• Significant vs. Non-‐significant
Interdisciplinary Science
Physicians-‐Follow Patients-‐Collect Samples-‐Apply new therapies
Biologists-‐Retrieve specimen (Cells, DNA, RNA etc.) from samples-‐Manipulate genes to validate hypothesis on animals and cells
Bioinformaticians-‐Analyze genomics data and identify target genes-‐Computationally validate the effect of genomic changes
Real Life Example
Sample Pipeline
Intratumor Heterogeneity
• Background: Intratumor heterogeneity and loss of tissue structure is a hallmark of cancers
• Question: Can we use heterogeneity to predict the clonal development of tumors?
• Data: From a single patient, we generated and analyzed;• Exome Sequencing (50 Samples)• SNP array for large genomic deletions and gains (46 Samples)• mRNA microarrays for expression (28 Samples)
• Method: Phylogenetic Trees
• Results: • Therapy successfully destroys initial tumor clone • A new distant clone is responsible to repopulate the tumor area• Focused therapies should be supported with targeting surrounding tissue
Tumor Heterogeneity and MonoClones
PolyClonal GSC
Parental Tumor
PolyClonal ex vivo GSC
MonoClonal GSC
Secondary MonoClonal GSC
-‐Mutation Profiles -‐DNA Gains-‐Losses-‐mRNA Profiles
Advanced Results ModelCommon ancestor
203 MCs
Homozygous TP53 Mutation, p16 deletion Common
ancestor
7 gain/10 lossT6-‐7, T7-‐
11,T7-‐12 T7-‐20
Rest of 303 MCs
7 gain/10 loss
PIK3CB,.. mutationsPTCH1,.. mutations
0203
Is T6-‐T7 or Others are the source?
0203
Is T6-‐T7 or Others are the source?
XRTCTx
T2T3T4T5T6T7
0203
Is T6-‐T7 or Others are the source?
XRTCTx
7 gain 10 loss
T2T3T4T5T6T7
0203
Is T6-‐T7 or Others are the source?
XRTCTx
7 gain 10 loss
T2T3T4T5T6T7
0203
Is T6-‐T7 or Others are the source?
XRTCTx
7 gain 10 loss
Acknowledgements
• NIH• Svetlana Kotliarova• Yuri Kotliarova• Gregory Riddick• Margaret Cam• Susie Ahn• Jennifer Walling• Wei Zhang• Hua Song• Holly Stevenson• Paul Meltzer
• UC Davis– Kevin Woolard
• Marquette University– Serdar Bozdag
• Cornell– Howard Fine– Amanda Linkous