Personalized Medicine in the era of `omics` data Ugur Sezerman

Human Genome Project

Goals: ■ identify all the approximate 30,000 genes in human DNA, ■ determine the sequences of the 3 billion chemical base pairs that make up human DNA, ■ store this information in databases, ■ improve tools for data analysis, ■ transfer related technologies to the private sector, and ■ address the ethical, legal, and social issues (ELSI) that may arise from the project. Milestones: ■ 1990: Project initiated as joint effort of U.S. Department of Energy and the National Institutes of Health ■ June 2000: Completion of a working draft of the entire human genome (covers >90% of the genome to a depth of 3-4x redundant sequence) ■ February 2001: Analyses of the working draft are published ■ April 2003: HGP sequencing is completed and Project is declared finished two years ahead of schedule

U.S. Department of Energy Genome Programs, Genomics and Its Impact on Science and Society, 2003 http://doegenomes.org http://www.sanger.ac.uk/HGP/overview.shtml

`Omics` Data

DNA Methylation

http://www.cellscience.com/reviews7/Taylor1.jpg

Hypomethylation Hypermethylation

Genome-wide association studies (GWAS)

Figure 1. Genome-wide association studies (GWAS) Excerpted from Genomewide Association Studies and Assessment of the Risk of Disease, Manolio TA. N Engl J Med 2010;363:166-176.

Our Methodology (PANOGA)

Partial Epilepsy Dataset

•  1429 patients with epilepsies of unknown cause (classified as “cryptogenic”), 919 cases with mesial temporal lobe epilepsy with hippocampal sclerosis, 241 with cortical malformations and 222 patients with various tumors, other smaller subgroups such as trauma, stroke, perinatal insults, infections, etc.

•  Cochran–Mantel–Haenszel test results were used as the genotypic p-values of the identified SNPs.

•  Using P<0.05 cutoff: •  28,450 SNPs were included.

# of Cases

# of Controls

# of genotyped SNPs

Platform

3,445 6,935 528,745 SNPs Illumina, Human610-Quadv1 genotyping chips

Table 5. Summary of Partial Epilepsy (PE)dataset (Kasperaviciute, et al., 2010).

Table 6. Comparison of the top 20 SNP-targeted pathways with the pathways of the known genes, as associated to partial epilepsy.

KEGG Term   p values  SNPs in GWAS  

SNP Targeted

Genes  

 

Previous Studies Showing Support  

Wang et al. Study   OMIM  

GWAS on PE  

CNV Study on Epilepsy  

Epi  GAD  

Rogic et al. Study  

Complement and coagulation cascades   2,16E-25   34   12  

(Aronica, et al., 2008; Okamoto, et al., 2010)  -   Y   -   -   -   Y  

Cell cycle   1,03E-24   24   14  

(Aronica, et al., 2008; Jimenez-Mateos, et al., 2008;

Limviphuvadh, et al., 2010)   -   Y   -   -   -   Y  Focal adhesion   7,10E-23   97   20   (Brockschmidt, et al., 2012)  Y   Y   Y   -   -   Y  ECM-receptor interaction   1,62E-22   62   14   (Aronica, et al., 2008)  Y   Y   -   -   -   Y  

Jak-STAT signaling pathway   1,16E-21   24   16  (Jimenez-Mateos, et al., 2008;

Okamoto, et al., 2010)  Y   Y   -   -   -   Y  

MAPK signaling pathway   2,32E-19   73   23  (Jimenez-Mateos, et al., 2008;

Okamoto, et al., 2010; Zhou, et al., 2011)  Y   Y   Y   -   Y   Y  Proteasome   1,15E-18   11   4   (Lauren, et al., 2010)   -   -   -   -   -   -  Ribosome   1,57E-18   2   2   (Lauren, et al., 2010)   -   -   -   -   -   Y  

Calcium signaling pathway   5,73E-18   154   22  

(Jimenez-Mateos, et al., 2008; Limviphuvadh, et al., 2010;

Okamoto, et al., 2010; Zhou, et al., 2011)  Y   Y   Y   Y   Y   Y  Regulation of actin cytoskeleton   9,23E-18   88   19   Y   Y   -   Y   -   Y  Adherens junction   1,01E-17   79   13   -   -   Y   -   -   Y  Pathways in cancer   3,94E-17   112   22   Y   Y   Y   -   -   Y  Gap junction   6,32E-17   147   18   (Lauren, et al., 2010)  Y   Y   Y   -   -   Y  Apoptosis   3,72E-16   37   13   (Jimenez-Mateos, et al., 2008)  Y   Y   -   -   -   Y  Long-term depression   2,90E-15   151   15   (Lauren, et al., 2010)  Y   Y   Y   Y   Y   Y  

Axon guidance   4,01E-15   59   12  (Jimenez-Mateos, et al., 2008;

Limviphuvadh, et al., 2010)   -   -   -   -   -   Y  Fc gamma R-mediated phagocytosis   2,22E-14   66   12   Y   Y   Y   Y   -   Y  Tight junction   2,82E-14   82   13   Y   Y   Y   -   -   Y  ErbB signaling pathway   4,04E-14   86   12   Y   Y   Y   -   -   Y  Wnt signaling pathway   6,28E-14   44   13   (Aronica, et al., 2008; Okamoto, et al., 2010)  Y   Y   Y   -   -   Y  

Intracranial Aneurysm Dataset

Population # of Cases

# of Controls

# of genotyped SNPs

Platform

European 2,780 12,515 832,000 Illumina

Japanese 1,069 904 312,712 Illumina,

Table 7. Summary of Intracranial Aneurysm (IA)dataset.

•  In both datasets, each SNP’s genotypic p-value of association is calculated via Cochran-Armitage trend test.

•  Using P<0.05 cutoff: •  44,351 SNPs were included for EU population, •  14,034 SNPs were included for JP population.

Table 8. The top 10 KEGG pathways identified for both populations in IA. 7 out of the top 10 pathways are shown in red. * Pathway found to be associated with aneurysm related diseases in KEGG Disease Pathways Database.

P-values   Rank  

# of  Associated SNPs in GWAS    

 # of  Common SNPs in GWAS  

 # of SNP Targeted Genes (STGs)  

 # of Com-mon STGs    

% Common Genes in Both Populations    

Common SNPs in GWAS  KEGG Term   EU   JP   EU   JP   EU   JP   EU   JP   EU   JP  

MAPK signaling pathway *   3.53E-27   2.70E-18   1   8   133   43  

1  14   18   2   14.29   11.11   rs791062  

Cell cycle   2.35E-25   2.81E-19   2   4   76   18  1  

11   10   2   18.18   20   rs744910  

TGF-beta signaling pathway *   6.26E-24   2.41E-17   3   9   126   20  

3  

15   9   5   33.33   55.56  

rs2053423. rs1440375. rs744910  

ErbB signaling pathway   9.52E-22   2.47E-15   4   16   50   15  

0  6   4   0   0   0  

Focal adhesion *   9.55E-22   5.60E-21   5   2   117   45  1  

21   14   5   23.81   35.71   rs4678167  

Proteasome   2.36E-21   4.55E-11   6   35   32   1  0  

6   1   0   0   0  Adherens junction*   4.91E-19   2.58E-21   7   1   85   34  

1  13   11   2   15.38   18.18   rs1561798  

Notch signaling pathway   2.14E-18   4.74E-12   8   31   26   13  

0  8   4   1   12.5   25  

Regulation of actin cytoskeleton *   2.28E-18   4.05E-17   9   10   102   36  

1   18   14   1   5.556   7.143   rs4678167  

Neurotrophin signaling pathway   2.49E-18   1.93E-18   10   7   68   14  

0   7   7   1   14.29   14.29  

EU population JP population

# of SNP Targeted Genes in Top 10 Pathways

62 15 51

EU population JP population

# of SNPs from GWAS in Top 10 Pathways

724 6 195

Figure 17. KEGG pathway map for MAPK signaling pathway. The set of genes shown in blue includes genes that are found for EU dataset; yellow includes genes that are found for JP dataset; red includes genes that are found both by EU and JP GWAS of IA.

Found in EU popln.

Found in JP popln.

Found both in EU and JP poplns.

Behcet’s disease dataset Population # of

Cases # of Controls

# of genotyped SNPs

Platform

Turkish 1,215 1,278 311,459 Illumina, Infinium assay

Japanese 612 740 500,568 Affymetrix Gene Chip Human Mapping 500K

Table 10. Summary of Behcet’s disease dataset.

•  In both datasets, each SNP’s genotypic p-value of association is calculated via calculated via allelic chi-squared test.

•  Using P<0.05 cutoff: •  18,479 SNPs were included for TR population, •  20,594 SNPs were included for JP population.

Table 11. The top 10 KEGG pathways identified for both populations in Behcet’s disease. 5 out of the top 10 pathways are shown in red.

P-values   Rank  

# of  Associated SNPs in GWAS    

 # of SNP Targeted Genes (STGs)  

 # of Com-mon STGs    

 % Common Genes in Both Populations    

Is Common Genes more than 50% in

any population?  KEGG Term   TR   JP   TR   JP   TR   JP   TR   JP   TR   JP  

Notch signaling pathway   1,53E-25   4,66E-17   1   10   37   11   9   6   5   55.55   83.33   Y  

Ribosome   7,62E-24   1,28E-15   2   14   5   4   4   2   0   0.0   0.0   N  

Focal adhesion 1,15E-20   2,20E-18   3   4   65   80   25   20   7   27.99   34.99   N  Jak-STAT signaling pathway 1,28E-20   2,26E-18   4   5   32   44   16   16   3   18.74   18.74   N  Complement and coagulation cascades   2,48E-20   2,60E-12   5   33   25   27   13   8   3   23.07   37.49   N  

Long-term potentiation 4,86E-20   2,69E-18   6   6   59   88   14   16   8   57.14   49.99   Y  

Long-term depression   3,30E-19   1,22E-14   7   18   59   73   15   10   9   59.99   89.99   Y  

Pathways in cancer 4,30E-19   1,18E-17   8   9   79   98   25   26   4   15.99   15.38   N  

Proteasome   1,55E-18   2,65E-16   9   12   2   3   1   3   0   0.0   0.0   N  

ECM-receptor interaction   1,27E-17   4,56E-12   10   38   19   41   10   10   4   39.99   39.99   N  

Found  in  TR  popln.    

Found  in  JP  popln.    

Found  both  in  TR  and  JP  poplns.  

Scoring for a Path •  A sub path in a KEGG pathway is

composed of an input node, an output node and the intermediate nodes that constitute the path in-between. If there are alternatives in-between, each is accepted as a distinct path.

•  Pathways that contain the most affected sub paths are considered to be highly affected.

Common pathways in Turkish and Japanese Populations

Antigen processing and presentation  Adipocytokine signaling pathway  

Aldosterone-regulated sodium reabsorption  Amoebiasis  AMPK signaling pathway  Axon guidance  cAMP signaling pathway  cGMP-PKG signaling pathway  Circadian rhythm  ErbB signaling pathway  Fc gamma R-mediated phagocytosis  Herpes simplex infection  Inflammatory mediator regulation of TRP channels  

Jak-STAT signaling pathway  MAPK signaling pathway  

Maturity onset diabetes of the young  NOD-like receptor signaling pathway  Notch signaling pathway  PPAR signaling pathway  Prolactin signaling pathway  Rap1 signaling pathway  Ras signaling pathway  Tight junction  Tuberculosis  Wnt signaling pathway  

* Common pathways (25) in first 40 pathways of each population

Highest scoring Jak-STAT path in Turkish population

Highest scoring Jak-STAT path in Japanese population

•  In Turkish population HIF-1 signaling pathway and Rap1 signaling pathway have more than five highly affected distinct (Jaccard index < 0.5) paths.

•  In Japanese population AMPK signaling pathway, Axon guidance, ErbB signaling pathway, Jak-STAT signaling pathway, MAPK signaling pathway, NOD-like receptor signaling pathway, Notch signaling pathway, PPAR signaling pathway, Ras signaling pathway, Tight junction, Wnt signaling pathway have more than five highly affected distinct (Jaccard index < 0.5) paths.

•  Tight junction, Jak-STAT signaling pathway and Wnt signaling pathway have many highly affected overlapping paths in both populations.

Proteomics analysis of MS

46 CIS patients 37 RRMS patients

32 Healthy control 42other Neurological Disease control

Proteomics analysis of MS Disease vs Healthy control Disease vs Healthy+OND

Control

Personalized  Medicine  in  Cancer  

Molecular  analysis  

Therapy  matched  to  genomic  altera;on  

Andre, ESMO, 2012

Target identification

What is the optimal Biotechnology ?

What is the optimal Algorithm ?

Clinical evidence

Frequent  cancers  include    high  number  of  very  rare  genomic  segments  

•  Somatic mutation •  Copy Number Variation

Stephens,  Nature,  2012  (whole genome sequencing breast cancers)

EvoluCon:  GENOMIC  DISEASES  ARE  BECOMING  TO  RARE  OR  COMPLEX  TO  ALLOW  DRUG  DEVELOPMENT  IN  GENOMIC  SEGMENTS  

How  to  move  forward  ?  

Stephens, Nature, 2012

Are  we  going  to  make  a  drug  development  for  this  AKT1  mut  /  FGFR1  amp  segment  ?  

Patient1

Patient 2

Case Study

•  20 y-old a left frontal intraaxial tumor with a volume of roughly 140cm3, involving the frontal opercular cortex as well as a large subcortical volume.

•  Postoperatively the patient has received fractionated radiotherapy combined with Temozolamide treatment per Stupp protocol

Exome Sequencing

•  Both tumour and blood samples •  156 somatic single nucleotide changes

•  TP53 (0.74; damaging), KRAS (0.38; tolerated), IDH1 (0.32; damaging), NASP (0.05; damaging)

•  Allelic losses in CDKN2D, CIC, PBRM1, NF2, ATRX genes were detected.

•  Gains for MYC, CCND2, FOXM1 and KRAS were detected.

SNPs Chr Pos Ref ->

Alt Genome Protein Effect

Gene dbSNP CGC* Tumor Type

DrugBank

2 209113112

C -> T R -> H

Missense

IDH1 rs121913500

Glioblastoma

-

17 7577545 T -> C M -> V

Missense

TP53 rs483352695

rs397516437

Glioma Acetylsalicylic

acid

* Cancer Gene Census

SNPs Chr Pos Ref ->

Alt Genome Protein Effect

Gene dbSNP CGC* Tumor Type

DrugBank

2 209113112

C -> T R -> H

Missense

IDH1 rs121913500

Glioblastoma

-

17 7577545 T -> C M -> V

Missense

TP53 rs483352695

rs397516437

Glioma Acetylsalicylic

acid

* Cancer Gene Census

SNPs

SNPs Chr Pos Ref -> Alt

Genome Protein Effect

Gene dbSNP CGC* Tumor Type

DrugBank

2 209113112

C -> T R -> H

Missense

IDH1 rs121913500

Glioblastoma

-

17 7577545 T -> C M -> V

Missense

TP53 rs483352695

rs397516437

Glioma Acetylsalicylic

acid

* Cancer Gene Census

SNPs

INDELs Chr

Pos Ref Alt Normal GT

Tumor GT

CGC* Tumor Type

DrugBank

5 67575323

ATT A 0/1 0/1 Glioblastoma

Isoprenaline

17 7579643 CCCCCAGCCCTCCAG

GT

C 0/0 0/1 Glioma Acetylsalicylicacid

* Cancer Gene Census

INDELs Chr Pos Ref Alt Normal

GT Tumor

GT CGC*

Tumor Type DrugBank

5 67575323

ATT A 0/1 0/1 Glioblastoma

Isoprenaline

17 7579643

CCCCCAGCCCTCCAG

GT

C 0/0 0/1 Glioma Acetylsalicylicacid

* Cancer Gene Census

INDELs

INDELs Chr

Pos Ref Alt Normal GT

Tumor GT

CGC* Tumor Type

DrugBank

5 67575323

ATT A 0/1 0/1 Glioblastoma

Isoprenaline

17 7579643 CCCCCAGCCCTCCAG

GT

C 0/0 0/1 Glioma Acetylsalicylicacid

* Cancer Gene Census

INDELs

Copy Number Variation Chr Start End Normal

Depth Tumor Depth

Log Ratio

8 29523990

29524007

20.6 4.3 -2.348

CNV  type   Disease   PlaUorm   Pubmed  Dele;on   Medulloblastoma   SNP  arrays   21979893  

Loss  Glioblastoma  mul;forme   CGH   19960244  

Loss  Glioblastoma  mul;forme  

conven;onal  CGH   21080181  

Loss  Glioblastoma  mul;forme   aCGH   21080181  

Loss   Medulloblastoma   CGH   16968546  

CNV Annotation

Copy Number Variation

Copy Number Variation Chr Start End Normal Depth

Tumor Depth

Log Ratio

11 74113323

74113342

24.0 4.0 -2.559

CNV  type   Disease   PlaUorm   Pubmed  Dele;on   Medulloblastoma   SNP  arrays   21979893  

Loss  Glioblastoma  mul;forme   CGH   19960244  

Loss  Glioblastoma  mul;forme  

conven;onal  CGH   21080181  

Loss  Glioblastoma  mul;forme   aCGH   21080181  

CNV Annotation

Copy Number Variation

Affected Pathways KEGG Term Rank Term Pvalue Corr Bonf Pathway Associated Genes Found in SubnetworksPathways in cancer 1 6.10905E-49 RET , GSK3B, CSF3R , HSP90AB1 , SPI1, PTEN , CBLC , CBLB , PIK3CB , BRCA2 , CRKL , IKBKB , CASP8 , AKT2 , MYC , AKT3, AKT1 , EP300 , RAC1, HRAS, JAK1 , PDGFRB , PDGFRA , HSP90AA1 , MAP2K1 , ARHGEF12 , MAP2K2 , CHUK, TPM3 , NCOA4 , ARNT , MITF , AXIN2 , RHOA , RUNX1 , BCR , MSH6 , AR , MSH2 , PAX8 , PIK3CA , CCNE1 , KIT , RARA , ARHGEF1 , PPARG , RAF1 , CRK , MET , BIRC3 , CEBPA, PDGFB , CXCR4, PIK3R1 , CBL , HIF1A, EGFR , NRAS , GNA11 , ERBB2 , GNA12, ABL1 , PLCG2, MAPK1 , PLCG1 , VHL , RALGDS, RUNX1T1 , NTRK1 , SMAD2 , STAT5B , CDKN2B , CREBBP , JUN , SMAD4 , CDKN2A, EGF , ZBTB16 , STAT3 , BRAF , MLH1 , NFKB1, PML , IL6 , CDK6 , RAD51 , APC , CDK4 , GNAQ , BCL2 , CCDC6 , GNAS , MDM2 , CTNNB1 , FGFR3Chronic myeloid leukemia 2 4.17954E-24 CBLC , CBLB , PIK3CB , PIK3R1 , CBL , CRKL , IKBKB , NRAS , AKT2 , MYC , AKT3, ABL1 , AKT1 , MAPK1 , HRAS, STAT5B , MAP2K1 , SMAD4 , MAP2K2 , CHUK, CDKN2A, BRAF , PTPN11 , NFKB1, RUNX1 , BCR , CDK6 , PIK3CA , CDK4 , MDM2 , RAF1 , CRKTranscriptional misregulation in cancer 3 2.0489E-23 ATF1 , CEBPA, CEBPB, DDX5 , SPI1, MLLT3 , HOXA11 , ELK4 , LYL1 , HOXA9 , MYC , ELANE, MEN1 , RUNX1T1 , NTRK1 , SS18 , ZBTB17, CDKN2C , FUS , ZBTB16 , H3F3A , LMO2 , HMGA2 , ETV1 , PAX5 , ETV4 , NFKB1, PML , RUNX1 , IL6 , BCL6 , PAX8 , EWSR1 , SP1, WT1 , DDIT3 , ID2, MDM2 , RARA , REL , ATM , PPARG , ERG, TCF3 , MET , BIRC3Prostate cancer 4 2.84454E-23 GSK3B, HSP90AB1 , PDGFB , PTEN , PIK3CB , PIK3R1 , EGFR , IKBKB , NRAS , AKT2 , AKT3, ERBB2 , AKT1 , EP300 , MAPK1 , HRAS, PDGFRB , PDGFRA , CREBBP , HSP90AA1 , MAP2K1 , MAP2K2 , CHUK, EGF , BRAF , NFKB1, AR , CREB1 , PIK3CA , CCNE1 , BCL2 , MDM2 , CTNNB1 , RAF1HTLV-I infection 5 8.25703E-23 ATF1 , GSK3B, SPI1, TRRAP , PIK3CB , ELK1, ELK4 , IKBKB , XPO1 , AKT2 , MYC , AKT3, AKT1 , EP300 , HRAS, JAK3 , JAK1 , PDGFRB , MAP2K4 , PDGFRA , TBP, CHUK, KAT2B, KAT2A, CREB1 , PIK3CA , LCK , SRF, PDGFB , GPS2, PIK3R1 , RELB, NRAS , SMAD2 , RANBP1 , STAT5B , CDKN2B , CREBBP , JUN , SMAD4 , CDKN2C , CDKN2A, NFATC2 , IL2 , NFKB1, IL6 , APC , CDK4 , IL2RA, CTNNB1 , ATM , CALR , TCF3 , ATRAcute myeloid leukemia 6 2.70535E-21 CEBPA, SPI1, PIK3CB , PIK3R1 , IKBKB , NRAS , AKT2 , MYC , AKT3, AKT1 , MAPK1 , HRAS, RUNX1T1 , STAT5B , MAP2K1 , MAP2K2 , CHUK, ZBTB16 , STAT3 , BRAF , NFKB1, PML , RUNX1 , PIK3CA , KIT , RARA , RAF1ErbB signaling pathway 7 3.15948E-21 GSK3B, CBLC , CBLB , PIK3CB , PIK3R1 , CBL , ELK1, EGFR , CRKL , NRAS , ERBB3 , AKT2 , MYC , AKT3, ERBB2 , ABL1 , PLCG2, ABL2 , AKT1 , MAPK1 , PLCG1 , HRAS, MAP2K4 , STAT5B , JUN , MAP2K1 , MAP2K2 , EGF , BRAF , PIK3CA , RAF1 , CRKT cell receptor signaling pathway 8 1.42535E-19 GSK3B, ITK , CBLC , CBLB , BCL10 , PIK3CB , PIK3R1 , CBL , IKBKB , NRAS , AKT2 , AKT3, AKT1 , MAPK1 , PLCG1 , HRAS, JUN , MAP2K1 , MAP2K2 , CHUK, NFATC2 , IL2 , NFKB1, RHOA , ZAP70, PTPRC , PIK3CA , CDK4 , LCK , NFKBIE , RAF1 , CARD11, LATHepatitis B 9 2.72767E-19 PTEN , PIK3CB , PIK3R1 , ELK1, IKBKB , NRAS , CASP8 , AKT2 , MYC , AKT3, AKT1 , EP300 , MAPK1 , STAT6 , HRAS, JAK1 , MAP2K4 , STAT5B , CREBBP , JUN , MAP2K1 , SMAD4 , MAP2K2 , CHUK, STAT3 , NFATC2 , NFKB1, DDB2 , IL6 , CDK6 , CREB1 , PIK3CA , CCNE1 , CDK4 , BCL2 , IRF7, RAF1 , MYD88B cell receptor signaling pathway 10 3.22606E-19 G SK 3B, BCL10 , PIK3CB , PIK3R1 , CD79B , IKBKB , CD79A , NRAS , AKT2 , AKT3, PLCG2, AKT1 , MAPK1 , RAC1, HRAS, LYN, JUN , MAP2K1 , MAP2K2 , CHUK, SYK , NFATC2 , NFKB1, PIK3CA , FCGR2B , NFKBIE , RAF1 , CARD11Pancreatic cancer 11 3.42002E-19 PIK 3C B , PIK3R1 , BRCA2 , EGFR , IKBKB , AKT2 , AKT3, ERBB2 , AKT1 , MAPK1 , RAC1, RALGDS, JAK1 , SMAD2 , MAP2K1 , SMAD4 , CHUK, CDKN2A, EGF , STAT3 , BRAF , NFKB1, CDK6 , RAD51 , PIK3CA , CDK4 , RAF1PI3K-Akt signaling pathway 12 6.18997E-19 G SK 3B, CSF3 , CSF3R , HSP90AB1 , PTEN , PIK3CB , IKBKB , STK11 , TCL1A , PPP2R1A , AKT2 , MYC , AKT3, KDR , AKT1 , RAC1, JAK2 , HRAS, JAK3 , JAK1 , PDGFRB , PDGFRA , HSP90AA1 , MAP2K1 , MAP2K2 , CHUK, SYK , TSC1 , CREB1 , PIK3CA , CCNE1 , CDC37, KIT , MTCP1, RAF1 , MET , PDGFB , PIK3R1 , EGFR , NRAS , MAPK1 , MCL1 , EGF , IL2 , NFKB1, COL1A1 , IL6 , CDK6 , CDK4 , IL7 , RHEB , IL2RA, BCL2 , MDM2 , FGFR4 , IL7R , FGFR3Complement and coagulation cascades 13 6.78228E-19 C 3, C 4B, C 5, C R1, F12, SERPIN G 1, M A SP1, F2, C2 , MBL2Endometrial cancer 14 1.76633E-18 G SK 3B, MAP2K1 , MAP2K2 , EGF , PTEN , BRAF , AXIN2 , PIK3CB , PIK3R1 , MLH1 , ELK1, EGFR , NRAS , APC , PIK3CA , AKT2 , MYC , AKT3, ERBB2 , AKT1 , CTNNB1 , MAPK1 , RAF1 , HRASColorectal cancer 15 1.63194E-17 G SK 3B, PIK3CB , PIK3R1 , AKT2 , MYC , AKT3, AKT1 , MAPK1 , RAC1, RALGDS, SMAD2 , JUN , MAP2K1 , SMAD4 , BRAF , AXIN2 , MLH1 , RHOA , MSH6 , APC , MSH2 , PIK3CA , BCL2 , CTNNB1 , RAF1Glioma 16 6.47438E-17 PD G FB , PTEN , PIK3CB , PIK3R1 , EGFR , NRAS , AKT2 , AKT3, PLCG2, AKT1 , MAPK1 , PLCG1 , HRAS, PDGFRB , PDGFRA , MAP2K1 , MAP2K2 , CDKN2A, EGF , BRAF , CDK6 , PIK3CA , CDK4 , MDM2 , RAF1Renal cell carcinoma 17 1.00526E-16 PD G FB , PIK3CB , PIK3R1 , HIF1A, CRKL , NRAS , AKT2 , AKT3, AKT1 , EP300 , MAPK1 , RAC1, VHL , HRAS, CREBBP , JUN , MAP2K1 , MAP2K2 , ARNT , BRAF , PTPN11 , PIK3CA , RAF1 , CRK , METCentral carbon metabolism in cancer 18 1.54676E-16 RET , PTEN , PIK3CB , PIK3R1 , HIF1A, EGFR , NRAS , AKT2 , MYC , AKT3, ERBB2 , AKT1 , MAPK1 , HRAS, NTRK1 , PDGFRB , PDGFRA , MAP2K1 , MAP2K2 , NTRK3 , PIK3CA , KIT , RAF1 , FGFR3, METSignaling pathways regulating pluripotency of stem cells 19 6.81689E-16 G SK 3B, PIK3CB , PIK3R1 , NRAS , AKT2 , MYC , AKT3, AKT1 , MAPK1 , JAK2 , HRAS, JAK3 , JAK1 , ACVR1 , SMAD2 , MAP2K1 , SMAD4 , MAP2K2 , STAT3 , LIFR , PAX6, AXIN2 , KLF4 , APC , PIK3CA , ID2, ID1, CTNNB1 , TCF3 , FGFR4 , IL6ST , RAF1 , FGFR3, BMPR1A

Target! Cancer! Variation type! Marker! Drug! Test!

EGFR" Lung cancer" Mutation" Predict benefit to EGFR TKIs" Erlotinib" DNA"

Gefitinib"

ALK" Lung cancer" Rearrangement" Predict response to ALK inhibitors" Crizotinib" FISH"

ROS" Lung cancer" Rearrangement" Predict response to TKIs" Crizotinib" FISH"

RET" Lung cancer" Rearrangement" Predict response to TKIs" Vandetanib" FISH"

BRAF" Melanoma" Mutation" Predict response to BRAF inhibitors" Vemurafenib" DNA"

Dabrafenib"

KRAS" Colorectal cancer" Mutation"Predict lack of response to anti-EGFR antibodies"

Panitumumab" DNA"

Cetuximab"

HER2" Breast cancer" Amplification" Predict response to anti-HER2 antibodies"Trastuzumab" FISH, IHC"

Gastric cancer" Overexpression" Lapatinib"Pertuzumab"

KIT" GIST" Mutation" Predict response to c-Kit inhibitors" Imatinib" IHC"

Estrogen receptor" Breast cancer" Overexpression" Predict response" Examestane" IHC"Fulvestrant"Letrozole"Tamoxifen"

Progesterone receptor" Breast cancer" Overexpression" Predict response" Examestane" IHC"

Letrozole"

Molecular selection markers for approved anticancer agents

Potential Treatment Analysis •  Most of the affected genes are

targets of sunitinib (Angiogenesis inhibitors appear to be promising therapies for highly vascularized tumors such as glioblastoma multiforme (GBM). Sunitinib is a multitargeted tyrosine kinase inhibitor with both antiangiogenic and antitumor activities due to selective inhibition of various receptor tyrosine kinases, including those important for angiogenesis

•  Most affected path that are part of MTOR signalling pathway (controls cell growth by regulating mRNA translation, metabolism, and autophagy so PI3K/AKT/MTOR inhibitors are potential treatment )

Potential Applications

Personalized Treatment Imatinib

•  Thanks to •  Burcu Bakir Gungor

•  Ahmet Sinan Yavuz •  Basar Batu Can

•  Suveyda Yeniterzi

•  AND •  THANK YOU for Listening