personalized medicine in the era of `omics` data ugur sezerman
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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
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