Integrative Radiation Biology
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1 Dr. Kristian Unger Integrative Biology Group Research Unit of Radiation Cytogenetics Department of Radiation Sciences Helmholtz-Zentrum München
2 1 Key Questions Molecular mechanisms of radiation-induced tumourigenesis? Biomarkers of radiation-induced tumours? Molecular mechanisms of tumour radiation sensitivity? Biomarkers used for stratification in personalised radiotherapy?
3 2 Radiation Marker Radiation marker Improved risk assessment by integrating molecular biology and epidemiology Investigation of the mechanisms of radiation induced carcinogenesis Marker and target in the diagnosis and treatment of radiation induced cancer and personalised radiotherapy
4 3 Multi-level Integration Metabolome Signalosome Proteome Transcriptome Radiation Genome Epigenome
5 3 Multi-level Integration Metabolome Signalosome Proteome Transcriptome Radiation Genome Epigenome
6 3 Multi-level Integration Metabolome Signalosome Proteome Transcriptome Radiation Genome Epigenome Rearrangements Deletions Amplifications Point mutations
7 3 Multi-level Integration Metabolome Signalosome Proteome Transcriptome Radiation Genome Epigenome Rearrangements Deletions Amplifications Point mutations
8 3 Multi-level Integration Metabolome Signalosome Proteome Transcriptome Radiation Genome Epigenome Rearrangements Deletions Amplifications Point mutations
9 3 Multi-level Integration Metabolome Signalosome Proteome Transcriptome Radiation Genome Epigenome Rearrangements Deletions Amplifications Point mutations
10 3 Multi-level Integration gene - 2 copies mrna protein gene - 1 copy mrna protein gene - 3 copies mrna protein
11 3 Multi-level Integration gene - 3 copies mrna protein gene - 1 copy mrna protein
12 3 Multi-level Integration gene - 3 copies mrna protein gene - 1 copies mrna protein Rarely linear causality between DNA copy number and its effects on transcriptome and protein level!
13 3 Multi-level Integration Protein mirna Translation mrna transcription factors DNAmeth Transcription DNA
14 3 Multi-level Integration prote-, transcript-, gen-, ome protein mirna mrna DNA DNA methylation
15 3 Multi-level Integration prote-, transcript-, gen-, ome protein mirna mrna DNA DNA methylation DNA copy number loss
16 3 Multi-level Integration prote-, transcript-, gen-, ome protein mirna mrna DNA DNA methylation DNA copy number loss
17 3 Multi-level Integration prote-, transcript-, gen-, ome protein mirna mrna DNA DNA methylation DNA copy number loss
18 3 Multi-level Integration prote-, transcript-, gen-, ome protein mirna mrna DNA DNA methylation DNA copy number loss
19 3 Multi-level Integration prote-, transcript-, gen-, ome protein mirna mrna DNA DNA methylation DNA copy number loss
20 3 Data matching
21 3 Multi-level Integration Exploiting functional relationships between different molecular levels WHY? informative interpretation of the biomarkers resulting from association analysis mechanistic insights using causality chains better chance (statistical power) of finding relevant information from high-dimensional data in radiation biology genomic changes as a starting point
22 4 GENRISK-T - Integrative approach Exposed Unexposed born before 26th April exposed to radioiodine fallout born after 1st January not exposed to radioiodine fallout papillary thyroid carcinomas (PTC) average estimated organ dose: ~ 150mGy estimated proportion of radiation-induced tumours: 85% (Jacob et al. 2006) median age at exposure: 2 years total RNA and DNA samples from Chernobyl Tissue Bank (CTB - Global DNA copy number and mrna expression from same set of samples
23 5 The 7q11 marker GENRISK-T discovery set: n=52 (33 exposed; 19 unexposed) Heß et al. 2011, PNAS validation in a replication set: n=28 (16 exposed; 12 unexposed) - narrower region 7q11 (7q ) Exclusive and specific gain of 7q11 in exposed cases
24 6 7q11 - CLIP2 unexposed Gene Transcript Protein exposed CAP-GLY domain potentially involved in microtubule-associated organelle transport chromosome segregation Function and role of CLIP2 in the radiation context?
25 7 Strategy to clarify role of CLIP2 Correlation networks (global mrna) Identification of CLIP2 module a priori Time-series characterisation after CLIP2/radiation perturbation Reconstruction of CLIP2 perturbation network Validation of key players in tumours
26 8 correlation network analysis tumour 1 tumour 2 tumour 3 tumour... tumour...n x y.. m gene x 1x 2x 3x...x nx -> vector x x 1 cor_xy cor_x... cor_xm gene y 1y 2y 3y...y ny -> vector y y cor_xy 1 cor_y... cor_ym gene n... -> vector cor_x... cor_y... 1 cor...m gene m 1m 2m 3m...m nm -> vector m m cor_xm cor_ym cor_...m 1 Genes whose GE vectors show high level of correlation are functionally related the more connections two elements share the closer they are topologically nodes with highest connectivity levels are central hubs i.e. key players
27 9 weighted correlation network analysis WGCNA (weighted correlation network analysis) modules with genes that are likely to be interconnected (correlation and topology) correlation with external data (clinical data, phenodata, exposure status) via Eigengenes Identification of genes that are close to CLIP2 (topology) and networks that may interact with CLIP2
28 9 weighted correlation network analysis Cluster Dendrogram Height Module colors d hclust (*, "average") salmon 114 genes brown 786 genes (incl. CLIP2)
29 10 Reactome CLIP2 FI network - brown FUS LSM2 NHP2L1 POLR2K PTRF TCEB1 UBE2B ASB9 ZBTB16 UBE2W RPA3 GEMIN2 ORC4 SNRPE PRIM1 SNRPG LSM5 GEMIN6 SARS SKI MMP28 SMURF2 UBE3B JUP BCL6 CREM NFATC2 ATF1 PPP3R1 SMARCA4 NFAT5 RNF128 SMARCE1 KSR1 SLC12A2 PRKX PRKACB BRAF ING3 YEATS4 ATP5F1 SPAG9 ATP5L TRIB3 NDUFS1 ARL1 NDUFS4 NDUFB3 CKMT2 NDUFA11 NDUFB5 CKM RPL31 RPS21 SEC11C SSX3 DDIT3 RPL23 EIF5B ATXN1 SSR3 BCR ASF1A RAD51B BRCA1 HIPK2 TCF7 FAM175A HNRNPM PPIL1 SRRM2 SF3A2 KIAA1967 ALDH2 ZNF880 ZNF226 ZNF415 MYH2 ZNF697 ZNF596 ZNF208 ZNF107 ZNF891 ZNF124 NR2F2 ZNF585A ZNF528 ZNF627 ZNF614 ZNF814 CTTN CENPL WASF2 CD47 LAMB1 CLIP1 ITGB3BP NSL1 MYO10 PTN CXCL12 DLG1 PTK2 ABLIM1 TRIO TNS3 COL9A2 ITGA8 ITGB3 ITGAE IGF1R COL16A1 COL4A4 INSR COL3A1 COL15A1 MYH11 CEP164 TNRC6B CEP70 CALD1 UQCR10 UQCRB TNRC6C TNNC1 FBLN5 SNAP29 HSCB MFAP4 ISCA1 DNAJC19 MYL1 EEA1 MLL WDR61 TLE1 HIC2 NIPBL HIST3H2A CBX5 RUNX2 DACH2 HDAC8 PEX2 TBL1X MED7 MED30 MED31 TOMM5 PCDH8 SOX9 CELSR1 SPEN CTNNAL1 TOMM20 TOMM22 SYNE1 SYCP3 ZNF512B SMC3 PDS5A ATRX MPHOSPH6
30 10 Reactome CLIP2 FI network - brown spliceosome FUS LSM2 NHP2L1 POLR2K PTRF TCEB1 UBE2B ASB9 ZBTB16 UBE2W RPA3 GEMIN2 ORC4 SNRPE PRIM1 SNRPG LSM5 GEMIN6 SARS SKI MMP28 SMURF2 UBE3B JUP BCL6 CREM NFATC2 ATF1 PPP3R1 SMARCA4 NFAT5 RNF128 SMARCE1 KSR1 SLC12A2 PRKX PRKACB BRAF ING3 YEATS4 ATP5F1 SPAG9 ATP5L TRIB3 NDUFS1 ARL1 NDUFS4 NDUFB3 CKMT2 NDUFA11 NDUFB5 CKM RPL31 RPS21 SEC11C SSX3 DDIT3 RPL23 EIF5B ATXN1 SSR3 BCR ASF1A RAD51B BRCA1 HIPK2 TCF7 FAM175A HNRNPM PPIL1 SRRM2 SF3A2 KIAA1967 ALDH2 ZNF880 ZNF226 ZNF415 MYH2 ZNF697 ZNF596 ZNF208 ZNF107 ZNF891 ZNF124 NR2F2 ZNF585A ZNF528 ZNF627 ZNF614 ZNF814 CTTN CENPL WASF2 CD47 LAMB1 CLIP1 ITGB3BP NSL1 MYO10 PTN CXCL12 DLG1 PTK2 ABLIM1 TRIO TNS3 COL9A2 ITGA8 ITGB3 ITGAE IGF1R COL16A1 COL4A4 INSR COL3A1 COL15A1 MYH11 CEP164 TNRC6B CEP70 CALD1 UQCR10 UQCRB TNRC6C TNNC1 FBLN5 SNAP29 HSCB MFAP4 ISCA1 DNAJC19 MYL1 EEA1 MLL WDR61 TLE1 HIC2 NIPBL HIST3H2A CBX5 RUNX2 DACH2 HDAC8 PEX2 TBL1X MED7 MED30 MED31 TOMM5 PCDH8 SOX9 CELSR1 SPEN CTNNAL1 TOMM20 TOMM22 SYNE1 SYCP3 ZNF512B SMC3 PDS5A ATRX MPHOSPH6
31 10 Reactome CLIP2 FI network - brown MYH2 ZNF880 ZNF697 ZNF596 ALDH2 ZNF226 ZNF415 ZNF627 ZNF208 WASF2 NR2F2 DLG1 PTN ZNF528 ZNF614 CTTN ZNF107 ZNF814 ABLIM1 ZNF124 ZNF891 ZNF585A TRIO CXCL12 MYO10 CENPL CD47 LAMB1 CLIP1 PPIL1 TNS3 integrin signalling/ EM organisation/ focal adhesion PTK2 COL4A4 ITGA8 COL3A1 ITGB3 COL9A2 COL16A1 COL15A1 ITGAE ITGB3BP NSL1 SRRM2 KIAA1967 IGF1R INSR spliceosome LSM5 HNRNPM SF3A2 FUS LSM2 NHP2L1 SNRPE SARS SNRPG POLR2K PTRF ORC4 RPA3 TCEB1 UBE2W UBE2B ASB9 UBE3B ZBTB16 SMURF2 JUP MMP28 PRKX PRKACB SLC12A2 BRAF KSR1 GEMIN6 PRIM1 BCL6 GEMIN2 SKI ATF1 RNF128 FAM175A BRCA1 CREM NFATC2 PPP3R1 NFAT5 ASF1A RAD51B TCF7 HIPK2 SMARCA4 SYCP3 BCR SMARCE1 HIC2 SMC3 CBX5 SYNE1 TLE1 HIST3H2A NIPBL HDAC8 ATRX MPHOSPH6 PDS5A RUNX2 ZNF512B PEX2 DACH2 TBL1X MED7 MED30 MED31 NDUFS4 SSX3 EIF5B RPS21 SPEN SOX9 TOMM5 TOMM22 CTNNAL1 PCDH8 DNAJC19 HSCB ISCA1 MLL WDR61 FBLN5 MFAP4 EEA1 SNAP29 MYL1 TNNC1 TNRC6B TNRC6C CALD1 MYH11 CEP164 CEP70 UQCR10 UQCRB NDUFA11 NDUFB5 DDIT3 RPL31 SEC11C TOMM20 CELSR1 NDUFB3 RPL23 NDUFS1 ATP5F1 ATP5L TRIB3 SSR3 ATXN1 CKM CKMT2 ARL1 SPAG9 ING3 YEATS4
32 10 Reactome CLIP2 FI network - brown MYH2 ZNF880 ZNF697 ZNF596 ALDH2 ZNF226 ZNF415 ZNF627 ZNF208 WASF2 NR2F2 DLG1 PTN ZNF528 ZNF614 CTTN ZNF107 ZNF814 ABLIM1 ZNF124 ZNF891 ZNF585A TRIO CXCL12 MYO10 CENPL CD47 LAMB1 CLIP1 PPIL1 TNS3 integrin signalling/ EM organisation/ focal adhesion PTK2 COL4A4 ITGA8 COL3A1 ITGB3 COL9A2 COL16A1 COL15A1 ITGAE ITGB3BP NSL1 SRRM2 KIAA1967 IGF1R INSR spliceosome LSM5 HNRNPM SF3A2 FUS LSM2 NHP2L1 SNRPE SARS SNRPG POLR2K PTRF ORC4 RPA3 TCEB1 UBE2W UBE2B ASB9 UBE3B ZBTB16 SMURF2 JUP MMP28 PRKX PRKACB SLC12A2 BRAF KSR1 GEMIN6 PRIM1 BCL6 GEMIN2 SKI ATF1 RNF128 FAM175A BRCA1 CREM NFATC2 PPP3R1 NFAT5 RAD51B chromosome maintenance ASF1A SYCP3 SMC3 BCR TCF7 HIC2 HIPK2 SMARCE1 SMARCA4 CBX5 SYNE1 TLE1 HIST3H2A NIPBL HDAC8 ATRX MPHOSPH6 PDS5A RUNX2 ZNF512B PEX2 DACH2 TBL1X MED7 MED30 MED31 NDUFS4 SSX3 EIF5B RPS21 SPEN SOX9 TOMM5 TOMM22 CTNNAL1 PCDH8 DNAJC19 HSCB ISCA1 MLL WDR61 FBLN5 MFAP4 EEA1 SNAP29 MYL1 TNNC1 TNRC6B TNRC6C CALD1 MYH11 CEP164 CEP70 UQCR10 UQCRB NDUFA11 NDUFB5 DDIT3 RPL31 SEC11C TOMM20 CELSR1 NDUFB3 RPL23 NDUFS1 ATP5F1 ATP5L TRIB3 SSR3 ATXN1 CKM CKMT2 ARL1 SPAG9 ING3 YEATS4
33 11 Reactome FI network - salmon POLE2 GINS1 E2F7 E2F8 DTL CDC7 MCM10 CDC6 MCM4 MCM6 GINS2 G1/S phase Regulation of DNA replication CDKN1A CDT1 TOP2A BARD1 CHEK1 CCNA2 PLK2 FOXM1 CCNE2 LMNB1 MELK CCNB2 TTK PBK NEK2 UBE2C Aurora A and B signalling AURKA DLGAP5 TPX2 AURKB CDC20 BUB1 M/G1 phase KIF4A BUB1B FEN1 KIF2C CENPH CENPM CENPF NCAPG CENPK NUF2 NDC80 CASC5 MLF1IP STMN1 ZWINT CENPA BIRC5 OIP5 CENPE PRC1 TYMS KIF11 KIF20A NUSAP1 KIF3A KIF23 RACGAP1 KIF14 ASF1B UHRF1
34 11 Reactome FI network - salmon POLE2 GINS1 E2F7 E2F8 CDC7 GINS2 MCM10 MCM4 CDC6 MCM6 DTL CDKN1A CDT1 TOP2A BARD1 CHEK1 CCNA2 PLK2 FOXM1 CCNE2 LMNB1 MELK CCNB2 TTK PBK NEK2 UBE2C BUB1 CDC20 Aurora A and B signalling AURKA DLGAP5 TPX2 AURKB KIF4A BUB1B FEN1 KIF2C CENPH CENPM CENPF NCAPG CENPK NUF2 NDC80 CASC5 MLF1IP STMN1 ZWINT CENPA BIRC5 CENPE OIP5 PRC1 TYMS KIF11 KIF20A NUSAP1 KIF3A KIF23 RACGAP1 KIF14 ASF1B UHRF1
35 11 Reactome FI network - salmon POLE2 GINS1 E2F7 E2F8 CDC7 GINS2 DTL MCM10 CDC6 MCM4 MCM6 G1/S phase Regulation / DNA replication CDKN1A CDT1 TOP2A BARD1 CHEK1 CCNA2 PLK2 FOXM1 CCNE2 LMNB1 MELK CCNB2 TTK PBK NEK2 UBE2C Aurora A and B signalling AURKA DLGAP5 TPX2 AURKB KIF4A NCAPG CDC20 BUB1 BUB1B FEN1 KIF2C CENPH CENPM CENPF CENPK NUF2 M/G1 phase NDC80 CASC5 MLF1IP STMN1 ZWINT CENPA BIRC5 OIP5 CENPE PRC1 TYMS KIF11 KIF20A NUSAP1 KIF3A KIF23 RACGAP1 KIF14 ASF1B UHRF1
36 12 Conclusion non-linear causality between genome, transcriptome and proteome requires integrative approaches 7q11 and CLIP2 succesful example for using integrative approaches correlation networks give meaningful results to be used a priori in systems biology CLIP2 network reconstruction
37 12 Acknowledgements Research Unit of Radiation Cytogenetics Herbert Braselmann Julia Heß Martin Selmansberger Verena Zangen Horst Zitzelsberger Partners from Genrisk-T, EpiRadBio and INT-Thyr C. Maenhaut, G. Dom, B. Jarzab W. van Wieringen, M. van der Wiel, P. Jacob, E. Cardis, E. Pernot, J. Grellier, M. Lushchyk, Y. Demidchik HMGU Imperial College London G. Thomas U. Schötz A. Galpine National International
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