THE UMD TP53 MUTATION DATABASE UPDATES AND BENEFITS. Pr. Thierry Soussi

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1 THE UMD TP53 MUTATION DATABASE UPDATES AND BENEFITS Pr. Thierry Soussi

2 TP53: 33 YEARS AND COUNTING STRUCTURE FUNCTION RELATIONSHIP OF WILD AND MUTANT TP Crawford et al. 37 C P. May 1983 Oren et al. 37 C 1988 Soussi et al. 40 C 1987 Soussi et al. 25 C 1988 Soussi et al Levine et al. 25 C

3 TP53: 33 YEARS AND COUNTING STRUCTURE FUNCTION RELATIONSHIP OF WILD AND MUTANT TP Crawford et al. 37 C P. May 1983 Oren et al. 37 C 1988 Soussi et al. 40 C 1987 Soussi et al Soussi et al. 25 C 2000 Levine et al. 25 C A. Pompeii 2015 Soussi et al. 60 C Which TP53 residues are important for TP53 folding? How these residues have evolved to ensure TP53 function from 0 to 60 C What is the status of these residues in human cancer? Wild type? mutant? associated with more deleterious clinical features?

4 TP53 MUTATIONS IN HUMAN CANCER GENE MUTATION: FROM DISCOVERY TO APPLICATION THE TP53 MUTATION DATABASE TP53 MUTATION ANALYSIS IN THE NEXT DECADE

5 TP53 MUTATIONS IN HUMAN CANCER IT is STORY TIME! TP53! A NEW MUTATED GENE IN HUMAN CANCER!

6 PHASE I Discovery Variant detection Causes Incidence Total number of variants (cumulated) Discovery rate of novel variants

7 PHASE I Discovery Variant detection Causes Incidence Total number of variants (cumulated) Discovery rate of novel variants I.F. Consequences Incidence Publications Inconsistant reports

8 PHASE II Incidence Discovery Clinical value Consolidation Variant detection X No clinical value Total number of variants (cumulated) Causes Discovery rate of novel variants

9 PHASE II Incidence Discovery Clinical value Consolidation Variant detection X No clinical value Total number of variants (cumulated) Causes Discovery rate of novel variants I.F. Consequences Incidence Publications Inconsistant reports

10 PHASE III Discovery Consolidation Application Causes Variant detection Incidence Total number of variants (cumulated) Discovery rate of novel variants

11 PHASE III Discovery Consolidation Application Causes Variant detection Incidence Total number of variants (cumulated) Discovery rate of novel variants I.F. Consequences Incidence Publications Inconsistant reports

12 TP53 MUTATIONS IN HUMAN CANCER GENE MUTATION: FROM DISCOVERY TO APPLICATION THE TP53 MUTATION DATABASE TP53 MUTATION ANALYSIS IN THE NEXT DECADE

13 THE p53 MUTATION DATABASE "unbiased" compilation of "every" TP53 mutations published in the literature + TCGA and ICGC unpublished data October ,900 PUBLICATIONS 60,000 MUTATIONS TP53 VARIANTS 1750 missense variants 5450 indel variants Bernard Leroy and Thierry Soussi

14 TP53 MUTATION HETEROGENEITY AND DATABASE FREQUENCY hot spot variants R175H (1 600 x) R273H (1 100x) TP53 MUTANTS (1,750 VARIANTS) rare variants 800 variants 1x 400 variants 2x WHAT IS THE BIOLOGICAL SIGNIFICANCE OF THESE RARE VARIANTS????

15 TP53 MUTATION HETEROGENEITY 2,314 p53 mutants representing all possible amino acid substitutions caused by a point mutation throughout the entire protein (5.9 substitutions per residue) have been constructed The transcriptional activity of all these mutants was analyzed in a yeast based functional assay For each p53 mutant, a quantitative value of the remaining activity is available

16 TP53 MUTATION HETEROGENEITY 175 ACTIVITY (% wt p53) ONLY TUMORS HAVE BEEN INCLUDED IN THE ANALYSIS > x 10-49x 6-9x 3-5x 2x 1x FREQUENCY

17 REMAINING ACTIVITY OF MUTANT TP53 ACCORDING TO THEIR FREQUENCY IN THE DATABASE 4 P< NS NS ACTIVITY (%) 0 NS _+ 81_160 41_80 21_40 9_ _3 FREQUENCY 1 Tumors cell line germ line

18 TP53 MUTATION HETEROGENEITY What is the contribution of these "rare" mutations to tumorigenesis passenger mutations? PCR and sequencing artefacts? Weak mutations? Are they randomly distributed in the literature? Are they associated with other unusual features?

19 TP53 MUTATION HETEROGENEITY Each publication was ranked according: Frequency of each mutant in the db Remaining activity of each mutant Number of tumors with multiple mutations Tum 2x Tum 3x Tum >3x Number of tumors synonymous mutations Number of tumors with recurrent mutations 2,018 publications with 5 or more reported mutations Edlund, K. et al. Data-driven unbiased curation of the TP53 tumor suppressor gene mutation database and validation by ultradeep sequencing of human tumors. Proc Natl Acad Sci U S A 109, (2012).

20 THE p53 MUTATION DATABASE Included >90% of the publications Outliers 7 % of the publications (2 sigma) or 1% (5 sigma) Multiple tumours with more than 1 TP53 mutations Multiple tumours with rare variants Multiple tumours with a high frequency of synonymous va PARAFFIN EMBEDDED TISSUE

21 THE p53 MUTATION DATABASE Only 2 papers describing TP53 mutations in lymphoma have been flagged Both papers used DNA from paraffin embeded tissue

22 TP53 MUTATIONS IN HUMAN CANCER GENE MUTATION: FROM DISCOVERY TO APPLICATION THE TP53 MUTATION DATABASE TP53 MUTATION ANALYSIS IN THE NEXT DECADE

23 THE TP53 PATHWAY: A COMPLEX NETWORK

24 THE TP53 PATHWAY: A COMPLEX NETWORK TP53 activities are modulated by the ratio of the various isoforms

25 TP53 ISOFORMS: WHATS IMPORTANCE? p.p110l 6.6% 17.8%

26 TP53 ISOFORMS: WHATS IMPORTANCE? TP53 exon 9 beta and gamma are poorly analyzed They are not included in the majority of commercial exon selection kit They are excluded from most analytical pipelines (labeled as intron) They are excluded in custom arrays

27 TP53 ISOFORMS: WHATS IMPORTANCE? Revised guidelines for screening of TP53 gene mutations Bernard Leroy, Mandy L. Ballinger, Gareth L. Bond, Antony Braithwaite, Nicole Concin, Lawrence A. Donehower, Wafik S. El-Deiry, Pierre Fenaux, Gianluca Gaidano, Anita Langerød, Eva Hellstrom-Lindberg, Richard Iggo, Jacqueline Lehmann-Che, Phuong L. Mai, David Malkin, Ute M. Moll, Jeffrey N. Myers, Kim E. Nichols, Sarka Pospisilova, Davide Rossi, Sharon A. Savage, Louise C. Strong, Patricia N. Tonin, Robert Zeillinger, Thorsten Zenz,Joseph F. Fraumeni Jr., Pierre Hainaut and Thierry Soussi

28 ASSESSING TP53 STATUS IN HUMAN CANCER STRATEGY METHODOLOGY MUTATION VALIDATION

29 ANALYSIS OF TP53 MUTATIONS IN HUMAN CANCER Application phase All coding exons with +/- 5 intronic extension

30 ANALYSIS OF TP53 MUTATIONS IN HUMAN CANCER Discovery phase screening beta and gamma exons Intron 9

31 ASSESSING TP53 STATUS IN HUMAN CANCER Discovery phase Intron 4 promoter Intron 4 TP53 RE Int Promoter

32 ASSESSING TP53 STATUS IN HUMAN CANCER Discovery phase 3 UTR? Intron 9 TP53 RE Int Promoter Intron 4 3'UTR

33 ASSESSING TP53 STATUS IN HUMAN CANCER STRATEGY METHODOLOGY MUTATION VALIDATION

34 ASSESSING TP53 STATUS IN HUMAN CANCER CONVENTIONAL SEQUENCING SANGER HIGH-THROUGHPUT SEQUENCING TUMOR HETEROGENEITY TUMOUR WITH MULTIPLE TP53 MUTATIONS Multiple tumoral clones with different mutations

35 ASSESSING TP53 STATUS IN HUMAN CANCER STRATEGY METHODOLOGY MUTATION VALIDATION Sequencing artefacts? Passenger mutations? Very rare neutral SNP? Driver mutations?

36 TP53 SNP CAN BE VERY UNFREQUENT More than 150,000 TP53 gene have been sequenced P36P P47S R72P R213R N235S R290H R283C R283H R156H P222L?

37 TP53 SNP CAN BE VERY UNFREQUENT More than 150,000 TP53 gene have been sequenced P36P P47S R72P R213R N235S R290H R283C R283H R156H P222L?

38 P36P R72P R213R

39 ASSESSING TP53 STATUS IN HUMAN CANCER STRATEGY METHODOLOGY MUTATION VALIDATION Sequencing artefacts? Passenger mutations? Very rare neutral SNP? Driver mutations?

40 PHASE III Discovery Consolidation Application Causes Variant detection Incidence Total number of variants (cumulated) Discovery rate of novel variants I.F. Consequences Incidence Publications Inconsistant reports

41 TP53 MUTATION HETEROGENEITY AND DATABASE FREQUENCY hot spot variants R175H (1 600 x) R273H (1 100x) TP53 MUTANTS (1,750 VARIANTS) cut off: TP53 frequeny Remaining activity Frequency in outliers studies rare variants 800 variants 1x 400 variants 2x

42 THE p53 MUTATION DATABASE TP53 Mutation 60,000 mutations TP53 Variants 7,200 Variants Each publication are flagged with a statistical index

43 Protein variant p.r175h p.r175h p.r175h p.r136h p.r136h p.r136h p.r43h p.r43h p.r43h p.r16h p.r16h p.r16h ALL ISOFORMS Frequency 2307 Hemopathies 138 CLL MDS for ERIC Comment Frequency This mutation is very frequent Comment Activity No activity cdna variant Comment Isoforms c.524g>a This mutation targets the 12 TP53 isoforms Comment Prediction Damaging wt p.r175h RESIDUAL ACTIVITY Polyphen: Damaging Mutassessor: Damaging Condel: Damaging Provean: Damaging Splice assessor: Splice Neutral Variant PREDICTION Comment Outliers No specific association Comment Splice No defect predicted

44 Protein variant p.t125t p.t125t p.t125t p.t86t p.t86t p.t86t p.= p.= p.= p.= p.= p.= ALL ISOFORMS Frequency 74 Hemopathies 4 CLL MDS 4 0 for ERIC Comment Frequency This mutation is not frequent Comment Activity No data cdna variant Comment Isoforms c.375g>t This mutation targets 6 TP53 isoforms Comment Prediction No data wt No data p.t125t RESIDUAL ACTIVITY Polyphen: No data Mutassessor: No data Condel: No data Provean: No data Splice assessor: Splice Neutral Variant PREDICTION Comment Outliers No specific association Comment Splice This exonic mutation, close to a splice site, has been predicted to impair splicing; high confidence index

45 Protein variant p.n235s p.n235s p.n235s p.n196s p.n196s p.n196s p.n103s p.n103s p.n103s p.n76s p.n76s p.n76s ALL ISOFORMS Frequency 39 Hemopathies 8 CLL MDS 5 0 for ERIC Comment Frequency This mutation is no frequent Comment Activity Fully active cdna variant Comment Isoforms c.704a>g This mutation targets the 12 TP53 isoforms Comment Prediction Probably Benign wt p.n235s RESIDUAL ACTIVITY Polyphen: Tolerated Mutassessor: Low Condel: Benign Provean: Damaging Splice assessor: Splice Neutral Variant PREDICTION Comment Outliers No specific association Comment Splice No defect predicted

46 TP53 MUTATIONS ARE HETEROGENOUS R175H deficient for apoptosis and cell cycle arrest R175P deficient for apoptosis only EXON 4-9

47 TP53 MUTATIONS AND ERIC 17p deletion Notch deletion association with another TP53 mutation early versus late TP53 MUTATIONS IN CLL ERIC program Other parameters can be discussed

48 ACKNOWLEDGEMENTS Ola Larsson Thierry Soussi Karolina Edlund Magnus Sundström Patrick Micke Johan Botling Bernard Leroy Jean L. Fournier Adam Ameur Ignas Bunikis Ulf Gyllensten

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