Uploaded byFran Supek
Synonymous mutations as drivers in human cancer genomes.
1) The document discusses how synonymous mutations, which do not change the protein sequence, are frequently found to act as drivers in carcinogenesis. 2) Using data from thousands of human cancer genomes, the author finds that oncogenes are enriched for synonymous mutations, showing synonymous mutations can be selected for during cancer development. 3) Further analysis suggests some synonymous mutations may impact mRNA folding, splicing, or microRNA binding sites and thus influence gene expression, providing a means for them to act as drivers.
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Synonymous mutations as drivers in human cancer genomes.
- 1. Synonymous mutations -from bacterial evolution to somatic changes in human cancer Fran Supek 1) Lehner group, CRG/EMBL Systems Biology Unit, Barcelona 2) Division of Electronics, RBI, Zagreb, Croatia XXI Jornades de Biologia Molecular Barcelona, 11.6.2014 Part 2: Synonymous mutations frequently act as drivers in carcinogenesis.
- 2. synonymous mutations = changesin the gene sequence that don’t alter the protein sequence
- 3. Synonymous mutations • (some)synonymous mutations are subject to evolutionary pressures • clearly shown for many bacteria and yeasts • likely also higher Eukarya (but weaker signal) • how does selection for/against synonymous changes relate to gene function in (a) evolution of bacteria and (b) in carcinogenesis? evolutionary trace across ~1000 bacterial genomes somatic mutations in ~4000 human cancers malignant transformationadaptation to diverse environments ( plush microbes in photos are from http://www.giantmicrobes.com/ )
- 4. A deluge ofhuman cancer genomic data 3851 cancer exomes from 11 tissues (>200 samples each) 292,405 missense and 123,193 synonymous somatic mutations ARE THE SYNONYMOUS MUTATIONS SELECTED FOR IN CARCINOGENESIS?
- 5. from Lawrence etal (2013) Nature. Mutation rate varies widely across the genome and correlates with DNA replication time and expression level.
- 6. from Schuster-Böckler andLehner (2012) heterochromatin correlates to SNV rates
- 7. Drivers vs. passengers •manysomatic mutations in cancer = „passengers” •a driver = a gene that confers a selective advantage. Recurrently mutated (ie. more than expected) 1. For missense, could be measured using the dN/dS 2. 3. commonly: find backgroud mut. frequencies for patient from entire exome see if a gene is above that background Intronic rates as a baseline: INVEX test Hodis et al. (Cell 2012)
- 8. 0 0.25 0.5 0.75 1 oPC2(24.3%) carcinoma, 1Mb non-carcinoma, 1Mb pooled,200kb liver, 200kb liver, 1Mb breast, 1Mb H3K9me3, 1Mb hypothalamus liver skeletal & heart muscle 6 tissues mRNA levels 0 0.2 0.4 0.6 0.8 1 9 0.6 0.8 1 oncogenes: translocation (217) missense (40) copy number (12) tumor suppressors: all mechanisms (84) Cancer Gene CensusA recurrently mutated genes (self-reported in literature) B known cancer genes in Census others: 336 39 38 C # mutation (110 cancers, heterochrom levels in 1 M 19 1821 missense- activated oncogenes recurrently mutated (from literature) oncogenes 0 0.2 0.4 0.6 0.8 1 0.1 0 0 0.25 0.5 0.75 1 oPC2(24.3%) carcinoma, 1Mb non-carcinoma, 1Mb pooled, 200kb liver, 200kb liver, 1Mb breast, 1Mb H3K9me3, 1Mb hypothalamus liver skeletal & heart muscle 6 tissues mRNA levels 0 0.2 0.4 0.6 0.8 1 9 19 D+ = 0 P = 0.0 0 0.2 0.4 0.6 0.8 1 9 19 29 D- = 0.256 P = 0.005 0.6 0.8 1 oncogenes: translocation (217) missense (40) copy number (12) tumor suppressors: all mechanisms (84) Cancer Gene CensusA recurrently mutated genes (self-reported in literature) B known cancer genes in Census others: 336 39 38 C # mutations per 200 kb (110 cancers, pooled tissues) heterochromatin (H3K9me3 levels in 1 MB windows) # mutations per (110 cancers, poole heterochromatin ( levels in 1 MB wi 0.6 0.8 1 D P 0 0.2 0.4 0.6 0.8 1 0.1 0.3 D+ = 0.215 P = 0.025 D 19 1821 missense- activated oncogenes recurrently mutated (from literature) oncogenes 0 0.2 0.4 0.6 0.8 1 0.1 0.3 0.5 D- = 0.185 P = 0.061 „classical” cancer genes: newly discovered, from cancer genomes: Oncogenes get activated by missense mutations, duplications, translocations.... Tumor suppressors get inactivated by missense/nonsense mutations, deletions, promoter methylation...
- 9. -0.5 -0.25 0 0.25 0.5 0.75 1 -1 -0.75 -0.5-0.25 0 0.25 0.5 0.75 1 correlationtoPC2(24.3%) correlation to PC1 (30.4 % variance) carcinoma, 1Mb non-carcinoma, 1Mb pooled, 200kb liver, 200kb liver, 1Mb breast, 1Mb H3K9me3, 1Mb GC3 RepliSeq, 1Mb hypothalamus liver skeletal & heart muscle 6 tissues regional mutation rates mRNA levels 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 oncogenes: translocation (217) missense (40) copy number (12) tumor suppressors: all mechanisms (84) Cancer Gene CensusA recurrently mutated genes (self-reported in literature) B known cancer genes in Census others: 336 39 38 # (11 het le rep s 19 1821 missense- activated oncogenes recurrently mutated (from literature) oncogenes 0 0.2 0.4 0.6 0.8 1 0 Detecting positive selection on synonymous mutations in cancer • create „matched sets” of genes closely following the oncogenes in: • regional mutation rates • In 1 Mb and 200 kb windows • expression levels in different tissues • Heterochromatin, replication timing • G+C content
- 10. How to finda good set of genes? A genetic algorithm. An optimization technique that can (relatively) easily handle many criteria at once. Quite efficient. Many parameters. Operators: ...crossover ...random mutation
- 11. carcinoma, 1Mb non-carcinoma, 1Mb pooled,200kb liver, 200kb liver, 1Mb breast, 1Mb H3K9me3, 1Mb alamus muscle regional mutation rates mRNA levels 0 0.2 0.4 0.6 0.8 1 9 19 29 D+ = 0.313 P = 0.0004 0 0.2 0.4 0.6 0.8 1 9 19 29 D- = 0.256 P = 0.005 0.2 0.4 0.6 0.8 1 D+ =0.211 P = 0.026 recurrently mutated genes (self-reported in literature) known ncer genes in Census others: 336 39 38 C # mutations per 200 kb (110 cancers, pooled tissues) heterochromatin (H3K9me3 levels in 1 MB windows) # mutations per 200 kb (110 cancers, pooled tissues) heterochromatin (H3K9me3 levels in 1 MB windows) 0.2 0.4 0.6 0.8 1 D- = 0.199 P = 0.043 0 0.2 0.4 0.6 0.8 1 0.1 0.3 0.5 D+ = 0.215 P = 0.025 D 19 1821 nse- ted enes recurrently mutated (from literature) oncogenes 0 0.2 0.4 0.6 0.8 1 0.1 0.3 0.5 D- = 0.185 P = 0.061 Oncogenes: Tumor suppressors: Distributions of regional mutation rates (1Mb and 200 kb), heterochromatin, etc. in the optimized sets of non-cancer genes closely match the cancer genes. Genetic algorithm tries to minimize the K-S statistic.
- 12. -0.5 -0.25 0 0.25 0.5 0.75 1 -1 -0.75 -0.5-0.25 0 0.25 0.5 0.75 1 correlationtoPC2(24.3%) correlation to PC1 (30.4 % variance) carcinoma, 1Mb non-carcinoma, 1Mb pooled, 200kb liver, 200kb liver, 1Mb breast, 1Mb H3K9me3, 1Mb GC3 RepliSeq, 1Mb hypothalamus liver skeletal & heart muscle 6 tissues regional mutation rates mRNA levels 0 0.2 0.4 0.6 0.8 1 -2 0 2 D- = 0.224 P = 0.017 0 0.2 0.4 0.6 0.8 1 9 D P 0 0.2 0.4 0.6 0.8 1 -2 0 0 0.2 0.4 0.6 0.8 1 9 19 29 D- = 0.256 P = 0.005 0 0.2 0.4 0.6 0.8 1 D+ =0.211 P = 0.026 earlylate oncogenes: translocation (217) missense (40) copy number (12) tumor suppressors: all mechanisms (84) Cancer Gene CensusA recurrently mutated genes (self-reported in literature) matched sets of noncancer genes: 1517 genes (for oncogenes) 693 genes (for tumor suppressors) B known cancer genes in Census others: 336 39 38 C # mutations per 200 kb (110 cancers, pooled tissues) heterochromatin (H3K9me3 levels in 1 MB windows) replication timing (RepliSeq signal in 1 MB windows) mRNA levels, avg. of 6 tissues # mutatio (110 cancers heterochrom levels in 1 replication t signal in 1 mRNA levels, 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0.1 D+ = P = 0 39 oncogenes (recurrently mutated) 38 tumor suppressors (recurr. mutated) D 19 1821 missense- activated oncogenes recurrently mutated (from literature) oncogenes 0 0.2 0.4 0.6 0.8 1 0.1 0.3 0.5 D- = 0.185 P = 0.061 Expected: the oncogenes and the tumor suppressors are highly enriched with missense mutations (~1.5 - 2.5x). However, the oncogenes are also enriched with synoynmous mutations over their matched sets, ~1.2x.
- 13. Introns of oncogenes(from whole- genome sequencing) are not enriched with SNVs, compared to matched sets. The matched sets method agrees with Invex, and with simply using neighboring genes as a baseline.
- 14. Tissue-specific oncogenes aremore enriched with synonymous mutations in the corresponding tissue. This effect is not due to mutation showers/clustered mutations, as the same cancer samples don't tend to contain both a synonymous and a missense mutation in same gene. Synonymous enrichment in oncogenes is detectable across cancer types.
- 15. Some oncogenes aremore highly enriched with synonymous mutations than others, e.g. PDGFRA, EGFR, GATA1, ELN, NTRK1, JAK3, ALK and others (n=16). The synonymous SNV enrichment in these genes is not paralleled by intronic SNV enrichment.
- 16. The synonymous mutationstend to cluster together to a similar extent as the missense mutations in the affected oncogenes. They also (less prominently) cluster with missense mutations.
- 17. 0% 10% 20% 30% 40% 50% 60% optimal codon gain optimal codon loss no change %ofsynonymousmutations leadingtooutcome n.s. -18 -13 -8 -3 mRNAfoldingfreeenergy aroundmutatedsites(kcal/mol) 50nt windows w.t. mRNA mut. mRNA -31 -26 -21 -16 -11 -6 100ntwindows w.t. mRNA mut. mRNA 0% 10% 20% 30% 40% ≤30 nt 31-70 nt >70 nt p < 10-4 1.75 1.26 0.45 -2 -1 0 1 2 1 2 3 4 5 6 7 log2RPKMofexon exon # in transcript ENST00000334286 30 random samples w/o point mutations 6 samples w/ synonymous exonic mutations EDNRB gene, colorectal cancer -0.5 -0.3 -0.1 0.1 0.3 0.5 whole cDNA sites w/ phyloP>1.0net#ofgainedmiRNAseed sitespersyn.mutation 16 oncogenes matched set -0.3 -0.2 -0.1 0 0.1 0.2 normalized difference (Glass' delta) between properties of mutated positions in oncogenes vs. matched set Relative preference value at C-cap (of α helices) Normalized frequency of turn in all-α class Alpha-helix indices for α-proteins t-test FDR 0% 10% 20% 30% enh. gain enh. loss sil. gain sil. loss %syn.mutations(within30nt ofsplicesite)leadingtoevent Ke et al. 2012 hexamers 1.53 0.83 0.60 1.90 p = 0.02 enh. gain enh. loss RESCUE-ESE p = 0.003 1.90 0.53 sil. gain sil. loss FAS-hex2 p = 3·10-4 0.37 2.73 A B C D E G F 10% 20% p=0.05 n.s. n.s. 1.43 1.12 0.79 20% 30% 40% 50% actual synonymous mutations randomized mutation H Use of „optimal codons” miRNA binding sites Secondary structures in mRNA What could the synonymous mutations do?
- 18. 0% 10% 20% 30% 40% 50% 60% optimal codon gain optimal codon loss no change %ofsynonymousmutations leadingtooutcome n.s. -18 -13 -8 -3 mRNAfoldingfreeenergy aroundmutatedsites(kcal/mol) 50nt windows w.t. mRNA mut. mRNA -31 -26 -21 -16 -11 -6 100ntwindows w.t. mRNA mut. mRNA 0% 10% 20% 30% 40% ≤30 nt 31-70 nt >70 nt p < 10-4 1.75 1.26 0.45 -2 -1 0 1 2 1 2 3 4 5 6 7 log2RPKMofexon exon # in transcript ENST00000334286 30 random samples w/o point mutations 6 samples w/ synonymous exonic mutations EDNRB gene, colorectal cancer -0.5 -0.3 -0.1 0.1 0.3 0.5 whole cDNA sites w/ phyloP>1.0net#ofgainedmiRNAseed sitespersyn.mutation 16 oncogenes matched set -0.3 -0.2 -0.1 0 0.1 0.2 normalized difference (Glass' delta) between properties of mutated positions in oncogenes vs. matched set Relative preference value at C-cap (of α helices) Normalized frequency of turn in all-α class Alpha-helix indices for α-proteins t-test FDR 0% 10% 20% 30% enh. gain enh. loss sil. gain sil. loss %syn.mutations(within30nt ofsplicesite)leadingtoevent Ke et al. 2012 hexamers 1.53 0.83 0.60 1.90 p = 0.02 enh. gain enh. loss RESCUE-ESE p = 0.003 1.90 0.53 sil. gain sil. loss FAS-hex2 p = 3·10-4 0.37 2.73 A B C D E G F 10% 20% p=0.05 n.s. n.s. 1.43 1.12 0.79 20% 30% 40% 50% actual synonymous mutations randomized mutation H Use of „optimal codons” miRNA binding sites Secondary structures in mRNA No general effect was detected in any of these cases (although they may still be important in specific examples).
- 19. Exonic Splicing Enhancer ~and ~ Exonic Splicing Silencer From Cartegni, Chew & Krainer. Nat Rev Genet. 2002 3(4),285-98. AGAAGA enh GAAGAT enh GACGTC enh GAAGAC enh .... CTTTTA sil CTTTAA sil TAGGTA sil TAGTAG sil
- 20. Synonymous SNVs tend tobe closer to splice sites in oncogenes. They also tend to cause gains of known exonic splicing enhancer motifs, and losses of exonic splicing silencer motifs.
- 21. They more oftenaffect exons with weaker (noncanonical) splice sites. The exonic splicing enhancers created may resemble SF2/ASF motifs. The ESS sites that are lost upon mutation sometimes resemble hnRNP A2/B1, H2 and A1 motifs.
- 22. Roughly ½ ofthe putatively causal synonymous mutations alter splicing, as evidenced by examining RNA-seq data from cancer. We don't (yet) know what the other ½ is doing. One possibility may be affecting protein folding.
- 23. In yeast: Pechmann& Frydmann Nature Struct Mol Biol 2013 F 0% 10% 20% α-helix, 1st a.a. α-helix, middle α-helix, last a.a. p=0.05 n.s. n.s. 1.43 1.12 0.79 0% 10% 20% 30% 40% 50% coil actual synonymous mutations randomized mutation positions 0% 10% 20% middle next to coil only next to β-sheet p = 4·10-5 0.97 1.01 2.60 α-helix parts: 0% 10% 20% 30% 40% 50% coil G H N’’ N’ Ncap Ccap C’ C’’ α-helix turn -0.3 -0.2 -0.1 0 0.1 0.2 normalized difference (Glass' delta) between mutated sites in oncogenes vs. matched set relative preference value at C-cap normalized frequency of turn in all-α class α-helix indices for α-proteins relative preference value at N' relative preference value at N'' normalized frequency of α-helix in all-α class FDR <10% ...also in cancer: we observe an enrichment of synonymous mutations at N-termini of alpha- helices, esp. if close to beta-sheets. Suggestive of effects on folding.
- 24. known novel TP53 gene hasa large excess of synonymous mutations, which are always near splice sites. We found three examples of recurrent SNV that inactivate the nearby splice site. causes a frameshift
- 25. Dosage sensitive oncogeneshave many point mutations in their 3' UTRs
- 26. Take-home messages: • oncogenescontain an excess of synonymous mutations in human cancers • a subset of synonymous mutations target splicing motifs • 1/5 to 1/2 synonymous mutations in oncogenes reported to-date are acting as driver mutations • ~6 – 8% of all driver mutations due to single nucleotide changes are likely to be synonymous mutations • TP53 has recurrent synonymous mutations that disrupt splice sites • an excess of mutations of 3’ UTRs of dosage-sensitive genes published in: Supek et al. (2014) Cell. http://dx.doi.org/10.1016/j.cell.2014.01.051
- 28. Thank you! Fran Supek 1)Lehner group, CRG/EMBL Systems Biology Unit, Barcelona 2) Dept of Electronics, RBI, Zagreb, Croatia XXI Jornades de Biologia Molecular Barcelona, 11.6.2014 End of Part 2. Part 1 deals with inferring microbial gene function from evolutionary change in codon biases, and is available separately.