RARE Germline variability in pediatric leukemia.

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RARE Germline variability in pediatric leukemia.

• Cancer Biology Series
• January 29, 2013
• Todd Druley, MD, PhD
• Assistant Professor of Pediatric and Genetics

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Presenter Disclosure InformationTodd E. Druley,
M.D., Ph.D.Druley Lab / WUSM CGSSB
In compliance with ACCME policy, WU requires the
following disclosures to the session audience
Research Support/P.I. No relevant conflicts of interest to declare
Employee No relevant conflicts of interest to declare
Consultant No relevant conflicts of interest to declare
Major Stockholder No relevant conflicts of interest to declare
Speakers Bureau No relevant conflicts of interest to declare
Scientific Advisory Board No relevant conflicts of interest to declare
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Why study rare variation?

• Whole genomes show 2-4 million variants PER
  PERSON!
• Only about 25 33 of these are common (gt2
  MAF).
• There are roughly 22,000 human genes
• This equals 40,000,000 nucleotides total for all
  of our genes.
• 1.5 of the entire genome
• If 2 individual genomes differ by
• 2M x 0.67 1,340,000 nucleotides
• There are 1.8 x 1012 possible combinations
  between the two genomes!!

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Common vs. Rare Variants

• Critical differences between common and rare
  variant analysis include
• Rare variants have greater effect sizes average
  OR3.7 (Bodmer Nat Genet
  2008)
• Disruptive rare variants are more likely to act
  dominantly (Fearnhead Cell Cycle
  2005)
• Rare variants are individually rare, but
  collectively common when collapsed (binned)
  within a genetic locus or metabolic pathway
  (Cohen Science 2004 Ji Nat
  Genet 2008)

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Antonarakis SE et al. Nature Rev Genet 2009.
Private
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Antonarakis SE et al. Nature Rev Genet 2009.
Were operating here
Private
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Example

• Cystic Fibrosis
• Originally thought that only the ?F508 mutation
  was causative for CF.
• Sequencing of the CFTR gene was initiated.
• Now over 1000 mutations in CFTR have been
  documented.
• Cause various severities of cystic fibrosis.

http//www.ccb.sickkids.ca/index.php/cystic-fibros
is-mutation-database.html
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Complex diseases demonstrating increased rare
variation
AJHG 80, 779-791 2007

• Obesity

• High Cholesterol

• Sequenced two groups of 128 individuals each

• Psychiatric illness, cancer, autoimmune
  disorders, heart disease, height,
• extreme longevity, many others

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What about pediatric cancer?

• Early onset cancer defined as cancer lt50
  years old
• Germline cancer causing gene alleles (TP53,
  APC, BRCA1) average age of disease onset is
  20s
• Cannot explain the incidence of pediatric cancer
  by somatic mutation.
• Epi studies have failed to explain exposures
  causing these cancers.
• Almost all pediatric cancer patients have a
  negative family history.
• So why do we see 3 children/week with a new
  cancer??

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Infant acute leukemia worst outcomes

• 50 mortality, 67 with MLL-rearrangements
• MLL regulates developmental transcription (HOX
  genes)
• Survivors often left with developmental problems
• COG AE24 Epidemiology of Infant Leukemia
• Largest case-control study to date looking for
  pre/perinatal exposures associated with infant
  leukemia
• Topoisomerase II inhibitor exposure during
  pregnancy
• Only associated with AML, but didnt impact
  survival
• Ross JA, J Nat Cancer Inst Monogr 2008

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Pilot exome sequencing experiment

• GERMLINE exome sequencing from 25 pairs of
  mothers and infants with MLL-negative acute
  leukemia
• Julie Ross, PhD (PI) and Amy Linabery, PhD.
• We are looking at genes with rare variants in
  affected infants, but also inherited from mothers
• These parents typically dont have leukemia or
  other cancers.
• We hypothesize a combinatorial effect from
  parental variants contributes to the early
  onset/short latency of leukemia.

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Demographics
25 pairs of Caucasian mothers and infants 12
ALL, 13 AML
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Validated bioinformatics

• We analyzed exome data using a validated
  bioinformatic pipeline
• Align using Novoalign
• Call variants with SAMtools
• Sensitivity 97
• Specificity 99.8

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Variant calls in COSMIC genes

• Prioritize by comparing our variant calls in
  genes already associated with hematologic
  malignancies in the COSMIC database.
• http//www.sanger.ac.uk/genetics/CGP/cosmic/
• ALL (126 ALL-associated genes)
• Infants 695 total variants (481 known, 214
  novel)
• Mothers 728 total (588 known, 140 novel 65)
• AML (657 AML-associated genes)
• Infants 5517 total (3961 known, 1556 novel)
• Mothers 4735 total (4264 known, 471 novel 30)

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Permutation testing
Average ALL 5 variant genes/infant, AML
6 variant genes/infant
Null distribution
Null distribution
Both sets of infants have a statistically
significant (Plt10-7) enrichment of novel,
non-synonymous, deleterious germline variants in
genes associated with hematopoietic malignancies
(COSMIC).
Mark Valentine
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Validation

• No significant enrichment in randomly chosen gene
  sets in infants
• No significant enrichment in random or leukemia
  gene sets in Caucasian unaffected exomes
• Unlikely to see the same novel variant in only
  related mother infant pairs by chance.
• 45 in ALL 23 in AML
• Consistent with maternal totals of 65 30,
  respectively
• Sanger validation of other variants is ongoing

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micro-RNA regulation?

• Many variant candidate genes are regulated by
  MIRs independently associated with leukemia and
  cell cycle regulation

Nick Sanchez
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Pathway Analysis

• ABC transporters
• Developmental defects
• Chloride channel regulator activity
• Transcription factor dysregulation
• YYI, Cdx, HNF1, MAF, EA2
• TDG glycosylase mediated binding and cleavage of
  a thymine, uracil or ethenocytosine opposite a
  guanine

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Implications / Conclusions

• Supports the hypothesis that infants with
  leukemia are born with a putatively functional
  enrichment of variation in genes associated with
  leukemogenesis.
• Infants with AML have an excess of novel,
  nonsynonymous, deleterious variation not from
  mother.
• Paternal age de novo mutation during
  spermatogenesis?
• De novo mutation during embryogenesis?
• Can we identify discreet biological/developmental
  and regulatory mechanisms leading to early onset
  leukemia?
• MIRs
• ABC transporters
• Specific transcription factors

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Future work

• SHORT TERM
• Complete the bioinformatic analysis
• Compare to existing data (TARGET and PCGP)
• Exome sequencing of 25 MLL-positive pairs
• LONG TERM
• Validate results in a second cohort of triads
• Establish model systems to study complex genetic
  interactions
• Integrate information into clinical trials?

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High-risk pediatric ALL Pooled sequencing

1. Patient germline (N96)
2. Patient leukemia (N96)
3. Unaffected controls (N93)

55 genes per pool
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Candidate genes for pooled sequencing

• 55 genes selected for pooled sequencing
• All genes have been published in relation to
  pediatric ALL
• 43 were identified near significant tagged-SNPs
  on the prior array (asterisks)
• Various cellular functions

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Pooled sequencing pilot project

• Sequenced 94.5 of coding regions from all three
  pools.
• 420 kb per person 1.2 x 108 total bases covered

Total Variants Coverage/Allele
Unaffected 4209 80-fold
Germline 3929 86-fold
Leukemia 3822 101-fold
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• Validation at 384 base positions by custom
  Illumina GoldenGate array

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Overlap

• 49 of called variants are unique to the ALL
  Germline pool
• Only 2.5 of Leukemia variants were NOT seen in
  the Germline pool (97.5 overlap)
• Somatic mutations

Germline pool NOT in Unaffected Leukemia pool NOT in Germline
Total variants 1915 (49) 96 (2.5)
Coding substitutions 233 (12) (20) 22 novel mutations in UTRs 5 within putative splice site
Novel 175 (75) 15 (79)
Non-synonymous Synonymous 162 (70) 71 14 (74) 5
Damaging (per SIFT) 89 (38) 84 missense 5 nonsense 9 (47) all missense
Coding Insertions/Deletions 9 7 11 in UTR or splice site
Causes protein dysfunction (per SIFT)? 6 3 MLL, 1 ATM, 1 PAX5, 1 LEF1 7 6 MLL, 1 TCF3
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Visualizing the dataset
Leukemia SNPs (x)
Germline SNPs ()
Amplicons
Control SNPs (?)
High
Conservation Across Species
Low
Joe Giacalone Mark Valentine
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Visualizing the dataset
Leukemia SNPs (x)
Germline SNPs ()
Amplicons
Control SNPs (?)
High
Conservation Across Species
Low

1. No variants in control group
2. Multiple variants in affected germline
3. Overlap with highly conserved region

Joe Giacalone Mark Valentine
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Mark Valentine
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Exome variant server overlay
Drew Hughes
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• All looking at known ancestral polymorphisms and
  the incidence of acute leukemia.
• None involve sequencing to demonstrate novel/rare
  variants in the same genes.

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Overexpressed genes

1. ATM
2. CDKN1A
3. CYP1A1
4. CYP3A5
5. IKZF1
6. MDM2
7. MLL
8. MTHFR
9. NAT2
10. NQO1
11. PAX5
12. PTPN11
13. TCF3
14. TPMT

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Overexpressed genes

1. ATM
2. CDKN1A
3. CYP1A1
4. CYP3A5
5. IKZF1
6. MDM2
7. MLL
8. MTHFR
9. NAT2
10. NQO1
11. PAX5
12. PTPN11
13. TCF3
14. TPMT

6 of 14 overexpressed genes (43) are involved in
drug metabolism.
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Additional gene expression profiles

• Similar expression differences in 18 additional
  genes (5 overexpressed CYPs).
• All genes possess 1 novel coding variant in
  P9906 patients.
• No clear connection between genetic variation and
  gene expression.

Drew Hughes
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Implications / Conclusions

• Overexpression of specific genes involved in
  metabolism of anti-leukemia agents identifies a
  subgroup of children with inferior EFS.
• Private sequence variation in drug/energy
  metabolism genes is not coupled to expression
  profiles, but may predispose to leukemia or
  modulate therapeutic response through defective
  metabolism.
• Pathogenesis vs. pharmacogenomics?
• Therapeutic implications
• Can look for these genomic signatures at
  diagnosis existing precedent
• Dose modification or direct to bone marrow
  transplant

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Future work

• Validation and identification of individual
  profiles.
• Delve more into the underexpressed genes as well.
• Analyze sequencing results of 700 additional
  drug/energy metabolism genes.
• Functional iPSC-based assays from patient
  fibroblasts.
• Introduction into immune-deficient mice for
  functional study.

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Acknowledgements Funding

• Wash U
• Bob Hayashi
• Alan Schwartz
• Rob Mitra
• F. Sessions Cole
• COG
• Julie Ross
• Logan Spector
• Mignon Loh
• Rick Harvey
• Druley Lab
• Nick Sanchez
• Mark Valentine
• Joe Giacalone
• Drew Hughes
• Andrew Young

1K08CA140720-01A1
Eli Seth Matthews Leukemia Foundation