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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. Presenter Disclosure Information Todd E. Druley , M.D., Ph.D. Druley Lab / WUSM CGSSB.

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Rare germline variability in pediatric leukemia

RARE Germline variability in pediatric leukemia.

Cancer Biology Series

January 29, 2013

Todd Druley, MD, PhD

Assistant Professor of Pediatric and Genetics

Presenter disclosure information todd e druley m d ph d druley lab wusm cgssb
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:

Why study rare variation
Why study rare variation?

  • Whole genomes show 2-4 million variants PER PERSON!

    • Only about 25 – 33% of these are common (>2% 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!!

Common vs rare variants
Common vs. Rare Variants

  • Critical differences between common and rare variant analysis include:

    • Rare variants have greater effect sizes [average OR=3.7] (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)

Antonarakis SE et al. Nature Rev Genet 2009.


Antonarakis SE et al. Nature Rev Genet 2009.

We’re operating here



  • Cystic Fibrosis

    • Originally thought that only the ΔF508 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.


Complex diseases demonstrating increased rare variation
Complex diseases demonstrating increased rare variation

AJHG 80, 779-791; 2007

  • Psychiatric illness, cancer, autoimmune disorders, heart disease, height,

    extreme longevity, many others…

  • Obesity

  • High Cholesterol

  • Sequenced two groups of 128 individuals each

What about pediatric cancer
What about pediatric cancer?

  • “Early onset cancer” = defined as cancer <50 years old

  • Germline “cancer causing gene alleles” (TP53, APC, BRCA1) – average age of disease onset is 20’s

  • 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??

Infant acute leukemia worst outcomes
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 didn’t impact survival

      • Ross JA, J Nat Cancer InstMonogr 2008

Pilot exome sequencing experiment
Pilot exome sequencing experiment

  • GERMLINE exome sequencing from 25 pairs of mothers and infants with MLL-negativeacute 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 don’t have leukemia or other cancers.

    • We hypothesize a combinatorial effect from parental variants contributes to the early onset/short latency of leukemia.


25 pairs of Caucasian mothers and infants: 12 ALL, 13 AML

Validated bioinformatics
Validated bioinformatics

  • We analyzed exome data using a validated bioinformatic pipeline:

    • Align using Novoalign

    • Call variants with SAMtools

    • Sensitivity = 97%

    • Specificity = 99.8%

Variant calls in cosmic genes
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%)

  • Permutation testing
    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 (P<10-7) enrichment of novel, non-synonymous, deleterious germline variants in genes associated with hematopoietic malignancies (COSMIC).

    Mark Valentine


    • 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

    Micro rna regulation
    micro-RNA regulation?

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

    Nick Sanchez

    Pathway analysis
    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

    Implications conclusions
    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

    Future work
    Future work:


    • 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?

    High risk pediatric all pooled sequencing
    High-risk pediatric ALL: Pooled sequencing

    • Patient germline (N=96)

    • Patient leukemia (N=96)

    • Unaffected controls (N=93)

    55 genes per pool

    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

    Pooled sequencing pilot project
    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

    Overlap GoldenGate array

    • 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

    Visualizing the dataset
    Visualizing the dataset GoldenGate array

    Leukemia SNPs (x)

    Germline SNPs (+)


    Control SNPs (Δ)






    Joe Giacalone

    Mark Valentine

    Visualizing the dataset1
    Visualizing the dataset GoldenGate array

    Leukemia SNPs (x)

    Germline SNPs (+)


    Control SNPs (Δ)






    • No variants in control group

    • Multiple variants in affected germline

    • Overlap with highly conserved region

    Joe Giacalone

    Mark Valentine

    + GoldenGate array




    Mark Valentine

    Exome variant server overlay
    Exome variant server overlay GoldenGate array

    Drew Hughes

    Overexpressed genes
    Overexpressed genes: incidence of acute leukemia.

    • ATM

    • CDKN1A

    • CYP1A1

    • CYP3A5

    • IKZF1

    • MDM2

    • MLL

    • MTHFR

    • NAT2

    • NQO1

    • PAX5

    • PTPN11

    • TCF3

    • TPMT

    Overexpressed genes1
    Overexpressed genes: incidence of acute leukemia.

    • ATM

    • CDKN1A

    • CYP1A1

    • CYP3A5

    • IKZF1

    • MDM2

    • MLL

    • MTHFR

    • NAT2

    • NQO1

    • PAX5

    • PTPN11

    • TCF3

    • TPMT

    6 of 14 overexpressed genes (43%) are involved in drug metabolism.

    Additional gene expression profiles
    Additional gene expression profiles incidence of acute leukemia.

    • 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

    Implications conclusions1
    Implications / Conclusions: incidence of acute leukemia.

    • 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

    Future work1
    Future work incidence of acute leukemia.:

    • 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.

    Acknowledgements funding
    Acknowledgements & Funding incidence of acute leukemia.

    Wash U:

    • Bob Hayashi

    • Alan Schwartz

    • Rob Mitra

    • F. Sessions Cole


    • Julie Ross

    • Logan Spector

    • Mignon Loh

    • Rick Harvey

      Druley Lab:

    • Nick Sanchez

    • Mark Valentine

    • Joe Giacalone

    • Drew Hughes

    • Andrew Young


    Eli Seth Matthews Leukemia Foundation™