RARE GERMLINE VARIABILITY IN PEDIATRIC LEUKEMIA.

Cancer Biology Series

January 29, 2013

Todd Druley, MD, PhD

Assistant Professor of Pediatric and Genetics

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

Research Support/P.I.Research Support/P.I. No relevant conflicts of interest to declareNo relevant conflicts of interest to declare

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In compliance with ACCME policy, WU requires the following disclosures to the session audience:

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

• 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.

“Private”

We’re operating here

We’re operating here

Antonarakis SE et al. Nature Rev Genet 2009.

“Private”

Example:

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.

http://www.ccb.sickkids.ca/index.php/cystic-fibrosis-mutation-database.html

Complex diseases demonstrating increased rare variation

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

extreme longevity, many others…

AJHG 80, 779-791; 2007

Obesity

High Cholesterol Sequenced two groups of 128 individuals each

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

• ~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 Inst Monogr 2008

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 don’t have leukemia or other cancers.• We hypothesize a combinatorial effect from parental variants contributes

to the early onset/short latency of leukemia.

Demographics

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

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

• 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/

1.ALL (126 ALL-associated genes)Infants = 695 total variants (481 known, 214 novel)Mothers = 728 total (588 known, 140 novel – 65%)

2.AML (657 AML-associated genes)Infants = 5517 total (3961 known, 1556 novel)Mothers = 4735 total (4264 known, 471 novel – 30%)

Permutation testing

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).

Average: ALL = 5 variant genes/infant, AML = 6 variant genes/infant

Mark Valentine

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

micro-RNA regulation?

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

Nick Sanchez

• 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

Pathway Analysis

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:

SHORT TERM:1.Complete the bioinformatic analysis

2.Compare to existing data (TARGET and PCGP)

3.Exome sequencing of 25 MLL-positive pairs

LONG TERM:1.Validate results in a second cohort of triads

2.Establish model systems to study complex genetic interactions

3.Integrate information into clinical trials?

High-risk pediatric ALL: Pooled sequencing

1. Patient germline (N=96)

2. Patient leukemia (N=96)

3. Unaffected controls (N=93)

55 genes per pool

• 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

Function Gene Name Function Gene NameJAK1 * MLH1 *

JAK2 * MSH3

JAK3 PAX5 *

NAT1 * TCF3/E2A *

NAT2 * EBF1

NQO1 LEF1 *

MPO IKZF1 *

MTHFR IKZF3 *

RFC1 * ETV6/TEL *

TYMS * BTG1 *

GGH ERG *

CCND1 * ATM *

Thiopurine metabolism TPMT * CCNC *

TNFAIP1 FHIT

IL10 * BLNK *

NR3C1 * HOXA7 *

PTPN11 * HOXA9 *

NRAS * HOXA10 *

KRAS2 * HOXB4 *

FLT3 * AML1/RUNX1 *

RB1 * MLL *

TP53 * PBX1 *

MDM2 * MEIS1 *

CDKN2A i soform 1 (P16ink4a) * Drug efflux ABCB1 *

CDKN2A i soform 4 (P14arf) * CYP1A1 *

CDKN1A (P21cip1) CYP2E1

CDK4 CYP3A5 *

CDK6 *

DNA repair

B-cell development

Hematopoietic stem cell differentiation

Cytochromes

JAK tyrosine kinases

Oxidative stress response

Folate metabolism

Steroid metabolism

Cell cycle signaling

Candidate genes for pooled sequencing

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

• Validation at 384 base positions by custom Illumina GoldenGate array

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%) 19 (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

Visualizing the dataset

Leukemia SNPs (x)

Germline SNPs (+)

Control SNPs (Δ)Amplicons

ConservationAcrossSpecies

High

Low

Joe GiacaloneMark Valentine

Visualizing the dataset

Leukemia SNPs (x)

Germline SNPs (+)

Control SNPs (Δ)Amplicons

ConservationAcrossSpecies

High

Low

1. No variants in control group2. Multiple variants in affected germline3. Overlap with highly conserved region

Joe GiacaloneMark Valentine

+ +++

Mark Valentine

Exome variant server overlay

Drew Hughes

All looking at known ancestral polymorphisms and the incidence of acute leukemia.•None involve sequencing to demonstrate novel/rare variants in the same genes.

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

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.

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

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

Future work:

1. Validation and identification of individual profiles.• Delve more into the underexpressed genes as well.

2. Analyze sequencing results of ~700 additional drug/energy metabolism genes.

3. Functional iPSC-based assays from patient fibroblasts.

4. Introduction into immune-deficient mice for functional study.

Acknowledgements & FundingWash 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™