Genomic mapping in outbred mice reveals overlap in genetic susceptibility for HZE ion and γ-ray induced tumors

Ph.D. Defense, 9/13/2016, MIP 103, Noon

Transcript

1. Elijah Edmondson, DVM DACVP elijah.edmondson@colostate.edu Weil Lab, CSU Genomic mapping in outbred mice reveals overlap in genetic susceptibility for HZE ion and γ-ray induced tumors Final Defense MIP Seminar September 13, 2016 Chromosome 2
2. NASA: NNJ11ZSA001N The use of genetically diverse populations of mice to represent human heterogeneity that will elucidate the spectrum of tumor types caused by HZE nuclei as well as possible differences compared to γ-rays.
3. Background The use of (2) genetically diverse populations of mice to represent human heterogeneity that will elucidate the spectrum of tumor types caused by (1) HZE nuclei as well as possible differences compared to γ-rays.
4. GCR: • 85% protons • 14% helium • 1% HZE ions
5. HZE ions “Heavy ions” • High (H) atomic number (Z) and energy (E) charged particle – Accelerated by supernova explosions – Atomic nuclei stripped of electrons – Travel at relativistic speeds (up to 85% speed of light) – Contain very high energies (cannot shield) – Ionization is proportional to charge^2
6. • Assumption: Terrestrial Radiation (γ-ray) exposures increase the risk of the same tumor types as celestial radiation (HZE-ion) exposures. • If this assumption holds, the use of existing epidemiological data, most notably the atomic bomb survivors, of human populations exposed to γ-ray is valid. Hiroshima Nagasaki Current Assumptions
7. Uncertainties surrounding HZE ion exposures 1. Is the spectrum of HZE ion-induced tumors different from γ-induced tumors in a genetically diverse population? 2. Are the mechanisms of tumorigenesis different? Dudley Goodhead, et al. Photon (gamma-ray) Particle (HZE ion)
8. Concepts 1. Model Organism Genetics 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS • Inbred strains • Forward vs Reverse genetics • GWAS Particle Accelerator
9. Genetically Identical to Parents? Genetically Unique to Parents? BALB/CJ BALB/CJ vs Poll 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS
10. Genetically Identical to Parents? Genetically Unique to Parents? BALB/CJ BALB/CJ vs Poll 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS
11. http://faculty.virginia.edu/mammgenetics/mousestrains.html 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS
12. 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS
13. Advantages to inbred mice for toxicity studies: • strains produce lower phenotypic variance • thus fewer mice are needed to detect statistical differences Disadvantages of using inbred mice for toxicity studies: • strain-specific responses may obscure the variability we expect in a genetically diverse population such as humans Toxicity Studies using Inbred mice 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS
14. Forward Genetics (unbiased) 1. Define a phenotype 2. Determine gene(s) Reverse Genetics (biased) 2. Determine phenotype(s) 1. Alter a gene 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS
15. 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS Genotyping (SNP) Large, Genetically Diverse Control Population Large, Genetically Diverse Diseased Population Statistical Analysis Genome Wide Association Study
16. Single Nucleotide Polymorphism (SNP) - Spelling mistakes in the book of life (genome) - “Chapters” (chromosomes): - 23 for humans - 20 for mice - Quantitative trait locus (QTL): - location of genotype associations - Referred to as “Markers” in GWAS bc they simply tell tell us the important “page numbers”, SNP not necessarily causative - QTL contain structural variant, in/del, repeats, etc 1. Concepts 1. Inbred Strains 2. Forward vs Reverse Genetics 3. GWAS
17. Genetic Diversity: Important to us for two reasons 1. Phenotypic diversity is necessary to discriminate between genetic effects and radiation quality effects 2. Provides the ability for QTL mapping and to determine QTL overlap—susceptibility loci act as surrogates for carcinogenesis mechanism
18. Genetic Diversity: Important to us for two reasons 1. Phenotypic diversity is necessary to discriminate between genetic effects and radiation quality effects 2. Provides the ability for QTL mapping and to determine QTL overlap—susceptibility loci act as surrogates for carcinogenesis mechanism
19. Genetic Diversity: Important to us for two reasons 1. Phenotypic diversity is necessary to discriminate between genetic effects and radiation quality effects 2. Provides the ability for QTL mapping and to determine QTL overlap—susceptibility loci act as surrogates for carcinogenesis mechanism
20. •Each marker is informative •Lower mapping resolution •Less phenotypic diversity • High mapping resolution (<3 Mb) • Abundant phenotypic diversity • Large numbers needed • Functional follow-up is challenging in HS *DO more balanced than HS (multiple funnels used) and allow the ability to utilize CC to test candidate genes Mouse models of genetic diversity Woods LC, 2013. Physical Genomics. QTL Mapping in outbred populations: successes and challenges.
21. Heterogeneous Stock (HS/Npt) multiparent cross, model of population diversity Woods LC, 2013. Physical Genomics. QTL Mapping in outbred populations: successes and challenges.
22. “Reverse Engineered” outbred mice with highly recombinant genomes and balanced allele frequencies
23. MegaMUGA (illumina SNP Array)
24. Each mouse is a genetically unique mosaic of the 8 parental strains. *All of these strains have been completely sequenced. Mouse 20
26. 0.4 Gy HZE ions 28Si or 56Fe 3.0 Gy 137Cs gamma rays Unirradiated Controls Experimental Design 613 mice, ♀ and ♂ 615 mice, ♀ and ♂ 622 mice, ♀ and ♂ • Irradiate at 7-12 weeks old, ♀ / ♂ • Monitor to 800 days of age • Necropsy, all organ systems • Tumor classification (Histo, IHC) • Neurobehavioral & Ocular exams • Map Quantitative Trait Loci (QTL)
27. Results 3. Precise phenotyping 4. SNP genotyping and bioinformatics 2. Mouse monitoring and necropsy 1. Mouse irradiations
28. Results 3. Precise phenotyping 4. SNP genotyping and bioinformatics 2. Mouse monitoring and necropsy 1. Mouse irradiations Aim 1: Tumor Pathology Aim 2: Tumor GWAS Aim 3: Cataract Aim 4: Dose-rate effect on Tumors
29. Tumor Phenotyping Total Met Area Total Lung Area _____________ *Molecular phenotyping:
 AML - Chromosome 2 deletions HCC - fusion gene PulACA - Surface markers ( )x100 Quantifying Metastatic Density
30. Aim 1 • Are the tumor spectra similar following HZE ion and γ-ray irradiation? 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
31. Burden of Malignant Tumors Overall Survival • Competing mortalities • Time at risk HZE Gamma (γ) Unirradiated 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
32. Aim 2 • Are the tumor susceptibility QTL following γ-ray irradiation similar to the tumor susceptibility QTL following HZE ion irradiation? 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
33. Mapping tumor incidence in 18 unique tumor types (>1% incidence) • 51 QTL for 11 distinct tumor histotypes • 95% CI: 3.4 Mb (range 0.02 - 7.54) • Biologic effect size: 3.72% (range 0.75 - 7.46%) *All publicly available at https://github.com/elijahedmondson/HZE * Overall picture: many loci each contributing a small proportion to the variance. Overview: QTL Mapping in HS mice 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
34. • Tumor incidence 1. Thymic PreT LSA — chr 4 2. Thyroid adenoma — chr 2 3. B cell LSA — chr 11 4. AML — chr 2 5. DLBCL LSA — chr 17 6. HCC — chr 2 7. B cell LSA — chr 3 8. HCC — chr 15 9. HCC — chr 8 10.Thyroid adenoma — chr 10 11.Thymic PreT LSA — chr 17 • Tumor Latency 1. B cell LSA — chr 7 2. Thymic PreT LSA — chr 4 3. B cell LSA — chr 1 14 Large Effect QTL (QTL explains > 5% of variance) 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
35. • Tumor incidence 1. Thymic PreT LSA — chr 4 2. Thyroid adenoma — chr 2 3. B cell LSA — chr 11 4. AML — chr 2 5. DLBCL LSA — chr 17 6. HCC — chr 2 7. B cell LSA — chr 3 8. HCC — chr 15 9. HCC — chr 8 10.Thyroid adenoma — chr 10 11.Thymic PreT LSA — chr 17 • Tumor Latency 1. B cell LSA — chr 7 2. Thymic PreT LSA — chr 4 3. B cell LSA — chr 1 14 Large Effect QTL (QTL explains > 5% of variance) 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
36. Thyroid Follicular Adenoma 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
37. Thyroid Follicular Adenoma Chromosome 2 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
38. Thyroid Follicular Adenoma: chr 2 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
39. Thyroid Follicular Adenoma: chr 2 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
40. Brown Coat Color, gamma Genome Scan Albino Coat Color, HZE Genome Scan Visualizing coincident QTL 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
41. Unsupervised Hierarchical Clustering of QTL: Coat Color Black Locus Tyrosinase Locus Dilution factor Locus 99% confidence 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
42. Clustering occurs based on tumor histotype Clustering occurs based on radiation exposure vs. Unsupervised Hierarchical Clustering of QTL 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
43. Clustering occurs based on tumor histotype Clustering occurs based on radiation exposure vs. Unsupervised Hierarchical Clustering of QTL 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
44. Clustering occurs based on tumor histotype Clustering occurs based on radiation exposure vs. Unsupervised Hierarchical Clustering of QTL 1. HZE vs γ: are histotypes the same? 2. HZE vs γ: is tumorigenesis the same? 1. Overview 2. Thyroid Adenoma 3. Genome Scan Clustering
45. Limitations for a mouse carcinogenesis study comparing acute Gamma-ray (3 Gy) and HZE ion (0.4 Gy) exposures Single doses—not dose responses Species differences—modeling carcinogenesis is a significant challenge Dose rate—technical limitations of protracted HZE exposures
46. Single doses—not dose responses Species differences—modeling carcinogenesis is a significant challenge Dose rate—technical limitations of protracted HZE exposures Limitations for a mouse carcinogenesis study comparing acute Gamma-ray (3 Gy) and HZE ion (0.4 Gy) exposures
47. Effect of dose rate on tumor histotype: Limitations for a mouse carcinogenesis study comparing acute Gamma-ray (3 Gy) and HZE ion (0.4 Gy) exposures
48. Effect of dose rate on tumor histotype: Limitations for a mouse carcinogenesis study comparing acute Gamma-ray (3 Gy) and HZE ion (0.4 Gy) exposures
49. Conclusions • The genetics of susceptibility often overlap for tumors in HZE ion and γ-ray irradiated populations *These findings support the current NASA risk model • Broader significance: 14 major effect QTL and 37 moderate effect QTL for 11 neoplasms have been mapped — many of which are novel and may represent new mouse models of tumorigenesis
50. Funding NASA / CSU NNX12AB54G NIH / CSU T32 OD 10437-15 NIH/ Jackson Labs R01GM070683 Acknowledgements CONTACT: Elijah Edmondson, DVM DACVP elijah.edmondson@gmail.com Committee Michael M. Weil Sue VandeWoude Doug Thamm Deborah Kamstock Christin Olver Colorado State University Weil Lab Chrissy Fallgren Elvin Garcia Paula Genik Todd Bass Debra Kamstock Jackson Labs Churchill Lab Daniel M. Gatti Oregon Health Science University John Belknap Columbia Norman Kleiman Eric Hall M.D. Anderson Cancer Center Kathy Mason Nancy Hunter
51. Funding NASA / CSU NNX12AB54G NIH / CSU T32 OD 10437-15 NIH/ Jackson Labs R01GM070683 Acknowledgements CONTACT: Elijah Edmondson, DVM DACVP elijah.edmondson@gmail.com Committee Michael M. Weil Sue VandeWoude Doug Thamm Deborah Kamstock Christin Olver Colorado State University Weil Lab Chrissy Fallgren Elvin Garcia Paula Genik Todd Bass Debra Kamstock Jackson Labs Churchill Lab Daniel M. Gatti Oregon Health Science University John Belknap Columbia Norman Kleiman Eric Hall M.D. Anderson Cancer Center Kathy Mason Nancy Hunter CODE: github.com/ elijahedmondson/ SLIDES: speakerdeck.com/ elijahedmondson/
52. Funding NASA / CSU NNX12AB54G NIH / CSU T32 OD 10437-15 NIH/ Jackson Labs R01GM070683 Acknowledgements CONTACT: Elijah Edmondson, DVM DACVP elijah.edmondson@gmail.com Committee Michael M. Weil Sue VandeWoude Doug Thamm Deborah Kamstock Christin Olver Colorado State University Weil Lab Chrissy Fallgren Elvin Garcia Paula Genik Todd Bass Debra Kamstock Jackson Labs Churchill Lab Daniel M. Gatti Oregon Health Science University John Belknap Columbia Norman Kleiman Eric Hall M.D. Anderson Cancer Center Kathy Mason Nancy Hunter CODE: github.com/ elijahedmondson/ SLIDES: speakerdeck.com/ elijahedmondson/