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Approaches to modeling precursor lesions in cancer etiology: applications to testicular and colorectal cancers Duncan C. Thomas Victoria Cortessis University.

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Presentation on theme: "Approaches to modeling precursor lesions in cancer etiology: applications to testicular and colorectal cancers Duncan C. Thomas Victoria Cortessis University."— Presentation transcript:

1 Approaches to modeling precursor lesions in cancer etiology: applications to testicular and colorectal cancers Duncan C. Thomas Victoria Cortessis University of Southern California

2 Cancer Epidemiol Biomark Prev 2013:22(4): 521-7

3 Statistics Sweden maintains a ‘Multigeneration Register’ in which offspring, born in Sweden in 1932 and later, are registered with their parents (as declared at birth) and they are organized as families (Hemminki et al, 2001a). The Family-Cancer Database, which covered years 1961- 2000 from the Swedish Cancer Registry, included 4082 testicular cancers in sons of ages 0–68 years and 3878 fathers with testicular cancer (Table 1). Seminoma accounted for 49.8% and teratoma 48.4% in sons, while in fathers the proportions were 59.1 and 38.2%,

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7 J Clin Edocrin Metab 2012;92:E393-9

8 Dependent Data! Between two phenotypes Within families Between two organs

9 CO l CO r TC l TC r G1G1 G2G2 G3G3 Conceptual DAG for Genetic Etiology of Cryptorchidism and Testicular Germ Cell Tumors

10 Schemes for Defining Testicular Phenotype SchemeDefined PhenotypesParametersExamples of Use TC2TC-TC+marginal G2basis of GWAS scans of TGCT TC3TC-TCuTCbmarginal G2, marginal F2 post scan stratified analyses of TGCT TC2CO2TC- CO- TC- CO+ TC+ CO- TC+ CO+ marginal G 1, marginal G 2 post scan stratified analyses of TGCT TC3CO3TC- CO- TC- COu TC- COb TCu CO- TCu COu TCu COb TCb CO- TCb COu TCb COb marginal G 1, marginal F 1, marginal G 2, marginal F 2 equivalent to model for precursor and disease of unpaired organ TC4CO4TC- CO- TC- COl TC- COr TC- COb TCl CO- TCl COl TCl COr TCl COb TCr CO- TCr COl TCr COr TCr COb TCb CO- TCb COl TCb COr TCb COb G 1, F 1, G 2, F 2, G 3 present analysis

11 FamiliesIndividualsN/family (max) Phase 017,844 1 (1) Phase 1*5,70232,9494.8 (29) Phase 2**69723,86733 (118) Phase 2 w SNPs5271,6393.1 (16) Total17,51464,315 4,994 697 11,824 4,994 696 35,482 23,143 * Consenting consenting probands who returned a family history questionnaire and their first-degree relatives ** Probands with bilateral TC or unilateral TC plus either a personal history of CO or a family history of CO or TC Families Individuals

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13 CO il CO ir TC il TC ir G i1 G i2 G i3 X i1 X i2 CO jl CO jr TC jl TC jr G j1 G j2 G j3 X j1 X j2

14 Model Form and Fitting Penetrance models logit Pr(CO il =1) = α 0 + α 1 G i1 + α 2 X i1 logit Pr(TC il =1) = β 0 + β 1 G i2 + β 2 X i2 + γ 1 CO il + γ 2 CO il × G i3 MCMC fitting: – Update G i and X i given CO i, TC i, G (-i), X (-i), e.g. Pr(G i1 | CO i1,G (−i)1, α)  Pr(CO i1 | G i1, α) Pr(G i1 | G (−i)1 ) = N [ μ(G i1 ) + α (CO i* − 2p i ) V(G i1 ), V(G i1 ) ] – Update α,β,γ conditional on G,X,CO,TC

15 Ascertainment Correction Prospective ascertainment-corrected likelihood Implemented by random sampling y r =(CO,TC) vectors meeting ascertainment criteria and applying importance sampling to compute AR(θ’:θ) Works for estimating penetrance parameters, not MAFs or LD ( would require sampling (y,g|Asc) )

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18 Full model estimates by subset of data

19 GWAS hits from literature Available on 1639 individuals from 527 phase 2 families

20 Updating the MGs Linked MGs are updated conditional on subject’s and immediate relative’s measured genotypes (if any), subject’s own phenotype, all other covariates, and model parameters – Assuming no recombination – Assuming LD between GWAS and causal SNPs – So far unable to jointly estimate LD, MAFs, and RRs.

21 Linked MG Univariate Effects CO model TC baseline CO->TC transition

22 Estimates of linked gene effects by whether PG, FR, residual MG included

23 Estimates of PG, FR, residual MG effects across alternative models

24 GeneSNPlnRR (S.E.) CO model UCK2rs3790672– 0.44 (0.41) TERT/CLPT1rs4635969– 1.74 (0.44) CNPErs4699052+ 1.04 (0.41) Frailty + 3.28 (0.20) TC baseline risk model SPRY4rs4624820 – 0.39 (0.22) KITLGrs995030– 0.51 (0.24) UCK2rs6703280+ 0.46 (0.21) Frailty+0.41 (0.19) CO to TC transition model CO status+ 1.17 (0.29) BAK1rs210138 + 0.93 (0.70) TERT/CLPT1 rs4635969 +1.26 (0.71) Frailty+1.27 ((0.45)

25 Wish list for TC-CO paper Linkage between 3 major genes and correlation between 3 polygenes Age-dependent frailty model for TC Additional genotype data at GWAS hits Covariates: birth order, left/right side, histology, race/ethnicity Better treatment of missing data and selection bias

26 … And now for something completely different! Colorectal Polyps and Cancer Similar model structure, but set in a time-to-event framework Combining 3 (simulated) datasets – Case-control data on prevalent polyps – Short-term longitudinal study of subsequent polyps – Cohort study of cancer incidence Secondary aim to model folate metabolism combining ODEs with statistical model

27 Y 10 u 21 u 20 U,Y2U,Y2 X1X1 X3X3 X2X2 Y1lY1l First discovered adenoma Recurrent adenomas Carcinoma from adenoma Carcinoma without prior adenoma Observable carcinoma and adenoma history X = Generic vector of risk factors: exposures, genes, interactions, predicted metabolite concentrations and reaction rates, etc. denotes a deterministic link function Z2Z2 Experimental animal data t1nt1n Complete adenoma history T0T0 TlTl λ(α,k) μ(γ,m 1 ) ν(δ,m 0 ) Time at screening Follow-up times

28 Model Details Polyps prevalence λ i (t) = t k exp(α 0 + α 1 X i1 + a i ) Polyps recurrence Y 1l = Σ j I(T il < t ij ≤ T i,l+1 ), l = 1,…,N fu Cancer incidence μ i (u 1 ) = exp(γ 0 + γX i2 ) Σ j|t ij < u1 (u 1 - t ij ) m 1 ν i (u 0 ) = exp(δ 0 + δX i3 ) u m 0

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30 Conclusions Joint modeling of precursors and cancer is feasible and avoids some potential nasty biases: – E.g., polyps & cancer in family studies (under review) Can be informative about genetic co-determinants of two traits

31 Mechanistic Modeling of Folate Pathway System of ODEs for metabolism – Duncan, Reed & Nijhout, Nutrients 2013 – Ulrich et al, CEPB 2008 Combined with stochastic models for disease and inter-individual variation in metabolism given genotypes – Thomas et al, Hum Genom 2012 Simulation of “virtual population” of 10K individuals with genotypes, exposures, enzyme activity rates, intermediate metabolites, and disease Fitting by Approximate Bayesian Computation – Jung & Marjoram, Stat Appl Genet Mol Biol 2011

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33 X G V C p,e Y B μ,σ α,ω φ β exposures genotypes enzyme reaction rates metabolites biomarkers disease phenotypes precursor & enzyme input indicators

34 C ms C p mr s V e mr s α mrs ω ms r = 1,…,P m, s = 0,2 Cm1Cm1 XmXm α m01 α m0s

35 Definitely a work in progress !

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42 ßSNPCrudeAdjusted for PG and FR Also adjusted for unlinked MG Unlinked residual MG estimate Genes for CO SPRY4rs4624820–0.37 (0.38)–0.01 (0.41)+0.06 (0.36)+1.90 (0.64) BAK1rs210138–1.31 (0.44)–0.72 (0.39)–0.87 (0.40)+2.25 (0.34) KITLG rs1508595–0.29 (0.65)–0.16 (0.44)–0.15 (0.56)+1.48 (0.39) rs995030–0.54 (0.43)–0.15 (0.44)–0.23 (0.39)+2.17 (0.31) UCK2 rs4657482–1.01 (0.31)–0.59 (0.40)–0.54 (0.37)+2.87 (0.30) rs3790672–1.14 (0.38)–0.69 (0.45)–0.82 (0.41)+2.45 (0.43) rs6703280+0.81 (0.35)+0.38 (0.44)+0.14 (0.44)+2.27 (0.43) TERTrs4635969–2.01 (0.41)–1.23 (0.34)–1.72 (0.49)–1.31 (1.03) CNPErs4699052+1.83 (0.49)+0.82 (0.37)+0.55 (0.47)+2.15 (0.31) BNC2rs3814113–0.78 (0.33)–0.31 (0.39)–0.35 (0.47)+1.56 (0.69) Genes for TC baseline risk SPRY4rs4624820–0.35 (0.21)–0.28 (0.27)–0.27 (0.27)+0.00 (0.23) BAK1rs210138+0.27 (0.20)+0.15 (0.33)+0.21 (0.31)+0.05 (0.23) KITLG rs1508595–0.27 (0.25)–0.31 (0.32)–0.24 (0.32)+0.02 (0.21) rs995030–0.46 (0.25)–0.48 (0.32)–0.48 (0.30)–0.01 (0.23) UCK2 rs4657482+0.08 (0.22)+0.05 (0.25)+0.05 (0.26)–0.05 (0.23) rs3790672+0.01 (0.21)+0.15 (0.27)+0.06 (0.27)+0.07 (0.22) rs6703280+0.13 (0.48)–0.04 (0.59)+0.01 (0.33)+1.34 (0.24) TERTrs4635969+0.10 (0.25)+0.09 (0.23)+0.12 (0.25)–0.05 (0.23) CNPErs4699052–0.13 (0.23)–0.24 (0.28)–0.20 (0.28)+0.06 (0.24) BNC2rs3814113–0.07 (0.20)–0.05 (0.24)+0.01 (0.25)+0.00 (0.26) Genes for CO to TC transition SPRY4rs4624820–0.04 (0.65)+0.08 (0.62)+0.76 (0.85)+1.23 (1.03) BAK1rs210138+0.29 (0.59)+0.31 (0.62)+0.05 (0.85)–0.43 (0.86) KITLG rs1508595+0.19 (0.61)+0.05 (0.59)+0.09 (0.98)–0.23 (1.36) rs995030+0.07 (0.64)+0.06 (0.59)+0.48 (0.65)+0.63 (0.64) UCK2 rs4657482+0.07 (0.63)+0.15 (0.63)+0.70 (0.77)+0.88 (0.91) rs3790672–0.10 (0.59)+0.20 (0.64)–0.42 (0.78)–0.76 (0.79) rs6703280+0.17 (0.58)+0.29 (0.63)+0.77 (0.66)+0.78 (0.62) TERTrs4635969+0.49 (0.53)+0.43 (0.61)+1.73 (0.77)–1.91 (0.77) CNPErs4699052+0.04 (0.56)+0.06 (0.59)–0.33 (0.93)–0.51 (1.10) BNC2rs3814113+0.18 (0.59)+0.18 (0.60)–0.93 (1.29)–1.79 (1.66)


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