Susceptibility Prediction in Familial Colon Cancer Giovanni - - PowerPoint PPT Presentation

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Susceptibility Prediction in Familial Colon Cancer Giovanni - - PowerPoint PPT Presentation

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Susceptibility Prediction in Familial Colon Cancer Giovanni Parmigiani gp@jhu.edu Cancer Risk Prediction Models: A Workshop on Development, Evaluation, and Application NCI, May 2004 C ROSS -P LATFORM C OMPARISON AND V ALIDATION 1

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SLIDE 1

Susceptibility Prediction in Familial Colon Cancer

Giovanni Parmigiani gp@jhu.edu Cancer Risk Prediction Models: A Workshop on Development, Evaluation, and Application NCI, May 2004

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SLIDE 2

CROSS-PLATFORM COMPARISON AND VALIDATION 1

SUSCEPTIBILITY PREDICTION MODELS

Family history can be very informative about the presence of a mutation Predicting mutations is possible and useful in two contexts:

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SLIDE 3

CROSS-PLATFORM COMPARISON AND VALIDATION 1

SUSCEPTIBILITY PREDICTION MODELS

Family history can be very informative about the presence of a mutation Predicting mutations is possible and useful in two contexts: HIGH RISK CLINICS: Counseling about testing decisions Interpretation test outcomes for individuals Predicting who will develop cancer

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SLIDE 4

CROSS-PLATFORM COMPARISON AND VALIDATION 1

SUSCEPTIBILITY PREDICTION MODELS

Family history can be very informative about the presence of a mutation Predicting mutations is possible and useful in two contexts: HIGH RISK CLINICS: Counseling about testing decisions Interpretation test outcomes for individuals Predicting who will develop cancer GENE CHARACTERIZATION RESEARCH: Selecting high risk subjects Building measures of susceptibility

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SLIDE 5

CROSS-PLATFORM COMPARISON AND VALIDATION 2

POLYGENETIC / ENVIRONMENTAL CHANCE CLUSTER OTHER FAMILIES "HIGH RISK" FAMILIES MLH1 MSH2 FAP

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SLIDE 6

CROSS-PLATFORM COMPARISON AND VALIDATION 3

EMPIRICAL MODELING

P    Positive Genetic Test Pedigree Information   

  • Correlates genetic testing results to features of family history
  • Relies on AI/statistics to infer

the genotype | phenotype relationship and the mode of inheritance

  • Generally gives broad classes of families

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SLIDE 7

CROSS-PLATFORM COMPARISON AND VALIDATION 4

MENDELIAN MODELING

P    Deleterious Mutation at Susceptibility Gene Pedigree Information   

  • Derives carrier probabilities from genetic parameters
  • Relies on statistics to infer

the phenotype | genotype relationship

  • Relies on Mendel’s laws for the mode of inheritance.

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SLIDE 8

CROSS-PLATFORM COMPARISON AND VALIDATION 5

RELATIONSHIP BETWEEN SCALES OF EMPIRICAL AND MENDELIAN PREDICTIONS

P    Positive Genetic Test Pedigree Information    = β × P    Deleterious Mutation at Susceptibility Gene Pedigree Information    β: Test Sensitivity; Specificity assumed complete EMPIRICAL MENDELIAN

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SLIDE 9

CROSS-PLATFORM COMPARISON AND VALIDATION 6

LOGIC BEHIND MENDELIAN RISK PREDICTION: notation

γ Genotype vector. γ∗ (the 0 vector) indicates the wildtype. θ Penetrance-related parameters π Prevalence-related parameters H History of relevant phenotypes for an individual r = 1, . . . , R Index of relative of a counselee within a family (counselee indexed by 0) F A family history, vector F = (H0, H1, . . . , HR) T Genetic test result Carrier Probability: p(γ0|H0, H1, . . . , HR, π, θ)

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SLIDE 10

CROSS-PLATFORM COMPARISON AND VALIDATION 7

LOGIC BEHIND MENDELIAN RISK PREDICTION: general approach

Updating: p(γ0|H0, . . . , HR, π, θ) = p(γ0|π)p(H0, H1, . . . , HR|γ0, θ, π)

  • all γ0’s p(γ0|π)p(H0, . . . , HR|γ0, θ, π).
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SLIDE 11

CROSS-PLATFORM COMPARISON AND VALIDATION 7

LOGIC BEHIND MENDELIAN RISK PREDICTION: general approach

Updating: p(γ0|H0, . . . , HR, π, θ) = p(γ0|π)p(H0, H1, . . . , HR|γ0, θ, π)

  • all γ0’s p(γ0|π)p(H0, . . . , HR|γ0, θ, π).

Integration: p(H0, H1, . . . , HR|γ0, θ, π) =

  • all γ1 . . . γR’s

p(H0, . . . , HR|γ0, . . . γR, θ)p(γ1, . . . , γR|γ0, π).

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CROSS-PLATFORM COMPARISON AND VALIDATION 8

LOGIC BEHIND MENDELIAN RISK PREDICTION: sources of information

p(γ0) Prevalence studies p(γ1, . . . , γR|γ0) Mendel’s laws + Prevalence Studies p(H0, . . . , HR|γ0, . . . γR) Penetrance studies p(H0, . . . , HR|γ0, . . . γR) =

  • r p(Hr|γr)

Conditional independence

to HNPCC example ◭ ◭ × i 2p ◮ ◮

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SLIDE 13

CROSS-PLATFORM COMPARISON AND VALIDATION 9

CRCAPRO

GENOTYPE: MLH1 & MSH2 FAMILY HISTORY: I-st and II-nd degree relatives of counseland Colorectal and endometrial cancer history (m & f) MSI testing Age of onset, age of death or current age PENETRANCES: Meta-analysis. Independent estimates in progress using Creighton data. PREVALENCES: Meta-analysis.

Hopkins GI SPORE. ◭ ◭ × i 2p ◮ ◮

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CROSS-PLATFORM COMPARISON AND VALIDATION 10

♥

65

  • 96

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79

♥

  • 85

♥

  • 70

87

⑦

CRC 60

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  • CRC 71
  • 94

50

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23

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47 12

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16

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19

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22

  • ✒

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CRC 57

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32

Pedigree Mendelian Wijnen 1 As in Figure above 0.028 .0019 2 No information about father 0.277 .0019 3 Father with CRC@60, pat. aunt unaff. 0.357 .0019 4 Sister with EC@50 0.597 .0099 5 Living maternal aunt with EC@50 0.057 .0099 ◭ ◭ × i 2p ◮ ◮

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CROSS-PLATFORM COMPARISON AND VALIDATION 11

SOFTWARE

BayesMendel: R environment for Mendelian risk prediction, including:

  • BRCAPRO
  • CRCAPRO
  • Sets of genetic parameters that are specific to ethnic groups
  • Functionality to build Mendelian Models for other syndromes

CaGene:

  • Inclusion of CRCAPRO (via BayesMendel) completed
  • Legal details pending

web search for BayesMendel

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SLIDE 16

CROSS-PLATFORM COMPARISON AND VALIDATION 12

> library(BayesMendel) > data(testfam) > testfam [1,] 1 1 0 3 2 0 0 57 57 0 0 0 [2,] 2 4 0 9 8 0 1 70 69 0 0 0 ..... > data(HNPCCpenet) > crcapro(testfam,penetrance=HNPCCpenet) [,1] [,2] [,3] [1,] 2.498343e-18 2.923043e-13 1.895220e-08 [2,] 1.813742e-13 2.073328e-08 1.100074e-03 [3,] 6.683116e-09 6.653272e-04 9.982346e-01

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SLIDE 17

CROSS-PLATFORM COMPARISON AND VALIDATION 13

VALIDATION

Data: 60 families tested for MSH1 and MLH2 at JHU. Goal: Compare CRCAPRO to Wijnen OVERALL PERFORMANCE by RMSE CRCAPRO 0.30 Wijnen 0.44 LOGISTIC PREDICTION of POSITIVE TEST RESULT Estimate Std. Error z value Pr(>|z|) (Intercept)

  • 2.7342

0.7224

  • 3.785 0.000154 ***

CRCAPRO 2.9138 1.0087 2.889 0.003867 ** Wijnen 0.6476 1.5523 0.417 0.676549

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SLIDE 18

CROSS-PLATFORM COMPARISON AND VALIDATION 14

CALIBRATION

CRCAPRO Wijnen

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

MODEL−BASED PROBABILITY PROPORTION POSITIVE

  • 0.0

0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

MODEL−BASED PROBABILITY PROPORTION POSITIVE

  • ●
  • RED: prior to adjustment for mutation screening sensitivity

GREEN: after adjustment for mutation screening sensitivity

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SLIDE 19

CROSS-PLATFORM COMPARISON AND VALIDATION 15

DISCRIMINATION: ROC curves

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

FALSE POSITIVE FRACTION TRUE POSITIVE FRACTION

CRCAPRO Wijnen

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SLIDE 20

CROSS-PLATFORM COMPARISON AND VALIDATION 16

Credits Lab: Karl Broman, Sining Chen, Ed Iversen, Wenyi Wang Clinical collaborators: Ken Kinzler, Francis Giardiello, David Euhus SPORE collaborations: Chris Amos, Steve Gruber, Sapna Syngal, Patrice Watson

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