Integration of multiple sources of evidence in clinical classification of vus
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Integration of multiple sources of evidence in clinical classification of VUS. David Goldgar University of Utah School of Medicine. What do we mean by a “VUS”?. Sequence variant in a gene with a clearly established role in a given disease

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Integration of multiple sources of evidence in clinical classification of VUS

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Integration of multiple sources of evidence in clinical classification of VUS

David Goldgar

University of Utah School of Medicine


What do we mean by a“VUS”?

  • Sequence variant in a gene with a clearly established role in a given disease

    • Usually rare in the general population (or in the clinically tested population)

    • If pathogenic would be clinically important to the individual carrying the variant

  • Typically missense, intronic, or in-frame deletions (but could include others)


Evidence potentially useful for classification of VUS

  • Direct:

    • Co-segregation of VUS with disease in pedigrees

      • Powerful direct evidence but often difficult to get additional samples from family members.

    • Co-occurrence (in trans) with deleterious mutations

      • Only useful if homozygotes/compound heterozygotes are ~embryonically lethal

    • Distribution of family history of probands carrying a VUS

  • Indirect:

    • Severity of amino acid change and evolutionary conservation of wt residue

    • Effects on protein structure (if known)

    • Functional evaluation in model systems

    • Other evidence relevant to cancer susceptibility genes:LOH, pathology, expression array/CGH signatures, MSI


Genetic vs. Functional/Sequence-based Approaches

  • Genetic approaches normally require multiple observations to be useful;

  • However, most VUS occur <5 times

  • Functional and sequence-based analyses can be done (in theory) on any variant

  • Relationship between functional assay and disease risk typically unknown

  • If valid relationships could be established, many more VUS could be classified


Select UV

Quantifiable Individual

or family data

Co-occurrence

Family History

Co-segregation

More data

LR 1

LR 2

LR 3

Combined evidence

 (LRi)

LR>1000

or LR <0.01

Yes

UV classified

Validation set

for functional

and conservation data

No

Incorporate evidence

from conservation and

functional data using

existing models

Initial model

Refine model

No

LR>1000

or LR <0.01

Yes

Goldgar et al. AJHG 75:535-44. 2004.


Easton et al. AJHG 2007


Align-GVGD

  • An extension of the original Grantham Difference to protein multiple sequence alignments. It uses two variables, GV and GD.

  • Grantham Variation (GV)

  • A quantitative measure of the range of variation present at a position in a protein multiple sequence alignment.

  • GV=0: position is invariant

  • GV> ~60: non-conservative substitution is tolerated

  • Grantham Deviation (GD)

  • A quantitative measure of the fit between a missense substitution and the range of variation observed at its position in the protein.

  • GD=0: substitution is within the observed range of variation

  • GD> ~60: substitution is non-conservatively beyond the range of variation

  • Website http://agvd.iarc.fr

50:0:0


Analysis of rare missense substitutions:Distribution of risk in the GV-GD plane

C65

C55

C45

C35

Risk estimates

C25

>4.00

C15

3.00-4.00

2.50 to 3.00

2.00 to 2.50

1.67 to 2.00

1.33 to 1.67

1.10 to 1.33

C0

0.90 to 1.10

≤ 0.90

=0.81

=0.66

=0.29

GD

=0.00

GV

Tavtigian et al., Human Mutation, (almost) in press

50:0:0


5 x GAL4 bs

luciferase

Transcription Activation Assay in Mammalian cells (293T)

controls

-

-

+

+

120

100

LOW

RISK

80

%WT (luc activity)

60

40

HIGH

RISK

20

0

-Gal4

WT

F1695L

Y1853X

A1830T

L1844R

P1771L

F1662S

R1726G

R1751Q

H1746N

M1783T

S1613G

R1751P

M1783L

P1859R

M1775R

 Raw data normalized by Renilla luciferase driven by a constitutive promoter. Results from triplicate experiments in which a Gal4 DBD: BRCA1 1396-1863 is co-transfected with the reporter (shown above graph) are plotted as percent of wild type activity.

Marcelo Carvalho & Alvaro Monteiro


Estimation of sensitivity and specificity of functional assays (simple approach)

  • For each variant with functional data, use prior probability based on sequence analysis and log-odds from genetic data to get posterior probability of being pathogenic

  • Sample each variant as being pathogenic or neutral from posterior distribution

  • Calculate sensitivity and specificity etc., from this simulated data set

  • Average over many replicates to get estimated sensitivity/specificity and confidence interval

  • For Transcriptional Activation assay, estimates were 0.85 (0.67 - 1.0) for sensitivity and 0.65 (0.58 - 0.75) for specificity


The Lyon Meeting on VUS4-5 February, 2008

  • Organised by Sean Tavtigian at IARC

  • Goal to have a highly focused knowledge transfer exercise representing diverse opinions

  • Representatives from MMR, p16, and BRCA worlds

  • Assembled expertise: clinical cancer genetics, functional assays, sequence analysis, genetic epidemiogy, etc.

  • International: US, UK, NL, Australia, France


Series of papers to be written for Human Mutation

  • Introduction to the series

  • Genetic variant classification using clinical and epidemiological data

  • In vitro and ex vivo assessment of functional effects of genetic variants

  • Splice site alteration assessment

  • Tumour characteristics as an analytic tool

  • Integration - the nuts and bolts of combining across data types

  • Locus specific databases

  • Clinical utility and risk communication


Issues in Integration

  • Transferability of results from one kind of mutation to others (e.g., truncating to missense)

    • LOH, Pathology, Co-occurrence

  • Choice of appropriate prior probability

  • Independence of evidence from different sources

  • Incorporating discrete types of evidence into a probabilistic framework

  • Combining everything -

    • Mixture Models via MCMC

    • Cluster analysis type methods


How to disseminate VUS information to the research and clinical communities

  • Should research information be separate/different from clinical use?

  • Qualitative vs. Quantitative information

  • What is the appropriate place to store this information?

    • Locus specific databases, e.g. BIC?

    • clinical databases?

    • Human Variome database?

    • All of the above?

  • How much detail of the evidence should be presented?


Issues in Transfer of Knowledge to Clinical Practice

  • What are appropriate thresholds for causality and neutrality respectively?

  • What should be reported?

    • Only those variants that have been definitively (by above threshold) classified?

    • Should the ‘current’ odds of causality?

    • Intermediate discrete categories, e.g., likely deleterious’, probably neutral’?

  • What if variants confer intermediate risk? Can the methods be adapted to estimate risks? Would it be useful?


Unified Framework for Genetic Testing (including VUS)

  • Prior probability of an affected proband being a carrier of a pathogenic mutation in gene X based on proband phenotype (including e.g., pathology, MSI, IHC, etc.) and family history and locus heterogeneity;

    • Could be model based

  • Add result of genetic testing of proband

    • wildtype or sequence variant (excluding common polymorphisms)

  • Add variant specific information

    • Sequence analysis (A-GVGD, SIFT)

    • Functional/structural assay if available and quantifiable

    • Co-segregation analysis if additional family members available to be tested


Unified Framework: Translation into disease risk

  • From previous information can calculate the posterior probability that the individual carries a pathogenic mutation or wildtype (or a variety of intermediate risks if reliably estimated)

  • Then disease risk for an at-risk relative of a proband discovered to have variant V is:

    If V+ : P(V=path)P(D|path)+P(V=wt)P(D|wt; fam hx)

    If V- : P(V=path)P(Dpop)+P(V=wt)P(D|wt; famhx)

  • Could be integrated into a single Web-based tool (including sequence, family history, co-segregation, family hx, environmental factors, etc.)


Acknowledgements

BRCA2 functional assays: F. Couch, D. Farrugia, M. Argawal, L. Wadum

Data Preparation: A. Deffenbaugh, D. Bateman, C. Frye – Myriad Genetics

Sequence Analysis: S. Tavtigian, A. Thomas, G. Byrnes

BRCA1 functional assays: A. Monteiro, M. Carvalho – Moffitt Cancer Center

Statistical Aspects: D. Easton, D. Thompson – Cambridge

E. Iversen – Duke University

The BIC steering committee;

Grants P50CA116201 & R01CA116167 ACS:RSG-040220-01-CCE (FC)

and CA92309 (AM)


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