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

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

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

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 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


Integration of multiple sources of evidence in clinical classification of vus

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.


Integration of multiple sources of evidence in clinical classification of vus

Easton et al. AJHG 2007


Integration of multiple sources of evidence in clinical classification of vus

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


Integration of multiple sources of evidence in clinical classification of vus

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


Integration of multiple sources of evidence in clinical classification of vus

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

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 vus 4 5 february 2008

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

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

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

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

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

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

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

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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