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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

Select UV

Quantifiable Individual

or family data


Family History


More data

LR 1

LR 2

LR 3

Combined evidence

 (LRi)


or LR <0.01


UV classified

Validation set

for functional

and conservation data


Incorporate evidence

from conservation and

functional data using

existing models

Initial model

Refine model



or LR <0.01


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



  • 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



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





Risk estimates





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


0.90 to 1.10

≤ 0.90







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



5 x GAL4 bs


Transcription Activation Assay in Mammalian cells (293T)











%WT (luc activity)
























 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.)

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)