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Patrick Sosnay on behalf of the CFTR2 team Johns Hopkins University

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Presentation on theme: "Patrick Sosnay on behalf of the CFTR2 team Johns Hopkins University"— Presentation transcript:

1 CFTR2 – Part 2 Using CFTR2 to examine how CFTR mutations affect clinical outcome
Patrick Sosnay on behalf of the CFTR2 team Johns Hopkins University Perdana University Graduate School of Medicine

2 CFTR Mutations Cystic Fibrosis Genotype Phenotype

3 CFTR Mutations Cystic Fibrosis CFTR Gene CFTR Protein Epithelial Cells
Organs Clinical Traits

4 Pancreatic status differs by the type of mutation
Mutation Type Example “severe” Pancreatic Insufficient “mild” Pancreatic Sufficient Missense G551D, R347H Single amino acid deletion F508del Stop Codon G542X Splice Junction 1717-1G>A Frameshift 3659delC Kristidis P, Bozon D, Corey M et al. Am J Hum Genet

5 Pancreatic status can be predicted from mutation class
CFTR I. RNA Expression II. Folding and modification IV. Channel function V. Reduced expression III. Channel activation Welsh and Smith, Cell, 1993 PS Golgi Rough endoplasmic reticulum Nucleus PI

6 Incomplete correlation between genotype and phenotype
Good correlation with pancreatic status (insufficient vs. sufficient) Moderate correlation with sweat chloride concentration when patients are grouped according to pancreatic status Weak correlation with lung function

7 Individual mutations do not appear to correlate with lung function
The Cystic Fibrosis Genotype-Phenotype Consortium NEJM 1993

8 Specific Genotype Specific Trait Mutations grouped by type or class Discrete variable: Pancreatic sufficient or pancreatic insufficient

9 ? Specific Genotype Specific Trait
Continuous variable: Sweat chloride or lung function Individual mutations

10 Clinical Data from CFTR2

11 Continuous variable: Sweat chloride or lung function
CFTR Gene CFTR Protein Epithelial Cells Organs Clinical Traits Use CFTR function measurements in cell lines as a way of describing genotype Specific Genotype Specific Trait Continuous variable: Sweat chloride or lung function 1100 mutations in CFTR2

12 CFTR chloride channel function correlates with sweat chloride concentration of patients that carry the same mutations R1070Q M470V I148T

13 CFTR chloride channel function correlates with sweat chloride concentration of patients that carry the same mutations

14 The relationship between log10 CFTR function and sweat chloride is linear
r=0.78, p<0.001

15 The relationship between log10 CFTR function and lung function is linear
r=0.56, p<0.001

16 Consequences of exponential relationship on lung function and sweat chloride concentration
Mean lung function increases 8% predicted (95% CI 4-12) 0- 5% function Mean sweat Chloride decreases 27 mEq/L (95% CI 20-33)

17 Consequence of exponential relationship on lung function and sweat chloride concentration
Mean lung function increases 1.4% predicted (95% CI ) Mean sweat chloride decreases 4.7 mEq/L (95% CI ) 5- 10% function

18 Environment Other genes
Why is there greater change in sweat chloride than in lung function with restoration of CFTR function? CFTR Gene CFTR Protein Epithelial Cells Organs Clinical Traits Environment Other genes CFTR channel function plays a greater role in determining sweat chloride concentration than FEV1

19 Opportunities for future studies
Collection of clinical data from patients in other regions To examine global variability To inform disease liability of rare variants Correlate genotype with longitudinal measures of lung function and other complications of CF (e.g. lung infection) Examine the relationship of other CFTR functions (e.g. ENaC regulation, HCO3- transport) with sweat chloride concentration, pancreatic status, and lung function

20 Summary Data from nearly 40,000 CF patients in the CFTR2 database has been instrumental in: Increasing the list of clinically, functionally and genetically vetted ‘CF-causing’ mutations from 23 to ~160 (more to follow..) Demonstrating that CFTR chloride channel function displays an exponential relationship with sweat chloride concentration and lung function. Revealing that improvement in low function CFTR mutations will have the greatest effect on CF phenotype.

21 With tremendous gratitude
Chris Penland Preston Campbell Bruce Marshall Leslie Hazle Cindy George Bob Beall Mentors: Garry Cutting Rachel Karchin Charlie Wiener JHH CF Team: Michael Boyle, Noah Lechtzin, Christian Merlo, Meghan Ramsay, Sue Sullivan, Marsha Davis, Rebecca Smith, Karen VonBerg, Kathie Bukowski CFTR2 Team: Michelle Lewis Karen Siklosi Johanna Rommens Mary Corey Ruslan Dorfman Julian Zielenski Carlo Castellani Fred Van Goor Phil Thomas, Margarida Amaral, Claude Ferec, Milan Macek, Phil Farrell Adi Gherman, Kyle Kaniecki, Jessica LaRusch, Darci Ferrer, Dave Masica, Kathleen Naughton, Neeraj Sharma


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