Redefining asthma: Identification of different types of ... · There are novel treatments under...

Professor Sven-Erik Dahlén (MD, PhD)

The Unit for Experimental Asthma and Allergy Research

at The National Institute for Environmental Medicine (NIEM)

and CfA-The Centre for Allergy Research

Redefining asthma: Identification of different types

of asthma to realise personalised treatments

1/3 of the population will get asthma, diabetes or cancer

during their lifetime(To et al Am J Respir Crit Care Med 181:337–343, 2010)

Asthma starts early and causes handicap all life

Large cumulated costs for society and individual

There are novel treatments under development!

Asthma Asthma

Diabetes

Cancer

Diabetes

Cancer

SED Diagnostics WS July 2017

Adrenaline

Oral steroidsTheophylline

Inhaled β2-agonists

Inhaled anticholinergics

Inhaled steroids

Long-actingdrugs

Anti-Leukotrienes

Anti-IgE

1900 1960 1970 1980 1990 2000

The evolution of asthma treatments

New biologics New combinations

2015

Sodium cromoglycate

Anti-IL5

Asthma

as painted

by a teenager

with asthma


What is asthma?

“ Airways that constrict

too much, too often and too easily,

resulting in impaired lung physiology

and quality of life”

Ann J. Woolcock


Bronchoscopy of subject with asthma

Stable condition Asthma attack

Smooth Muscle

Constriction

Airway inflammation

and oedema


Features in asthma:

• Bronchoconstriction

• Airway Hyperresponsiveness (AHR)

• Airway Inflammation

• Airway Remodelling


The Lancet 368:705, 2006


The current awareness

Asthma is not a homogenous disease

but a syndrome comprised of many

phenotypes!

Allergic Non-allergic

Eosinophilic Non-eosinophilic

Neutrophilic Paucigranulocytic

Fatal Near fatal

Brittle Chronic severe

Cough-variant Occupational

Leukotriene-dependent Exercise-induced

Aspirin/NSAID-intolerant Skier’s obstruction

Intermittent Steroid-insensitive

Identification of phenotypes requires improved diagnostics

Challenges for asthma sub-phenotyping studies

1) There are as of date no specific biomarkers ofasthma sub-phenotypes nor endotypes

2) The vast majority of studies are cross-sectional3) Few studies include interventions4) Most studies include only few biomarkers such

as IgE, eosinophils and exhaled NO

Kupczyk, Siddiqui, James, Walker & DahlénMS for ERJ


The rapid development of ‘omics technology platforms

Next “Genomics”The ’omics cascade

What can happen

What appears tobe happening

What makes ithappen

What has happened

& is happening


MAMild to

moderate asthman=88

HCHealthy controlsn=101

SASevere asthmanon-smokers;

n=311smokers; n=110

U-BIOPRED study of biomarkers in severe asthma


Extensive investigation of clinical and physiologal outcomesat baseline, including bronchoscopies

Broad collection of biomarker samples for omics

PI Sir Peter SterkAmsterdam


U-BIOPRED Clinical clusters; Lefaudeux et al JACI 2017


Biomarker:

A molecule that may be measured to

- predict risk to develop disease

- establish diagnosis

- reflect severity

- relate to mechanisms

- identify pathobiology

- indicate drug target engagement

- assess effects of treatment

- monitor adherance to treatment

- monitor environmental exposure

- Bronchoscopy material

(Biopsies, Fluids, Cells)

- Nasal material (Cells & Fluids)

- Induced sputum (Cells & Fluids)

- Saliva

- Blood

- Urine

- Exhaled air

Matrices for biomarkers of asthma

Invasive

Non-Invasive

FENO E-NOSE

PEXASED Diagnostics WS July 2017

CE-marked medical devicesHunt Developments UK

Nasosorption

fibres foam

Hansel TT, Johnston SL & Openshaw PJ. Lancet 2013; 381: 861-73


Induced sputum to monitor airway inflammation

Green et al The Lancet 2002

Maciej Kupczyk

PHENOTYPE STABILITY IN BIOAIR

Half of the patients changed phenotype defined

by sputum cells after 1 year of follow up

Phenotypes should not be defined by

single cross-sectional measurement

1 year

Kupczyk et al Allergy 2014


Induced sputum to monitor airway inflammation: Opportunities by the use of ‘omics and bioinformatics but induction, processing and technologies for analysis need to be developed

LC-IMS-MSE Analysis (duplicate injections)

Data pre-processing: quantitation,

normalisation, batch effect correction

Standard statistics: R, DanteR, Matlab,

OPLS

Data mining and unsupervised

grouping

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Neutrophil 1

Neutrophil 2

Mixed Group 1

Tru

e P

ositiv

e R

ate

False Positive Rate

Predictive biomarkers

Breathomics

FENOE-NOSE (VOCs) PExA

(particles)

'

Exhaled NO during repeated low dose allergen challenge

pp

b

40

50

60

70

80

90

budesonide+formoterol

formoterol

placebo

F M T O T F M T W

Weekday

Healthy

Asthma


Dahlén B et al. Eur Respir J.

2009;33:747-53

Fens N et al. Am J Respir Crit Care Med

2009:180:1076-82

Almstrand AC et al. Anal Chem

2009;81:662-668.

> 15 yrs

Less exacerbations Less steroid use

Triphasic pattern of FeNO levels during lifetime

20 40 60 80

25

20

15

10

5

Males

FemalesM: 15.7 years

F: 14.1 years

F: 45.4 years

M: 59.5 years

Fe

NO

(p

pb

)

Age

Tiago Jacinto, Kjell Alving et al. Journal of Breath Research 2017 in press

Patients/

Knowledge

based healthcare

IT-tools

Clinical

research

EMR

eCRF

Patient

reporting

QC-

Registers

Biobank

Better

health

Quality

assurance

New

treatments

New

diagnostics

New

knowledge

The near future: Internet-aided transfer of

biomarker status and airway function!

Internet-based monitoring of asthma

using point-of-care biomarkers:

Improves management

Empowers the patient

Cost-effective

Round the clock

Secures evidence-based

treatments


Increased use of electronic medical records (EMR) in the USand decreased costs of whole genome sequencing

Victor Ortega & Dara Torgerson AJRCCM 2017;195:412-414.

Exacerbations

Lung function monitoring:1 year of electronic diary data

Thorax 2013; 68(7):611-8.


Fluctuation Based Clustering: Constructing

lung function profiles for each patient

Delphine Meier, Edgar Delgado-Eckert & Urs Frey

http://www.proteinatlas.org/

>25000 antibodiestargeting

17000 proteins

Severe asthma (n= 362) vs mild-to-moderate asthma (n = 94) in U-BIOPRED

Volcano plot

Fold change = log2(median group 1 / median group 2)

Higher in severe asthmaHigher in mild asthma


Human Protein Atlas Affinity Proteomics in Blood Samples

Maria Mikus et al, MS in preparation

BronchoconstrictionPro-inflammatory

Oxidative stress Vascular & Plateleteffects + TXA2

bronchoconstriction

CysLTs Isoprostanes PGI2 TXA2 PGD2 PGE2

UPLC-MS/MS platform for urinary lipid mediatorsBalgoma et al Anal Chem 2013

BronchoconstrictionPro-inflammatory

Bronchoprotective

David Balgoma

Marcus Sjödin

Johan Kolmert

Craig Wheelock

Cristina Gomez


Log

(ng

/mm

ol c

reat

inin

e)

SevereAsthma

MildAsthma

Healthy Control

smokersnon-smokers

Urinary LTE4 monitoring CysLTs


Extreme value analysis

Log (

ng/m

mol cre

atinin

e)

0,0

5,0

10,0

15,0

20,0

25,0

Cohort A Cohort B Cohort C Cohort D

<25:th percentile

>75:th percentile

Sputum eosinophils (%)

(Patient selection)

n=410

High

Low SAns SAs MA HC

SA= Severe Asthma

ns=non-smoking

s=smokers

MA= Mild Asthma

HC=Healthy Controls


Ubiased consensus clustering

Final model: PAM, log2, Z-scores, Euclidean

distance, K=5


(28)

(16)

Molecular phenotyping in the U-BIOPRED study:

Patient clusters identified urinary lipid mediator profiles


Kolmert et al, MS in preparation!

Improved asthma phenotyping

Recommended strategies

Prioritise:1) longitudinal studies with repeated measures2) Interventional trials

Apply broad and unbiased biomarker panels

Perform careful clinical investigations

Aim for measures suitable at the point-of-care

Involve all stakeholders


Redefining asthma: Identification of different types of ... · There are novel treatments under...

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