Download Predictive, Preventive and Personalized Medicine

PPPM (Predictive, Preventive and Personalized Medicine)
as a Novel Avenue and a Source of New Translational Tools
to Predict and to Prevent Chronic Autoimmune Disorders
Dr Sergey Suchkov, MD, PhD
Professor in Immunology & Medicine
I.M.Sechenov First Moscow State
Medical University and
A.I.Evdokimov Moscow State Medical &
Dental University,
Moscow, Russia
ISPM (International Society for
Personalized Medicine), Tokyo, Japan
PMC (Personalized Medicine Coalition),
Washington, DC, USA
EPMA (European Association for
Predictive, Preventive and
Personalized Medicine),
Brussels, EU
Dr Trevor Marshall, MD, PhD
Professor in Immunology
Autoimmunity Foundation, LA, CA, USA
The Train of PIFAS
and associated Subclinical Disorder and
Clinical Illness
Autoimmune diseases (ADs) are a family of complex heterogeneous disorders
with similar underlying mechanisms characterized by immune responses
against self.
Autoimmune diseases are the third leading cause of morbidity and mortality in
the industrialized world!
Most of the autoimmune diseases follow a chronic course that can be compared
with a lengthy train ride. And a substantial need does exist for developing and
validating a range of biomarkers that would address a number of important
unmet clinical needs in the autoimmunity field.
The journey mentioned begins based on pre-existing genetic equipment
followed by the onset of a self directed autoimmune response. And the earliest
evidence that the journey has begun is often found in the form of rising
autoAbs. The pace of the journey can be ascertained by the soluble mediators
of autoimmunity found in the blood in the form of cytokines and autoAbs again
as well.
If the train spirals out of control, a train wreck may follow. We may think of the
autoimmune disease as the train wreck that follows this long journey of
autoimmunity. It is a journey that can be timed in hours or days, but more
frequently re-quires weeks, months or years. Generally it is only when the train
wreck has occurred that the disease reaches the threshold of attention by the
patient and the physician. Too often diagnosis and therapy of an autoimmune
disease is delayed only after irreversible damage has taken place.
There are many risk factors for autoimmune diseases, other
than cytokines and autoAbs; some of them genetic and
others related to the patients lifestyle, diet and environment.
Moreover, autoimmune diseases have a genetic predisposition.
The abnormal immune response probably depends upon
interactions between susceptibility genes to secure
surveillance
over
the
immune
control,
and
various
environmental factors.
The most potent genetic influence on susceptibility to autoimmunity is the
major histocompatibility complex (MHC/HLA), which has been known for over
two decades to affect susceptibility to a variety of autoimmune disorders.
Different HLA alleles are linked to different autoimmune diseases.
Among those alleles are:
●
A1
and
DQW1
associated with SLE;
● DQ3-DR4, DQ3-DR9,
DQ5-DR1,
DQ5-DR10
associated with RA;
● DR3 and B8 associated
with
RA,
SLE,
autoimmune thyroiditis,
celiac, multiple sclerosis,
and myasthenia gravis,
etc.
Other genetic risk factors
confirmed
are
polymorphisms in
specific genes encoding
molecules involved in
antigen
presentation,
such as Tap-1 and
proteosomes, as well as
many other candidate
genes.
More precisely, autoimmunity results from disruption of different combinations
of genes participating in the same immunologic function designated as tolerance.
Meanwhile, some susceptibility loci are shared by several autoimmune diseases,
whereas others are more disease-specific. Neverltheless, due to the highly
multifactorial nature of most autoimmune diseases, individual loci do not seem
significant enough to be used as biomarkers for susceptibility or as diagnostic
tools.
In this sense, many exciting things are happening, including an expanded
understanding of the genetic basis of human health and autoimmune disease
through research efforts, the rapid lowering of the cost of genetic sequencing,
new precision drugs for a number of the diseases based on genetic targets,
including autoimmune conditions. Let me stress the the link that might exert
reliable control over morbidity, mortality and disabling rates and significantly
optimize the cost of treatment for those who had fallen ill whilst having either of
the autoimmune disorder. The name of the link is
Predictive, Preventive and
Personalized Medicine (PPPM)
(slide)
PPPM associated with
Subclinical and Predictive Diagnostics
To achieve the practical implementation of
PPPM concept, it is necessary to create a
fundamentally new strategy based upon the
pre-early
(subclinical)
recognition
of
biomarkers of hidden imbalances and
defects long before the illness clinically
manifests itself.
This strategy would give a real
opportunity
to
secure
preventive
measures whose personalization could
have a significantly positive influence on
demographics! (slide)
Impacts to be assumed for
the practical implementation of
PPPM into the practice
to predict the
likelihood of
developing
disease
to estimate the
length of the
asymptomatic
period
to serve as a
warning to avoid
potential diseasetriggering factors
to provide predictive
information about
disease course,
severity, and
complications
identify high-risk
individuals who might
be suitable candidates
for preventive
intervention trials
PPPM as thus the big change to
forecast, to predict and to prevent is
rooted in a big and new science to be
rooted from the achievements of
genomics, proteomics,
metabolomics and bioinformatics
which are being implemented into the
daily practice to secure visualizing of
lesion foci that was previously
unknown to clinicians (slides x2)
In reality,
Genomics
as a set of molecular tools
to probe genome and to thus identify and
to select genomic
biomarkers
has allowed for identifying newer
genetic variations that affect health
to form subclinical and predictive risks
to be screened and unveiled, and then
the subclinical pathology to be diagnosed,
monitored and terminated
to prevent tolerance breakage and thus
the clinical illness
Over the last decade genome-wide association studies (GWASs) have
identified many loci associated with late-onset autoimmune conditions
including T1D, MS, SLE, RA, AIM and others
For this model, about half of the total risk is genetically predisposed, and
about half of the risk is in the HLA and other regions to be useful for
gene-based predictive testing!
HLA-I
HLA-III
HLA-II
The genetic map of MS: the location of genes and SNPs on the chromosome
The genetic map of MS: the location of genes and SNPs on the chromosome
Gene products associated with systemic lupus erythematosus (SLE) that might affect
the adaptive immune system
Gene products associated with rheumatoid arthritis (RA) that might affect
the adaptive immune system
Well, genes
can say a lot about
an individual’s
predisposition
to an autoimmune
disorder,
but cannot reveal
what is happening in
cells at the protein
level.
The latter would
attribute to
proteomics
to identify
individual proteins and
their epitopes
to be valuable for
predictive
diagnosing and thus
may eventually have a
great impact on PPPM
Predictive & Prognostic
Diagnosing
Predictive & Prognostic
Diagnosing
Predictive & Prognostic
Diagnosing
Meanwhile,
a combination of genomic and
proteomic biomarkers
are becoming of great
significance to predict risks of
the chronification!!!
LOOK AT:
Genomic biomarkers
Proteomic
biomarkers
Genomic biomarkers
Proteomic
biomarkers
Proteomic
biomarkers
Proteomic
biomarkers
And thus to predict risks of
disabling since
chronic autoimmune diseases
are preceded by
a long subclinical
(symptom-free) phase
or a period of latency (slides xx)
A stepwise progression of autoaggression
Stage of
subclinical
autoaggression
Stage of
full-term
autoaggression
Clinical
illness
Subclinical (cryptic) latency
A stepwise (subclinical-clinical) course to be developed
Notwithstanding the underlying mechanism
of disease, almost all autoimmune disorders
are
associated
with
circulating
autoantibodies (immunomics as a portion of
the proteomics) which bind self protein.
Some of those antibodies can be detected in
the patients serum long before clinical
disease manifests itself. Several studies
showed the predictive ability of anticycliccitrullinated-peptide
(anti-CCP)
for
rheumatoid arthritis (RA).
Anticyclic-citrullinated-peptide (anti-CCP) in
rheumatoid arthritis (RA).
In reality,
proteomics per se is the continuation of functional genomics and, at the
same time, a prologue to metabolomics
Genome
Proteome
106 human proteins
25,000 human genes
Proteome
Genome
Transcriptomic modifications
Alternative splicing
(mRNA)
Transcriptome
Posttranslational
modifications (PTMs) of
proteins
Metabolome
The latter (metabolomics)
illustrates the
functional state of the cell at the level of metabolism
on a real time basis, requiring the use of the term
'metabolome', demonstrating a set of metabolic
pathways in the cell at a given time point
Tissue-derived information
we would accumulate from the
sources including:
● molecular architectonics and profiling
of the autoimmune disorder at its
clinical and subclinical stages;
●
laboratory
and
instrumental
armamentarium;
● predictive testing;
● susceptibility and resistance testing
might be
combined with
the:
● individual's
medical records;
● family history;
● data from
imaging;
●instrumental
and laboratory
tests
to develop
personalized and
preventive
treatments.
But, in this
sense, how is
the whole
databank
provided by
omicstechnologies
could be
comprehended?
It is bioinformatics
to suit the goal by applying mathematical modeling
techniques to thus secure constructing and
maintaining unified biobanks and datasets
necessary for personal health monitoring
based on principles of
genotyping and phenotyping.
As a result, the patient becomes a data carrier,
whilst learning about possible risks of a disease,
and the physician can reasonably select a kind of
preventive and personalized protocol
rooting from the predictive assays made
(slides).
Bioinformatics
would service PPPM
The diagram shows the integrated “knowledge environment” that enables clinicians to query critical information from across
disparate data sources to find relationships between an individual patient’s EMR information of persons-at-risk family records
By integrating bioinformatics and clinical healthcare informatics,
both offers unique infrastructure, tools, techniques and applications
to bridge those areas.
This facilitates the sharing of data and information across diverse disciplines and
professional sectors to unveil the clinical case illustrating the autoimmunity condition
Individuals, selected in the first (genotypic)
stage, undergo
the second stage, which uses a panel of
phenotypic biomarkers,
whilst monitoring every:
● potential patients,
● persons-at-risks
predisposed to the autoimmune disease,
and/or
● persons at subclinical stages
of the disease.
Biobanks would thus supplement the proper
information and datasets about patient's
proteomic, genomic and metabolic profiles
to be used to tailor medical care due to
the individual's needs and
personalized scenarios.
An understanding of the factors underlying
the burden of a disorder and later on
of the clinical illness
would provide policymakers, healthcare providers
and administrators and medical educators with
an opportunity
to guide preventive initiatives at both
individual and community levels (next Fig)
PPPM
Biobanking as
applicable to PPPM
Impact on Sustainability in
Three Main Dimensions of
Biobanking
PPPM will require the integration of clinical
information, stable and dynamic genomics, and
molecular phenotyping.
And you might understand that bioinformatics
will be crucial in translating these data into useful
applications, leading to improved diagnosis,
prediction, prognostication and treatment.
So, our viewpoint would support the
contributions of genomic and proteomic
approaches in developing a preventive and
personalized approach to manage
chronic autoimmune diseases and
to thus monitor the patients and persons-at-risk
PPPM thus uses diagnostic and predictive tests of
newer generations based on biomarkers,
to individually determine the health conditions
a person is predisposed to the autoimmune
conditions and to reveal biomarkers of the probable
or the already existing pathological processes, and
thus to select the targets.
PPPM-oriented survey should be based on
biomarkers and algorithms to differ essentially from those
employed in traditional clinical strategies, namely,
(i) algorithms for predictive and subclinical diagnostics
on one hand, and
(ii) algorithms for preventive therapy,
on the other one
Integrating High-Throughput Measurements with the Phenotype:
from Science to Medicine
(Omenn & Athey, NCIBI, 2010)
In the era of personalized medicine there is increased
interest in the incorporation of individual biomarkers in
risk score algorithms in order to improve autoimmune
disorders-related risk stratification followed by the
prompt initiation of preventive measures whilst evaluated
the predictive and prognostic value of currently used
biomarkers like
canonical autoAbs,
cytokines,
specific proteins and ligands
as well as promising future-affiliated biomarkers like
noncoding RNA,
silencing sequences,
pathway-associated biomarkers, and
combinatorial biomarkers as the latest ones!!!
Most of the latter are directly associated with
development of
chronic autoimmune diseases.
So, we would need absolute proofs to confirm
clinical applications of the biomarkers
mentioned, evaluating not only their diagnostic
and prognostic value but also
their integrability into routine practice
Although a single biomarker is a tangible entity
that can be easily understood,
aggregate measures comprised of multiple
genes are also informative as
the combinatorial biomarkers.
The simultaneous addition of combinatorial biomarkers of
autoimmune disorders to the score index panel
substantially improves the risk stratification for death and
disabling.
And clinical potential may be evaluated by asking three
fundamental questions:
(1) Can the clinician measure the biomarker as applicable to
chronic autoimmune diseases?
(2) Does it add new information?
(3) Does it help the clinician to manage the patients?
The incorporation of the multiplexed but consolidated
biomarker-based panels mentioned into clinical practice
for the prediction of death and disabling causes
could be accomplished quickly,
since the measurement of these biomarkers
is already well established for diagnostic use.
And there is urgent clinical need
to identify
predictive combinatorial
biomarkers
for
subclinical PIFAS
(post-infectious autoimmune
syndrome)
to allow preventive/treatment strategies
to be instituted early in the disease running.
A schematic
representation of
the postinfectious
autoimmune syndrome
(PIFAS)
The primary microbial pathogen triggers
PIFAS through activation of two different
mechanisms:
(i) depletion of intrinsic (antigenic) molecular
mimicry
pools
of
cross-reacting
(mimicking) epitopes of the pathogen (red
arrows);
(ii) generation, by the infectious pathogen, of
antigen-nonspecific signals (blue arrows)
able to induce inflammation and thus
enhance immune responsiveness (so-called
adjuvant effect)
A crucial role in formation of PIFAS and
progression to develop clinical illness is played
by inborn (in the first place, HLA-associated)
predisposition coupled with
impaired immune responsiveness of
the human body invaded and/or
having had signs of dysbiosis
since recent data has stressed the importance of
the endomicrobiota in immune homeostasis.
In this sense, then how the endomicrobiota
influences host immune responses
to provoke autoaggression?
When microbes are critical for development of PIFAS, several
mechanisms can be discriminated! Which ones, in specific terms?
Mechanisms
by which microbes
contribute to
initiation or
severity of PIFAS
In all cases the end
point is the destruction
of tissue target cells
(center) by self-reactive
cells
(in
red)
or
antibodies (not shown).
Microbial
peptides
could be identical or
similar enough to selfpeptides (left) to induce
cross-reactivity to selftissues - “molecular
mimicry.”
The mimicry is not necessary if APCs are activated by microbes through innate
receptor signaling and happen to express self-Ag when a self-reactive T cell is present.
This is “bystander activation” (right).
The third mechanism is “amplification of autoimmunity by cytokines” elicited by
microbial activation of professional APCs and the so-called innate lymphoid cells
(ILCs), which together produce or elicit cytokine production by T cells. These cytokines
target self-reactive T cells and their target tissues (bottom). They can also reduce the
efficiency of regulatory cells, which are responsible for active tolerance to self
Innate-adaptive connection mechanisms (Ag
presentation and costimulation;
green
arrows) are required to
activate
self-reactive
cells.
As you know,
antimicrobial immunity is based on
sequential reactivity of the innate and
adaptive immune systems.
And the final version of PIFAS is controlled
by the coordinated functioning of
innate and adaptive arms on one hand,
and the interaction of the arms with
endomicrobiota,
on the other hand.
As you see,
the host’s genetics defines
the interactions
with the
endomicrobiota,
and the outcome of those interactions
affects
the autoimmunity risks
Indeed, autoaggression provoked by
insufficient coordination between two
immune arms and hyperactivity of
the adaptive one is a dominant feature of
PIFAS to serve as
a combinatorial biomarker
to monitor PIFAS and then the PIFASdependent autoimmune disorder.
Its unique feature is a broad repertoire of autoAbs
responsible for multiseropositivity and thus to
autoimmune inflammation biomarkers,
e.g., anti-B7-HI autoAbs
Several examples.
Autoimmune myocarditis (AIM)
usually develops in
genetically predisposed
individuals infected with
the CVB3 or related viruses to
represent typical manifestations
of molecular mimicry.
Initiation and progression of
autoimmune myocarditis
PIFAS is initialized
moderate PIFAS
full-term PIFAS
The presence of CM-AR CTLs and anti-CM autoAbs is
prerequisite to AIM development to initiate
myocardial lesions.
Clinical manifestations of AIM, with distinct onset, vary
from asymptomatic to fatal. But the biomarkers to predict
the course at initial presentation
have not yet been established.
Meanwhile, an improved knowledge of the mechanisms
of infections to preceed the illness should help to get
PIFAS defined and then be used as
a combinatorial biomarker
to develop preventive strategies for quenching PIFAS
at the subclinical stages
T1D is a chronic autoimmune inflammation comprising stages of
subclinical pathology and clinical manifestations and resulting in a
destruction of pancreatic beta-cells capable of producing insulin
Subclinical stage
T1D
Clinical stage
Subclinical stages are usually determined by
identification of
proteomic biomarkers, i.e., antiislet autoAbs as early as
5-10 years before the clinical onset of disease
A stepwise progression of autoaggression
Stage of
subclinical
autoaggression
Stage of
full-term
autoaggression
Clinical
illness
Subclinical (cryptic) latency
A stepwise (subclinical-clinical) course to be developed
But! Look again at the evolution of Chronic Autoimmune Disorder – a
lot of factors are involved into the subclinical stage! And PIFAS as a
Combinatorial Biomarker may serve to suit the needs!
A stepwise development of T1D
Stage 3
Glucose
intolerance
Stage 2
T1D
clinical manifestations
(latent or asymptomatic
insulin deficiency
Benign autoimmunity
(autoimmune insulitis)
Stage 1
Population of β-cells
to function
Pathogenic autoimmunity
Genetic predisposition
Factors
to provoke
T1D
НТГ
Stage 4
Clinical illness
100% death
of β-cells
Stage 5
Stage 6
A subclinical stage is characterized by depletion of β-cells and fall in insulin secretion levels to have a biased burst.
Clinical manifestations link to β-cell death to illustrate ceasing in insulin secretion.
Thus, the need for biomarkers of newer
generations, newer algorithms and
software applications
to support integrative data analysis and to
secure the databanks becomes critical to
the discovery of linkages and concordance
between different data types
such as genomics-, proteomics-,
immunomics-, metabolomics- and
clinically-rooted!
Multifactorial risk factor modification and control
can have a profound and favorable impact on
decreasing the incidence of initial and recurrent
autoimmunity-related events.
Meanwhile, because the adult tissues and organs have
negligible regenerative capacity, healing of the latter
damaged and affected is dependent on sequential
activation of anti-inflammatory and anti-fibrogeneic signals.
So, a strategy of preventive treatment should contain,
at least, two critical steps for managing
autoimmune disorders:
(i) quenching of inflammation and degeneration;
and,
(ii) restoration of the tissue damaged and affected.
PPPM is thus a model of
healthcare services
being tailored to the individual and
dictates a construction of
PPPM-based algorithms
to diagnose,
to predict, and
to prevent
autoimmune conditions
in time!
Due to our viewpoint, all healthcare professionals of the future
should be educated to deliver patient-centric care as members of
interdisciplinary teams, emphasizing evidence-based practice,
quality improvement approaches and bioinformatics.
That concerns the need for novel training programs since the
society is in bad need of large-scale dissemination of
novel systemic thinking and minding.
And upon construction of the new educational platforms
in the rational proportions, there would be not a primitive
physician created but a medical artist to be able to enrich
flow-through medical standards with creative elements to gift
for a patient a genuine hope to survive but, in turn, for a personat-risk – a trust for being no diseased.
So, the existing medical education would strongly need to be
restructured to involve along with traditional graduate and
post-graduate training, pre-graduate preliminaries to disclose
for schoolchildren the mysteries of the evidence-based medicine
and PPPM as the entity
EPMA-World Congress 2011
September 15th19th, Bonn, Germany
International
Research Team of Youngers
Based on current trends
and own experience,
we have tried a
non-canonical approach
towards reshuffling
the traditional educational
tandem
“School-University”
to create a team of
talented and gifted
teenagers to be engaged
into PPPM-related areas.
The Team has been given
a roof under the aegis of
The First Anglo-Russian Students’ Workshop
on PPPM and Translational Medicine
European Association of
Predictive, Preventive
and Personalized
Medicine (EPMA),
Brussels, EU,
Lancaster University
4th September 2012
and started up
to move ahead now
Location: TR1/TR2 Gordon Manley building
Chairs:
Professors Frank Martin, PhD (UK)
Director, Environmental and Biophotonics Center, and Chairman,
Dept for Biochemistry, Lancaster University, UK
Professor Sergey Suchkov, MD, PhD (Russia)
Dept of Pathology, School of Pharmacy, I.M.Sechenov First Moscow
State Medical University, and Dept of Clinical Immunology, Moscow
State Medical & Dentistry University, First Vice-President and Dean,
School of PPPM, University of World Politics and Law, Moscow,
Russia
EPMA World Congress 2013
Europarliament, Brussels, EU, Sep 2013
Section For Young Professionals (Session)
And, finally, in Aug-Sep 2015,
on the invitation of
American and Canadian universities
The EPMA-YPC Team of Young
Researchers and Clinicians
were participating in a series of
the Workshops and Summits
organized jointly with young students of
the inviting universities whilst demonstrating
their creative potential and activity
The First US-EPMA-Russian
Students’ Workshop
“Chronic Disorders through
the View of Preventive,
Personalized and
Translational Medicine”
UCSD,
San Diego, CA, USA
The First US-EPMA-Russian
Students’ Workshop
“Chronic Disorders through
the View of Preventive,
Personalized and
Translational Medicine”
UCSF,
San Francisco, CA, USA
SENIOR
YOUNG MEMBERS of THE
EPMA TEAM
Georgia
Israel
Russia
Russia
Russia
JUNIOR
YOUNG MEMBERS of THE
EPMA TEAM
USA
Russia
Austria
Italy
Russia
VIRGIN
YOUNG MEMBERS of THE
EPMA TEAM
Russia
Australia
Russia
Germany
SLovakia
Russia
This approach
should be based on
postulates
which will change
the incarnate culture
and social mentality.
Our global challenge is that
the new guidelines should create
the robust juristic, social, ethical and
economic platforms for
advanced medical services utilizing
the cost-effective models of risk
assessments followed by
tailored preventive treatments
focused on the precursor stages of
chronic autoimmune diseases,
in particular!