Developing a Urine Test for Hepatitis B Related Liver Cancer
The promise of metabonomics in gastroenterology and
hepatology
The changing face of 21st century medicine.
The concept of the patient as a totality of all their organs and
tissues, held in a chemical equilibrium under the control of
genetic and environmental factors, has only come to the fore in the
last decade. The mindset of medical specialities being treated asin
discrete silos with patients attending separate clinics for each
condition still persists in mainstream medicine. The move towards
personal or stratified medicine calls for a patient-centric
approach where a global view of health demands knowledge of the
integrated output of multiple systems and organs. The realisation
that if we are to truly understand disease mechanisms, then we need
to adopt a global investigative approach combining multiple
bio-organizational layers, such as the genome, proteome, metabolome
and, inflammasome has propelled the development of “Systems
Biology” and “Systems Medicine”. Parallel development of ‘omics’
tools and advanced bioinformatics can be harnessed to help achieve
this goal.
Depersonalised or ‘one-size-fits-all’ treatments are the default
approaches used for most patients and this can negatively impact
negatively on the success of clinical trials, the
cost-effectiveness of therapies and ultimately on patient safety.
Variations in genetic profile, differential gene expression and
gene-environment interactions all influence an individual’s
response to drugs and indeed the risk of developing a particular
disease. Genetic classifications of disease sub-types are well
established, and modern genomic methods can provide rich
information on large numbers of patients at relatively modest
costs. However, there are orthogonal and highly complementary
approaches to patient stratification, based on proteomic and
metabolic information that can help place underlying genetic
information in a biochemical or physiological context; these can
also measure differential environmental, dietary and gut microbial
contributions to patient diversity and response. Thus, even within
selected genetic sub-classes of disease there is observed variation
in therapeutic response that is dependent on environmental factors
and conditional gene-environment interactions. In particular,
metabolic and other clinical phenotyping approaches have been shown
to be of great value in patient characterisation and can also be
used to monitor direct responses to therapies through the patient
journey{Holmes, 2008 #3;Nicholson, 2012 #4}. The 21st century
heralds a move towards predictive, preventive, personalized and
participatory (P4) approaches to medicine where each person serves
as their own control over time and is interactive with their
genetic and environmental background, with a responsibility for
their own healthcare{Hood, 2012 #6}.
The fundamental paradigm of clinical metabolic phenotyping is
that any localised metabolic, physical or histological perturbation
in the human body will result in global, systems level changes,
which are detectable by profiling hundreds or thousands of system
parameters in biological samples such as urine, serum (systemic
snap shot and time-averaged signatures respectively) or tissue
biopsies (intact or extracted). Localised and systemic metabolic
phenotypes in tissue compartments and biofluids are the products of
gene-environment interactions and so are both statistically and
biologically connected to disease risk factors{Holmes, 2008 #3},
and individual patients responses to therapy. Alterations in
metabolic profiles will be characteristic of the origin, behaviour
and outcome of the original disease manifestation. We have
developed novel and innovative molecular diagnostic phenotyping
approaches for patient stratification across a range of technology
platforms with application to many different pathological disease
states and clinical conditions{Robinette, 2013 #9}. Modeling the
responses to therapy via metabolic phenotyping can be viewed as a
“dynamic stratification” process, which adds a new temporal and
physiological dimension to understanding therapeutic response
classes{Nicholson, 2012 #4}.
Background to the use of metabonomics in the clinic.
Metabolic profiling of biofluids using predominantly NMR
spectroscopy and MS (often combined with a chromatographic
separation) provide relatively high throughput generation of
molecular fingerprints at low cost per sample and have been used to
characterise a wide range of pre-pathological and pathological
conditions including cardiovascular disease{Holmes, 2008 #10},
cancer, neurodegeneration, metabolic disorders{Maher, 2008 #11} and
infection{Shariff, 2011 #12}. Profiling can be carried out in
screening mode to obtain broad coverage of the metabolome without
the need for a priori hypotheses, or in targeted mode to give deep
coverage for selected metabolite classes, for example amino acids,
eicosanoids (inflammatory conditions), bile aids (liver disease) or
short chain fatty acids.
NMR spectroscopy is rapid and non-destructive and has the
advantage of high reproducibility. It is the only spectroscopic
tool that can deliver atom-centred information, giving it premium
position in molecular structural elucidation [REF]. Mass
spectrometry, being inherently more sensitive than NMR, offers
complementary molecular information. One of the first medical
applications of both NMR and MS spectroscopic profiling in
stratified medicine was in identification of infants with inborn
errors of metabolism, and this technology is now routinely used in
hospitals. Ultra performance liquid chromatography (UPLC), used as
a molecular separation phase prior to MS detection, provides rapid
analysis and delivers excellent chromatographic resolution{Plumb,
2004 #13}. Concatenation of NMR systems with liquid chromatographic
devices, directly coupled to mass spectrometers, offer exquisite
capability for enhanced structure elucidation{Shockcor, 1996 #14}
and is equally applicable to endogenous and drug metabolite
identification, although the majority of publications are focussed
on drug metabolism {Duarte, 2009 #15}. Metabolic screening of
biofluids has been at the forefront of profiling technologies in
the clinical arena, but other spectroscopic tools have been
developed for assessment of tissue biopsies. Amongst these magic
angle spinning (MAS) NMR methods, operating on a 5-10 min per
sample timescale, allow metabolic characterisation of intact tissue
biopsies with one exemplar area being the differentiation of benign
and malignant tissue from colon cancer biopsies with further
correlation of the profile with tumour grade or stage (REF{Jimenez,
2013 #23). Related developments in molecular imaging of tissues to
improve clinical decision-making and augment conventional
histological and clinical pathology measurements are also being
undertaken mainly in cancer diagnostics {Veselkov, 2014 #2} and are
ready for further development in an experimental medicine
environment. Current histopathological techniques offer little
interpretable molecular information, without the use of specific
non-routine stains or immunohistochemical procedures. Advances in
DESI (invented by Takats et al.{Takats, 2004 #29}) and MALDI-IMS
technology{Crecelius, 2005 #30} enable multi-modal tissue imaging
utilising thousands of simultaneously collected molecular ion
traces, which can be used to provide new molecular biomarker
information relevant to disease aetiology and diagnostics as well
as new imaging modalities. Comment by str31: Do you need to define
this?Comment by str31: ditto
In parallel with spectroscopic developments, many computer-based
pre-processing and multivariate modelling techniques have been
developed to facilitate the analysis and interpretation of the
complex high-density spectral data. Computational modelling tools
have been developed to analyse, map and interpret spectroscopic
data and these can be roughly divided into: i) pre-processing
methods (e.g.such as spectral alignment and de-noising{Veselkov,
2009 #16}); ii) multivariate analysis methods (extensions to linear
projection methods, Bayesian probabilistic models and so
onetc){Trygg, 2007 #17}; iii) biomarker extraction algorithms,
based on statistical spectroscopic tools{Cloarec, 2005 #19}, and;
iv) pathway integration and mapping tools (e.g.such as
MetabonetworksTM{Posma, 2014 #20}). Data filtering strategies and
other pre-processing methods can be used to optimize models, and to
increase the interpretability of the models focussing on important
biochemical changes relating to single or combinatorial
pathologies{Trygg, 2003 #21} by removing extraneous variation. Date
fusion methods have also been developed to relate multiple data
matrices, such as NMR and clinical chemistry or gene
expression{Rantalainen, 2006 #70} in order to achieve a more
holistic picture of the dynamic and interactive biological
processes occurring in an organism. Statistical correlation methods
applied to spectroscopic data can be used to structurally identify
candidate biomarkers of disease, to identify drugs and their
metabolites (REF){Keun, 2008 #66} and to establish pathway
connections between molelcules. Modifications of the basic
statistical spectroscopy focus on identification of metabolites in
relatively small subsets, otherwise hidden in large patient numbers
or detection of idiosyncratic responses to drugs (subset
optimisation by referencing matching, STORM){Posma, 2012 #64}. All
of these methods and approaches aid the efficient recovery and
validation of biomarker structure information, which is essential
to provide the mechanistic underpinning of the diagnostic and
prognostic information generated by spectroscopic phenotyping.
These enabling technologies will be complemented by development
of innovative bioinformatic and modelling methods to enhance
relationships with other omics data. The proposed infrastructure
framework bridges gaps between population phenotyping and real-time
diagnostics and between systems biology and molecular technology
framework that will facilitate harnessing the power of omic
technology for mainstream use in clinical decision making, based on
an incremental step in generation, organisation and extraction of
information to establish a set of blueprints and technology units
for carrying out efficient patient stratification with respect to
disease diagnosis, intervention and prognosis.
Importance of the gut microbiota in disease.
Widespread realisation that the interaction between the gut
microbiome and host metabolism has a critical impact on human
health with long reaching effects. The microbiome is key to the
status of the immune system with capacity to affect a diverse range
of tissues and organs, including the liver and brain and is
implicated in the aetiology or development of many diseases
including inflammatory bowel disease, cardiovascular disease,
asthma and even neurodevelopmental conditions, such as autism.
Other examples of microbial-related differences in the phenotype
include the fact that malignant gastrointestinal tumours carry
differential 16S RNA bacterial profiles{Marchesi, 2011 #69}.
Critical biosynthetic pathways that significantly extend host
metabolic capacity are provided by the microbes. Metagenomic
analysis can map the microbial community of faecal or intestinal
samples. Parallel metabolic profiling of biofluids such as urine,
plasma or faecal water can provide a window for investigating the
functionality of the microbiome and assessing the consequences on
human health and can demonstrate altered microbial activity
reflected in microbial-host co-metabolite profiles such as
phenolics and biogenic amines.
and potential windows of impact for spectroscopic
phenotyping.
Increase in IBD,
Unmet needs in Gastroenterology and Hepatology
Death from liver disease is a major cause of mortality which is
increasing in developed and developing countries alike with the
combined burden of viral hepatitis, increased alcohol consumption
amongst young people and the prevalence of obesity causing a steady
rise in patients with cirrhosis. Over the past three decades, in
the United Kingdom alone, deaths from chronic liver disease have
increased eight-fold in men aged 35-44 and seven-fold in women
(REF). Hepatocellular carcinoma (HCC) is the commonest primary
liver cancer worldwide, killing up to one million people annually,
most of whom have established cirrhosis as a precursor. Many of
these deaths are preventable as HCC is curable if patients with
pre-existing fibrotic and cirrhotic liver disease are regularly
screened and the cancer is diagnosed early. However, small tumours
are asymptomatic and standard diagnostic screening tests lack
sensitivity and specificity. Comment by str31: Hepatocellular
carcinoma: current trends in worldwide epidemiology, risk factors,
diagnosis and therapeutics.Shariff MI, Cox IJ, Gomaa AI, Khan SA,
Gedroyc W, Taylor-Robinson SD.Expert Rev Gastroenterol Hepatol.
2009 Aug;3(4):353-67. doi: 10.1586/egh.09.35
Similarly the incidence of inflammatory Bowel Disease (IBD),
particularly Crohn’s Disease and uUlcerative colitis have increased
xxxx significantly in the developedWestern world in the last
decade. Much of this increase in prevalence has been attributed to
lifestyle changes, such as increased consumption of fast foods with
a corresponding decrease in dietary fibre.Comment by str31:
Age-related differences in presentation and course of inflammatory
bowel disease: an update on the population-based
literature.Duricova D, Burisch J, Jess T, Gower-Rousseau C, Lakatos
PL; On Behalf of ECCO-EpiCom.J Crohns Colitis. 2014 Jun 17. pii:
S1873-9946(14)00179-2. doi: 10.1016/j.crohns.2014.05.006. [Epub
ahead of print]
The following sections describe applications of metabolic
profiling in gastroenterology and hepatology and explore potential
avenues for its application with respect to addressing key unmet
needs in these areas.
Inflammatory Bowel Disease
There has been considerable interest in developing urine and
serum biomarker diagnostic strategies in inflammatory bowel disease
(IBD): both Crohn’s disease (CD) and ulcerative colitis (UC) have
been investigated. Biomarkers of disease could be a useful
non-invasive adjunct to current methods of diagnosis. One of the
critical issues remains the differentiation of CD and UC from each
other and from other inflammatory bowel conditions. Diagnosis is
based upon clinical symptoms, endoscopic examination, radiographic
data and histological evidence and is therefore an invasive and
costly process (B. H. Stephen, W. Sandborn. Am. J. Gastroenterol.
2001, 96, 635–643.). Thus, specific biomarkers of the variants of
IBD would be of immense clinical value. Since the manifestation of
CD is heterogeneous and the severity of disease does not always
mirror the endoscopic profile, new measures of disease presence and
stage are needed. To this end, numerous genomic xxxx have been
conducted. Comment by str31: What?
To date, there have only been a few metabolic studies conducted
on serum or plasma. Dawiskiba et aland colleagues conducted an
NMR-based metabolic profiling study in IBD patients (n=19 CD; n=24
UC; n=17 healthy controls) did not find a significant difference
between the metabolic profiles of patients with CD and UC, although
there was some evidence of predictivity of the metabolic profiles
for IBD with serum providing a slightly stronger diagnostic
(p=0.002) than urine (0.003) (REF). N-acetylated glycoproteins,
often associated with inflammation, and phenylalanine were found to
be upregulated in serum in patients with IBD, whereas the urine of
patients with IBD was characterized by higher glycine and lower
acetoacetate levels. Additional metabolites were found to be
increased (leucine, isoleucine, 3-D- hydroxybutyrate, acetoacetate,
glu]ycine and lactate) or decreased (creatine, histidine, choline)
in the serum, when only the cases with active disease were compared
with healthy controls. Similarly, urine profiles showed
differentiation based on reduced excretion of hippurate,
trigonelline citrate and taurine, when only active disease cases
were compared with controls. In contrast to the results of
Dawiskiba et al, Williams et aland colleagues found that it was
possible to differentiate CD from UC patients in a similar sized
cohort (n=24 CD; n=20 UC; n=23 healthy controls), based on NMR
serum profiles and that the key discriminatory metabolites were
N-acetylated glycoproteins, choline and lipoproteins (XXXXXXX).
Comment by str31: Serum metabolic profiling in inflammatory bowel
disease.Williams HR, Willsmore JD, Cox IJ, Walker DG, Cobbold JF,
Taylor-Robinson SD, Orchard TR.Dig Dis Sci. 2012 Aug;57(8):2157-65.
doi: 10.1007/s10620-012-2127-2. Epub 2012 Apr
10.PMID:22488632[PubMed - indexed for MEDLINE]
Fathi et aland colleagues performed random forest analysis of
low molecular weight serum profiles to classify CD from healthy
participants in 26 CD participants and 26 age- and gender-matched
controls. The resulting model predicted CD with a sensitivity of
100% and a specificity of 88%, based on elevated isoleucine
concentrations and lower valine concentrations in the CD
participants. The altered levels of these amino acids was were
attributed to the altered nutritional status often found in CD
patients. The authors further assessed the profiles with respect to
serum zinc levels, since low Cu2+-Zn2+ super oxide dismutase
activity has been associated with IBD. A correlation between serum
zinc levelrs and serum glutamine and lysine was established. One
hypothesis arising from this study, given that glutamine is an
essential nutrient for immune cells, was that CD patients may be
susceptible to glutamine depletion (Fathi et al).Comment by str31:
Ref needed
Previous studies have demonstrated the importance of gut flora
in modulating the disease process in inflammatory bowel disease
(REF). The systemic effect of gut bacterial alterations in IBD is
exemplified by a large urinary metabonomic investigation conducted
by Williams and colleagues (REF). The group compared the urinary
metabolic profiles from UC patients, CD patients, as well as
healthy control subjects and concluded that specific urinary
metabolites could differentiate between the cohorts. Statistically
significant differences were found in hippurate, 4-cresol sulphate
and formate: all metabolites of gut microbial activity. Most
significant was hippurate, which was found to be lowered in CD
patients, compared to UC patients and healthy controls. It has been
hypothesised that lowered hippurate levels occurs in conjunction
with reduced gut Clostridia spp. in IBD (REF). Future studies
should attempt to clarify this relationship by correlating urinary
hippurate levels with faecal studies of the gut moicrobiome,
including the relative contribution of Clostridia spp.Comment by
str31: Characterization of inflammatory bowel disease with urinary
metabolic profiling.Williams HR, Cox IJ, Walker DG, North BV, Patel
VM, Marshall SE, Jewell DP, Ghosh S, Thomas HJ, Teare JP,
Jakobovits S, Zeki S, Welsh KI, Taylor-Robinson SD, Orchard TR.Am J
Gastroenterol. 2009 Jun;104(6):1435-44.
Colorectal Cancer
Colorectal cancer (CRC) is the fourth most common cause of death
due to malignancy and therefore remains an active area of
investigation (REF). The use of NMR spectroscopy and MS to
metabolically profile solid CRC tumours has expanded knowledge of
tumour pathogenesis (REF).Comment by str31: Long-term mortality
after screening for colorectal cancer.Shaukat A, Mongin SJ, Geisser
MS, Lederle FA, Bond JH, Mandel JS, Church TR.N Engl J Med. 2013
Sep 19;369(12):1106-14.
Many of the current metabolic phenotyping approaches to
stratified medicine worldwide are subjective, empirical, labour
intensive, unreliable and usually housed in disparate uncoordinated
centres. A key goal will be to standardise upon reliable and robust
protocols and applications that show statistically superior
performance to existing pathology/clinical methods and to diffuse
these nationally using the hub and spoke model, thereby providing
added value to other UK metabolic phenotyping facilities. In terms
of clinical adoption of the new technologies we propose, it should
be emphasised that new discoveries in the medical field have to
overcome barriers of conservative attitudes that place a burden on
new methodologies to be proven at least better or more cost
effective. For this reason, the early excitement and promise of the
new methods in metabolic profiling now need significant investment
to broaden their application base, and to create a community of
users who will collaborate to create exemplars of the uses and
robustness of the new methods in a range of clinical areas. In
addition, by the nature of the novelty of these approaches,
exemplars as yet undetermined will emerge from the user community
to explore how the basic science can be applied to many areas of
patient stratification (frameworks for stratification of
therapeutic response, diagnostics, prognostics and improved
mechanistic understanding of disease aetiologies) and it is
difficult at this early stage to determine where the biggest
impacts will lie.
Hepatitis
Hepatitis C virus (HCV) infection is a global health problem,
with 130-170 million people currently infected worldwide. The
infection may lead to chronic liver inflammation, fibrosis,
cirrhosis and ultimately, HCC. Comment by str31: Hepatocellular
carcinoma: epidemiology, risk factors and pathogenesis.Gomaa AI,
Khan SA, Toledano MB, Waked I, Taylor-Robinson SD.World J
Gastroenterol. 2008 Jul 21;14(27):4300-8.
The clinical spectrum and natural history of chronic liver
disease is varied with some individuals having a rapidly
progressive course and others having relatively indolent disease.
There is wide geographical variability. For example, in northern
Europe, chronic infection rates are estimated to be between 0.1 and
1%; in southern Europe, these are higher at 2.5%-3.5%. Egypt is
worst affected with a prevalence of 22% or higher, owing to the
high transmission of HCV in the parenteral antischistosomal therapy
campaign in the 1970s and 19880s (REF). In about 80% of new cases,
the infection becomes chronic. About 30% of individuals progress
inexorably to develop cirrhosis over a variable period of up to 20
years after the initial infection, which is usually 5-10 years
after initial medical presentation. Current treatment regimens are
designed to prevent the progression in disease from mild hepatitis
through significant fibrosis and subsequently, cirrhosis
(REF).Comment by str31: Hepatitis C virus infection epidemiology
among people who inject drugs in Europe: a systematic review of
data for scaling up treatment and prevention.Wiessing L, Ferri M,
Grady B, Kantzanou M, Sperle I, Cullen KJ; EMCDDA DRID group,
Hatzakis A, Prins M, Vickerman P, Lazarus JV, Hope VD, Matheï
C.PLoS One. 2014 Jul 28;9(7):e103345. doi:
10.1371/journal.pone.0103345. eCollection 2014.Comment by str31:
Comparing staging systems for predicting prognosis and survival in
patients with hepatocellular carcinoma in Egypt.Gomaa AI, Hashim
MS, Waked I.PLoS One. 2014Comment by str31: Treatment of hepatitis
C: a systematic review.Kohli A, Shaffer A, Sherman A, Kottilil
S.JAMA. 2014 Aug 13;312(6):631-40. doi: 10.1001/jama.2014.7085.
Review
The late presentation of disease, as well as expensive
serological tests and polymerase chain reaction (PCR) to identify
viral RNA, present diagnostic conundra, especially in developing
countries.To date, metabonomic studies of plasma in patients with
hepatitis have largely concentrated on profiling of the lipid
content. An exemplar of this is provided by Cassol and colleagues
(REF). Distinct clusters of altered lipids correlated with markers
of inflammation (interferon-α and interleukin-6), microbial
translocation (lipopolysaccharide (LPS) and LPS-binding protein),
and hepatic function (bilirubin). Lipid alterations showed
substantial overlap with those reported in non-alcoholic fatty
liver disease (NAFLD) (REF). Increased bile acids were associated
with noninvasive markers of hepatic fibrosis and correlated with
acylcarnitines, a marker of mitochondrial dysfunction (REF).
Urinary metabolomics has been shown to be useful in studies from
China, an example of which is from Zhang and colleagues, where a
panel of 11 discriminant metabolites was found using UPLC-MS (REF).
In the largest study of its kind, Ladep and colleagues found in an
African population discriminant metabolites that distinguish
patients with hepatitis B from those with cirrhosis and those with
liver cancer using urinary NMR profiling (REF)Comment by str31:
Management of liver disease in Nigeria.Ladep NG, Taylor-Robinson
SD.Clin Med. 2007 Oct;7(5):439-41. Review
Non-Alcoholic Fatty Liver Disease (NAFLD)
Fatty liver, or hepatic steatosis, is a common finding in the
general population and is a frequent cause for elevated serum
aminotransferase levels (REF). This condition is the hepatic
manifestation of the metabolic syndrome, where insulin resistance
is the underlying factor with diabetes mellitus, obesity and
hypertriglyceridaemia prominent clinical sequela. Steatosis may
occur with other assaults on the liver, particularly in alcohol
abuse and also in chronic HCV infection. NAFLD is defined as the
presence of hepatic steatosis in the absence of excessive alcohol
consumption. It encompasses a spectrum of disease, ranging from
simple steatosis to steatohepatitis (NASH), with or without the
development of fibrosis and cirrhosis. Estimates of hepatic
steatosis prevalence vary, but incidence rates are increasing the
world over (REF). Hepatic steatosis is also the commonest
histological abnormality of the liver, affecting about 50% of
patients who abuse alcohol. In addition to the well-known
progression of patients with alcohol-related liver disease, those
with NAFLD may also develop fibrosis and subsequently cirrhosis and
liver cancer (REF). Metabonomic studies in NAFLD, show
dysregulation of bile acid and phospholipid homeostasis (REF) with
a concomitant upregulation of fatty acid β-oxidation. These studies
may provide insight into hepatic metabolism in health and disease
and aid targeted drug discovery.Comment by str31: Non-alcoholic
fatty liver disease: a practical approach to diagnosis and
staging.Dyson JK, Anstee QM, McPherson S.Frontline Gastroenterol.
2014 Jul;5(3):211-218. Epub 2013 Dec 24. ReviewComment by str31:
Obesity and liver disease: the epidemic of the twenty-first
century.Corey KE, Kaplan LM.Clin Liver Dis. 2014 Feb;18(1):1-18.
doi: 10.1016/j.cld.2013.09.019. ReviewComment by str31: Diagnosis
of hepatocellular carcinoma.Gomaa AI, Khan SA, Leen EL, Waked I,
Taylor-Robinson SD.World J Gastroenterol. 2009 Mar
21;15(11):1301-14. Review
Liver Fibrosis and Cirrhosis
Chronic liver injury over a period of months to years may lead
to fibrosis. There are a variety of causes of liver injury, which
include viral hepatitis, alcohol abuse, metabolic insults such as
accumulation of iron, copper or fat, autoimmune disease and drugs.
Liver injury leads to initiation and perpetuation of inflammatory
processes, which lead to hepatic stellate cell (HSC) activation and
resultant fibrosis. Traditionally, fibrosis has been considered
reversible, while the end-stage, cirrhosis, is irreversible.
However, with elimination of the cause of liver injury, a number of
studies have demonstrated regression of fibrosis in animal models
and in humans.(Dufour, DeLellis, & Kaplan 1997;Hammel et al.
2001;Iredale et al. 1998;Iredale 2001;Kweon et al. 2001;Poynard et
al. 2002). Elucidation of the process of fibrogenesis enables
markers of disease severity and potential targets for therapeutic
intervention to be developed.
The development of liver fibrosis is a complex process, which
involves a number of mechanisms and pathways. HSCs have been shown
to be integral to initiation and perpetuation of fibrosis through
interaction with metabolites, growth factors, other cell types and
ROS. Oxidative stress and consequent lipid peroxidation has been
demonstrated to occur in a number of hepatic disease states and may
represent a “common pathway” in fibrogenesis (REF).Comment by
str31:
Given Inflammation, steatosis, fibrosis and fibrogenesis are
complex multistep processes. It would be surprising if a single
biomarker were able to describe liver disease completely.
Accordingly, combinations of markers and modalities may describe
disease more accurately and reproducibly than one marker alone.
Studies of marker combinations should be performed to establish
optimal combinations, in terms of numbers of tests, accuracy of
combinations and the provision of complementary information from
the test components. Candidate markers differ widely in the
equipment and expertise required, so cost-benefit analyses compared
to routine liver biopsy are warranted. Serum and urinary panel
markers need to be investigated longitudinally in response to
intervention in a number of disease states. As histological
assessment of liver biopsy is itself a surrogate marker of liver
disease, the challenge is to develop and validate protocols
correlated to clinically meaningful outcome measures. Further
research into non-invasive technologies for the assessment of
chronic liver disease is required to optimise these techniques, to
correlate with clinical outcomes and to incorporate them into
validated management algorithms.
Bile duct disorders
Liver Cancer
HCC is the third commonest cause of cancer death worldwide. Most
HCC occur on the background of chronic liver disease and while the
majority arise in Africa and Asia, where chronic hepatitis B and C
virus infection (HBV and HCV) are the most important risk factors,
the additional problems of excess alcohol consumption amongst the
young, and increasing obesity in the population means that even in
the developed world, HCC is rising steadily on the background of an
alarming rise in the numbers of patients who have cirrhosis (1). In
many countries the prognosis for patients with HCC is poor, with
5-year survival rates of less than 5%. With early diagnosis, HCC is
curable by liver transplantation, surgical resection,
radiofrequency ablation or chemoembolisation (2). HCC survival
rates are still low, even in the Europe and North America, because
patients often present late, when tumours are too large for
curative treatment. Current gold-standard screening tests for HCC
diagnosis are 6-monthly ultrasound scanning and blood analysis for
alpha fetoprotein (AFP) levels in patients with known liver
disease. However, these tests are expensive, only have 75-80%
sensitivity and specificity, and are not performed routinely in
primary care in the UK. Furthermore, AFP analysis alone only picks
up around 70% of HCC cases (3). A diagnostic urine test for HCC
would be a paradigm shift in liver cancer screening. It would
provide a practical and cost-effective test, easy to use in primary
care, and help save thousands of lives. Clinically and economically
such an approach would have a global impact, not only in the UK but
also in severely resource limited settings, such as sub-Saharan
Africa. Urinary metabolomics studies have shed light on potential
discriminant biomarkers in an Egyptian population with hepatitis C
and a West African population with hepatitis B, distinguishing
those with mild liver disease from those with cirrhosis, from those
with cancer (REFS). If borne out in larger studies, then this may
hold promise for the ultimate development of a urinary test, such
as a dipstick, that could be used at village level to screen for
the complications of chronic liver disease, such as HCC, in order
to reduce the burden of late presentation and improve outcomes.
Hepatic Encephalopathy
Hepatic encephalopathy (HE) is characterized by neuropsychologic
sequela that complicate the natural history of cirrhosis patients,
as a result of essentially unfiltered blood reaching the brain
owing to hepatocellular failure and/or portosystemic shunting.
Diagnosis is currently achieved using psychometric tests (REFS).
The main disadvantage is that they are time-consuming to perform in
a busy liver clinic and there is debate about the applicability of
some of these tests across cultural, linguistic and educational
ranges. There is therefore an interest to determine objective
biomarkers of HE in order to develop a rapid assessment of disease
in terms of metabolic profile, rather than cognitive function. To
date, most metabonomic studies have concentrated on changes to the
urinary or plasma metabolic profile induced by hepatic
encephalopathy treatment, which causes alteration to the gut
microbiome (REFS).Comment by str31: Neuropsychological tools in
hepatology: a survival guide for the clinician.Montagnese S, Schiff
S, De Rui M, Crossey MM, Amodio P, Taylor-Robinson SD.J Viral
Hepat. 2012 May;19(5):307-15.
Phenotypically augmented patient stratification
The use of spectroscopic techniques to extract deep phenotypic
descriptors and to follow the progression of these features through
time is unprecedented in the clinical environment and is a powerful
approach to dynamic patient stratification based on enhanced
diagnostics. This will be made possible via extension of approaches
such as pharmacometabonomics{Clayton, 2009 #38}, novel high
fidelity MS imaging techniques, and new statistical tools for
spectral{Robinette, 2013 #9} and chemical image{Veselkov, 2014 #2}
analysis that facilitate enhanced biomarker recovery and
identification of mechanistic pathways. The demand for improved
monitoring and treatment of individual patients as they undergo a
series of unique investigative and therapeutic procedures within
the healthcare system is driven by a combination of scientific,
social and economic factors. Biologically similar sub-populations
of patients are likely to share similar post-interventional
response trajectories that can be monitored in a dynamic
environment. Combining the potential of advanced metabolic
profiling technology with enhanced data processing capacity will
enable the step change in ‘translation,’ to meet the growing demand
for dynamic patient stratification to improve the delivery and
sustainability of optimised healthcare. We envisage a model that
permits sampling of patients throughout their entire patient
journey from primary diagnosis through intervention and
response
One of the most effective approaches for understanding the
pathogenesis of various chronic diseases is the longitudinal
deep-phenotyping of patients at various stages of the disease or
the clinical patient journey. Deep phenotyping encompasses the
phenotypic analysis of all accessible biological fluid samples
including blood, urine, saliva, mucosal smears etc. which may carry
relevant information regarding the underlying disease. While the
corresponding metabolic phenotyping includes the untargeted NMR
spectroscopic and mass spectrometric methods already offered
Pharmacogenomic and Pharmacometabonomic Approaches to “Dynamic”
Stratification of Patient Populations: Pharmacogenomics, or the
study of the impact of genomic variation on treatment response, has
been used as framework on which to base patient stratification. For
example, TrastuzumabTM is effective as a chemotherapeutic for only
30% of breast cancer patients, corresponding to those who
overexpress the ERBB2 gene that encodes the human epidermal growth
factor receptor (HER2) protein{Arteaga, 2012 #5}. The
pharmacogenomic approach to selecting appropriate patients can be
complemented by pharmacometabonomics, or prediction of an
individual’s response to a drug based on a baseline profile, which
can capture the influence of both genetic and environmental
contributions and has been shown to be a useful tool in predicting
adverse drug reactions{Kwon, 2011 #8}. We, and others have applied
pharmacometabonomics in dynamic personalised medicine studies, for
example in identifying colorectal cancer patients with high
susceptibility to capacetabine toxicity based on NMR spectroscopic
models of blood plasma composition taken prior to
intervention{Backshall, 2011 #7}. WeHere we propose to apply deep
metabolic phenotyping and integration of genomic and metagenomic
data to extend this concept in order to select sub-populations of
patients that would benefit from specific treatment regimens. The
MCSMT presents a new dynamic national resource for stratified
medicine that has potential to impact beneficially on current
healthcare guidelines and practices by identifying more effective
means of patient management for both acute and chronic conditions
as well as rare diseases. An important role of the MCSMT will be to
build upon current research and training programmes in Systems
Medicine being undertaken at Imperial (IC), to increase the UK
cadre of clinicians and scientists who can utilize multimodal data
streams to improve clinical diagnostics and prognostics. Comment by
str31: This whole section doesn’t sit very well – looks as if it
has been liftedComment by str31: ????
Population phenotyping
For population studies of chronic conditions such as
cardiovascular disease, metabolic syndrome, and neurodegenerative
diseases we developed the metabolome-wide association study (MWAS)
approach to identifying associations between metabolic patterns and
risk / prevalence of disease based on spectral data{Holmes, 2008
#10}.
Surgical Metabonomics
More recently the applicants achieved a major breakthrough
regarding the in-vivo metabolic profiling of living cells including
human tissues, cell cultures, bacteria or protozoa. The underlying
Rapid evaporative Ionization Mass Spectrometry (REIMS) technique is
based on the thermal disintegration of cells followed by mass
spectrometric analysis of ionized metabolic constituents. Since
thermal tissue disintegration is widely used in interventional
medicine as diathermy or ablation, the hyphenation of these medical
technologies with on-line mass spectrometric detection results in a
family of capable of the in-situ, in-vivo, real-time metabolic
profiling of vital tissues or associated microorganisms. The REIMS
technique has been demonstrated to provide a solution for the
long-standing problem of intrasurgical tissue identification (cf.
iKnife), for point of care characterisation of needle biopsy
samples and for identification of mucosa-associated bacterial
strains,{Balog, 2013 #34;Strittmatter, 2013 #37} and has the
potential to transform many in situ analytical procedures such as
endoscopy.
Oncological surgery has remained largely unchanged for decades
and clearance of tumour tissue is typically based on arbitrary and
non-objective measurements of clearance, which are often inadequate
with potentially serious implications for the patient. For example,
30% of breast cancers require re-excision for positive margins.
Both NMR and MS technologies have been used to improve surgical
diagnosis. Magic angle spinning NMR technology has been installed
in the surgical unit at St Mary’s hospital (first in the world in
2009) and is able to robustly determine the difference between
benign and malignant tissue in breast, and published in colon
cancer with a high degree of sensitivity and specificity{Mirnezami,
2013 #67}. Mass spectrometric imaging (MSI), of tissues in-situ
creates a new layer of detailed molecular information that can be
overlaid onto traditional histopathological images and opens up
exciting new avenues in molecular pathology. The MSI segment
utilizes Desorption Electrospray Ionization (DESI) method for
imaging, which is particularly well-suited for spatially resolved
metabolic profiling of tissues. DESI was originally invented by
Takats{Takats, 2004 #29}, who has developed the approach further at
IC improving spatial resolution, sensitivity and information
recovery from frozen section material. The method currently allows
the 10-500 µm variable resolution imaging of frozen histological
sections. The MSI metabolic profile can be interrogated by
real-time multivariate statistical analysis of the data to achieve
histopathological classification{Veselkov, 2014 #2}. However, the
specificity of the spectral profiles exceeds the level of standard,
morphology-based classification enhancing diagnostic capability and
providing more refined guidelines for tumour resection etc.
Information earlier accessible only by immunohistochemistry or DNA
sequencing can also be obtained from metabolic imaging datasets as
is shown in the example in in Figure 4 for determination of KRAS
mutation status or HER status of colorectal adenocarcinoma and
breast cancer, respectively. In principle, it should be possible to
replace the complete histological processing of tumour samples
including morphological staining, series of immunohistochemical
stains and genetic assays with a single imaging MS assay.
Furthermore, imaging mass spectrometry is capable of detecting
changes not visible in histological segmentation (e.g. metabolic
changes in the tumour environment), which give a new dimensionality
to tissue analysis. Nevertheless, the limitations of metabolic
tissue imaging are yet to be determined; one of the main goals of
the proposal is to harmonize DESI imaging with other approaches of
molecular pathology and develop an optimized workflow.
Intelligent surgical device (or iKnife) technology that combines
standard surgical dissection techniques with mass spectrometric
analysis of the by-products (smoke, liquefied tissue) of the
dissection is planned to be coupled with biopsy analysis and
endoscopy. In the former case the main driving force is the
instantaneous analysis of biopsy samples, which enables quick
decision making based on a more detailed portfolio of information
than the surgeon’s clinical scoring criteria and intuition. This
improves potential for patient stratification and subsequent
treatment. Spectral interconversion algorithms are being developed,
bridging MSI and iKnife approaches in order to fully utilize the
capabilities of ISD technologies. Briefly, the algorithm enables
the use of high-resolution MSI data for the real-time
identification of ISD data in the pathology laboratory or surgical
environment, which further emphasizes the importance of a database
featuring the metabolic fingerprints of human healthy and diseased
tissue types. The combination of MSI and ISD technologies can lead
to a general paradigm change in histopathology, when the tissue
types are defined by their chemical topology based on concentration
distributions of hundreds of well-defined chemical species)
describing multidimensional data vectors.
Although MS imaging has been used to demonstrate localisation of
proteins in tumours{Le Faouder, 2014 #31} and for experimental
studies in colon cancer{Pevsner, 2009 #32}, we have now moved
towards translating this approach to direct clinical use using
surgical gastrointestinal (GI) tissue analysis for exemplar studies
mapping KRAS mutation status{Gerbig, 2012 #33}.
Towards the Future
metabolic phenotyping and molecular pathology to enrich
stratified medicine research, through the development and
integration of novel analytical technology hubs. This will involve
the development of a suite of new high throughput technologies for
population phenotyping, clinical image based diagnostics and
patient journey modelling, each with potential to impact
significantly on healthcare, building on and extending existing
national facilities.
i) Patient cohort stratification studies linking genes to deep
metabolic phenotypes.
ii) Patient journey phenotyping to monitor and classify patient
responses to therapy through time.
iii) Chemical image enhanced molecular pathology of tissues.
iv) Novel biomarker structure elucidation technologies and
clinical biomarker database construction.
v) Pathways to translation of integrative multi-omic
stratification technologies, tools and models.
The opportunity to create a paradigm shift in clinical
capability leading to changes in practice afforded by the MCSMT is
made possible by a combination of factors: i) the proven synergy of
clinicians and physical scientists embedded in the Faculty of
Medicine and the availability of large, well-defined ethically
approved patient study groups as piloted in multiple BRC projects;
ii) novel and innovative analytical technology and statistical
information recovery tools developed in-house by the applicants;
iii) the ability to capitalise on the analytical framework and data
processing pipelines already established in the MRC-NIHR NPC; iv)
the enhanced computational power afforded by the new MRC-funded MED
BIO programme; v) the longstanding and productive strategic
relationship between the applicants and the principal phenotyping
technology providers (Bruker BioSpin and the Waters Corporation).
Comment by str31: This is all a bit jargon-richComment by str31:
Will need to explain
Many of the current metabolic phenotyping approaches to
stratified medicine worldwide are subjective, empirical,
labour-intensive, unreliable and usually housed in disparate
uncoordinated centres. A key goal will be to standardise upon
reliable and robust protocols and applications that show
statistically superior performance to existing pathology/clinical
methods and to diffuse these internationally such that data can be
efficiently and freely shared thereby increasing the statistical
power of biomarker discovery studies.
In terms of clinical adoption of the new technologies we propose
in this review, it should be emphasised that new discoveries in the
medical field have to overcome barriers of conservative attitudes
that place a burden on new methodologies to be proven at least
better or more cost effective. For this reason, the early
excitement and promise of the new methods in metabolic profiling
now need significant investment to broaden their application base,
and to create a community of users who will collaborate to create
exemplars of the uses and robustness of the new methods in a range
of clinical areas. In addition, by the nature of the novelty of
these approaches, exemplars as yet undetermined will emerge from
the user community to explore how the basic science can be applied
to many areas of patient stratification (frameworks for
stratification of therapeutic response, diagnostics, prognostics
and improved mechanistic understanding of disease aetiologies) and
it is difficult at this early stage to determine where the biggest
impacts will lie.
References
(1) Shariff et al. Hepatocellular carcinoma: current trends in
worldwide epidemiology, risk factors, diagnosis and therapeutics.
Expert Rev Gastroenterol Hepatol 2009; 3(4), 353-367.
(2) Patel M, et al. Hepatocellular carcinoma: diagnostics and
screening. J Eval Clin Pract. 2010;10: 1365-2753
(3) Farinati F et al. Diagnostic and prognostic role of
alpha-fetoprotein in hepatocellular carcinoma: both or neither? Am
J Gastroenterol 2006; 101:524-532.
Fathi F, Majari-Kasmaee L, Mani-Varnosfaderani A, Kyani A,
Rostami-Nejad M,
Sohrabzadeh K, Naderi N, Zali MR, Rezaei-Tavirani M, Tafazzoli
M, Arefi-Oskouie
A. 1H NMR based metabolic profiling in Crohn's disease by random
forest
methodology. Magn Reson Chem. 2014 Jul;52(7):370-6. doi:
10.1002/mrc.4074. Epub
2014 Apr 22. PubMed PMID: 24757065.
Dawiskiba T, Deja S, Mulak A, Ząbek A, Jawień E, Pawełka D,
Banasik M,
Mastalerz-Migas A, Balcerzak W, Kaliszewski K, Skóra J, Barć P,
Korta K,
Pormańczuk K, Szyber P, Litarski A, Młynarz P. Serum and urine
metabolomic
fingerprinting in diagnostics of inflammatory bowel diseases.
World J
Gastroenterol. 2014 Jan 7;20(1):163-74. doi:
10.3748/wjg.v20.i1.163. PubMed PMID:
24415869; PubMed Central PMCID: PMC3886005.
Williams HR, Willsmore JD, Cox IJ, Walker DG, Cobbold JF,
Taylor-Robinson SD,
Orchard TR. Serum metabolic profiling in inflammatory bowel
disease. Dig Dis Sci.
2012 Aug;57(8):2157-65. doi: 10.1007/s10620-012-2127-2. Epub
2012 Apr 10. PubMed
PMID: 22488632.
Figure 1: Relationships between Research Programmes (A-D) and
underpinning technology and informatics systems (E-G): Bridging the
gap between stratified medicine and public healthcare research
paradigms in an integrative technology and computational framework.
Requested infrastructure key: FT-MS – Fourier Transform Ion
Cyclotron Resonance Mass Spectrometer, Q-IMS-ToF MS – quadrupole
ion mobility time-of-flight mass spectrometer, NMR – nuclear
magnetic resonance spectrometer, UPLC- ultra performance liquid
chromatograph, IHC – immunohistochemistry, FISH – fluorescent
in-situ hybridization, SFC-MS – supercritical fluid
chromatograph-mass spectrometer.
{Jimenez, 2013 #23}; Other examples of metabolic phenotyping
enhancements of contributions to understanding of disease
mechanisms include the identification of the toxic mechanisms
responsible for melamine toxicity in baby milk{Zheng, 2013 #24},
ifosfamide toxicity{Foxall, 1997 #25}, Hirmi Valley liver
disease{Robinson, 2014 #27} and in the characterisation of
metabolic associations with genetic mutations (missense mutation in
EHHADH) underlying mitochondrial dysfunction in a subset of renal
Fanconi patients{Klootwijk, 2014 #28}.
That said, a number of applications, such as tissue pathology
using hyperspectral mass spectrometric imaging{Fonville, 2013
#1;Veselkov, 2014 #2} have been identified that could offer
significant benefit and be immediately translatable to UK clinical
environments
We have used NMR, which is an inherently extremely reproducible
technology, as a sentinel technology to screen every sample prior
to MS analysis to identify and remove contaminated outlier samples
(e.g. blood contaminated plasma) that can comprise up to 2% in
epidemiology sets and disrupt UPLC-MS analytical procedures.
Coupled with dedicated instrumentation for specific analytical jobs
and highly trained technicians results in extreme reliability in
the field that cannot easily be replicated with multipurpose
instruments with multi-user bases.
10
MCSMT: PATIENT JOURNEY PHENOTYPING
MRC-‐NIHR POPULATION PHENOTYPING
DATA INTEGRATION, COMPUTATIONAL MEDICINE
AND INFORMATICS: DATA TO KNOWLEDGE
Integrated metabolic databases, clinical
and omics data-‐fusion, pathway
modeling & visualisaPon
A GENERAL
POPULATION PHENOTYPING (disease risk)
B CLINICAL
POPULATION PHENOTYPING
(straPfied medicine)
C PATIENT
STRATIFICATION AND
DIAGNOSTICS
D INTERVENTIONAL
METABOLIC RESPONSE
MONITORING
E BIOMARKER STRUCTURE ELUCIDATION
PLATFORMS
F GENOMIC &
MICROBIOMIC PLATFORMS
(systems reference data)
G MOLECULAR PATHOLOGY (hyperspectral
chemical imaging & biofluid
analysis)
Research
Programmes
Enabling
Techno
logies
PCR, Sequencer, Robotics
Imaging MS, IHC, FISH
Q-IMS-TOF MS, NMR, FT-MS
4 UPLCs Robotics
12 UPLC-MS, 2 NMR
8 UPLC-MS, HT-MS, SFC-MS
Raw data storage, post acquisition processing
MCSMT:PATIENTJOURNEYPHENOTYPINGMRC-NIHRPOPULATIONPHENOTYPING
DATAINTEGRATION,COMPUTATIONALMEDICINEANDINFORMATICS:DATATOKNOWLEDGE
Integratedmetabolicdatabases,clinicalandomicsdata-fusion,pathwaymodeling&visualisaon
A
GENERAL
POPULATION
PHENOTYPING
(diseaserisk)
B
CLINICAL
POPULATION
PHENOTYPING
(strafiedmedicine)
C
PATIENT
STRATIFICATION
AND
DIAGNOSTICS
D
INTERVENTIONAL
METABOLIC
RESPONSE
MONITORING
E
BIOMARKER
STRUCTURE
ELUCIDATION
PLATFORMS
F
GENOMIC&
MICROBIOMIC
PLATFORMS
(systemsreference
data)
G
MOLECULAR
PATHOLOGY
(hyperspectral
chemicalimaging&
biofluidanalysis)
R
e
s
e
a
r
c
h
P
r
o
g
r
a
m
m
e
s
E
n
a
b
l
i
n
g
T
e
c
h
n
o
l
o
g
i
e
s
PCR, Sequencer,
Robotics
Imaging MS, IHC,
FISH
Q-IMS-TOF MS,
NMR, FT-MS
4 UPLCs
Robotics
12 UPLC-MS,
2 NMR
8 UPLC-MS, HT-MS, SFC-MS
Raw data storage, post acquisition processing
MRC-‐NIHR NATIONAL PHENOME CENTER
LARGE SCALE PHENOTYPING CAPACITY
FOR MOLECULAR EPIDIMIOLOGY (NMR AND
MS)
(exis;ng)
LARGE SCALE PHENOTYPING CAPACITY
FOR CLINICAL COHORT STRATIFICATION
(NMR AND MS) (proposed – capacity
building)
TRAINING, STANDARISATION AND SOPs
(exis;ng)
MRC CENTRE FOR STRATIFIED MEDICINE
TECHNOLOGIES
(proposed capability and capacity
building)
BIOMARKERS STRUCTURE ELUCIDATION ENGINES
(STATISTICAL INTEGRATION OF NMR AND
MS)
NOVEL OMICS DATA INTEGRATION METHODS
FOR STRATIFICATION
MOLECULAR PATHOLOGY AND MS IMAGING
TECHNOLOGIES FOR SYSTEMS MEDICINE
PATIENT JOURNEY PHENOTYPING (NMR AND
MS) AND NEW PATHWAYS TO
TRANSLATION)
INTEGRATION OF HARMONIZATION OF
DATABASES, BIOMARKER DISCOVERY SOFTWARE
INTERNATIONAL OUTREACH – LINKS TO
NIH AND OTHER CENTRES –
DATABASES SHARING
WATERS CORP. AND BRUKER BIOSPIN
TECHNOLOGY PARTNERS FOR BOTH CENTRES
Linked gene-‐environment models of
disease risk in the general
popula;on
Phenotypic classifica;on of disease
sub-‐
popula;ons for common and rare
diseases
Na;onal enhancement of exper;se in
systems
medicine
Improved understanding of disease
mechanisms
and improved stra;fica;on/diagnos;cs
Improved understanding of localised
;ssue
heterogeneity-‐linking pathways and pathology
Linked gene;c and phenotypic
stra;fica;on of pa;ents in the
clinic
LIKELY HEALTHCRE IMPACTS
LIKELY HEALTHCRE IMPACTS
