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Strategies for Rational and Personalized Cancer Biomarker Discovery
Thu, September 20 2012, 12:00 AM
Posted By:
Sciclips
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"A biological marker or biomarker is defined as a characteristic that is objectively
measured and evaluated as an indicator of normal biologic processes, pathogenic
processes, or biological responses to a therapeutic intervention". A biomarker can be
a physiologic, pathologic, or anatomic characteristic or measurement that is thought
to relate to some aspect of normal or abnormal biologic function or process. (Source:
US FDA (1). Ironically,
most of the reported studies, knowingly or unknowingly, undermine this definition.
For example, more than 8000 biomolecules differentially expressed in pancreatic cancers, based
on the data collected from published literature, are included in the compendium of
potential biomarkers of pancreatic cancer (2). Moreover, this data does
not include biomarkers that are reported in patents or patent applications, which may
not be published in scientific journals that are indexed by PubMed or similar databases.
Probably, more than ten thousand biomolecules can be considered as potential pancreatic
cancer biomarkers, which may or may not be functionally related to pancreas or may not
be even associated with pancreatic cancer. Likewise, more than 100 serum biomarkers
have been reported for lung cancer in never smokers from a single report (3).
The question is how will we justify the presence of thousands of biomarkers for a single
disease like pancreatic cancer? It is true that cancer is a complex disease, however,
the "complexness" associated with cancer should not be justified as a factor that
contributes towards the incidence of multiple biomarkers for a particular type of cancer.
Despite the fact that thousands of biomarkers can be associated with a cancer type, this
may have significant scientific and clinical consequences. Most importantly, scientific
reports on the presence of thousands of biomarkers for a single disease can slow down
the development of clinical diagnostic tools, increase the cost of developing disease
detection assays, and may also lead to the loss of confidence in biomarkers and its
potential in therapeutic and diagnostic applications, especially in personalized medicine.
In order to address these issues, we have attempted to understand and dissect potential
scientific reasons behind the occurrence of thousands of biomarkers for a specific
disease. Based on our analysis, we believe that an efficient and robust biomarker
discovery platform, which can lead to the development of reliable and robust diagnostics
assays, should be developed by integrating comprehensive understanding of patients
phenotypic, genetic and socio-environmental characteristics along with biological and
functional relevance of all biomolecules that may be potentially identified and called as
biomarkers. In order to achieve this, we believe, it is necessary to adopt fundamental
changes in our current scientific biomarker discovery approaches and philosophical
perceptions on biomarkers. Therefore, we propose targeted discovery platforms as a
viable and innovative option. Concurrently, we put forward several innovative strategies
for developing rational and personalized biomarker discovery platforms, which might
open up new avenues for efficient and reliable biomarker discovery for clinical
diagnostics applications.
Are all identified biomolecules can be called as biomarkers?
Biomarkers can be very powerful and attractive tool for the early detection of diseases
such as cancer, autoimmune diseases, neurological diseases, cardiovascular diseases etc.
Early detection of diseases will help in implementing effective preventive measures as
well as treatment methods, which will prevent morbidity and mortality from diseases.
Biomarkers can also be used for stratifying patients for specific treatment methods, for
predicting toxicity and side effect effects of drugs etc, which may lead to the development
of robust personalized medicine strategies. Moreover, discovery of predictive biomarkers
may lead to the development of non-invasive (non-surgical) methods for the early detection
of diseases using biological fluids such as blood, saliva, urine or imaging techniques such
as CT scan, nuclear imaging (PET and SPECT) and magnetic resonance imaging (MRI).
Imaging biomarkers offers unique advantages over serum biomarkers for the early detection
of diseases like cancer, primarily because of the fact that cancer specific biomarkers may not
be always present in the serum or other body fluids.
Whether body fluids or tissues are used in the discovery of biomarkers, thousands of
biomarkers that are currently being reported may not be true biomarkers of the target
disease, rather it may be a complex mixture of biomarkers, which may include target
disease specific biomarker as well as biomarkers or biomolecules associated with other
diseases, infections, gender, race/ethnic backgrounds, geographic-environmental factors,
psychiatric condition/diseases and nutritional factors (nutritional biomarkers) (Fig. 1).
Potentially, these non-target biomolecules present in the test sample, like serum, can be
misidentified as target disease specific biomarkers depend on the control samples used.
Incidence, prognosis and drug response in cancers have been shown to be associated with
several factors, in addition to genetic or epigenetic factors (4,5,6,7), such as
geographical location (weather and latitude) (8), environment (9,10), gender (11,12,13,14,15), race (16,17,18), food habits (19), deprivation (a key environmental factor) (20,21,22,23), diet-
disease interactions (24) etc. Besides, same type of
cancer can have differential expression of biomolecules induced by different mutant
alleles of a specific cancer gene (25). Probably, gene-environmental interactions are
associated with differential regulation of genes or downstream signaling pathways of
mutant genes and SNPs (26,27,28), though some studies have
shown that environmental factors were not associated with SNPs in prostate cancer risk
(29). The most studied ovarian
cancer biomarker, CA-125 levels, was shown to be associated with age, hormone-
replacement therapy, smoking habits, and use of oral contraceptives (30). Likewise, association of the
expression of invasive breast cancer biomarkers with age and molecular subtypes (31), favorable profiling of
cardiac biomarkers with increased walking duration in elderly subjects (32), gender-related pathophysiological
conditions with biomarkers of acute coronary syndromes (33) and weight loss with breast cancer
biomarkers (34) have been reported. In addition to the above-
mentioned factors, incidence of neuropsychological diseases/disorders can influence
biomarker identification, depending on patients neuropsychological conditions test
samples may also contain biomarkers or biomolecules associated with these disease/
disorders. Gene or protein expression changes or epigenetic modifications, mostly
present in serum, have been reported to be associated with mood disorders (35,36,37), personality disorders (38, 39, 40), depression (41,42,43,44,45,46), anxiety/stress (47,48,49,50), schizophrenia (51,52,53,54),
emotions (55), intellectual
inability (56,57), deprivation-social experiences and social stress (58,59,60, 61), suicidal tendency (62,63,64), child abuse (65), drug abuse (66,67) etc. Salivary protein analysis was
shown to be a potential diagnostics tool for various psycho-physiological states such as
depression (68). These
studies indicate that biomarkers associated with various diseases or disorders, including
response biomarkers to various drug treatments, may be present in the test sample.
Fig. 1: Complexities associated with serum based biomarker discovery
Possibly, these biomolecules can be wrongly identified as a biomarker of the target disease, if
the control samples are not identical to the test samples with respect to the genetic and
environmental factors mentioned elsewhere in this blog. Probably, non-disease target
biomarkers may influence the expression of a target disease specific biomarker through
protein-protein interactions, transcriptional regulation or epigenetic modifications etc.
Occurrence of diseases or disorders other than the target disease in patients may result in
aberrant expression of biomarkers specific target disease or may result in the generation
of new biomarkers due to disease-disease interactions and its influence in genetic or
cell signaling pathways. Perhaps, the incidence of multiple biomarkers for a specific
disease or variations in the occurrence of biomarkers from different studies or the lack of
reproducibility in biomarker studies may be due to the presence of other contaminating
biomarkers, which are present in test samples and absent in control samples. These
contaminating biomarkers may appear to be functionally associated with the target
disease (e.g. immune response markers, cell signaling biomlecules etc.). We are not
sure how many reported studies, including genomics or proteomics based studies that
generally report multiple biomarkers for a specific disease, have analyzed patients
history or the presence of other diseases or disorders in patients whose samples were
analyzed for discovering biomarkers. The lack of in-depth understanding of patients
genotypic, phenotypic and environmental background may be one reason why we could
not identify cancer biomarkers with high predictive value, though several biomarkers
were reported to have high sensitivity and specificity.
Probably, some biomarkers may be associated with body response to cancer, not really
relevant to cancer, and may represent the solid tumor induced changes in the surrounding
tissues (69) or immune
system (70). Therefore, it
may be very difficult to differentiate cancer biomarkers from biomarkers associated with
immune response to tumor formation (71) or biomarkers of other types
of diseases or disorders. Another source of scientific confusion is with respect to the
reports on same protein as a biomarker for multiple diseases or disorders. For example,
growth-differentiation factor 15 (GDF-15) was reported as a potential serum biomarker
of not only for colorectal cancer (72) and liver cancer (73) but also for increased mortality in cardiovascular disease (74,75,76), determining the risk by chrysene
exposure (77), diabetes mellitus (78), thalassemia (79, 80), systemic sclerosis pulmonary
arterial hypertension (SSc-PAH) (81), response to aurora kinase inhibitor
(Danusertib) treatment (82) or histone deacetylase inhibitor treatment (83). Another example is myeloperosxidase
(MPO), which is associated with both inflammation and oxidative stress and is expressed
in activated neutrophils (84).
MPO was shown to be a potential serum biomarker of breast cancer (85) and ovarian cancer along with free
iron (86). MPO was also shown to be
a biomarker for diagnosis or risk stratification of patients with acute coronary syndrome
(ACS) and other cardiovascular diseases (87,88,89,90,91,92,93,2,94,2,95,96,97), for risk stratification in
patients with heart failure (98), skin irritation and inflammation (99),
greater White matter hyperintensities volume (WMHV) in stroke-free individuals (100) and for assessing disease activity and
response to therapy in ulcerative colitis patients (101). In addition to this, elevated expression of MPO was
shown to be associated with significant increase in the risk of death or myocardial
infarction (MI) patients with ACS (102
). Identification of a biomarker with multiple disease targets, whether it is specific or
non-specific to the target diseases, may affect the diagnostic sensitivity and specificity of
biomarkers, and the cost associated with screening these biomarkers for its diagnostic
potential. For example, the MUC-1 mucin glycoproteins CA 15.3 and BR 27.29 were
identified as the best breast cancer biomarkers by the European Group of Tumor
Markers identified, but due to the low sensitivity and specificity they could not
recommend these marker proteins for breast cancer diagnosis (103).
Increased expression of CA 15-3 was also shown to be associated with chronic active
hepatitis, liver cirrhosis, sarcoidosis (104), hypothyroidism (105), megablastic anemia (106), and beta-thalassaemia (107). In biomarker discovery,
resources could be effectively utilized for developing clinical biomarkers with high
predictive values in less time, if the biomarkers were identified and characterized in a
systematic way by integrating multiple scientific parameters, without any technology or
scientific/laboratory expertise driven biasness.
Prevalence and predicative value of biomarkers in personalized biomarker
discovery
In current biomarker based clinical diagnostic development, predictive values of
biomarkers are determined based on the prevalence of diseases (e.g. cancer) in general
population. Thus, biomarkers with high specificity and sensitivity can have low
predictive value because of the low prevalence of cancer. Is it truly a right approach for
personalized biomarker development for clinical diagnostics applications? For example,
liver cancer can be caused by hepatitis B virus (HBV) infection, hepatitis C virus (HCV)
infection, aflatoxin, cirrhosis caused by chronic alcohol consumption, female hormones
and anabolic steroids or Opisthorchis felineus infection (108,109). It has been
reported that the aflatoxin-DNA adducts can be used as a biomarker for the diagnosis of
liver cancer in patients who live in regions with aflatoxin exposure (110). On the contrary, if aflatoxin-
DNA adducts is used as a biomarker for the diagnosis of liver cancer caused by viral
infections or carcinogens, it may be unsuccessful. In this case, aflatoxin-DNA adduct biomarker will have very low predictive value because the prevalence is calculated based on general population, without considering the epidemiological impact. Moreover, lack of patients
with aflatoxin induced liver cancer in the study population may also result in the rejection of
this biomarker for further clinical analysis. However, aflatoxin-DNA adduct biomarker may
have high predictive value if prevalence of liver cancer is calculated based on the population in
eastern China with high aflatoxin exposure, rather than the general population. Likewise, liver cancer caused by hepatitis B virus (HBV) is relatively uncommon in regions where
HBV infection is not endemic, therefore the predictive value of biomarker associated
with HBV induced liver cancer should be calculated based on prevalence of liver cancer
in HBV endemic areas (111). It is assumable that biomarkers associated with HCV induced liver cancer can be
different from HBV induced liver cancer, because of the differential associations of two
infections in complex biological pathways. Another example is the incidence of lung
cancer in smokers and never smokers, where the identification of common biomarker(s)
for the detection of lung cancer in these two groups of population can be a difficult task
because the genetic risk factors and pathways of molecular carcinogenesis are different in
smokers from never smokers (112,113,114,115).
Fig. 2: Prevalence and predicative value of biomarkers in personalized biomarker discovery
The predictive values of biomarkers associated with smokers can be low if prevalence is calculated based on the occurrence of lung cancer in the
general population where smoking is not prevalent or prohibited. Therefore, for
developing personalized diagnostics or prognostics biomarkers, current methods of
determination of predictive value biomarkers based on prevalence of diseases in
general population needs to be reconsidered. We propose a modified approach for
calculating the predictive value of biomarkers in association with prevalence of disease
in patients geographical location, ethnicity, socio-environmental background etc., which
are derived from epidemiological and population genetics studies (Fig. 2). This
personalized biomarker discovery approach may help in developing robust clinical
cancer diagnostic tools, which may vary among cancer types. Another potential
challenge will be in calculating the predictive value of personalized biomarkers, which
are highly specific to an individual or a small group of patients. For example,
personalized tumor biomarkers have been reported for detecting patient-specific
translocations in leukemias and lymphomas using next generation sequencing method
(116). In this case,
the predictive value of personalized rearranged ends reported in this work will be low
because of the low specificity of this marker in detecting leukemias and lymphomas,
which are detected by more general translocation markers. Therefore, diagnostic
application of personalized biomarkers derived from clinically validated biomarkers,
especially gene mutation or SNP biomarkers, may require a different approach for
determining the predictive value of biomarkers.
Strategies for rational or targeted biomarker identification
Rational biomarker discovery approach has been proposed, especially by proteomics
aproaches that involve integration of genetics, animal models, clinical studies and
computational biology (117,118). Considering the potential complexities associated with biomarker discovery,
as described earlier, the proposed proteomics based rational biomarker identification
may have limitations since this approach does not consider non-genetic factors,
moreover, technical limitations of proteomics methods can be a concern (119,120). Thus, we propose five different approaches for
rational biomarker discovery such as 1) Comprehensive genome-scale analysis based
rational genetic biomarker discovery 2) Cell or tissue or organ specific function based rational
or targeted biomarker discovery 3) Use of validated tissue/organ specific biomarkers or
therapeutic drug targets for identifying non-invasive biomarkers, 4) Epidemiology-driven
biomarker discovery for developing personalized diagnostic tools and 5) Integrated
bioinformatics approaches for rational biomarker discovery.
1. Comprehensive genome-scale analysis based rational genetic biomarker
discovery
Comprehensive genome scale analysis of various diseases can be a powerful tool in
identifying potential therapeutic drug targets and biomarkers, especifically rational
genetic biomarkers. Various methods such as whole genome or exome sequencing,
mapping of epigenetic modification, mRNA/miRNA profiling and DNA copy number
variations have been adopted for analyzing disease specific genome wide aberrations.
National Cancer Institute (NCI) has initiated a project, Cancer Genome Atlas, to map
genetic changes in 20 cancers (121) and the
results from these studies have been published for ovarian cancer (122) and
colorectal cancer (123). Although these studies provide in-depth information
on cancer specific mutations or somatic copy number alterations (SCNAs) or DNA
methylation variations, currently available data may be inadequate for developing
reliable clinical biomarkers or personalized diagnostics tools. The most obvious reason is
because these studies used limited number of patient samples, without considering the
genetic (e.g. population heterogeneity) and non-genetic factors (e.g. environmental) that
may influence the occurrence of disease specific gene mutations or genetic variations.
Moreover, the mixed results obtained from IGF2 response to dalotuzumab (anti-IGF1R
antibody) and its correlation with cancer genomics studies (124) raise more
challenges in genome scale analysis based biomarker discovery. Incidence and prognosis
of colorectal cancer have been reported to be genetically associated with several factors
such as with tumor location and gender (125), geographical variations and diet differences (126,
127,128), ethnicity (129) etc. In a Danish
case-cohort study, colorectal cancer risk was found to be associated with diet and
lifestyle, which may modify inflammatory response in colon by altered IL-10 expression
(130).
Similarly, ovarian cancer risk was shown to be associated with environmental factors (131), diet (132), oral contraceptive use, tubal
ligation (133), family
history (hereditary) (134, 135), population specific differences in
cancer susceptibility and allele frequency of gene polymorphism (136, 137) etc. These
observations indicate that in comprehensive genome scale analysis population specific
genetic heterogeneity influenced by environmental factors or lifestyle need to be
critically correlated with the genetic incidence of diseases. It is obvious that genome
scale studies can be a powerful tool in rational discovery of genetic biomarkers for the
early detection and predicting the risk in developing specific diseases. However, for
developing reliable clinical diagnostics tools, genome scale analysis should include more
patients and integrate patient characteristics (geographical location, disease states,
presence of non-target diseases and disorders, gender, sex, ethnicity, food habits etc.)
with genetic incidence of diseases. It has been shown that genetic background and diet
can significantly impact preclinical results and it has been proposed that genetic
background, sex, diet, and disease model should be considered in preclinical studies (138). Familial
incidence, environment interactions, genetic interactions and segregation of cancer
biomarkers needs to be considered for personalized biomarker discovery.
2. Cell or tissue or organ specific function based rational or targeted biomarker
discovery
In this approach, cellular or organ or tissue specific functions will be targeted for
identifying potential biomarkers that are altered during early tumor formation. The key
goal of this approach will be to identify early signs of cancer or tumor formation, not the
after partial or complete manifestation of tumors that can be diagnosed using anatomical
or physical symptoms, biopsy and biomedical tools/instruments. For example, initiation
of tumor formation in pancreas may most likely affect the normal functions of pancreas,
which in turn affect changes in gene expression, protein expression, post-translational
modifications, transcriptional machinery, cell signaling regulation, metabolic pathways
etc. (139,140,141,142,143,144). These changes or modification
can directly reflect on pancreas function or indirectly affect other organs/tissue functions.
Thus, the idea is to identify changes in levels of expression or modification or functional
alterations of pancreas specific biomolecules induced by biomolecules associated with
early tumor initiation process. Cell signaling pathways and biomolecules associated with
tumor microenvironment, epithelial-to-mesenchymal transitions and tumor
microenvironment interactions, stromal microenvironment (immune cells) (127,145,146), pseudopodium (147,148,149), cancer stem
cells (150) or exosomes
(151) could be potential targets for identifying biomarkers for the early
detection of cancers. The strategy is to identify the potential gene or protein or
metabolite changes that can occur in pancreas by tumor initiating factors and cross-
functional interactions of these molecular changes with other organs/cellular
compartments, can be achieved by intelligent data mining, pathway analysis and
predictive models. Considering the fact that angiogenesis, tumor invasion and metastasis
involve other parts of the body, identification of molecular changes in body fluids (e.g.
serum) induced by factors associated with cancer initiating biomolecules will be required
for the early detection of diseases. Systematic functional and pathway analysis can
pinpoint potential changes at molecular levels, which can be critically analyzed for the
identification of potential biomarkers. Cancer induced changes in the expression or
modification of biomolecules within the tissue or organ or cell can be used for imaging
applications, for example, using membrane protein targeting imaging agents or using cell
penetrating imaging agents that can detect target biomolecules inside a tissue or organ.
Identification of tissue or organ specific metabolomic biomarkers may offer unique
advantages over gene or protein markers for rational identification of biomarkers. Above
all, imaging based early detection of cancer may be an attractive and feasible approach,
because of the limitations to finding serum specific biomarkers that can predict early
tumor development in an organ/tissue. Imaging biomarkers for the early detection of
cancers may include tumor microenvironment biomolecules (proteins, metabolites,
miRNA etc) or cell-cycle regulated proteins or apoptosis related proteins or tumor
antigens or epigenetic modifications, which can be detected using non-invasive cancer
imaging techniques. Targeted identification and validation of these biomarkers are
required to achieve this goal.
3. Use of validated tissue/organ specific biomarkers or therapeutic drug targets
for identifying non-invasive biomarkers
Several experimentally validated tissue or organ specific biomarkers have been reported
for various cancers and other diseases (152,86). Biomarkers that are validated
using clinical trials or multiple patient derived samples can be used for identifying new
biomarkers for developing non-invasive methods for the early detection of diseases. In
this approach, combination of comprehensive data mining and functional-context based
pathway analysis of validated biomarkers might lead to the identification of new
biomarkers that can be used for non-invasive diagnostic method using body fluids or
imaging techniques. For example, tumor-associated calcium signal transducer 2 (TROP2)
was identified as a potential biomarker for various cancers such as colorectal cancer,
gastric cancer, SCC of the oral cavity, and pancreatic cancer (153,154,155,156). TORP2 is a cell-surface glycoprotein overexpressed only in
tumor cells, not in normal cells, and was shown to be associated with tumorigenicity,
aggressiveness, and metastasis (157,158).
However, TROP2 was only overexpressed only in 55% of pancreatic cancer patients and
64.1% of the squamous cell carcinoma (SCC) cases studied (159,160). This low predictive value
of TROP2 might prevent using this as a biomarker for diagnostic or prognostic
applications. The absence or low expression of TROP2 may be explained based on the
impact of genetic make-up of patients, target disease-non-target disease interactions, inter
and intra biomolecular interactions, environmental and other factors that we have
mentioned elsewhere in this blog. Since TOPR 2 is a tumor antigen, one can expect that
this protein may play a role in the induction of cancer, and the identification of
transcriptional or protein-protein interactions and other cell signaling pathways where
TROP2 is involved may aid in identifying potential biomarkers that are directly or
indirectly associated TROP2. TROP2 was shown to be associated with several
transcription factors such as TP63/TP53L, ERG, GRHL1/Get-1 HNF1A/TCF-1, SPI1/
PU.1, WT1, GLIS2, AIRE, FOXM1 and FOXP (161). Probably, through intelligent data mining and
comprehensive functional mapping, we might be able to identify changes or
modifications in biomolecules that are directly or indirectly associated with TORP2. In
addition to this approach, tumor related mutations can also be utilized for the
identification of tumor specific early biomarkers, primarily by analyzing the differences
in the expression of downstream activators or repressors induced by different cancer gene
specific mutant alleles (162). KRAS mutation associated altered pathways in lung
tumors have been identified using next-generation sequencing technology (163).
These and similar approaches will help in developing novel technologies for rational and
personalized cancer biomarker discovery.
Like validated biomarkers, therapeutic drug targets can also be used for rational
identification of biomarkers for the early detection of diseases, by using predictive
and comprehensive transcriptional regulatory or protein-protein interaction
mapping and pathway analysis, integrated with patients genetic, mental, physical
and environmental factors. Such biomarkers can be used for diagnostics as
well as theranostics (164) applications.
4. Epidemiology-driven biomarker discovery for developing personalized
diagnostic tools
Utilization of the data/information derived from epidemiological studies on the
incidence, mortality and prevalence of disease like cancer in biomarker discovery will
open up tremendous opportunities for identifying personalized biomarkers as well as for
addressing several challenges faced by current biomarker discovery approaches. The
incidence of cancers have been reported to be associated with vitamin D (165), sociodemographic factors (166), gender nutrition (167), arsenate exposure (168,169), smoky coal (170), low vegetable intake and low fruit
intake (171), red and
processed meat consumption (172),water pollution (173,174,175), infectious diseases ( bacterial, viral and parasitic
infections (176,177,178,160,179,180)
etc. The question is what will be the relevance of this epidemiological information in
personalized biomarker discovery? Several parasitic infections have been reported to be
associated with various cancer such as gall bladder cancer with Salmonella typhi,
bladder cancer with Schistosoma hematobium, squamous cell carcinoma of the
skin and gastric cancer with Helicobacter pylori; and cholangiocarcinoma and
hepatocellular carcinoma with Opisthorchis felineus (181,182,183). Most likely, biomarker(s) associated
with bladder cancer caused by S. hematobium infection can be different from
bladder cancer induced by mutations or carcinogens, because the genetic or epigenetic or
physiological or cell signaling changes that lead to the induction of bladder cancer by
Schistosoma can be different from bladder cancer induced other factors.
Inflammatory cells induced by Schistosoma infection produce carcinogens such
as N-nitrosamines or carcinogenic metabolites and hydroxyl radicals that can induce
mutations, or chromatid exchanges or DNA strand breaks, which may be responsible for
the cancer formation (184,185,186,187). These changes can affect the
gene/protein expression levels or patterns in Schistosoma induced bladder
patients and can be different from non-Schistosoma induced bladder cancer. On
the contrary, critical analysis of Schistosoma and non-Schistosoma
associated bladder cancer patients and the elimination of Schistosoma associated
biomarkers may lead to the identification of valid universal biomarker that can be used
for diagnosis of bladder cancer, though this will be a quite challenging task. Thus, by
understanding the geographical prevalence of diseases, we might be able to develop
rational or targeted biomarker discovery approaches. Information on the geographical
clustering of cancer incidence may provide opportunities for developing personalized
diagnostic tools. Few examples are the association of the incidence of
cholangiocarcinoma and hepatocellular carcinoma with the prevalence of O. felineus infection in T'umen' region of north-west Siberia and the high incidence of liver cancer
in eastern china where high aflatoxin exposure was observed (188,189). Based on these observations, we
believe that epidemiological or population genetics studies can be valuable tools in
developing rational and personalized biomarker discovery.
5. Integrated bioinformatics approaches for rational biomarker discovery
The thousands of biomarkers that have already been reported can be used for the
identification of "true" disease specific biomarkers by integrated bioinformatics
approaches using a combination of computational biology and manual data mining. A
strategy for this approach is shown in Fig. 3.
Fig.3 : Integrated bioinformatics approach for rational biomarker discovery
Advantages of rational biomarker discovery in clinical trials
The effect of multi-parameters such patients age, gender, disease subtypes etc are
considered in clinical trials (multivariate analysis). However, most of the genetic or non-
genetic factors (mentioned elsewhere in this blog) that can affect in identifying reliable
biomarkers are not generally considered in pre-clinical studies or clinical trials.
Rationally identified biomarkers may have unique advantages in clinical trials. These
advantages include 1) implementation of effective rational clinical trial designs, 2 )
development of effective patient recruitment strategies 3) selection and validation of the
most efficient and reliable biomarkers that favor positive outcome in clinical trials and 4)
may improve time and cost-effectiveness of clinical trials. Biomarkers play significant
role in clinical trials, especially in monitoring the efficacy of new or novel drug treatment
methods. For example, the aim of an ongoing clinical trial is to study the effect of
vitamin D supplementation and changes in mammographic density in female patients
using cancer biomarkers, IGF-1 and Ki67. (ClinicalTrials.gov Identifier: NCT01224678
(190). IGF was shown to be associated with
breast, prostate, lung and colorectal cancers (191,192,193,194,195), and vitamin D/vitamin D
analogues were shown to be useful in treating breast cancer (196,197,198,199).
On the contrary, a recent study has found no independent associations between molecules
associated with vitamin D pathway such as IGF-1 and mammographic breast density in
postmenopausal women, after adjustment for body mass index (BMI) (200). This study has also suggested that
the effect of vitamin D on breast cancer risk may not be associated with breast density,
further studies are required to establish these findings. Moreover, IGF1 was also shown
to be a biomarker for assessing metabolic, nutrional, health, and fitness status (201), early Parkinsons
disease (202); and it is also associated with osteoporosis, muscle
weakness, diabetes, hypertension, cardiovascular disease may result from subtle and
chronic vitamin D deficiency (203), metabolic syndrome (204), impaired cognitive
function (205), heart disease (206)
progression of diabetic retinopathy in puberty (207)
and in pregnancy (208),
stroke (209,210) etc. These
observations indicate that rationally identified biomarkers may help in developing
innovative strategies for efficient clinical utilization of biomarkers.
The concepts and proposed models described in this blog article can be used for
developing strategies for rational and personalized biomarker discovery for all types of
cancers as well as other human diseases such as cardiovascular diseases, neurological
diseases/disorders, autoimmune diseases, infectious diseases etc.
Note: This scientific blog is a contribution from Sciclips Consultancy team.
References
References are hyperlinked to respective abstracts or full articles. Please click the reference
numbers to the citation details
Related tools:
Comprehensive cancer biomarker database with companion
diagnostics pathway
Tumor and Tumor Cell
Assays and Protocols
Related blogs:
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for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs
Keywords: personalized biomarker, personal diagnostics, rational biomarker discovery,
targeted biomarker discovery, personalized medicine, theranostics, imaging biomarkers,
Epidemiology-driven biomarker discovery, personalized diagnostic tools, personalized
diagnostics, molecular diagnostics, next generation sequencing, nutritional biomarkers,
clinical diagnostics, cancer biomarkers, biomarkers, biomarker discovery, aflatoxin-
DNA adducts, non-invasive cancer imaging, non-invasive cancer biomarker, tumor
microenvironment, rational genetic biomarkers, personalized medicine, genome-scale analysis, non-invasive biomarkers, biomarker predictive value, biomarker prevalence
Categories:
Biomarkers
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