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Strategies for Rational and Personalized Cancer Biomarker Discovery

Thu, September 20 2012, 12:00 AM
Posted By: Sciclips

"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 Moreover, this data does not include biomarkers that are reported in patents or patent applications, which may not be published in scientific journals that are indexed by PubMed or similar databases.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

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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

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Dennis Cosmatos said
I have worked in Translational Medicine and Biomarker Development for many years and appreciate the issues you raise. I have identified several specific roadblocks to making clinical use of biomarkers that I believe need to be addressed before their promise of personalized medicine will come to be: 1) Understanding of biomarker behavior in HEALTHY individuals should be the first priority as only then will observation of differences from that healthy state be identifiable. The problem is funding large "healthy volunteer" studies and spending money to analyze biomarkers from these people is often difficult to justify to Coporate or Academic financial officers. 2) More care needs to be taken in biomarker validation. I worked with assays that had 20% margin of error and the observed change in diseased subject vs their healthy counterparts was on a 10% level of expression. This requires increadibly large stuides or impossible studies to be able to detect changes. We need to work on demanding more precise assays for these biomarkers, and if they are not available, move on to another biomarker. 3) We need to change our mindset that a specific "code" of biomarkers will bring about occurance of a disease. Simple biologic principles teach us redundancy is key in any biologic system. The example I use is chords on a guitar. They can be formed by hitting numeorus differnet strings on different frets producing the identical sound. There is a logical reason to believe genomic, proteomic, metabonomic and even biomarker expressions take on this redundancy. We need to be looking much futher "upstream" to identify the common "chord" they are producing. 4) Finally, being a statistician, I urge us not to take the simple way out in term of analyses of these complex data only because it uses a well know, easy-to-explain analysis methods. The multiple t-test approach used on early genomic studies was doomed to failure from the start. Even worse, a proper analysis using mathemtical matrices was developed but sits unused because it is too complex for non-mathematicians to understand, but as a biostatistician it makes perfect sense to me and has the advantage of being structured in a biologically appropriate basis as many biological systems may operate in the manner different cells of a mathematical matrix impact on each other in specifying its characteristics.
Posted on Mon, September 24 2012, 07:14 AM

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