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Posts Tagged ‘Biomarkers

Cancer biomarkers can be used for developing assays for clinical diagnosis, identifying patient’s response to a particular drug, optimizing personalized drug treatment regimen (drug dose, drug treatment schedule etc.), monitoring the efficacy of treatment (disease stage, tumor progression, tumor recurrence etc.) and in cancer theranostics. With the growing trend towards the advancement of personalized medicine concept, companion diagnostic tools may play a significant role in patient stratification by identifying patients with positive clinical response to an existing or novel treatment method. However, current limitations in identifying life-threatening side effects of therapeutic drugs may have negative impact on developing efficient drug therapy strategies, often difficult to identify short or long term side effects of drugs during clinical trials. Therefore, there is a need for developing predictive methods and assays for identifying secondary disease causing side effects of drugs. We propose disease specific diagnostic biomarkers as an attractive tool for predicting the occurrence of secondary diseases from a specific drug treatment method. In this blog, we tried to explore the potential of cancer diagnostic biomarkers for predicting therapeutic drug (non anti-cancer drugs) induced cancer occurrence in patients. For identifying biomolecules that might be potentially associated with pioglitazone induced bladder cancer development in patients, hypothesis driven functional integration and identification of biomolecules, incorporating traditional pathway analysis, linked to bladder cancer specific diagnostic biomarkers and drug target (PPARgamma) were adopted. Link to the full blog article: Cancer Biomarker Strategy to Develop Companion Diagnostics for Predicting Prescription Drug Induced Tumors – Analysis using pioglitazone (Actos) and bladder cancer

Related blogs on biomarkers:

Strategies for Rational and Personalized Cancer Biomarker Discovery

Cancer Theranostics – Potential Applications of Cancer Biomarker Database

How to Identify Clinically Successful Biomarkers?

Potential Use of Drug Response-Efficacy Biomarkers for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs

Metabolon vs. Stemina – Are Biomarkers Patents can be Considered as “True Inventions”?

Related tools:

Comprehensive cancer biomarker database with companion diagnostics pathway

Bioprotocols database

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This blog critically analyzes the limitations and pitfalls in biomarker patent process. According to the argument made in this blog, most of the biomarkers patents may not have commercial prospective, though patents are intend to protect the right to manufacture and sell invented products, due to the fact that these biomarkers were identified and characterized without following solid scientific principles and demonstrating clinical applications, which are indispensable for developing commercially viable products. Claimed inventions proposed in patents without adequate scientific research driven supporting evidences and reasonable interpretation of experimental results, as oppose to peer reviewed scientific publications, may not have any scientific value or may not be scientifically acceptable. These patents can also be scientifically mistaken and these patents may not only preclude innovation in biomarker discovery but also hinder the development of low-cost patient care diagnostics products. Read more: >> Metabolon vs. Stemina – Are Biomarkers Patents can be Considered as “True Inventions”?

Related blogs on biomarkers:

Strategies for Rational and Personalized Cancer Biomarker Discovery

Cancer Theranostics – Potential Applications of Cancer Biomarker Database

How to Identify Clinically Successful Biomarkers?

Potential Use of Drug Response-Efficacy Biomarkers for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs

Related tools:

Comprehensive cancer biomarker database with companion diagnostics pathway

Bioprotocols database

The decisive goal of clinical biomarker discovery should be intended for developing high quality and low-cost disease detection/monitoring assays with high diagnostic accuracy. Innovative approaches are warranted for the discovery of clinical biomarkers, with faster bench to clinics timeline, to provide high quality and efficient patient care. At the same time, if we continue with the strategic and technological approaches that are currently being adopted for biomarker discovery and validation, most likely, it may take several years to find clinically viable biomarker/s for complex diseases like cancer or neurodegenerative or autoimmune or other human diseases. Therefore, an ideal clinical biomarker discovery platform, which can lead to the development of reliable and robust clinical diagnostics assays, should adopt an integrated approach that consists of comprehensive understanding of patients’ phenotypic, genetic and socio-environmental characteristics as well as biological and functional relevance of all biomolecules. In order to achieve these goals, two models for the discovery, selection and validation of clinically viable biomarkers are proposed in this scientific blog. Read the full blog: How to Identify Clinically Successful Biomarkers?

Related blogs:

Strategies for Rational and Personalized Cancer Biomarker Discovery

Cancer Theranostics – Potential Applications of Cancer Biomarker Database

Potential Use of Drug Response-Efficacy Biomarkers for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs

Related tools:

Comprehensive cancer biomarker database with companion diagnostics pathway

This scientific blog critically analyzes potential complexities associated with current biomarker discovery approaches. According to the scientific arguments that have been put forward in this blog, 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. Based on our analysis, we believe that an ideal 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. Several innovative strategies for developing rational and personalized biomarker discovery platforms have been suggested in this blog. These strategies include 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. The relevance of disease prevalence and predictive value in biomarker discovery for personalized medicine, utility of rational or personalized biomarkers in clinical trials and applications of rationally identified biomarkers for diagnostics imaging or theranostics have also been discussed. Read the full blog (click on this title): Strategies for Rational and Personalized Cancer Biomarker Discovery

Related tools:

Comprehensive cancer biomarker database with companion diagnostics pathway

Tumor and Tumor Cell Assays and Protocols

Related blogs:

Cancer Theranostics – Potential Applications of Cancer Biomarker Database

Potential Use of Drug Response-Efficacy Biomarkers for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs


1. Plasma biomarkers for the diagnosis of age-related macular degeneration
2. Methylene blue for semen quality diagnosis
3. Gene expression markers for distinguishing between active and latent Mycobacterium tuberculosis infection
4. Hypoxia-related genes as biomarkers for colon cancer or lung cancer
5. Exhaled breath condensate biomarker for the diagnosis of lung cancer
6. Urinary biomarkers of bladder cancer
7. Free light chains as prognostic cancer biomarkers
8. TCPTP protein as a biomarker for the diagnosis of breast cancer
9. cRAF as a predictive biomarker for clinical management of a cancer treatment
10. Biomarkers determining a susceptibility to breast cancer
11. Diagnostic and prognostic biomarkers of renal injuries
12. Securin as a biomarker of decreased survival in adrenocortical carcinoma
13. LKB1 as a predictive biomarker of TOR kinase inhibitor sensitivity in patients with lung cancer or Peutz-Jeghers Syndrome
14. “Cosmetic biomarkers”- for identifying cosmetics that have antioxidant benefit to skin
15. Placental proteins responsible for pathophysiology of preeclampsia
16. pro-BNP as a biomarker for the diagnosis of sepsis
17. Biomarkers for evaluating the risk of death within a year for a person who has undiagnosed shortness of breath, undiagnosed chest pain, or undiagnosed chest discomfort
18. Cytosolic PLA2 alpha as a prosatate cancer biomarker
19. X-chromosome gene expression markers for the diagnosis of autism
Details: http://www.sciclips.com/sciclips/biomarker-news.do

1. Gene expression biomarkers for the detection of acute allograft rejection
2. Method for sequencing peptides and proteins using metastable-activated dissociation mass spectrometry
3. Epigenetic biomarkers for cervical cancer
4. Phospho-cErbB2 as a predictive biomarker for erbB2-directed cancer therapy
5. Metabolomic biomarkers for the diagnosis of prostate cancer relapse
6. Surrogate biomarkers for monitoring the efficacy of specific pharmacological chaperone in Pompe disease
7. miRNA biomarkers for diagnosing myelodysplastic disease syndrome
8. ADAM 12 as a urinary biomarker of bladder cancer
9. Metabolomic biomarkers for the diagnosis of heart failure
10. Biomarker for diagnosing the risk of developing cardiovascular diseases
11. Protein biomarkers for the diagnosis of schizophrenia
12. Noninvasive prenatal diagnosis of fetal aneuploidies using maternal blood
13. Lysyl tRNA synthetase (KRS) and laminin receptor (67LR) as biomarkers of lung cancer or breast cancer
14. Biomarkers for the diagnosis of B cell chronic lymphocytic leukemia (B-CLL)
15. A method of detecting circulating tumor cells (CTCs)
16. Autoantibody biomarkers of the cancer of gingivo-buccal complex
17. Direct quantitation of glycated hemoglobin in blood samples
18. Urinary Cyr61 protein as a diagnostic biomarker of breast cancer or ovarian cancer
19. Gene expression biomarkers for the prognosis in severe sepsis
20. Biomarker genes for the diagnosis of scleroderma
21. MicroRNA biomarkers for the prognosis of lung cancer
22. Biomarkers of systemic sclerosis
23. Biomarkers for diagnosing a form of lower back pain (LBP)
24. Biomarkers for the diagnosis of Kawasaki disease
25. Saposin D and FAM3C protein as biomarkers for Alzheimer’s disease
26. Biomarker of oxidative stress
27. Aging biomarker
28. CAIX as a biomarker for the prognosis in gastric cancer patients
29. Biomarkers for predicting pregnancy outcome
30. Collagen like gene (CLG) as a biomarker of prostate cancer and breast cancer
31. Prostate specific gene UC41 as diagnostic biomarker of prostate cancer
32. IgA anti-OmpC antibodies for the diagnosis of Crohn’s disease
33. Oxygen-18 labeled organic acids for diagnosing metabolic disorders

Details: http://www.sciclips.com/sciclips/biomarker-news.do

It is very interesting to read some of the recent studies and discussions in various scientific publications which revealed the negative aspects of biomarkers. Question is whether these reports or views really conveying the true message or simply trying to create a “negative hype” that the biomarker discoveries are a failure: a conclusion based on discouraging results from a particular technology area? We would like address two different aspects in this blog, first “biomarker hype” and second the current limitations of proteomics technology that may have created this “biomarker hype”.

The diagnostic biomarkers can be used not only for the detection of diseases but also for monitoring the drug effect as well as for defining drug dosage. It is a well-established fact that the use of biomarkers in multiple clinical applications may have significant impact on early diagnosis, clinical monitoring, fine-tune the efficacy of the drug. If the definition of a biomarker is based on the concept of “one biomarker-one disease”, it will be hard to find biomarkers for complex diseases, such as cancer. Our current understanding of clinical diagnostics, infrastructure, and expertise may dictate that the single biomarker approach would be more practical, feasible and reliable. At the same time, we should also consider the fact that major breakthroughs in recent years on gene and protein expression analysis have not only resulted in generating significant scientific discoveries for clinical applications of multiple biomarkers but also the involvement of these biomarkers in disease pathogenesis and progression. On the contrary, we are still spending valuable scientific resources to find perfect single biomarkers for various diseases. A recent study from National Cancer Institute (Cancer Prev. Res. (2011) 4:365) has been cited as a classic example of biomarker hype or the failure of biomarker discovery. According to this report the researchers have tested more than 35 ovarian biomarkers that have been claimed in many scientific publications to be better than CA125, a well established ovarian cancer biomarker. After analyzing hundreds of tissue samples, the researchers found that none of the biomarkers were better than CA125. Now the question is, based on this experimental data, can we truly claim that those biomarkers which did not perform well in this particular experiment are not better than CA125 and hence, are not true biomarkers? Several questions still remains unanswered such as: do we know for sure how many patients who were diagnosed using CA125 biomarker were responded completely or partially or none to the chemo or radiation therapies? If we would have carefully analyzed the CA125 selected patients along with other biomarkers (in combination), wouldn’t it be possible to predict a specific biomarker combination pattern that would have helped us to understand: a) the disease variation among patients and b) the patient response to a given therapy, which will ultimately help us to develop better treatment methods. The biomarkers are not only for the detection of a disease but also to provide clues for the disease state and variation in a particular patient. Expression level of a particular biomarker or a panel of biomarkers can be used to select a specific drug dose or a treatment regimen. Wouldn’t it be reasonable to think that the presence of other biomarkers in addition to CA125 in ovarian cancer patients might have some biological and clinical significance? Otherwise, a test sample from an ovarian cancer patient should not have shown positive to a second biomarker that is not present in healthy controls.

Based on our analysis using data from various clinical trials, we found out that it is very difficult to find biomarkers that have high specificity, in terms of diagnosing a particular cancer in all the patients who were enrolled in a clinical trial. It was also true that in some studies a particular biomarker that was found in majority of cancer patients were absent or merely detected in few patients, though these patients were all diagnosed with the same cancer like the biomarker positive patients. The data from clinical studies needs to be evaluated critically for developing innovative strategies for biomarker discovery and clinical diagnostics assays. All the biomarkers that are experimentally validated need to be carefully evaluated and considered for developing most efficient and clinically useful diagnostic/prognostic assays. Biomarker or proteomics researchers need to develop creative thinking, planning and execution of ideas with a “birds view” not with a “microscopic view” that fails to interpret the long-term impact of research findings beyond the areas of expertise. In other words, for developing an intelligent and smart research plan for high value areas like biomarkers, researchers need to develop broader understanding and knowledge on current and future clinical applications of biomarkers. Constant interaction with clinical and diagnostic researchers in academic and industrial laboratories could be a first step towards achieving this goal. Collaboration with researchers from established pharmaceutical or biotechnology companies will be an advantage to academic researchers to design their strategies and experiments that may lead to the discovery and validation of clinically viable biomarkers.

The false hopes that are created by current proteomics approaches can be blamed for the “biomarker hype”. More often the term “biomarkers” are confined to or projected as proteomics based biomarkers; mainly by the proteomics researchers. We sometime knowingly or unknowingly tend to ignore the fact that biomarkers can be proteins, peptides, DNA, mRNA, SNP, miRNAs or metabolites. It would be tough to predict whether protein or molecular biomarkers will be more commercially viable and successful; not to forget the fact that molecular biomarkers are more robust and cost effective than protein biomarkers. Even if we consider proteomics is the way for biomarker discovery, current proteomic approaches would need a 360° make over, especially in sample preparation and analysis methods that are suitable for clinically relevant biomarker discovery platform. Starting from sample preparation to enzyme digestion followed by fractionation are the major limitations of mass spectrometry (MS) based proteomics approach in a clinical set up. The difficulty in sample analysis, sample loss, lack of reproducibility and the high cost in identifying a biomarker are additional limitations. It is even more confusing when proteomics approaches claim absolute quantitation of biomarkers based on the data that have been generated through complex sample preparation that lacks reproducibility. Moreover, the biological significance and relevance of absolute quantitation of biomarkers in diagnostics applications are debatable. Unless, we have a robust sample preparation and analysis technique, MS based proteomics approaches may continue to generate false positives/negatives. Unfortunately, optimization of sample preparation techniques also includes development of new LC and MS instrument platforms that can handle heterogeneous “dirty” samples without specialized sample cleaning steps.

The lack of true collaboration between mass spectrometry scientists, biological and clinical researchers is another limitation in MS based approach. The protein mass spectrometry field is generally dominated by analytical scientists and there is an urgent need for more productive collaborative research, not just a core facility confined consultation, to identify true biomarkers that are biologically and clinically relevant. Analyzing the peak shifts and peptides may not be the only major thrust area, rather, the identification of biological relevance and validation of biomarkers should also be the priorities of proteomics researchers. Proteomics laboratories need to develop strategies, skills and infrastructure for true biomarker discovery. Currently, mass spectrometry based proteomics skills or expertise is confined to very few laboratories or core facilities that have independent mass spectrometer capabilities. This severely cripples innovations in proteomics based biomarker discoveries. Breakthroughs in biomarker discoveries may happen once portable mass spectrometers are available to research laboratories, like PCR machines or ELISA readers. It is noteworthy to point out that emerging areas such as MALDI imaging using tissue arrays and whole protein mass spectrometry analysis look very promising and hopefully these technologies might address the current limitations in proteomics based biomarker discovery.

The current criticism over biomarkers can be purely based on the putative or false biomarkers that were identified and claimed using mass spectrometry based proteomics approaches. Nonetheless, the failure of proteomics technology should not invalidate the biomarkers (proteins, SNPs, miRNA etc) that were discovered and validated through other conventional and modern biological tools, such as tissue arrays and various antibody based technologies, in a pre-clinical or clinical set-up. These technologies will continue to discover new biomarkers that may have the potential for developing novel diagnostic tools.

Related blog

1. Strategies for Rational and Personalized Cancer Biomarker Discovery


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