The introduction of technologies such as mass spectrometry and protein and DNA arrays, combined with our understanding of the human genome, has led to renewed interest in the discovery of novel cancer biomarkers. Furthermore, the morden technologies provide the means by which new, single cancer biomarkers could be discovered through use of reasonable hypotheses and novel analytical strategies. Despite the current optimism, a number of important limitations to the discovery of novel single cancer biomarkers have been identified, including study design bias, and artefacts related to the collection and storage of samples. Despite the fact that new technologies and strategies often fail to identify wellestablished cancer biomarkers and show a bias toward the identification of high-abundance molecules, these technological advances have the capacity to revolutionize cancer biomarker discovery.
Genomic microarrays represent a highly powerful technology for gene-expression studies. Over the past decade, a tremendous growth in the application of gene-expression profiling has been witnessed. This growth has contributed to the cancer subclassification theory, insights into cancer pathogenesis, and the discovery of a large number of diagnostic cancer biomarkers. Despite promising proof-of-principle data, the successful use of gene arrays to discover novel subtypes of various carcinomas, and the utilization of these technologies for discovery of diagnostic cancer biomarkers, these new tools are not yet recommended for widespread clinical use by either organizations issuing clinical guidelines or expert panels
Mass-spectrometry-based proteomic profiling
Proteomic-pattern profiling is a recent approach to cancer biomarker discovery. Given that mRNA information does not best reflect the function of proteins, which are the functional components within organisms, the use of proteomic patterns to enable tumor diagnosis or subclassification seems more promising. Mass-spectrometry- based methods of proteomic analysis have improved and include more-advanced technology that brings higher mass accuracy, higher detection capability, and shorter cycling times, thereby enabling increased throughput and more-reliable data.
In spite of the optimism regarding this approach, a number of important limitations have been identified. These shortcomings include bias from artefacts related to the clinical sample collection and storage, the inherent qualitative nature of mass spectrometers, failure to identify well-established cancer biomarkers, bias when identifying high-abundance molecules within the serum, and disagreement between peaks generated by different research laboratories. Another limitation concerns possible bioinformatic artefacts. Despite a substantial time lapse since the first report of this technology, no product has yet reached the clinic and no independent validation studies have been published. Guideline-developing organizations and expert panels do not currently recommend serum proteomic profiling for clinical use.
The low-molecular-weight plasma or serum proteome has been the focus of recent attempts to find novel cancer biomarkers. Peptidomic profiling might represent nothing more than peptides cleaved during coagulation or functions inherent to plasma or serum, including immune modulation, inflammatory response and protease inhibition. Many of the aforementioned caveats associated with mass-spectrometry- based protein profiling technologies also apply to peptidomics.
The premise for the 'cancer biomarker family' approach is that if a member of a protein family is already an established biomarker, then other members of that family might also be good cancer biomarkers. For example, PSA is a member of the human tissue kallikrein family. Kallikreins are secreted enzymes with trypsin-like or chymotrypsin-like serine protease activity. This enzyme family consists of 15 genes clustered in tandem on chromosome 19q13.4. PSA (KLK3) and KLK2 currently have important clinical applications as prostate cancer biomarkers. Other members of the human kallikrein family have been implicated in the process of carcinogenesis and are being investigated as biomarkers for diagnosis and prognosis. For example, KLK6 has been studied as a novel biomarker for ovarian cancer.
Secreted protein approach
In theory, a candidate serological tumor marker should be a secreted protein, because secreted proteins have the highest likelihood of entering the circulation. Examination of tissues or biological fluids near to the tumor site of origin could facilitate identification of candidate molecules for further investigation. The increasing evidence that tumor growth and progression is dependent on the malignant potential of the tumor cells as well as on the microenvironment surrounding the tumor (e.g. stroma, endothelial cells and immune and inflammatory cells) further supports this approach.
It should be noted that some of the widely used cancer biomarkers such as CEA, CA125 and HER2 are actually membrane-bound proteins, which are shed into the circulation. The identification of secreted proteins in tissues or other biological fluids does not necessarily imply that the proteins will be detectable in the sera of cancer patients. Serum-based diagnostic tests depend on the stability of the protein, its clearance, its association with other serum proteins and the extent of post-translational modifications.
Other prominent strategies
A number of other strategies for detecting cancer biomarkers exist. One approach that is gaining popularity is based on protein arrays. Another prevailing view is that tumor-associated antigens could serve as biosensors for cancer because tumors naturally elicit an immune response in the host. Moreover, breaking the cancer genetics dogma that hematologic malignancies result from chromosomal translocations and that mutations underlie epithelial solid tumors, gene fusions as a result of translocations in prostate cancer have been identified through use of gene-expression data sets. In addition, mass-spectrometrybased imaging of fresh-frozen tissue sections has yielded a number of potential candidate molecules. Besides proteomic profiling of serum, attempts have been made to decipher the serum proteome via numerous fractionation schemes to simplify and reduce the dynamic range of molecules present in serum. Finally, the use of animal models involving human tumor xenograft experiments has also shown promise for biomarker discovery.
The first cancer biomarker ever reported was the light chain of immunoglobulin in the urine, as identified in 75% of patients with myeloma in an 1848 study. The test for this marker is still employed by clinicians today, but with use of modern quantification techniques.
From 1930 to 1960, scientists identified numerous hormones, enzymes and other proteins, the concentration of which was altered in biological fluids from patients with cancer. The modern era of monitoring malignant disease, however, began in the 1960s with the discovery of alfa-fetoprotein and carcinoembryonic antigen (CEA), which was facilitated by the introduction of immunological techniques such as the radioimmunoassay.
In the 1980s, the era of hybridoma technology enabled development of the ovarian epithelial cancer marker carbohydrate antigen (CA) 125. In 1980, prostate-specific antigen (PSA [KLK3]), considered one of the best cancer markers, was discovered.
Every era of biomarker discovery seems to be associated closely with the emergence of a new and powerful analytical technology. The past decade has witnessed an impressive growth in the field of large-scale and high-throughput biology, which has contributed to an era of new technology development. The completion of a number of genome-sequencing projects, the discovery of oncogenes and tumor-suppressor genes, and recent advances in genomic and proteomic technologies, together with powerful bioinformatics tools, will have a direct and major impact on the way the search for cancer biomarkers is conducted. Early discoveries of cancer biomarkers were based mainly on empirical observations, such as the overexpression of CEA. The modern technologies are capable of performing parallel rather than serial analyses, and they can help to identify distinguishing patterns and multiple markers rather than just a single marker; such strategies represent a central component and a paradigm shift in the search for novel biomarkers.
Cancer biomarker discovery and development falls into five conceptual phases: preclinical exploratory studies; clinical assay and validation; retrospective longitudinal studies; prospective screening; and randomized control trials
Preclinical exploratory studies
In this phase, tumor and non-tumor specimens are compared to generate hypotheses for clinical tests for detecting cancer. Strategies such as geneexpression profiling, mass-spectrometry-based methods and other approaches to cancer biomarker discovery can be used to aid this phase.
Assay development and validation
A clinical assay that uses a specimen of choice (usually something that can be obtained noninvasively) is developed in this phase. The assay must discriminate individuals with cancer from those without. The patients assessed in this phase have established disease. The utility of the assay in detecting disease early is not demonstrated in this phase.
Retrospective longitudinal clinical repository studies
Specimens collected and stored from a cohort of healthy individuals who were monitored for development of cancer are used here. Evidence for the capacity of the biomarker to detect preclinical disease is demonstrated in phase 3. Criteria for 'positive' screening results are defined and used in phase 4.
Prospective screening studies
In this phase, individuals are screened with the assay and diagnostic procedures are applied to those who screened positive. This can help to establish the tumor stage or the nature of the disease at the time of detection.
Randomized control trials
The objective of this phase is to determine if screening reduces the burden of cancer in the population.
Canonical Wnt Pathway
Complement Activation Pathways
Death Receptor Signaling
EGFR Signaling Pathway
NF-kB (NFkB) Pathway
Non-Canonical Wnt Pathway
Notch Signaling Pathway
TGF-beta Signaling Pathway