Applications of molecular profiling in renal cell carcinoma


Applications of Molecular Profiling in Renal Cell Carcinoma.


To give an overview of applications of Molecular Profiling in Renal Cell Carcinoma.


Renal Cell Carcinoma or RCC as it is commonly called is the most common type of kidney cancer in adults also known as Renal Cell Cancer or Hypernephroma or Renal Cell Adenocarcinoma. 9 out of 10(90%) kidney cancers are found to be Renal Cell Carcinomas. Pathologically Renal Cell Carcinoma is classified in to subtypes:

  • Clear Cell RCC
  • Papillary RCC
  • Chromophobe RCC
  • Collecting Duct RCC
  • Medullary RCC
  • Sacromatoid RCC

80 percent of cases are Clear Cell Renal Cell Carcinomas and 15 percent papillary and the remaining 5 percent other types. Usually Renal Cell Carcinoma grows within the kidney as a single mass but sometimes tumors can also be found in various parts of the kidney or even in both the kidneys. Renal Cell Carcinoma is asymptotic in the initial phases of development. It can only be noticed only after it becomes quite large. Mostly the Renal Cell Carcinoma is found before it spreads to various other organs in the body. The Renal Cell Carcinoma can be diagnosed by using the imaging techniques like ultrasound, Magnetic Field, Computed Tomography (CT) and X-ray. Diagnosis of Renal Cell Carcinoma by biopsy of the kidney may involve complications and may not give accurate results. Treatment by surgery may cure the localized disease but mostly the patient relapses. Treatment of the advanced Renal Cell Carcinoma is exigent. Earlier Interferon-alpha was used to be the standard before the discovery of the targeted therapies but it had a low response rate and was toxic significantly. Interleukin-2 has a similar response as interferon-alpha but high dosage of it can cure three to five percent of patients approximately.

Tumor Markers

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The indicator which helps a clinician in detecting a cancer and whether a specific treatment will cure or at least decrease the risk of such events is called as the tumor marker. The stage and size of the tumor are the only viable tools to predict the prognosis as there is no tumor marker established for Renal Cell Carcinoma.A number of new molecular markers have been identified recently which are potential clinically but none of them have gained approved clinical application.

Molecular Profiling

Molecular Profiling is a method in which the biological specimens such as the blood, urine, and tissues are classified based on gene or protein or mRNA expression patterns or changes in the genome which is useful in diagnosing, prognosing and predicting the abnormalities. The traditional approach is to examine single molecule at a time but Molecular Profiling is to analyze thousands of molecules simultaneously.

Molecular profiling can be done on 3 different levels: Genomic, Transcriptomic and Proteomic. Different tools used for Molecular Profiling are as follows: Single Nucleotide Polymorphism (SNP), Comparative Genomic Hybridization (CGH), Array based CGH, Multicolor FISH, Spectral karyotyping, High-throughput analysis of methylation are the tools used at genomic level. Microarray, SAGE (Serial Analysis of Gene Expression), EST (Expressed Sequence Tags), SNP (Single Nucleotide Polymorphism), mRNA, microRNA, Quantitative RT-PCR, In-situ hybridization are the tools used at Transcriptomic level. Mass spectrometry, Chromatography, Tissue Microarray and Protein Microarray are the tools used at Proteomic level. Microarray technology and mass spectrometry are most widely used tools among the above mentioned tools.

Microarray Analysis:

Microarray is a small chip comprising several well-defined capture molecules such as proteins, mRNA transcripts, antibodies, and synthetic oligos etc., which are immobilized and can assay molecules by hybiridisation with a labeled probe. It is most widely used to compare the patterns of gene expression between different conditions simultaneously. It is used for analysis of profiles of gene expression in several malignancies through the study of alterations which lead to development of metastasis and also transition from condition to another for example from benign to dysplastic, to a invasive cancer. Microarrays play a key role in the discovery of biomarkers and hence are important techniques in the diagnostic pathology. Among the above mentioned techniques the mRNA microarray is the most commonly used technique. The rapid advancement in the field of Bioinformatics has led to better understanding of several malignancies and storing the database of the profiles of gene expression in a variety of malignancies which are now accessible to public(Sherlock G. et al.,).

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Microarray is most popular technology because of its high degree of sensitivity and also as it needs only very less amount of any tissues to be analyzed or the reactants for generating the results. There are also some disadvantages of microarray albeit the continuous improvement such as microarray lacks in reproducibility and standardization, the results are not the same at all times because of the kind of specimen and also its preparation. Moreover there is no quality control for the procedure. One of the main draw backs is microarray cannot reveal the post transcriptional gene control (Sturgeon et al.,).

Mass Spectrometry:

Mass Spectrometry is a vital method for characterization of protein structure and the sequences of their amino acid. In the case of malignancies proteomics play a crucial role than that of genomics as the agent responsible for the phenotype of malignancies is protein. Moreover some of the processes that cannot be identified by the RNA-based studies can be identified by proteomics. For example, the RNA-based studies may not be able to detect the alterations in the post translational modification but these alterations can be identified by the proteomics.

A number of techniques are available for profiling of the protein. The traditional technique being 2-D gel electrophoresis along with the mass spectrometry which revealed a number of potential biomarkers for cancer (Ornstein et al.). MALDI MS (Matrix Assisted Laser Desorption/Ionization Mass Spectrometry) and SELDI MS (Surface Enhanced Laser Desorption/Ionization Mass Spectrometry) are the other methods used for protein profiling.

Identification of biomarkers for the cancer which can be measured by ELISA (Enzyme Linked Immuno Sorbent Assay) is the primary objective of the most of the proteomic studies. There may be changes during the process of improvement of proteomics technology to develop proteomics technology directly in to clinical diagnostics tests. For example SELDI-TOF (Surface Enhanced Laser Desorption/Ionization Time Of Flight) along with some of the bioinformatics tools can help in discriminating the patients from those of with benign diseases.

Apart from the above mentioned Microarray and Mass Spectrometry techniques various other techniques are also being used for the study of cancers, such as SNP (Single Nucleotide Polymorphism), MicroRNAs etc.,

Molecular Profiling in Renal Cell Carcinoma:

Molecular Profiling of Renal Cell Carcinoma can be carried out at different levels such as genomic, Protein, RNA and miRNA. Molecular Profiling has a number of clinical applications in Renal Cell Carcinoma. Signature expression Profile in Renal Cell Carcinoma helps in knowing the difference between it and the normal tissue. The presence of the Signature expression Profile is investigated by the help of Molecular Profiling. Various scientists experimented and the studies have been carried out to analyze the differential gene expression in Renal Cell Carcinoma at mRNA level. Lenburg et al., noticed the poor overlap between these studies and stated the necessity to implement the techniques which are statistically accurate for microarray analysis and to make a distinction between the reliable and non reliable genes that are detected. The variation between the information acquired from the tissues and the cell lines is explained by Liou et al. When the tissues of Renal Cell Carcinoma micro dissected by laser were used for micro array analysis, the identified genes were found to be not the same as the bulk tissues which suggests that the accuracy in results is directly proportional to the purity of the malignant population.

Protein profiling of Renal Cell Carcinoma is helpful in detecting and identifying the potential biomarkers. Molecular Profiling has the competence to distinguish among the types of renal tumors. For example the kidney tumors such as oncocytoma and chromophobe Renal Cell Carcinoma are often seemed to be similar due to their microscopic similarity but they can be differentiated with the help of Molecular Profiling. Both the cancers are even found to have the similarity in the mitochondrial gene expression by the Microarray. However the gene analysis distinguished the variations in the profiles of gene expression between the two cancers.

A study proved the accuracy of Molecular Profiling in the classification of different subtypes of Renal Cell Carcinoma. Approximately about five percent of clear cell RCC comprise of sarcromatoid component, the nature of which is ambiguous. However this topic is coming to light by the studies with the help of Molecular Profiling. Jones et al compared the patterns of the allelic loss in sarcromatoid components of RCC and in clear cell RCC and finally concluded that both the components are originated from the same progeniter cell. Distinct paths of allelic loss have been noticed in clear cell and sarcromatoid components from the same patient, which specified genetic divergence during the clonal evolution of Renal Cell Carcinoma. In addition, the advanced performance of Molecular Profiling in the detection of mixed subtypes and cases with ambiguous histological patterns were shown by the retrospective analysis. A group of genes which can differentiate the chromophobe and the clear cell types of Renal Cell Carcinoma have been identified by another report. DNA microarrays were used by Higgins et al for the classification of papillary carcinomas from common Renal Cell Carcinoma on a molecular scale and also the cancers from various parts of the kidney. The distinctive chromosomal aberrations in paraffin embedded renal tumors can be detected by the SNP arrays, and thus the SNP arrays present a high-resolution, genome-wide method that can be used as an auxiliary study for classification and potentially for predictive stratification of these tumors. Gene signatures have been identified that discern Renal Cell Carcinoma from other cancers with 100% accuracy using microarray analysis. Differentially expressed genes have been also found during initial formation of the tumor and progression of tumor to metastatic Renal Cell Carcinoma. Another study identified a set of eighty genes that were adequate to classify tumors with accuracy. Diverse gene expression signatures were connected with chromosomal abnormalities of tumor cells, metastasis formation, and patient survival. Such studies accentuate the practical efficacy of Molecular Profiling in revealing the subtype and of the illness of the patient.

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Molecular profiling has significant predictive applications in Renal Cell Carcinoma. Several prognostic biomarkers have been identified by the use of microarrays. Patients can be stratified into prognostic risk groups and they by can be guided with therapy decisions in the future with the help of these markers. Two main subgroups within Renal Cell Carcinoma have been identified by a microarray analysis, depending on gene expression profiling, that vary in biological behavior regardless of similarity in histology(Skubitz et al.,2006). Another microarray-based analysis has revealed that approximately about forty genes can perfectly make the difference between patients with a comparatively non-aggressive form of the disease from those with aggressive disease. These molecular signatures have been shown to surpass usual staging in predicting outcome. Moch et al(1999) identified about eighty nine differentially expressed genes in Renal Cell Carcinoma. Vimentin, one of these differentially expressed genes, is a marker of poor prognosis.

An other important application of Molecular Profiling in Renal Cell Carcinoma is identifying prognostic markers. Molecular Profiling can be used to expect response to immunotherapy and targeted therapy. Profiling analysis can be very useful in recognizing the immunotherapy targets and also the targets for targeted molecular therapy (Bjelogrlic et al 2008). The important objective of Molecular Profiling is explaining the pathogenesis of Renal Cell Carcinoma. The next step of knowing the molecular interactions is elicited by the amassing of hundreds of dysregulated genes that are identified by different studies. Cytogenetic analysis has also been a precious tool to approach into the pathogenesis of Renal Cell Carcinoma. Previously studies revealed that chromosomal aberrations are involved in the Renal Cell Carcinoma development, and that they can direct our understanding of the molecular events required for development and also progression of Renal Cell Carcinoma.

Draw Backs of Molecular Profiling

One of the main challenges of Molecular Profiling is to obtain a multi dimensional Molecular Profile of the patient's specimen which needs a collective effort among various elements of the research scientists, statisticians and health care team. Preservation of the tissue and handling of the tissue is a major problem in molecular profiling. The tissues used in all the histopathologic diagnosis such as Formalin-fixed tissue cannot be used for Molecular Profiling. The best solution to this problem is to use non formalin alcohol based fixatives.

Tumor tissue is generally a mixture of several elements such as tumor, adjacent normal and stromal elements. Micro-dissection profiling can be used to over come this problem.

Lack of prospective studies is one more issue which is hampering the performance of many Molecular Profiling experiments. Cost of the equipment and running of Molecular Profiling is very high. How ever the cost of most generally used techniques such as micro arrays possibly will decrease as it becomes more widespread. Moreover the cost can be reduced by focusing on fewer targets.


Earlier diagnosis of cancer, prediction, and the successive treatments of the cancer thus diagnosed were all based on histopathologic parameters, generally the tissue of origin and the developmental stage and the grade of the tumor. Later individual molecular markers have been gradually introduced to improve the precision of predicting prognosis and prediction of effectiveness of the treatment. For example the immuno histo chemical estimation of the receptors of estrogen and progesterone in the case of breast cancer and also the pre-operative prostate specific antigen measurement.

There are three stages in the progress of clinically meaningful application of molecular profiling(Liotta et al.,2000). Identifying all the agents that take part in the pathogenesis of cancer is the first stage which is nearly completed. The main progress in programs of gene prediction, splice variants, several new genes and non-coding molecules have been recognized with the successful completion of human genome project (HGP). This begins the next stage of comparison of molecular profiling in normal against cancer and at different stages of cancer. Much more amount of data is collected and also being collected about differential gene and protein expression in RCC. The transformations of Cyto genetic and micro RNA are also accumulating. The most crucial and comparatively difficult stage is the third stage which is integration of these manifold parameters into one platform. The progression of analysis of protein-protein interactions and pathway analysis signify two vital steps which helps in knowing the significance of these pathological changes, and as a result applying them for diagnostic and treatment efforts. Bioinformatics plays a crucial role at this point. The availability of databases of the cancer and providing refined diagnostic tools that have the capability of analyzing an enormous amount of data is one of the main functions of Bioinformatics. Molecular Profiling now also presents an approach that is multi parametric in cancer biomarkers, where in a blend of multiple markers increases the sensitivity and specificity, when compared to individual markers. One commercially available kit is used to evaluate the chance of reappearance in certain subgroups of patients of breast cancer. The second kit uses profiling of gene expression for the identification of the tissue of origin in cancers. In conclusion, the approaches of Molecular Profiling should be tailored for different cancers as in Renal Cell Carcinoma.


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