6.6 Diagnostics

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Why molecular genetic methods?

 

Current clinical practice in oncology relies to some extent on diagnostic methods to guide treatment protocol. This is because some cancers are more susceptible to certain drugs or certain treatment strategies; for example, tamoxifen is effective in reducing tumour sizes in breast cancers positive for the estrogen receptor, but has little to no effect on many other kinds of tumours (1). However, the diagnosis of cancer via traditional methods, such as morphology and clinical presentation, can oftentimes be inaccurate because these qualities can change markedly in cancer cells, particularly in later stage cancers (2). Furthermore, the utilization of morphology and clinical presentation of a cancer is not necessarily a good predictor of clinical outcome of the disease (3). This being the case, molecular methods provide the possibility of classifying cancers into more specific subtypes in the hopes that these subtypes will give a better indication for prognostics (4). As such, oncologists and researchers have come to realize that molecular genetic methods in cancer diagnostics are very promising (2). 

 

 

Gene expression signatures

 

Gene expression signatures may be identified for specific cancer types through the use of gene microarray expression experiments and can be used to differentiate between specific cancer types.

 

A landmark study published in 1999 by Golub and his research group proposed the use of gene expression signatures in diagnosing acute myeloid leukemia (AML) from acute lymphoblastic leukemia (ALL).  Gene expression signatures are patterns of mRNA transcript levels that are associated with a phenotype.  For example, a gene called Highly Upregulated in Liver Cancer (HULC) is associated with greater transcript levels in liver cancer tissue than in normal cancer, thus it is considered a signature of liver cancer.  To test the diagnostic potential of gene expression signatures, Golub profiled the expression of 6817 genes in both normal and leukemic cells, and determined 1100 to be correlated with the leukemic cell phenotype. Of these 1100, 50 genes were selected and used in the profiling analysis, which was carried out on a microarray. For these 50 genes, there would be a specific expression pattern for each of AML, ALL and normal cell phenotypes, and therefore, specific microarray patterns would be able to differentiate cell types. Using this method, 36 of 38 leukemic cells were identified correctly, according to whether they were AML or ALL types.

 

 

This study is significant in that a single test was developed to differentiate AML from ALL.  Prior to microarray analysis of gene expression signatures, a hematopathologist would have to analyze cell morphology, histochemistry, immunophenotyping, and cytogenetic qualities of cancerous cells to attain a diagnosis (4).  More importantly, the study underlined the possible development of techniques that allowed cancers to be diagnosed based on their gene expression profiles (5).

 

In vivo fluorescence imaging

 

A diagnostic tool in development by many groups is in vivo fluorescence imaging (8, 9, 10).  A study recently showed that the duration of fluorescence of a probe conjugated to a small molecule targeting receptors on tumour cells can give a snapshot of the expression level of these tumour-associated receptors (8).  When the antibody-like protein is bound to the receptor, the probe fluoresces.  This study is significant because Human Epidermal Growth Factor 2 (HER2) receptor is a biomarker upregulated in ovarian and breast cancer, and greater HER2 expression levels are associated with resistance to particular chemotherapeutics (8).  One common primary cancer therapy uses antibodies to bind HER2 receptors, thereby preventing growth-promoting signal transduction (11).  In vivo fluorescence imaging may be a useful diagnostic test to determine which therapy may work best for an individual according to their HER2 receptor expression.  Treatment plans could be personalized by non-invasive imaging of tumour-associated biomarkers whose expression is predictive of drug response.  

 

Click this link (http://cubocube.com/dashboard.php?a=344&b=461&c=1) to learn about the role of tumor-derived exosomes as diagnostic markers for cancerdisease progression.

Proteomics as an extension of genomics

Many studies have shown that tumour cells have different gene expression patterns compared to their non-cancerous predecessors and therefore secrete different types of proteins (12). Cancer proteomics is an expanding field that aims to use protein data to distinguish between differentiated and undifferentiated (or transformed/non-cancerous) cells, as well as determine differences between different cancer subtypes. Proteomics has been used to study various cancers such as gastric cancers, breast cancer, and prostate cancer, and has been successful in identifying biomarkers that may be useful as potential diagnostic tools. (13,14)

Stable Isotope Labelling by Amino Acids (SILAC)

SILAC, or stable isotope labeling by amino acids in cell culture, is a proteomic technique developed by the Pandey Lab. This method is now widely used for proteomics analysis and quantitation of protein expression in various cell culture systems. This technique involves the in vivo incorporation of specific amino acids into all mammalian proteins. Mammalian cell lines are grown in media lacking a standard essential amino acid but supplemented with a non-radioactive, isotopically labeled form of that amino acid. Initial experiments demonstrated that the alternative amino acid was readily taken up by cells and that the modified media did not affect cell growth and differentiation. Proteins are harvested from the cells and mass spectrometry is then used to analyze the relative abundance of each protein of interest. (14)

 

References

 

  1. Powles, T., Ashley, S., Tidy, A., Smith, I., Dowsett, M. (2007). Twenty-year follow-up of the Royal Marsden randomized, double-blinded Tamoxifen breast cancer prevention trial. Natl Cancer Inst99 (4), 283-290.
     
  2. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J., Poggio, T., Gerald, W., Loda, M., Lander, E., Golub, T. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. PNAS, 98 (26), 15149-15154.
     
  3. Schmidt, U., Begley, C. (2003). Cancer diagnosis and microarrays. The international journal of biochemistry and cell biology, 35 (2), 119-124.
     
  4. Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lander, E. (1999). Molecular classification of cancer: class discover and class prediction by gene expression monitoring. Science, 286: 531-537.
     
  5. Ramaswamy, S., Golub, T. (2001). DNA microarrays in clinical oncology. J Clin Oncol, 20, 1932-1941.
     
  6. Sassen, S., Miska, E., Caldas, C. (2008). MicroRNA -- implications for cancer. Virchows Arch, 452 (1), 1-10.
     
  7. Chen, X., Ba, Y., Ma, L., Cai, X., Yin, Y., Wang, K., Guo, J., Zhang, Y., Chen, J., Guo, X., Li, Q., Li, X., Wang, W., Zhang, Y., Wang, J., Jiang, X., Xiang, Y., Xu, C., Zheng, P., Zhang, J., Li, R., Zhang, H., Shang, X., Gong, T., Ning, G., Wang, J., Zen, K., Zhang, J., Zhang, C. (2008). Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Research, 18, 997-1006.
     
  8. Ardeshirpour, Y. et al. In vivo fluorescence lifetime imaging monitors binding of specific probes to cancer biomarkers. PloS one 7, e31881, doi:10.1371/journal.pone.0031881 (2012).
     
  9. Corlu, A. et al. Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans. Optics express 15, 6696-6716 (2007).
     
  10. Wang,Y. & Yan, X. P. Fabrication of vascular endothelial growth factor antibody bioconjugated ultrasmall near-infrared fluorescent Ag2S quantum dots for targeted cancer imaging in vivo. Chemical communications 49, 3324-3326, doi:10.1039/c3cc41141a (2013).
     
  11. Ceran, C. et al. Novel anti-HER2 monoclonal antibodies: synergy and antagonism with tumor necrosis factor-alpha. BMC cancer.  12 : 450, doi:10.1186/1471-2407-12-450 (2012).
  12. Hanash S, Taguchi A. 2010. Timeline: The grand challenge to decipher the cancer proteome. Nat Rev Cancer 10: 652-660

  13. Marimuthu A, Subbannayya Y, Sahasrabuddhe NA, Balakrishnan L, Syed N, Sekhar NR, Katte TV, Pinto SM, Srikanth SM, Kumar P, Pawar H, Kashyap MK, Maharudraiah J, Ashktorab H, Smoot DT, Ramaswamy G, Kumar RV, Cheng Y, Meltzer SJ, Roa JC, Chaerkady R, Prasad TS, Harsha HC, Chatterjee A, Pandey A. 2013. SILAC-based quantitative proteomic analysis of gastric cancer secretome. Proteomics Clin Appl. 7(5-6):355-66.

  14. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1(5):376-86.

  15. "Silac". Licensed under Public Domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Silac.gif#/media/File:Silac.gif