Statistical and epidemiological data indicate that cancer is a growing global health problem. The World Health Organization (WHO) predicts an estimated 27 million new cancer cases worldwide by 2030. Cancer initiation and progression involves genetic and epigenetic changes that reprogram complex regulatory circuits. Within this context, Hanahan and Weinberg (Hanahan and Weinberg, 2011) characterized ten consensus processes, called cancer hallmarks, which are representative of oncogenesis.


Traditionally, a chemotherapy protocol is considered beneficial for an entire patient subpopulation with common tumor traits and is, therefore, referred to as one-size-fits-all. However, molecular diversity increasing with tumor development promotes therapy resistance (van Wieringen and van der Vaart, 2011; Banerji et al., 2015). Moreover, chemotherapy drugs may result in harmful side effects for patients due to their low selectivity that adversely affects both tumor and normal cells (Siegel et al., 2012). Thus, the process of therapeutic target identification is complex and implies the recognition of molecular differences between tumor and healthy cells, most of them based on gene regulation. Accordingly, the profile of upregulated genes in tumor tissues is used in a personalized (individualized) medicine approach. Personalized medicine is expected to bring higher benefits to patients.

The development of personalized medicine is directly related to high-throughput technologies that became available in recent years. High-throughput techniques, such as RNA sequencing, are essential tools for tumor and control cells’ characterization. These techniques allow a better understanding of tumor biology and demonstrate that each tumor is unique. At Physiotarget, we have several technological solutions to improve cancer theranostics.




Hanahan, D., Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell 144 (5), 646–674. doi: 10.1016/j.cell.2011.02.013

van Wieringen, W. N., van der Vaart, A. W. (2011). Statistical analysis of the cancer cell’s molecular entropy using high-throughput data. Bioinformatics 27 (4), 556–563. doi: 10.1093/bioinformatics/btq704

Banerji, C. R. S., Severini, S., Caldas, C., Teschendorff, A. E. (2015). Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer. PLoS Comput. Biol. 11 (3), e1004115. doi: 10.1371/journal.pcbi.1004115

Siegel, R., DeSantis, C., Virgo, K., Stein, K., Mariotto, A., Smith, T., et al. (2012). Cancer treatment and survivorship statistics, 2012. CA Cancer J. Clin. 62 (4), 220–241. doi: 10.3322/caac.21149