ReviewTowards personalized medicine of colorectal cancer
Introduction
The global healthcare burden of colorectal cancer (CRC) is enormous. In 2016, United States will have 70,820 estimated new cases and 26,020 estimated deaths due to CRC (Siegel et al., 2016). CRC is ranked among the highest incidence cancers across the world in both men and women. 1.6 million cases of CRC and 0.8 million deaths due to CRC were reported in 2015. (Global Burden of Disease Cancer C., 2016). However, large proportion of CRC cases are preventable and early detection is associated with good prognosis and better survival (Anon, 2017). A major challenge in the treatment of CRC is the heterogeneous nature of this disease. Intertumor and intratumor heterogeneity of cancer is being acknowledged as a major problem in devising accurate therapies (Ogino et al., 2012). Evidence is mounting in favor of the unique identity of a human being in health or diseased state. Colorectal cancer poses a formidable challenge in the form of molecular heterogeneity with involvement of several molecular pathways and molecular changes unique to an individual’s tumor (Linnekamp et al., 2015). Two main pathways often described in reference to colorectal cancer are chromosomal instability (CIN) and microsatellite instability (MSI) pathway accounting for 85% and 15% of total CRC cases respectively. Though these molecular pathways have been used to classify CRC patients and guide treatment regimens, there is a need to better customize treatment strategies keeping in view the heterogeneity of CRC found in every patient (Lugli, 2015, Sinicrope et al., 2016). In this scenario, the best option is to customize treatment strategies tailored to the need of an individual patient. This is known as Personalized Medicine.
Personalized medicine could be defined as customized form of treatment for an individual based on information available for its unique biological attributes. In its true sense, personalized medicine would mean customized prevention, therapy and management of a disease for a patient. The benefits of personalized medicine are twofold. On one hand, personalized medicine provides more accurate and precise way to prevent and cure a disease, while, on the other hand, it avoids unnecessary interventions. The concept of personalized medicine has been empirically practiced for some time, but evidence to support and drive this notion has only recently been available. We have yet to develop tools powerful enough to perfectly achieve this aim. What has been achieved is better classification of patients in more specific groups and the identification of biomarkers like KRAS proto-oncogene, GTPase (KRAS gene) (Zocche et al., 2015). The identification and validation of these [sub] groups is continuously being challenged by new data. Several types of omics e.g. genomics, transcriptomics etc., has made it feasible to record the molecular changes in CRC at an unprecedented scale. Though technological advancements have made it possible to generate the data needed for a dynamic model of customized therapy for individual patients, many challenges exist when it comes to analyzing the data. Moreover, the implementation of these analyses in the form of clinically relevant procedures and interventions has yet to be assessed. In CRC, any preventive intervention has to be carefully weighed against its benefits for every individual patient. For metastatic stage patients, intervention has to be affordable, both economically and physically, with lower cost and fewer toxic adverse effects.
In this review, we present the concept of personalized medicine in context to CRC. The current status of treating CRC based on available molecular evidence is insufficient to capture the molecular heterogeneity and thus necessitates a paradigm shift. Several omics technologies have been discussed that promise to provide data which would help in developing personalized medicine for CRC. The concept of personalized medicine as applied to CRC followed by the challenges that it would face are discussed. Probable solution to these challenges is provided to help design the future course of personalized medicine in CRC.
Section snippets
Molecular model for development of CRC and its clinical applications
Most of the sporadic CRC cases are explained using CIN model. This model for the development of CRC suggests a predictable progression with sequential accumulation of mutations in specific genes like APC, WNT etc. The model provides signs that can be used for risk assessment, early detection, prognosis and treatment of the disease (Fearon and Vogelstein, 1990, Kinzler and Vogelstein, 1996, Huang et al., 1996, Polyak et al., 1996, Morin et al., 1996). Several molecules have been known to be
Genomics
Microarrays, real-time polymerase chain reaction (RT-PCR) and next-generation sequencing (NGS) have been at the forefront of the technological advancements that allow the entire genome of an individual to be analyzed in a relatively timely and cost-effective manner (McShane et al., 2013, Gonzalez-Pons and Cruz-Correa, 2015, Newton et al., 2012, Vilar et al., 2009, Lee et al., 2012). Using microarray technology, it is possible to interrogate the entire human genome for the gene mutations, copy
Personalized medicine in CRC
As we acknowledge the heterogeneity of CRC and the use of new tools helps us to identify these heterogeneous factors, we are closer to understand the personalized nature of CRC. This would lead us to the real practice of personalized medicine in CRC. Biomarker discovery has been vital in implementation of this practice in clinics. Clinical management of CRC uses biomarkers that could serve to predict the drug response in a patient. These biomarkers are helpful in designing the treatment
Challenges for personalized medicine in CRC
Despite major breakthroughs in our capability to generate data that could help in developing personalized medicine for CRC; we face major challenges to make it a clinical practice. There are challenges which are generic to all types of cancer and some are specific for CRC. Personalized medicine faces a number of challenges from the perspective of data analysis as reviewed by Fernald et al. (2011). The potential strengths of personalized medicine could also be undermined by the lack of inclusion
Future directions
A paradigm shift has occurred in our understanding of cancer as a disease of an individual rather than searching for generalized biomarkers and drug targets. In CRC, patient groups are being classified into smaller subgroups to better identify drug targets and design customized therapies. Such classification would eventually result in the characterization of an individual as a separate class. But there would be statistical problems in dealing with data from an individual patient (n = 1). The
Conclusion
The current management of CRC is challenged by the extensive heterogeneity in this disease, suggesting that this is a complex disease that could benefit immensely from the implementation of a personalized medicine approach. Technological advances are allowing the rapid, large-scale and cost effective generation of the data needed to bring personalized medicine into the clinic. There has been an impressive increase in our ability to generate omics data from nucleic acids, and there are
Conflict of interest
The authors declare that they have no conflicts of interest.
Authors contribution
MAA conceived the idea and prepared the manuscript. ZY and AS provided critical feedback and suggestions. SM prepared graphics and discussed manuscript. BK provided critical feedback and clinical insights.
Acknowledgement
This project is supported by a research grant awarded to MAA [Grant number RC10/083] by King Abdullah International Medical Research Center [KAIMRC] and [M-S-20-36] by King Abdul Aziz City for Science and Technology [KACST] in Riyadh, Saudi Arabia.
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