Introduction
Mutations that are occured at oncogenes and tumor suppressor genes causes genomic alteration. These genomic alterations play a key roles to occur formation, progressing and metastasis of cancer.
To understand developing and progressing of cancer mechanism and also enhancing personalized cancer treatments are both associated with analyzing and distinguishing of all genomic alteration in cancer types.(1) Genes determines your eye colour and body shape also genes decides your sensitivity to disease and how you respond to medical treatment. After determination of entire sequence of human genome, distinguishing of function of genes became more important.(2) A lots of new knowledge about human genome and genetically differences between individuals has been learned thanks to completion of the human genome project at 2003. Under the light of these informations new technologies that are high throughput sequencing, single nucleotide polymorphism (SNP) genotyping, and transcript profiling were arised for the analysing of the genome. Seeing in details of DNA has been gained numerous benefits in area of bioinformatics and computer system, therefore it caused big step forward to the area of personalized medicine (PM) in medical care.(3)
What is Personalized Medicine?
Humans are not identical each other genetically or typically, these differences reflect in area of medicine. Although one dose drug can be efficient for one people, it can not be efficient for the other people. Or although side reaction of drug can be observed in some people, it can not be observed in other people. What is the reasons of the these situations? These are can be lighted with the personalized medicine.
Personalized medicine can be defined in many ways, but generally to decide and apply appropriate prevention, diognosis and treatment method of disease for appropriate person, at appropriate time and dose according to guide of the genetic profile and person’s protein structure.(4)
Why Personalized Medicine Is Needed
There are wide range effects and occasion of personalized medicine. We divided these effects into the four main subtitles.
Personalized
Personalized medicine contribute to the individualized healthcare medicine with the help of “-omic” technologies like a genomic, proteomic, pharmagenomic by combining information of personal genetic and protein profiles(5). Biomarker that is present in blood, urine or tissues is used to detect the presense of the cancer and state the current situation. Information about person’s genetic state of cancer helps to draw the way of treatment.(4)
Preventative
Personalized medicine gives an early interference chance to the disease before the symptoms appear with the skill of predict disease risk. Preventing of progression of disease with intervention save the life in many cases. For example, females that have mutations on their BRCA1 and BRCA2 genes have higher breast cancer risk than the normal females. Preventive treatments can be apply according the some tests that are done with breast cancer susceptibility genes.(6)
Predictive
Personalized medicine provides doctor to choose most proper therapy and avoid adverse drug effect. Some diagnostic tools that use biomarkers as a guide to divide subgroup who would gain the benefit which therapy. For example, Oncotype DX® uses a sixteen gene signature to determine chance of benefit of women that have current types of breast cancer from chemotherapy.(7) Tests like this are used to categorise the patients into subgroups. Therefore, with the help of these categorisation which group should treat with hormone or chemotherapy would know successfully.
Participatory
Personalized medicine can save the money that is waste for treatment, also can save the time and prevent the adverse drug reaction. Therefore patients who has seen the benefit of the personalized medicine would be more willing for the treatment.
The Scope of Clinical Applications of Personalized Medicine
Cancer that is a heterogeneous disease have wide-range variety in same type of cancer. Every individuals response differently to the treatment. These differences are interest era of personalized medicine. A clinical applications of personalized medicine has wide influence fields. These applications involve diagnosis, screening, prediction, prognosis of treatment efficacy, control of patient after surgery to detect recurrence earlier and classification of patient into the small subgroups.
These subgroups favourably lead to choose targeted therapies. Targeted therapies provide high efficiency to respond rate to the therapy and survival consequences.(8) There are some current test for the varied aspects of personalized medicine (Table 1). Also personalized medicine contribute to the pharmaceutical companies. Because they waste o lot of money for the drug design. (9)
Cancer screening
Genetic and environmental factors are both contributor of the predisposition of cancer (10). Knowing the nature of these contributers is important to prevent the diseases ( adapting lifetstyle and behavior to the conditions). Sometimes genetic factors and cancer that are associated with each other affect significantly clinical intervention. For instance, as mentioned before if mutations occur at breast cancer susceptibility gene 1 and 2 (BRCA1,BRCA2) and at the same time if mutations occur at tumor suppressor genes , there is higher risk to develop the breast, ovarian, hematologic and prostate cancers(11). For these reason, regular screening, surgical measures and receive adjuvant therapies would undergo to prevent. Also genetic tests are used to analyse the inherited mutations DNA mismatch repair genes. Risk of advencing of colon cancer is high at the MLH1 and MSH2 genes(12). Under the light of this information cancer can be precluded with early screening colonoscopy to early detect and treat for cancer. Cancer databases that are about mutation types and polymorphisms are updated for public. These resources can be used to identify new biomarkers for screening.(13)
Tumor classification and subtyping
Personalized medicine changes the traditional classification of cancers from histologic scale to the molecular scale. Although histological scale does’nt give more information about prognosis , personalized tretment alternatives and risk of recurrence, molecular scale offers to give a detailed information about diseases processes(14). DNA, RNA, miRNA and protein have been used for molecy-ular analyses to classfy different tumor types into the subtypes. Each of them have an unique prognostic outcome that can not be identified with the traditional morphologic ways(15). Generally molecular scale for classification is used for acute myeloid leukemia, glioblastoma, breast cancer , and renal cell carcinoma , and to differentiate between Burkitt’s lymphoma and diffuse B-cell lymphoma. This classification that offers prognosis and treatment options can help to the patients about management of disease.(16)
Targeted therapy and predictive markers for treatment efficiency
The basic aim of the personalized medicine is applying right therapy to the right population of people by defining disease at the moecular level. So, identifying differences among the individuals support the new treatment methods and pharmaceutical companies to develop new cancer drugs. Patients who have similar clinical outcome and histological tumor type can give different response to the same drug(17). Prediction of who will be a nonresponders reduces the harmfull effect of drug on nonresponders like a potential toxic effect of drug and cost effect. Also when drug companies develop new drug, they focus on the patient population that benefit from drug to increase positive responds(17).
U.S. Food and Drug Administration bringed development about targeted therapy. For example, to treat chronic myeloid leukemia and gastrointestinal stromal tumor(18) ,imatinib mesylate is used and to treat breast cancer(19), trastuzumab (Herceptin) is used. Molecular characteristics of these cancer types that are abnormal protein tyrosine kinase activity in chronic myeloid leukemia and gastrointestinal stromal tumor and HER-2 receptor in breastcancer is used as a predictive biomarker. By using these markers only individuals which have these molecular alteration is selected and it means they are favorable for the treatment. Using this way some cancer types’ survival rate is shifted from 0 to 70%(17).
This application is used in non-small cell lung cancer treatment with using of mutations screeing. In this cancer type mutation occurs in kinase domain of EGFR. Gefitinib (Iressa) and erlotinib are tyrosine kinase inhibitors drug are used to treat and patients give a higher response to the treatment(20). Also if patient that is never smoked Asian females have adenocarcinomas, these drugs efficient on them(21). On the other hand, if the mutatuions occur at downstream effector KRAS, patient is resistant to to erlotinib(22). Also mutations that is at KRAS have a resistance to cetuximab (Erbitux) and panitumumab (Vectibix) drugs in colon cancer patients. If the KRAS is wild type, these these drugs is effective on the patients(23). These responses that are specific and different are based on molecular profile. Some molecular test are done before the using of cetuximab or panitumumab to a colon cancer patient. Lung and colon cancer is concerned with targeted therapy that is guide to patient about treatment by understanding the structure of cancer(24).
Pharmacogenomics and treatment safety
Genes that have genetical variation encode enzymes which metobolize drug, drug transporters, or drug targets. Variation in genes that can predict dose and safety of treatment for different types of cancer patient can have harmful influence on these patients’ treatment(25). For instance, polymorphism where in cytochrome P450 enzymes could cause to metabolite to drug slowly or very fast. So patient give an overdose symptoms or no response to drug by changing the pharmacokinetics of drug metabolism, also it may cause an adverse drug reaction(26). Thereby , forecasting optimal dose of drug , inducing the harmful side effects can be provided by using polymorphism(27). In familial breast cancer, patients shows low survival rate to treatment with tamoxifen that is chemotherapeutic drug because of genetic variation in CYP2D6 that is seen as a poor metabolizer (28). There are some studies abour genetic testing on drug label including test for CYP450 polymorphisms.
Prognosis
Insteaf of using clinicopathologic parameters as a biomarker in biochemical testing for prognosis and selection of therapatic way for cancer patient , Genotyping or gene expression profiling by microarray and protein analysis by mass spectrometry is used for prognostic biomarkers with the understanding of the molecular mechanism of cancer subtypes(29).
Biomarkers can be used alone or with combination of other parameters for classify subgroups according to their risk rate and for leading to therapy decision. For example, tissue microarray analysis with combining molecular and clinical biomarker is more efficient than the clasical clinical parameter for patient who has renal cell carcinoma(30).
Approaches and Tools for Personalized Medicine
Instead of using PCR, fluorescence in situ hybridization, immunohistochemistry, and sequencing for personalized medicine testing, high throughput analyses that consist of microarray, mass spectrometry, second generation sequencing, array comparative genomic hybridization, and high-throughput single nucleotide polymorphism (SNP) analysis were started to use after human genome project . These techniques can analyse numerous target at the same time(31). New technologies improve sesitiveness, speciality, trueness of new biomarkers. In figure 1 , different ways of PM testing is shown.
(9)
High-throughput whole genome sequencing
Genome sequencing consist of three subprocess: sample preparation, physical sequencing, and reconstruction. Firstly in sample prepration phase, genome that will be sequenced is divided into the fragments. In physical sequencing, respectively identified individual bases of each fragmend is defined as the read lenght. In reconstruction phase, each fragments is overlapped according to original genome by using bioinformatic software . Traditianally first- genaration sequencing or Sanger sequencing was used for 30 years. Buy these methods is limited about reading long lenght of bases, costly and time consuming(32,33). Some cancer alleles couldn’t be detected with sanger sequencing method because of the lower level in cell.Now, next generation sequencing is preferred for genome analysis.
Deep sequencing(34) that is coverage of interested sequence by extansive repeating and paired-end sequencing(35) allow to understand cancer genome. Also , cancer cell DNA and RNA can be isolated for targeted sequencing by using laser capture microdissection (36). These methods provides to identify unique mutations or other type of alteration that cause tumorigenesis in cancer types. High- throughput sequencing studies have been continue to evolve.
SNP analysis and haplotype mapping
There are more than 30 million single-nucleotide polymorphisms that are like a finger print of genetic code in human genome(37). International Haplotype Mapping Project characterizes these SNPs in variety of population for public usage(38). Researchers can use these databases to identify association between disease risk .disease studies and genome- wide association studies linked by commercially available microarrays (SNP chips)(39). When specific allele of a SNP is present , a fluorescent signal is produced by using allele specific oligonucleotide probes for SNP arrays and array have skill of analyzing up to 1 million SNPs in a single sample(40). Also allelic imbalance, copy number variation, or loss of heterozygosity of cancer genome can be screened by SNP array.
Microarray analysis
Expression levels of thousand gene in cancer is analyzed with single experiment of microarray. Microarrays that are chips have immobilized capture molecules serve as probes to bind fluorescently labeled targets prepared from the two samples for comparing (41). These capture molecules can be oligonucleotides or cDNA. MRNA, miRNA, DNA and protein microarrays are most popular analysis. Gene expression profiling has been used for catogarizing unique subtypes of cancer, identifying invasive and non invasive cancer type’s phenotype, forecasting prognosis and response to treatment and risk of recurrence(42). New miRNA microarray platform data’s can be used as a cancer biomarker. To classify patients prognostic groups and treatment subgroups, miRNA signatures is used. Also misroarray is used to determine epigectic alteration that is contributed to tumorigenesis and direct to manage patient(43).
Proteomics by mass spectrometry
Changing of protein profiles in cancer cell is important to determine new biomarker and might help to classify of tumors subtypes(44). Proteomic analysis have more advantage than measurement of mRNA. Because protein is the final effector molecule and their level can not overlap the level of mRNA due to the posttranscriptional modifications(45). In addition to that , protein-protein interactions contribute to cellular pathways and carcinogenesis. Proteins are quanrified in mass spectrometry according to their mass to charge ratios by inonizing into smaller molecules. Various new biomarkers can be identified for breast , ovarian , prostate , and kidney cancers thanks to mass spectrometry(46). Proteomics can be used to classify tumor , select treatment, pharmacoproteomics, and identify new drug targets and maybe monitor the therapeutic drug.(47)
Genome-wide association studies
There are a lot of studies to examine genetic variation of tumor types. Genome-wide association studies (GWAS) try to extend scale of these variations that were limited previously. For instance, one of the studies is “Genetic Markers of Susceptibility Project” that was initiated by the National Cancer Institute and their goal is identfying genes that causes breast and prostate cancer by using single nucleotide polymorphism analysis. Examining all type of genetic abnormalities and alterations like a gene silencing, methylation and epigenetic mechanisms, gene translocation, amplifications, and deletions are studies area of “The Human Cancer Genome Project”(48).
Genome-wide association studies revealed some facts that 6q25.1 is sensitive locus for breast cancer(49) and in European ancestry men, two independennt loci included 8q24 that affect formation of prostate cancer(50). Also GWAS showed some differences between cancer types. For instance, 5p15.33 has locus for lung cancer and it was related with adenocarcinoma but not squamous or other subtypes(51). These revealed facts show that patient response to the treatment can be predicted by these unique mutations. Also, 20 SNPs that is related with efficiency of platinum-based chemotherapy in small cell lung cancer patient was revealed thanks to genome- wide scan studies for single nucleotide polymorphism(52). Despite there are studies to discover genetic loci and SNPs , more studies is needed to understand effect of these abnormalities to form disease risk(53).
Databases/bioinformatics
Bioinformatics that include information management and algorithm development is combining of biology and computer science(54). Reaching the database that is about all research is important for personalized medicine. Information that is obtained from previosly described studies in subtitiles can be used for integrating a patient’s clinical information and the genetic profiles of their tumor to predict the relationships of certain molecular changes to cancer.
Challenges
There are some challenges about personalized medicine because, it is a new expanding area. The most important challenge is higher cost for establishing a new technology. Substructure of personalized medicine is required higher spending. Addition to that people who pay for PM can be effected, because 5% of private insurance companies cover the genetic test. But in long term personalized medicine will be more beneficial. Other important issue is standardization of testing. Standardization of testing includes kind of sample to be analyzed, proper techniques to collect sample and storage, chosing of targer genes and appropriate labarotory conditions to test . Ethical issue is also important, necessary laws and policy should be defined.
Future Expectation
“Bench to bedside” approach will be adapted to reality. Indicidual biomarkers have been added to enhance sensibility of forecasting prognosis and treatment yiels. Traditional method histopatalogically classification and management of cancer is cahnging with personalized medicine by using data of high-throughput analysis. In the near future , traditional ways will be totally replaced with molecular approach. If necessary higher invesment comes true for personalized medicine, cost effective and beneficial medical practice can be arised. Personalized medicine encourage to collect every individual cancer typer information. According to fingerprint of tumor side effect and unnecessary treatment can be reduced and family members who may carry risk of disease can be protected with early detection.
Figure 2 also explains possible scenerio of expected personalized medicine future from prediction to treatment for cancer management.
Conclusion
In conclusion, firstly it should be considered that each cancer patient treat according to their DNA structure. We can easily say that not yet. Because, PM field is evolving day by day. Personalized medicine area is growing by discovering new personal genetic alteration . Combination of high-throughput technology and molecular knowledge new moecular marker are discovered. Explication of genomic sequencing will be further developed and stand a standardization. Not only people are different but also their response to the treatment are different. Therefore we need to get rid of one-dose-fits-all approach of medicine with personalized medicine.
References
1-Meyerson M, Gabriel S, Getz G: Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11:685-96, 2010
2-.Genomics at the FDA www. fda. gov/ cder/ genomics/
download time:26.12.2015
3-Frazer KA, Ballinger DG, Cox DR, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 2007;449:851–61.
4-Sang-Hoon Cho, Jongsu Jeon, Seung Il Kim. Personalized Medicine in Breast Cancer: A Systematic Review. Breast Cancer 2012 ;15: 265-272
5-BRCA1 and BRCA2: cancer risk and genetic testing. National Cancer Institute. http://www.cancer.gov/cancertopics/ factsheet/Risk/BRCA. Accessed 26.12.15.
6-Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, et al. Gene expression and benefit of chemotherapy in women with nodenegative, estrogen receptor-positive breast cancer. J Clin Oncol 2006;24:3726-34
7-Personalized Medicine Coalition. The Case for Personalized Medicine. 3rd ed. Washington, DC: Personalized Medicine Coalition; 2011.
8- Ginsburg GS, McCarthy JJ. Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol 2001;19:491–6
9-Diamandis, M., N. M. A. White, and G. M. Yousef. “Personalized Medicine: Marking a New Epoch in Cancer Patient Management.”Molecular Cancer Research, 2010, 1175-187. Accessed December 26, 2015. mcr.aacrjournals.org/content/8/9/1175.full.pdf html?sid=b092c8d6-3987-4611-97ab-5abe8210f6d9.
10- Perera FP. Environment and cancer: who are susceptible? Science 1997;278:1068–73.
11- Kadouri L, Hubert A, Rotenberg Y, et al. Cancer risks in carriers of the BRCA1/2 Ashkenazi founder mutations. J Med Genet 2007;44: 467–71
12- Peltomaki P, de la Chapelle A. Mutations predisposing to hereditary nonpolyposis colorectal cancer. Adv Cancer Res 1997;71:93–119
13-Forbes SA, Tang G, Bindal N, et al. COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer. Nucleic Acids Res 2010;38:D652–7.
14- Girard N, Ostrovnaya I, Lau C, et al. Genomic and mutational profiling to assess clonal relationships between multiple non-small cell lung cancers. Clin Cancer Res 2009;15:5184–90.
15- Overdevest JB, Theodorescu D, Lee JK. Utilizing the molecular gateway: the path to personalized cancer management. Clin Chem 2009;55:684–97.
16- Dave SS, Fu K, Wright GW, et al. Molecular diagnosis of Burkitt’s lymphoma. N Engl J Med 2006;354:2431–42.
17- Allison M. Is personalized medicine finally arriving? Nat Biotechnol 2008;26:509–17.
18- Tuveson DA, Willis NA, Jacks T, et al. STI571 inactivation of the gastrointestinal stromal tumor c-KIT oncoprotein: biological and clinical implications. Oncogene 2001;20:5054–8.
19- Baselga J, Norton L, Albanell J, Kim YM, Mendelsohn J. Recombinant humanized anti-HER2 antibody (Herceptin) enhances the antitumor activity of paclitaxel and doxorubicin against HER2/neu overexpressing human breast cancer xenografts. Cancer Res 1998;58:2825–31.
20- Paez JG, Janne PA, Lee JC, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004; 304:1497–500.
21-Pao W, Miller V, Zakowski M, et al. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A 2004;101:13306–11.
22- Massarelli E, Varella-Garcia M, Tang X, et al. KRAS mutation is an important predictor of resistance to therapy with epidermal growth factor receptor tyrosine kinase inhibitors in non-small-cell lung cancer. Clin Cancer Res 2007;13:2890–6.
23-Lievre A, Bachet JB, Le CD, et al. KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer. Cancer Res 2006;66:3992–5.
24. Laurent-PuigP,TaiebJ.LessonsfromTarcevainpancreaticcancer: where are we now, and how should future trials be designed in pancreatic cancer? Curr Opin Oncol 2008;20:454–8
25-Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999;286:487–91
26- Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 1998;279:1200–5.
27- Ingelman-Sundberg M. Polymorphism of cytochrome P450 and xenobiotic toxicity. Toxicology 2002;181–182:447–52.
28-Newman WG, Hadfield KD, Latif A, et al. Impaired tamoxifen metabolism reduces survival in familial breast cancer patients. Clin Cancer Res 2008;14:5913–8.
29- Macgregor PF, Squire JA. Application of microarrays to the analysis of gene expression in cancer. Clin Chem 2002;48:1170–7.
30-Kim HJ, Shen SS, Ayala AG, et al. Virtual-karyotyping with SNP microarrays in morphologically challenging renal cell neoplasms: a practical and useful diagnostic modality. Am J Surg Pathol 2009; 33:1276–86
31-Wheeler DA, Srinivasan M, Egholm M, et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 2008;452:872–6.
32-4. Metzker ML: Sequencing technologies: The next generation. Nat Rev Genet 11:31-46, 2009 33- Shendure J, Ji H: Next-generation DNA sequencing. Nat Biotechnol 26:1135-1145, 2009
34- ThomasRK,BakerAC,DebiasiRM,etal.High-throughputoncogene mutation profiling in human cancer. Nat Genet 2007;39:347–51.
35- Stephens PJ, McBride DJ, Lin ML, et al. Complex landscapes of somatic rearrangement in human breast cancer genomes. Nature 2009;462:1005–10.
36- Emmert-Buck MR, Bonner RF, Smith PD, et al. Laser capture microdissection. Science 1996;274:998–1001.
37-Cargill M, Altshuler D, Ireland J, et al. Characterization of singlenucleotide polymorphisms in coding regions of human genes. Nat Genet 1999;22:231–8.
38-A haplotype map of the human genome.Nature2005;437:1299–320
39- McCarthy MI, Abecasis GR, Cardon LR, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008;9:356–69.
40- Syvanen AC. Toward genome-wide SNP genotyping. Nat Genet 2005;37 Suppl:S5–10.
41- Simon R. Microarray-based expression profiling and informatics. Curr Opin Biotechnol 2008;19:26–9.
42- Kim MJ, Ro JY, Ahn SH, Kim HH, Kim SB, Gong G. Clinicopathologic significance of the basal-like subtype of breast cancer: a comparison with hormone receptor and Her2/neu-overexpressing phenotypes. Hum Pathol 2006;37:1217–26.
43-Esteller M. Epigenetics in cancer. N Engl J Med 2008;358:1148–59.
44-Wulfkuhle JD, Liotta LA, Petricoin EF. Proteomic applications for the early detection of cancer. Nat Rev Cancer 2003;3:267–75.
45-Rogers S, Girolami M, Kolch W, et al. Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinformatics 2008;24:2894–900.
46-van der Merwe DE, Oikonomopoulou K, Marshall J, Diamandis EP. Mass spectrometry: uncovering the cancer proteome for diagnostics. Adv Cancer Res 2007;96:23–50.
47- Bodovitz S, Joos T, Bachmann J. Protein biochips: the calm before the storm. Drug Discov Today 2005;10:283–7.
48- Garber K. Human Cancer Genome Project moving forward despite some doubts in community. J Natl Cancer Inst 2005;97:1322–4.
49- Zheng W, Long J, Gao YT, et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet 2009;41:324–8.
50- Yeager M, Orr N, Hayes RB, et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet 2007;39:645–9.
51- Landi MT, Chatterjee N, Yu K, et al. A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am J Hum Genet 2009;85: 679–91.
52-Wu C, Xu B, Yuan P, et al. Genome-wide examination of genetic variants associated with response to platinum-based chemotherapy in patients with small-cell lung cancer. Pharmacogenet Genomics 2010;20:389–95.
53- Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature 2009;461:218–23.
54- Pop M, Salzberg SL: Bioinformatics challenges of new sequencing technology. Trends Genet 24:142-149, 2008