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.

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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 personalize