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Precision Diagnostics
Precision diagnostics is a branch of precision medicine where a patient's health-care model is precisely managed, and specific diseases are diagnosed based on the patient's customised omics data analytics. the healthcare system is transformed from a conventional “one-size-fits-all” approach to a model that encompasses four newly features: predictive, preventive, personalised, and participatory(P4)

The general idea started in 2015 when U.S former president Obama's Precision medicine initiative was launched. A year after the launch of the precision medicine initiative, the Human Personal Omics Profiling study was launched to establish integrative multi-omics approaches that could be used for precision diagnosis.

Each person’s diseases are early diagnosed based on an individual's variability in DNA, environment, and lifestyle. This is achieved through recent technological advances in data acquisition from genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiome studies. Through precise monitoring of collateral molecular layers, the ‘whole picture’ of personal molecular profile in an unbiased manner is attained.

Plus, contemporary computational algorithms enhance data analysis from these omics data generated, and data management is further improved through digital technologies. Moreover, advancements in artificial intelligence, especially convolutional neural networks and extensive data analysis, are used to further predict the association between genotype and phenotype, which could improve sensitivity and specificity in precision diagnosis.

With the advancement of NGS, cancer diagnostics are achieved much more precisely than ever before. NGS offers complete perspective in decoding the genome over any other single gene assays. NGS-based molecular diagnostics provide genomic information about tumor-related variants and cancer-causing structural alterations. Having this highly accurate diagnosis, complementary targeted novel therapies are possible. In NGS, samples are collected through a buccal swab or peripheral blood or through tissue-specific biopsy, and DNAs are used to screen for single nucleotide variants, gene insertion/deletion, and copy number variants, while RNA is used for measuring gene expression.

Genomic Sequencing In Lymphoma Diagnostics
With recent advancements in genome sequencing and identification of mutations linking toward diagnosing lymphoma, more effect has been put in identifying key mutations and genetic aberrations to aid precision diagnostics for Lymphoma patients. Most lymphoma identities may be characterized by chromosome translocations, for example, follicular lymphoma (FL) t(14;18), diffuse large B cell lymphoma (DLBCL) t(8;14), and anaplastic large cell lymphoma (ALCL) t(2;5). Though these translocations are useful to identify lymphoma entities, translocations are not unique to each type of lymphoma. For instance, FL and DLBCL share translations of the 8th and 14th chromosomes. To address this problem, low low-throughput and low-resolution methods such as Sanger sequencing and fluorescence in situ hybridization (FISH) are used alongside commercial probes to detect translocation on desired chromosomes. Despite the mutational landscape of multiple lymphomas being highly heterogenous, large-scale sequencing projects using higher definition resolution revealed more key mutations in different lymphomas. Next-generation sequencing (NGS) revealed several essential mutations for T cell-associated lymphoma: TET2, IDH2, and RHOA mutations are commonly observed in peripheral T cell lymphomas (PTCL), while STAT3 and STAT5B mutations are unique to large granular lymphocytic (LGL) leukemia. Furthermore, transcriptomics analysis and visualization techniques has revealed key cellular receptors and pathways to specify diagnostics further. NOTCH signaling pathway, T-cell Receptor (TCR) signaling pathways, and T-cell associated genes (Tet2, Dmnt3) were found to be prominent in T cell, and B cell-related lymphomas and helped to diagnose subtypes of PTCL. On the other hand, subtypes of DLBCL and display mutations associated with B cells change B cell receptor (BcR), NOTCH signalling pathway, Toll-like receptor (TLR), and NF-κB signalling cascade. Simply put, the increasing knowledge of genetic aberration in lymphoma, provides more information to design precision diagnostic tests for major and subtype lymphomas.

Molecular Analysis In Cancer Diagnostics
Tumor sampling and molecular analysis is a common ways to determine the properties of cancers as well as cancer progression and host immune response. Cancers of unknown origin claim a small portion of all cancers globally. Previously unknown primary tumors were discovered from PD-1 mutations and amplifications thanks to high dimension molecular profiling. A suspected carcinoma or poorly differentiated one may also be justified to apply to medical care. Newer technologies such as endobronchial ultrasound-guided transbronchial needle aspiration biopsy (EBUS-TBNA) are currently used in lung cancer diagnostics with 95% sensitivity and over 95% specificity. This minimally invasive method collects samples for morphological diagnosis and IHC/ISH characterization to determine cancer subtype and corresponding drug for treatment. Whole smear slides (WSI) also show potential for newer molecular analysis. Able to create a digital library of WSIs from cytology data, clinicians can have more information at diagnosis in Rapid on-site evaluation. Conventionally, treatment of cancers has been reliant on the morphological diagnosis of the cell type and tissue, taking microphytic and simple biological techniques to identify cancer subtypes. However, this method is proven to be hard for metastatic tumors with primary tumors further away from the site of discovery. Upon using recent, high dimentional complete molecular sequencing, diagnostics results may also include mutations observed in tumours to better understand cancer types and aid future treatment plans. An extreme example of group of cancer, oesophageal adenocarcinomas, which are hardly distunguishable by morphology. This is due to the fact the nearly all oesophageal adenocarcinomas arise from the Barrett’s mucosa. Using cDNA microarrays, the genetic variations of subtypes of oesophageal adenocarcinomas is profiled and prognosis of invasive hot cancers of this category is greatly improved.

Microbiome
In recent years, the interest in microbiome research has been rising and has become one of the critical components in precision medicine. Microbiome research refers to studying microorganisms' interaction within and outside the host. Common microorganisms include different types of fungi, bacteria, and viruses, and the community of microorganisms is known as the microbiome. These microorganisms exist in most of our body parts, contributing to our health. According to research, this microbiome is crucial in regulating our physiology by altering our metabolism, immune system, and more. Hence, the changes in the microbial community can provide insights into the health condition of the specific host and patients. In precision medicine, patients' gut microbiome is often profiled to determine which treatment offers the most therapeutic value to them. Evidence shows that the microbiome is essential as it may increase the effectiveness of specific cancer therapeutic treatments. Therefore, scientists can identify the imbalance of the microbiome community within a patient and act upon it to enhance the success rate of treatments.

Advantages
As mentioned above, precision medicine brings a lot of valuable insights into personalized treatments based on genetic information. Compared to conventional healthcare technology, precision medicine has several short and long-term advantages. Firstly, healthcare professionals can use genetic data collected from the patients to determine a better-personalized treatment. Since every person has a different set of genome information, they may have different responses to the same treatment, making personalized treatment a crucial step forward in the medical field.

With the help of precision medicine, scientists can gain better insights into the underlying causes of diseases in the population with certain genome information. Subpopulations with similar genome information, such as close family members, have a relatively high chance of developing certain genetic conditions or diseases. By identifying the underlying causes, healthcare professionals can take the essential steps to prevent the patients from creating the conditions. For instance, the underlying causes of disease may include environmental and lifestyle reasons. When identified early, the medical professionals can perform an early intervention that can significantly improve and prevent the disease. In research about the onset of pneumonia, early intervention has reduced the mortality rate from 90% to 41%, reinforcing the importance of early diagnosis.

Moreover, information gained from precision medicine may lead to the reduced cost spent on healthcare services. Since genetic information often reveals the possible causes and trigger factors of the development of certain diseases, it can reduce the unnecessary costs spent on identifying conditions. According to research, eliminating unwarranted variations in medical care can reduce the cost of patient management by at least 35 percent. The healthcare professional can figure out the best possible treatment with the detailed patients' genetic information. The comprehensive information about the patients can avoid unnecessary diagnostic testing and scanning, which reduces the cost of healthcare.

Limitations
Despite all the advantages and benefits of precision medicine, it has several limitations and pitfalls for the patients. Firstly, precision medicine promotes individual benefits by providing necessary insights into the best treatment for a specific genome mutation population. However, the cost of collecting genome information will increase. There may be an increase in price for private medical consultation, limiting the number of people who can benefit from precision medicine. With the increased cost, fewer people can afford the medical service; it may only provide value to patients with sufficient financial capability. As stated that the improved quality in healthcare does not mean it is more cost-effective; it may further drive the economic inequality in the health system. This will limit precision medicine to an individual's benefit instead of improving the healthcare system as a collective benefit.

Since precision medicine proposes the customization and personalization of treatments, it is tailored to a particular subgroup of patients. Suppose the data collected reflected that a small subset of the patient population is unresponsive to specific drugs; large pharmaceutical companies might not be willing to develop alternative drugs for them due to financial reasons. It is only a small group, so it does not seem as big as an earning opportunity for pharmaceutical companies. Hence, data collected in precision medicine may introduce unfair treatment between different subgroups of patients.

Not to mention that precision medicine requires storing patients' information in a vast database. This begs the question regarding data privacy issues. As genetic information is a personal and sensitive insight into a person's life, privacy concerns need to be addressed. Even though there is legislation protecting patients from data privacy, it does not necessarily prevent attackers from hacking the database. It might introduce genetic discrimination where people are treated differently because of their genome information.

Future prospects
With the help of advanced technology and data collected in precision medicine, it improves clinical decision-making. Since every medical decision is based on factors related to the patients, such as genetic information, sociodemographic characteristics, etc. The large dataset in precision medicine allows medical professionals to approach the treatment with a handful of data, which allows for more accurate and effective treatment.

Another potential prospect would be health apps which can be digital diagnostics devices in the form of a wearable biosensor. By utilizing AI technology, patients can obtain essential information such as any physiological data. The data obtained from these health apps, where medical professionals can evaluate the information and determine the best possible treatment.

Besides from obtaining genome information, there is an on ‘Omics’-based biomarkers that could be one of the prospects in future precision medicine. The omics-based test is considered a form of biomarker which helps capture information to understand patients’ lives. The recent development in Omic-based biomarkers has improved the complexity of information obtained from patients, also reduces the cost of the process. This can be beneficial in future precision medicine as it makes obtaining patients’ health condition more cost-effective and gather more data.