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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 customized omics data analytics.^[1] the healthcare system is transformed from a conventional “one-size-fits-all” approach to a model that encompasses four newly features: predictive, preventive, personalized, and participatory(P4) ^[2]

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.^[3]

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.^[4]

With the advancement of Next generation sequencing (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.^[5]

Precision Diagnostics Techniques

DNA Seqeuncing

DNA sequencing is an essential component of modern scientific translational research and the use of DNA sequencing in the clinical environment was introduced first in clinical oncology. Whole genome sequencing (WGS) is used heavily for cancer patients.^[6] WGS is used to help give further genetic information about patient background as well as their eligibility to clinical trials that may be beneficial for them.^[7]^[8] The advantage of using WGS is that it reduces overall cost and time for the clinic to pass the diagnostics stage and apply treatments for the patient. Genetic sequencing can also be performed later on when a patient's disease progresses.^[9] Furthermore, using germline data, clinicals may evaluate cancer predisposition and pharmacogenomics information for earlier cancer identify and treatment.^[10] Despite some challenges such as accessibility to lower income patients, healthcare systems around the World have started to invest into holistic genomic sequencing and data infrastructure.^[11] The importance of fast access to high-dimensional output of genomic data is growing.^[12]

RNA Seqeuncing

Single-cell RNA-sequencing and dual host-pathogen RNA-sequencing are some of the commercially available RNA sequencing technologies. RNA-Seq allows clinicians to trace cancers when other diagnostic results are ambiguous. RNA sequencing allows further cell trajectory analysis that may give additional insight of cancer subtypes and patient background.^[14] As a more advanced version of the whole genome sequencing, RNA sequencing give additional information when creating an individual patient treatment plan. An increasing importance of RNA sequencing in diagnostics of malignant disorders, such as leukoplakia. Transcriptome analysis may also reveal disease progression in pro-malignant conditions.^[15]^[16] Such analysis allows for individualized prognosis for each patient.^[17] The utilities for sequencing of blood, bone marrow, or other bodily systems are becoming increasingly obvious. Using the database, clinicians are becoming more informed of the patient's situation.^[18]

Proteomics

Proteomics is a study of proteins. Proteins that are translated from messenger RNA go through post-transcriptional modifications that include phosphorylation, ubiquitination, methylation, acetylation, glycosylation, etc.^[19] Previously, immunoassay methods were used to study proteins but mass spectrometry is mostly used as a proteomic analyzing tool.^[20] In mass spectrometry analysis, proteins/peptides are fragmented. Then, peptides are ionized through either electrospray ionization(ESI) or matrix-assisted laser desorption/ionization (MALDI). In addition to this, a mass analyzer generates information rich ion mass spectra from fragmented peptides. Four types of mass analyzer include: ion trap, time-of-flight, quadrupole, and fourier transform ion cyclotron. Lastly, using computational bioinformatics tools and algorithms, collected proteomics data could be further analyzed and used for protein profiling.^[21]

Microbiome

In recent years, the interest in microbiome research has been rising and has become one of the critical components in precision medicine.^[22]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.^[23]According to research, this microbiome is crucial in regulating our physiology by altering our metabolism, immune system, and more.^[24] 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.^[25] Evidence shows that the microbiome is essential as it may increase the effectiveness of specific cancer therapeutic treatments. ^[26]Therefore, scientists can identify the imbalance of the microbiome community within a patient and act upon it to enhance the success rate of treatments.

Diagnostics in Specific Disease Conditions

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-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.^[27]

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 whole slide images 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 dimensional 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, makes morphological diagnosis extremely difficult. 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.^[28]

Evaluation of precision medicine

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 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%,^[29] 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.^[30] 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.^[31]

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.^[32] 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.^[33] Hence, data collected in precision medicine may introduce unfair treatment between different subgroups of patients.

Not to mention, precision medicine requires the storing of patients’ information in a vast database. This begs to question the data privacy issue. As genetic information is very personal and sensitive insights 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 being treated differently because of their genome information.^[34]

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 utilising 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.^[35]

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.^[36] This can be beneficial in future precision medicine as it makes obtaining patients’ health condition more cost-effective and gather more data.

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[1] Brown, Noah A.; Elenitoba-Johnson, Kojo S.J. (24 January 2020). "Enabling Precision Oncology Through Precision Diagnostics". Annual Review of Pathology: Mechanisms of Disease. 15 (1): 97–121. doi:10.1146/annurev-pathmechdis-012418-012735. PMID 31977297. S2CID 210891430.

[2] Wang, Qi; Peng, Wei-Xian; Wang, Lu; Ye, Li (March 2019). "Toward multiomics-based next-generation diagnostics for precision medicine". Personalized Medicine. 16 (2): 157–170. doi:10.2217/pme-2018-0085. PMID 30816060. S2CID 73488370.

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@@ Line 4: / Line 4: @@
 {{TOC right}}
-'''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 customized omics data analytics.<ref>{{cite journal |last1=Brown |first1=Noah A. |last2=Elenitoba-Johnson |first2=Kojo S.J. |title=Enabling Precision Oncology Through Precision Diagnostics |journal=Annual Review of Pathology: Mechanisms of Disease |date=24 January 2020 |volume=15 |issue=1 |pages=97–121 |doi=10.1146/annurev-pathmechdis-012418-012735}}</ref> the healthcare system is transformed from a conventional “one-size-fits-all” approach to a model that encompasses four newly features: predictive, preventive, personalized, and participatory(P4) <ref>{{cite journal |last1=Wang |first1=Qi |last2=Peng |first2=Wei-Xian |last3=Wang |first3=Lu |last4=Ye |first4=Li |title=Toward multiomics-based next-generation diagnostics for precision medicine |journal=Personalized Medicine |date=March 2019 |volume=16 |issue=2 |pages=157–170 |doi=10.2217/pme-2018-0085}}</ref>
+'''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 customized omics data analytics.<ref>{{cite journal |last1=Brown |first1=Noah A. |last2=Elenitoba-Johnson |first2=Kojo S.J. |title=Enabling Precision Oncology Through Precision Diagnostics |journal=Annual Review of Pathology: Mechanisms of Disease |date=24 January 2020 |volume=15 |issue=1 |pages=97–121 |doi=10.1146/annurev-pathmechdis-012418-012735|pmid=31977297 |s2cid=210891430 }}</ref> the healthcare system is transformed from a conventional “one-size-fits-all” approach to a model that encompasses four newly features: predictive, preventive, personalized, and participatory(P4) <ref>{{cite journal |last1=Wang |first1=Qi |last2=Peng |first2=Wei-Xian |last3=Wang |first3=Lu |last4=Ye |first4=Li |title=Toward multiomics-based next-generation diagnostics for precision medicine |journal=Personalized Medicine |date=March 2019 |volume=16 |issue=2 |pages=157–170 |doi=10.2217/pme-2018-0085|pmid=30816060 |s2cid=73488370 }}</ref>
-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.<ref>{{cite journal |last1=Wang |first1=Qi |last2=Peng |first2=Wei-Xian |last3=Wang |first3=Lu |last4=Ye |first4=Li |title=Toward multiomics-based next-generation diagnostics for precision medicine |journal=Personalized Medicine |date=March 2019 |volume=16 |issue=2 |pages=157–170 |doi=10.2217/pme-2018-0085}}</ref>
+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.<ref>{{cite journal |last1=Wang |first1=Qi |last2=Peng |first2=Wei-Xian |last3=Wang |first3=Lu |last4=Ye |first4=Li |title=Toward multiomics-based next-generation diagnostics for precision medicine |journal=Personalized Medicine |date=March 2019 |volume=16 |issue=2 |pages=157–170 |doi=10.2217/pme-2018-0085|pmid=30816060 |s2cid=73488370 }}</ref>
 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 [[Genomic sequencing|genomics]], [[Transcriptomics technologies|transcriptomics]], [[epigenomics]], [[proteomics]], [[metabolomics]] , and [[Human microbiome|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.<ref>{{cite journal |last1=Khatab |first1=Ziyad |last2=Yousef |first2=George M. |title=Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics |journal=Critical Reviews in Clinical Laboratory Sciences |date=17 November 2021 |volume=58 |issue=8 |pages=546–562 |doi=10.1080/10408363.2021.1943302}}</ref>
+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.<ref>{{cite journal |last1=Khatab |first1=Ziyad |last2=Yousef |first2=George M. |title=Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics |journal=Critical Reviews in Clinical Laboratory Sciences |date=17 November 2021 |volume=58 |issue=8 |pages=546–562 |doi=10.1080/10408363.2021.1943302|pmid=34297653 |s2cid=236212963 }}</ref>
-With the advancement of Next generation sequencing (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.<ref>{{cite journal |last1=Brown |first1=Noah A. |last2=Elenitoba-Johnson |first2=Kojo S.J. |title=Enabling Precision Oncology Through Precision Diagnostics |journal=Annual Review of Pathology: Mechanisms of Disease |date=24 January 2020 |volume=15 |issue=1 |pages=97–121 |doi=10.1146/annurev-pathmechdis-012418-012735}}</ref>
+With the advancement of Next generation sequencing (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.<ref>{{cite journal |last1=Brown |first1=Noah A. |last2=Elenitoba-Johnson |first2=Kojo S.J. |title=Enabling Precision Oncology Through Precision Diagnostics |journal=Annual Review of Pathology: Mechanisms of Disease |date=24 January 2020 |volume=15 |issue=1 |pages=97–121 |doi=10.1146/annurev-pathmechdis-012418-012735|pmid=31977297 |s2cid=210891430 }}</ref>
 ==Precision Diagnostics Techniques==
@@ Line 18: / Line 18: @@
 === DNA Seqeuncing ===
-DNA sequencing is an essential component of modern scientific translational research and the use of DNA sequencing in the clinical environment was introduced first in [[clinical oncology]]. [[Whole genome sequencing|Whole genome sequencing (WGS)]] is used heavily for cancer patients.<ref>{{cite journal |last1=Nik-Zainal |first1=Serena |last2=Davies |first2=Helen |last3=Staaf |first3=Johan |last4=Ramakrishna |first4=Manasa |last5=Glodzik |first5=Dominik |last6=Zou |first6=Xueqing |last7=Martincorena |first7=Inigo |last8=Alexandrov |first8=Ludmil B. |last9=Martin |first9=Sancha |last10=Wedge |first10=David C. |last11=Van Loo |first11=Peter |last12=Ju |first12=Young Seok |last13=Smid |first13=Marcel |last14=Brinkman |first14=Arie B. |last15=Morganella |first15=Sandro |last16=Aure |first16=Miriam R. |last17=Lingjærde |first17=Ole Christian |last18=Langerød |first18=Anita |last19=Ringnér |first19=Markus |last20=Ahn |first20=Sung-Min |last21=Boyault |first21=Sandrine |last22=Brock |first22=Jane E. |last23=Broeks |first23=Annegien |last24=Butler |first24=Adam |last25=Desmedt |first25=Christine |last26=Dirix |first26=Luc |last27=Dronov |first27=Serge |last28=Fatima |first28=Aquila |last29=Foekens |first29=John A. |last30=Gerstung |first30=Moritz |last31=Hooijer |first31=Gerrit K. J. |last32=Jang |first32=Se Jin |last33=Jones |first33=David R. |last34=Kim |first34=Hyung-Yong |last35=King |first35=Tari A. |last36=Krishnamurthy |first36=Savitri |last37=Lee |first37=Hee Jin |last38=Lee |first38=Jeong-Yeon |last39=Li |first39=Yilong |last40=McLaren |first40=Stuart |last41=Menzies |first41=Andrew |last42=Mustonen |first42=Ville |last43=O’Meara |first43=Sarah |last44=Pauporté |first44=Iris |last45=Pivot |first45=Xavier |last46=Purdie |first46=Colin A. |last47=Raine |first47=Keiran |last48=Ramakrishnan |first48=Kamna |last49=Rodríguez-González |first49=F. Germán |last50=Romieu |first50=Gilles |last51=Sieuwerts |first51=Anieta M. |last52=Simpson |first52=Peter T. |last53=Shepherd |first53=Rebecca |last54=Stebbings |first54=Lucy |last55=Stefansson |first55=Olafur A. |last56=Teague |first56=Jon |last57=Tommasi |first57=Stefania |last58=Treilleux |first58=Isabelle |last59=Van den Eynden |first59=Gert G. |last60=Vermeulen |first60=Peter |last61=Vincent-Salomon |first61=Anne |last62=Yates |first62=Lucy |last63=Caldas |first63=Carlos |last64=Veer |first64=Laura van’t |last65=Tutt |first65=Andrew |last66=Knappskog |first66=Stian |last67=Tan |first67=Benita Kiat Tee |last68=Jonkers |first68=Jos |last69=Borg |first69=Åke |last70=Ueno |first70=Naoto T. |last71=Sotiriou |first71=Christos |last72=Viari |first72=Alain |last73=Futreal |first73=P. Andrew |last74=Campbell |first74=Peter J. |last75=Span |first75=Paul N. |last76=Van Laere |first76=Steven |last77=Lakhani |first77=Sunil R. |last78=Eyfjord |first78=Jorunn E. |last79=Thompson |first79=Alastair M. |last80=Birney |first80=Ewan |last81=Stunnenberg |first81=Hendrik G. |last82=van de Vijver |first82=Marc J. |last83=Martens |first83=John W. M. |last84=Børresen-Dale |first84=Anne-Lise |last85=Richardson |first85=Andrea L. |last86=Kong |first86=Gu |last87=Thomas |first87=Gilles |last88=Stratton |first88=Michael R. |title=Landscape of somatic mutations in 560 breast cancer whole-genome sequences |journal=Nature |date=2 June 2016 |volume=534 |issue=7605 |pages=47–54 |doi=10.1038/nature17676}}</ref> [[Whole genome sequencing|WGS]] is used to help give further genetic information about patient background as well as their eligibility to [[clinical trial]]s that may be beneficial for them.<ref>{{cite journal |last1=Rusch |first1=Michael |last2=Nakitandwe |first2=Joy |last3=Shurtleff |first3=Sheila |last4=Newman |first4=Scott |last5=Zhang |first5=Zhaojie |last6=Edmonson |first6=Michael N. |last7=Parker |first7=Matthew |last8=Jiao |first8=Yuannian |last9=Ma |first9=Xiaotu |last10=Liu |first10=Yanling |last11=Gu |first11=Jiali |last12=Walsh |first12=Michael F. |last13=Becksfort |first13=Jared |last14=Thrasher |first14=Andrew |last15=Li |first15=Yongjin |last16=McMurry |first16=James |last17=Hedlund |first17=Erin |last18=Patel |first18=Aman |last19=Easton |first19=John |last20=Yergeau |first20=Donald |last21=Vadodaria |first21=Bhavin |last22=Tatevossian |first22=Ruth G. |last23=Raimondi |first23=Susana |last24=Hedges |first24=Dale |last25=Chen |first25=Xiang |last26=Hagiwara |first26=Kohei |last27=McGee |first27=Rose |last28=Robinson |first28=Giles W. |last29=Klco |first29=Jeffery M. |last30=Gruber |first30=Tanja A. |last31=Ellison |first31=David W. |last32=Downing |first32=James R |last33=Zhang |first33=Jinghui |title=Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome |journal=Nature Communications |date=December 2018 |volume=9 |issue=1 |pages=3962 |doi=10.1038/s41467-018-06485-7}}</ref><ref>{{cite journal |last1=The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium |title=Pan-cancer analysis of whole genomes |journal=Nature |date=6 February 2020 |volume=578 |issue=7793 |pages=82–93 |doi=10.1038/s41586-020-1969-6}}</ref> The advantage of using WGS is that it reduces overall cost and time for the clinic to pass the diagnostics stage and apply treatments for the patient. Genetic sequencing can also be performed later on when a patient's disease progresses.<ref>{{cite journal |last1=Zehir |first1=Ahmet |last2=Benayed |first2=Ryma |last3=Shah |first3=Ronak H |last4=Syed |first4=Aijazuddin |last5=Middha |first5=Sumit |last6=Kim |first6=Hyunjae R |last7=Srinivasan |first7=Preethi |last8=Gao |first8=Jianjiong |last9=Chakravarty |first9=Debyani |last10=Devlin |first10=Sean M |last11=Hellmann |first11=Matthew D |last12=Barron |first12=David A |last13=Schram |first13=Alison M |last14=Hameed |first14=Meera |last15=Dogan |first15=Snjezana |last16=Ross |first16=Dara S |last17=Hechtman |first17=Jaclyn F |last18=DeLair |first18=Deborah F |last19=Yao |first19=JinJuan |last20=Mandelker |first20=Diana L |last21=Cheng |first21=Donavan T |last22=Chandramohan |first22=Raghu |last23=Mohanty |first23=Abhinita S |last24=Ptashkin |first24=Ryan N |last25=Jayakumaran |first25=Gowtham |last26=Prasad |first26=Meera |last27=Syed |first27=Mustafa H |last28=Rema |first28=Anoop Balakrishnan |last29=Liu |first29=Zhen Y |last30=Nafa |first30=Khedoudja |last31=Borsu |first31=Laetitia |last32=Sadowska |first32=Justyna |last33=Casanova |first33=Jacklyn |last34=Bacares |first34=Ruben |last35=Kiecka |first35=Iwona J |last36=Razumova |first36=Anna |last37=Son |first37=Julie B |last38=Stewart |first38=Lisa |last39=Baldi |first39=Tessara |last40=Mullaney |first40=Kerry A |last41=Al-Ahmadie |first41=Hikmat |last42=Vakiani |first42=Efsevia |last43=Abeshouse |first43=Adam A |last44=Penson |first44=Alexander V |last45=Jonsson |first45=Philip |last46=Camacho |first46=Niedzica |last47=Chang |first47=Matthew T |last48=Won |first48=Helen H |last49=Gross |first49=Benjamin E |last50=Kundra |first50=Ritika |last51=Heins |first51=Zachary J |last52=Chen |first52=Hsiao-Wei |last53=Phillips |first53=Sarah |last54=Zhang |first54=Hongxin |last55=Wang |first55=Jiaojiao |last56=Ochoa |first56=Angelica |last57=Wills |first57=Jonathan |last58=Eubank |first58=Michael |last59=Thomas |first59=Stacy B |last60=Gardos |first60=Stuart M |last61=Reales |first61=Dalicia N |last62=Galle |first62=Jesse |last63=Durany |first63=Robert |last64=Cambria |first64=Roy |last65=Abida |first65=Wassim |last66=Cercek |first66=Andrea |last67=Feldman |first67=Darren R |last68=Gounder |first68=Mrinal M |last69=Hakimi |first69=A Ari |last70=Harding |first70=James J |last71=Iyer |first71=Gopa |last72=Janjigian |first72=Yelena Y |last73=Jordan |first73=Emmet J |last74=Kelly |first74=Ciara M |last75=Lowery |first75=Maeve A |last76=Morris |first76=Luc G T |last77=Omuro |first77=Antonio M |last78=Raj |first78=Nitya |last79=Razavi |first79=Pedram |last80=Shoushtari |first80=Alexander N |last81=Shukla |first81=Neerav |last82=Soumerai |first82=Tara E |last83=Varghese |first83=Anna M |last84=Yaeger |first84=Rona |last85=Coleman |first85=Jonathan |last86=Bochner |first86=Bernard |last87=Riely |first87=Gregory J |last88=Saltz |first88=Leonard B |last89=Scher |first89=Howard I |last90=Sabbatini |first90=Paul J |last91=Robson |first91=Mark E |last92=Klimstra |first92=David S |last93=Taylor |first93=Barry S |last94=Baselga |first94=Jose |last95=Schultz |first95=Nikolaus |last96=Hyman |first96=David M |last97=Arcila |first97=Maria E |last98=Solit |first98=David B |last99=Ladanyi |first99=Marc |last100=Berger |first100=Michael F |title=Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients |journal=Nature Medicine |date=June 2017 |volume=23 |issue=6 |pages=703–713 |doi=10.1038/nm.4333}}</ref> Furthermore, using germline data, clinicals may evaluate cancer predisposition and [[pharmacogenomics]] information for earlier cancer identify and treatment.<ref>{{cite journal |last1=Gerlinger |first1=Marco |last2=Rowan |first2=Andrew J. |last3=Horswell |first3=Stuart |last4=Larkin |first4=James |last5=Endesfelder |first5=David |last6=Gronroos |first6=Eva |last7=Martinez |first7=Pierre |last8=Matthews |first8=Nicholas |last9=Stewart |first9=Aengus |last10=Tarpey |first10=Patrick |last11=Varela |first11=Ignacio |last12=Phillimore |first12=Benjamin |last13=Begum |first13=Sharmin |last14=McDonald |first14=Neil Q. |last15=Butler |first15=Adam |last16=Jones |first16=David |last17=Raine |first17=Keiran |last18=Latimer |first18=Calli |last19=Santos |first19=Claudio R. |last20=Nohadani |first20=Mahrokh |last21=Eklund |first21=Aron C. |last22=Spencer-Dene |first22=Bradley |last23=Clark |first23=Graham |last24=Pickering |first24=Lisa |last25=Stamp |first25=Gordon |last26=Gore |first26=Martin |last27=Szallasi |first27=Zoltan |last28=Downward |first28=Julian |last29=Futreal |first29=P. Andrew |last30=Swanton |first30=Charles |title=Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing |journal=New England Journal of Medicine |date=8 March 2012 |volume=366 |issue=10 |pages=883–892 |doi=10.1056/NEJMoa1113205}}</ref> Despite some challenges such as accessibility to lower income patients, healthcare systems around the World have started to invest into holistic genomic sequencing and data infrastructure.<ref>{{cite journal |last1=Pereira |first1=Luisa |last2=Mutesa |first2=Leon |last3=Tindana |first3=Paulina |last4=Ramsay |first4=Michèle |title=African genetic diversity and adaptation inform a precision medicine agenda |journal=Nature Reviews Genetics |date=May 2021 |volume=22 |issue=5 |pages=284–306 |doi=10.1038/s41576-020-00306-8}}</ref> The importance of fast access to high-dimensional output of genomic data is growing.<ref>{{cite journal |last1=Rosenquist |first1=Richard |last2=Cuppen |first2=Edwin |last3=Buettner |first3=Reinhard |last4=Caldas |first4=Carlos |last5=Dreau |first5=Helene |last6=Elemento |first6=Olivier |last7=Frederix |first7=Geert |last8=Grimmond |first8=Sean |last9=Haferlach |first9=Torsten |last10=Jobanputra |first10=Vaidehi |last11=Meggendorfer |first11=Manja |last12=Mullighan |first12=Charles G. |last13=Wordsworth |first13=Sarah |last14=Schuh |first14=Anna |title=Clinical utility of whole-genome sequencing in precision oncology |journal=Seminars in Cancer Biology |date=25 June 2021 |doi=10.1016/j.semcancer.2021.06.018}}</ref> [[File:Whole genome sequencing process.jpg|thumb|Example workflow of whole genome sequencing <ref>{{cite web |title=Whole Genome Sequencing (WGS) {{!}} PulseNet Methods{{!}} PulseNet {{!}} CDC |url=https://www.cdc.gov/pulsenet/pathogens/wgs.html |website=www.cdc.gov |language=en-us |date=14 May 2019}}</ref>]]
+DNA sequencing is an essential component of modern scientific translational research and the use of DNA sequencing in the clinical environment was introduced first in [[clinical oncology]]. [[Whole genome sequencing|Whole genome sequencing (WGS)]] is used heavily for cancer patients.<ref>{{cite journal |last1=Nik-Zainal |first1=Serena |last2=Davies |first2=Helen |last3=Staaf |first3=Johan |last4=Ramakrishna |first4=Manasa |last5=Glodzik |first5=Dominik |last6=Zou |first6=Xueqing |last7=Martincorena |first7=Inigo |last8=Alexandrov |first8=Ludmil B. |last9=Martin |first9=Sancha |last10=Wedge |first10=David C. |last11=Van Loo |first11=Peter |last12=Ju |first12=Young Seok |last13=Smid |first13=Marcel |last14=Brinkman |first14=Arie B. |last15=Morganella |first15=Sandro |last16=Aure |first16=Miriam R. |last17=Lingjærde |first17=Ole Christian |last18=Langerød |first18=Anita |last19=Ringnér |first19=Markus |last20=Ahn |first20=Sung-Min |last21=Boyault |first21=Sandrine |last22=Brock |first22=Jane E. |last23=Broeks |first23=Annegien |last24=Butler |first24=Adam |last25=Desmedt |first25=Christine |last26=Dirix |first26=Luc |last27=Dronov |first27=Serge |last28=Fatima |first28=Aquila |last29=Foekens |first29=John A. |last30=Gerstung |first30=Moritz |last31=Hooijer |first31=Gerrit K. J. |last32=Jang |first32=Se Jin |last33=Jones |first33=David R. |last34=Kim |first34=Hyung-Yong |last35=King |first35=Tari A. |last36=Krishnamurthy |first36=Savitri |last37=Lee |first37=Hee Jin |last38=Lee |first38=Jeong-Yeon |last39=Li |first39=Yilong |last40=McLaren |first40=Stuart |last41=Menzies |first41=Andrew |last42=Mustonen |first42=Ville |last43=O’Meara |first43=Sarah |last44=Pauporté |first44=Iris |last45=Pivot |first45=Xavier |last46=Purdie |first46=Colin A. |last47=Raine |first47=Keiran |last48=Ramakrishnan |first48=Kamna |last49=Rodríguez-González |first49=F. Germán |last50=Romieu |first50=Gilles |last51=Sieuwerts |first51=Anieta M. |last52=Simpson |first52=Peter T. |last53=Shepherd |first53=Rebecca |last54=Stebbings |first54=Lucy |last55=Stefansson |first55=Olafur A. |last56=Teague |first56=Jon |last57=Tommasi |first57=Stefania |last58=Treilleux |first58=Isabelle |last59=Van den Eynden |first59=Gert G. |last60=Vermeulen |first60=Peter |last61=Vincent-Salomon |first61=Anne |last62=Yates |first62=Lucy |last63=Caldas |first63=Carlos |last64=Veer |first64=Laura van’t |last65=Tutt |first65=Andrew |last66=Knappskog |first66=Stian |last67=Tan |first67=Benita Kiat Tee |last68=Jonkers |first68=Jos |last69=Borg |first69=Åke |last70=Ueno |first70=Naoto T. |last71=Sotiriou |first71=Christos |last72=Viari |first72=Alain |last73=Futreal |first73=P. Andrew |last74=Campbell |first74=Peter J. |last75=Span |first75=Paul N. |last76=Van Laere |first76=Steven |last77=Lakhani |first77=Sunil R. |last78=Eyfjord |first78=Jorunn E. |last79=Thompson |first79=Alastair M. |last80=Birney |first80=Ewan |last81=Stunnenberg |first81=Hendrik G. |last82=van de Vijver |first82=Marc J. |last83=Martens |first83=John W. M. |last84=Børresen-Dale |first84=Anne-Lise |last85=Richardson |first85=Andrea L. |last86=Kong |first86=Gu |last87=Thomas |first87=Gilles |last88=Stratton |first88=Michael R. |title=Landscape of somatic mutations in 560 breast cancer whole-genome sequences |journal=Nature |date=2 June 2016 |volume=534 |issue=7605 |pages=47–54 |doi=10.1038/nature17676|pmid=27135926 |pmc=4910866 }}</ref> [[Whole genome sequencing|WGS]] is used to help give further genetic information about patient background as well as their eligibility to [[clinical trial]]s that may be beneficial for them.<ref>{{cite journal |last1=Rusch |first1=Michael |last2=Nakitandwe |first2=Joy |last3=Shurtleff |first3=Sheila |last4=Newman |first4=Scott |last5=Zhang |first5=Zhaojie |last6=Edmonson |first6=Michael N. |last7=Parker |first7=Matthew |last8=Jiao |first8=Yuannian |last9=Ma |first9=Xiaotu |last10=Liu |first10=Yanling |last11=Gu |first11=Jiali |last12=Walsh |first12=Michael F. |last13=Becksfort |first13=Jared |last14=Thrasher |first14=Andrew |last15=Li |first15=Yongjin |last16=McMurry |first16=James |last17=Hedlund |first17=Erin |last18=Patel |first18=Aman |last19=Easton |first19=John |last20=Yergeau |first20=Donald |last21=Vadodaria |first21=Bhavin |last22=Tatevossian |first22=Ruth G. |last23=Raimondi |first23=Susana |last24=Hedges |first24=Dale |last25=Chen |first25=Xiang |last26=Hagiwara |first26=Kohei |last27=McGee |first27=Rose |last28=Robinson |first28=Giles W. |last29=Klco |first29=Jeffery M. |last30=Gruber |first30=Tanja A. |last31=Ellison |first31=David W. |last32=Downing |first32=James R |last33=Zhang |first33=Jinghui |title=Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome |journal=Nature Communications |date=December 2018 |volume=9 |issue=1 |pages=3962 |doi=10.1038/s41467-018-06485-7|pmid=30262806 |s2cid=52878243 }}</ref><ref>{{cite journal |last1=The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium |title=Pan-cancer analysis of whole genomes |journal=Nature |date=6 February 2020 |volume=578 |issue=7793 |pages=82–93 |doi=10.1038/s41586-020-1969-6|pmid=32025007 |pmc=7025898 }}</ref> The advantage of using WGS is that it reduces overall cost and time for the clinic to pass the diagnostics stage and apply treatments for the patient. Genetic sequencing can also be performed later on when a patient's disease progresses.<ref>{{cite journal |last1=Zehir |first1=Ahmet |last2=Benayed |first2=Ryma |last3=Shah |first3=Ronak H |last4=Syed |first4=Aijazuddin |last5=Middha |first5=Sumit |last6=Kim |first6=Hyunjae R |last7=Srinivasan |first7=Preethi |last8=Gao |first8=Jianjiong |last9=Chakravarty |first9=Debyani |last10=Devlin |first10=Sean M |last11=Hellmann |first11=Matthew D |last12=Barron |first12=David A |last13=Schram |first13=Alison M |last14=Hameed |first14=Meera |last15=Dogan |first15=Snjezana |last16=Ross |first16=Dara S |last17=Hechtman |first17=Jaclyn F |last18=DeLair |first18=Deborah F |last19=Yao |first19=JinJuan |last20=Mandelker |first20=Diana L |last21=Cheng |first21=Donavan T |last22=Chandramohan |first22=Raghu |last23=Mohanty |first23=Abhinita S |last24=Ptashkin |first24=Ryan N |last25=Jayakumaran |first25=Gowtham |last26=Prasad |first26=Meera |last27=Syed |first27=Mustafa H |last28=Rema |first28=Anoop Balakrishnan |last29=Liu |first29=Zhen Y |last30=Nafa |first30=Khedoudja |last31=Borsu |first31=Laetitia |last32=Sadowska |first32=Justyna |last33=Casanova |first33=Jacklyn |last34=Bacares |first34=Ruben |last35=Kiecka |first35=Iwona J |last36=Razumova |first36=Anna |last37=Son |first37=Julie B |last38=Stewart |first38=Lisa |last39=Baldi |first39=Tessara |last40=Mullaney |first40=Kerry A |last41=Al-Ahmadie |first41=Hikmat |last42=Vakiani |first42=Efsevia |last43=Abeshouse |first43=Adam A |last44=Penson |first44=Alexander V |last45=Jonsson |first45=Philip |last46=Camacho |first46=Niedzica |last47=Chang |first47=Matthew T |last48=Won |first48=Helen H |last49=Gross |first49=Benjamin E |last50=Kundra |first50=Ritika |last51=Heins |first51=Zachary J |last52=Chen |first52=Hsiao-Wei |last53=Phillips |first53=Sarah |last54=Zhang |first54=Hongxin |last55=Wang |first55=Jiaojiao |last56=Ochoa |first56=Angelica |last57=Wills |first57=Jonathan |last58=Eubank |first58=Michael |last59=Thomas |first59=Stacy B |last60=Gardos |first60=Stuart M |last61=Reales |first61=Dalicia N |last62=Galle |first62=Jesse |last63=Durany |first63=Robert |last64=Cambria |first64=Roy |last65=Abida |first65=Wassim |last66=Cercek |first66=Andrea |last67=Feldman |first67=Darren R |last68=Gounder |first68=Mrinal M |last69=Hakimi |first69=A Ari |last70=Harding |first70=James J |last71=Iyer |first71=Gopa |last72=Janjigian |first72=Yelena Y |last73=Jordan |first73=Emmet J |last74=Kelly |first74=Ciara M |last75=Lowery |first75=Maeve A |last76=Morris |first76=Luc G T |last77=Omuro |first77=Antonio M |last78=Raj |first78=Nitya |last79=Razavi |first79=Pedram |last80=Shoushtari |first80=Alexander N |last81=Shukla |first81=Neerav |last82=Soumerai |first82=Tara E |last83=Varghese |first83=Anna M |last84=Yaeger |first84=Rona |last85=Coleman |first85=Jonathan |last86=Bochner |first86=Bernard |last87=Riely |first87=Gregory J |last88=Saltz |first88=Leonard B |last89=Scher |first89=Howard I |last90=Sabbatini |first90=Paul J |last91=Robson |first91=Mark E |last92=Klimstra |first92=David S |last93=Taylor |first93=Barry S |last94=Baselga |first94=Jose |last95=Schultz |first95=Nikolaus |last96=Hyman |first96=David M |last97=Arcila |first97=Maria E |last98=Solit |first98=David B |last99=Ladanyi |first99=Marc |last100=Berger |first100=Michael F |title=Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients |journal=Nature Medicine |date=June 2017 |volume=23 |issue=6 |pages=703–713 |doi=10.1038/nm.4333|pmc=5461196 }}</ref> Furthermore, using germline data, clinicals may evaluate cancer predisposition and [[pharmacogenomics]] information for earlier cancer identify and treatment.<ref>{{cite journal |last1=Gerlinger |first1=Marco |last2=Rowan |first2=Andrew J. |last3=Horswell |first3=Stuart |last4=Larkin |first4=James |last5=Endesfelder |first5=David |last6=Gronroos |first6=Eva |last7=Martinez |first7=Pierre |last8=Matthews |first8=Nicholas |last9=Stewart |first9=Aengus |last10=Tarpey |first10=Patrick |last11=Varela |first11=Ignacio |last12=Phillimore |first12=Benjamin |last13=Begum |first13=Sharmin |last14=McDonald |first14=Neil Q. |last15=Butler |first15=Adam |last16=Jones |first16=David |last17=Raine |first17=Keiran |last18=Latimer |first18=Calli |last19=Santos |first19=Claudio R. |last20=Nohadani |first20=Mahrokh |last21=Eklund |first21=Aron C. |last22=Spencer-Dene |first22=Bradley |last23=Clark |first23=Graham |last24=Pickering |first24=Lisa |last25=Stamp |first25=Gordon |last26=Gore |first26=Martin |last27=Szallasi |first27=Zoltan |last28=Downward |first28=Julian |last29=Futreal |first29=P. Andrew |last30=Swanton |first30=Charles |title=Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing |journal=New England Journal of Medicine |date=8 March 2012 |volume=366 |issue=10 |pages=883–892 |doi=10.1056/NEJMoa1113205|pmid=22397650 |pmc=4878653 }}</ref> Despite some challenges such as accessibility to lower income patients, healthcare systems around the World have started to invest into holistic genomic sequencing and data infrastructure.<ref>{{cite journal |last1=Pereira |first1=Luisa |last2=Mutesa |first2=Leon |last3=Tindana |first3=Paulina |last4=Ramsay |first4=Michèle |title=African genetic diversity and adaptation inform a precision medicine agenda |journal=Nature Reviews Genetics |date=May 2021 |volume=22 |issue=5 |pages=284–306 |doi=10.1038/s41576-020-00306-8|pmid=33432191 |s2cid=231587564 }}</ref> The importance of fast access to high-dimensional output of genomic data is growing.<ref>{{cite journal |last1=Rosenquist |first1=Richard |last2=Cuppen |first2=Edwin |last3=Buettner |first3=Reinhard |last4=Caldas |first4=Carlos |last5=Dreau |first5=Helene |last6=Elemento |first6=Olivier |last7=Frederix |first7=Geert |last8=Grimmond |first8=Sean |last9=Haferlach |first9=Torsten |last10=Jobanputra |first10=Vaidehi |last11=Meggendorfer |first11=Manja |last12=Mullighan |first12=Charles G. |last13=Wordsworth |first13=Sarah |last14=Schuh |first14=Anna |title=Clinical utility of whole-genome sequencing in precision oncology |journal=Seminars in Cancer Biology |date=25 June 2021 |doi=10.1016/j.semcancer.2021.06.018|pmid=34175442 |s2cid=235661249 }}</ref> [[File:Whole genome sequencing process.jpg|thumb|Example workflow of whole genome sequencing <ref>{{cite web |title=Whole Genome Sequencing (WGS) {{!}} PulseNet Methods{{!}} PulseNet {{!}} CDC |url=https://www.cdc.gov/pulsenet/pathogens/wgs.html |website=www.cdc.gov |language=en-us |date=14 May 2019}}</ref>]]
 === RNA Seqeuncing ===
-Single-cell RNA-sequencing and dual host-pathogen RNA-sequencing are some of the commercially available RNA sequencing technologies. RNA-Seq allows clinicians to trace cancers when other diagnostic results are ambiguous. RNA sequencing allows further cell trajectory analysis that may give additional insight of cancer subtypes and patient background.<ref>{{cite journal |last1=Zhang |first1=Yijie |last2=Wang |first2=Dan |last3=Peng |first3=Miao |last4=Tang |first4=Le |last5=Ouyang |first5=Jiawei |last6=Xiong |first6=Fang |last7=Guo |first7=Can |last8=Tang |first8=Yanyan |last9=Zhou |first9=Yujuan |last10=Liao |first10=Qianjin |last11=Wu |first11=Xu |last12=Wang |first12=Hui |last13=Yu |first13=Jianjun |last14=Li |first14=Yong |last15=Li |first15=Xiaoling |last16=Li |first16=Guiyuan |last17=Zeng |first17=Zhaoyang |last18=Tan |first18=Yixin |last19=Xiong |first19=Wei |title=Single‐cell RNA sequencing in cancer research |journal=Journal of Experimental & Clinical Cancer Research |date=December 2021 |volume=40 |issue=1 |pages=81 |doi=10.1186/s13046-021-01874-1}}</ref> As a more advanced version of the whole genome sequencing, RNA sequencing give additional information when creating an individual patient treatment plan. An increasing importance of RNA sequencing in diagnostics of malignant disorders, such as leukoplakia. Transcriptome analysis may also reveal disease progression in pro-malignant conditions.<ref>{{cite journal |last1=Lu |first1=Miaolong |last2=Zhan |first2=Xianquan |title=The crucial role of multiomic approach in cancer research and clinically relevant outcomes |journal=EPMA Journal |date=March 2018 |volume=9 |issue=1 |pages=77–102 |doi=10.1007/s13167-018-0128-8}}</ref><ref>{{cite journal |last1=Westermann |first1=Alexander J. |last2=Vogel |first2=Jörg |title=Cross-species RNA-seq for deciphering host–microbe interactions |journal=Nature Reviews Genetics |date=June 2021 |volume=22 |issue=6 |pages=361–378 |doi=10.1038/s41576-021-00326-y}}</ref> Such analysis allows for individualized prognosis for each patient.<ref>{{cite journal |last1=Westermann |first1=Alexander J. |last2=Vogel |first2=Jörg |title=Host-Pathogen Transcriptomics by Dual RNA-Seq |journal=Bacterial Regulatory RNA |date=2018 |volume=1737 |pages=59–75 |doi=10.1007/978-1-4939-7634-8_4}}</ref> The utilities for sequencing of blood, bone marrow, or other bodily systems are becoming increasingly obvious. Using the database, clinicians are becoming more informed of the patient's situation.<ref>{{cite journal |last1=Louis |first1=Irina Vlasova-St |title=Introductory Chapter: Applications of RNA-Seq Diagnostics in Biology and Medicine |date=13 October 2021 |doi=10.5772/intechopen.99882}}</ref>
+Single-cell RNA-sequencing and dual host-pathogen RNA-sequencing are some of the commercially available RNA sequencing technologies. RNA-Seq allows clinicians to trace cancers when other diagnostic results are ambiguous. RNA sequencing allows further cell trajectory analysis that may give additional insight of cancer subtypes and patient background.<ref>{{cite journal |last1=Zhang |first1=Yijie |last2=Wang |first2=Dan |last3=Peng |first3=Miao |last4=Tang |first4=Le |last5=Ouyang |first5=Jiawei |last6=Xiong |first6=Fang |last7=Guo |first7=Can |last8=Tang |first8=Yanyan |last9=Zhou |first9=Yujuan |last10=Liao |first10=Qianjin |last11=Wu |first11=Xu |last12=Wang |first12=Hui |last13=Yu |first13=Jianjun |last14=Li |first14=Yong |last15=Li |first15=Xiaoling |last16=Li |first16=Guiyuan |last17=Zeng |first17=Zhaoyang |last18=Tan |first18=Yixin |last19=Xiong |first19=Wei |title=Single‐cell RNA sequencing in cancer research |journal=Journal of Experimental & Clinical Cancer Research |date=December 2021 |volume=40 |issue=1 |pages=81 |doi=10.1186/s13046-021-01874-1|s2cid=232088301 }}</ref> As a more advanced version of the whole genome sequencing, RNA sequencing give additional information when creating an individual patient treatment plan. An increasing importance of RNA sequencing in diagnostics of malignant disorders, such as leukoplakia. Transcriptome analysis may also reveal disease progression in pro-malignant conditions.<ref>{{cite journal |last1=Lu |first1=Miaolong |last2=Zhan |first2=Xianquan |title=The crucial role of multiomic approach in cancer research and clinically relevant outcomes |journal=EPMA Journal |date=March 2018 |volume=9 |issue=1 |pages=77–102 |doi=10.1007/s13167-018-0128-8|pmid=29515689 |pmc=5833337 }}</ref><ref>{{cite journal |last1=Westermann |first1=Alexander J. |last2=Vogel |first2=Jörg |title=Cross-species RNA-seq for deciphering host–microbe interactions |journal=Nature Reviews Genetics |date=June 2021 |volume=22 |issue=6 |pages=361–378 |doi=10.1038/s41576-021-00326-y|pmid=33597744 |hdl=10033/622795 |s2cid=231952431 }}</ref> Such analysis allows for individualized prognosis for each patient.<ref>{{cite book |last1=Westermann |first1=Alexander J. |last2=Vogel |first2=Jörg |title=Host-Pathogen Transcriptomics by Dual RNA-Seq |journal=Bacterial Regulatory RNA |series=Methods in Molecular Biology |date=2018 |volume=1737 |pages=59–75 |doi=10.1007/978-1-4939-7634-8_4|isbn=978-1-4939-7633-1 }}</ref> The utilities for sequencing of blood, bone marrow, or other bodily systems are becoming increasingly obvious. Using the database, clinicians are becoming more informed of the patient's situation.<ref>{{cite book |last1=Louis |first1=Irina Vlasova-St |title=Applications of RNA-Seq in Biology and Medicine |chapter=Introductory Chapter: Applications of RNA-Seq Diagnostics in Biology and Medicine |date=13 October 2021 |doi=10.5772/intechopen.99882|isbn=978-1-83962-686-9 |s2cid=243094823 }}</ref>
 === Proteomics ===
-Proteomics is a study of proteins. Proteins that are translated from messenger RNA go through post-transcriptional modifications that include phosphorylation, ubiquitination, methylation, acetylation, glycosylation, etc.<ref>{{cite journal |last1=Duan |first1=Guangyou |last2=Walther |first2=Dirk |title=The Roles of Post-translational Modifications in the Context of Protein Interaction Networks |journal=PLOS Computational Biology |date=18 February 2015 |volume=11 |issue=2 |pages=e1004049 |doi=10.1371/journal.pcbi.1004049}}</ref> Previously, immunoassay methods were used to study proteins but mass spectrometry is mostly used as a proteomic analyzing tool.<ref>{{cite journal |last1=Chait |first1=Brian T. |title=Mass Spectrometry in the Postgenomic Era |journal=Annual Review of Biochemistry |date=7 July 2011 |volume=80 |issue=1 |pages=239–246 |doi=10.1146/annurev-biochem-110810-095744}}</ref> In mass spectrometry analysis, proteins/peptides are fragmented. Then, peptides are ionized through either electrospray ionization(ESI) or matrix-assisted laser desorption/ionization (MALDI). In addition to this, a mass analyzer generates information rich ion mass spectra from fragmented peptides. Four types of mass analyzer include: ion trap, time-of-flight, quadrupole, and fourier transform ion cyclotron. Lastly, using computational bioinformatics tools and algorithms, collected proteomics data could be further analyzed and used for protein profiling.<ref>{{cite journal |last1=Aebersold |first1=Ruedi |last2=Mann |first2=Matthias |title=Mass spectrometry-based proteomics |journal=Nature |date=March 2003 |volume=422 |issue=6928 |pages=198–207 |doi=https://doi.org/10.1038/nature01511}}</ref>
+Proteomics is a study of proteins. Proteins that are translated from messenger RNA go through post-transcriptional modifications that include phosphorylation, ubiquitination, methylation, acetylation, glycosylation, etc.<ref>{{cite journal |last1=Duan |first1=Guangyou |last2=Walther |first2=Dirk |title=The Roles of Post-translational Modifications in the Context of Protein Interaction Networks |journal=PLOS Computational Biology |date=18 February 2015 |volume=11 |issue=2 |pages=e1004049 |doi=10.1371/journal.pcbi.1004049|s2cid=11573752 }}</ref> Previously, immunoassay methods were used to study proteins but mass spectrometry is mostly used as a proteomic analyzing tool.<ref>{{cite journal |last1=Chait |first1=Brian T. |title=Mass Spectrometry in the Postgenomic Era |journal=Annual Review of Biochemistry |date=7 July 2011 |volume=80 |issue=1 |pages=239–246 |doi=10.1146/annurev-biochem-110810-095744|pmid=21675917 }}</ref> In mass spectrometry analysis, proteins/peptides are fragmented. Then, peptides are ionized through either electrospray ionization(ESI) or matrix-assisted laser desorption/ionization (MALDI). In addition to this, a mass analyzer generates information rich ion mass spectra from fragmented peptides. Four types of mass analyzer include: ion trap, time-of-flight, quadrupole, and fourier transform ion cyclotron. Lastly, using computational bioinformatics tools and algorithms, collected proteomics data could be further analyzed and used for protein profiling.<ref>{{cite journal |last1=Aebersold |first1=Ruedi |last2=Mann |first2=Matthias |title=Mass spectrometry-based proteomics |journal=Nature |date=March 2003 |volume=422 |issue=6928 |pages=198–207 |doi=10.1038/nature01511 |pmid=12634793 |s2cid=118260 }}</ref>
 === Microbiome ===
-In recent years, the interest in microbiome research has been rising and has become one of the critical components in precision medicine.''<ref>{{Cite journal |last=Cullen |first=Chad M. |last2=Aneja |first2=Kawalpreet K. |last3=Beyhan |first3=Sinem |last4=Cho |first4=Clara E. |last5=Woloszynek |first5=Stephen |last6=Convertino |first6=Matteo |last7=McCoy |first7=Sophie J. |last8=Zhang |first8=Yanyan |last9=Anderson |first9=Matthew Z. |last10=Alvarez-Ponce |first10=David |last11=Smirnova |first11=Ekaterina |date=2020 |title=Emerging Priorities for Microbiome Research |url=https://www.frontiersin.org/article/10.3389/fmicb.2020.00136 |journal=Frontiers in Microbiology |volume=11 |doi=10.3389/fmicb.2020.00136 |issn=1664-302X |pmc=7042322 |pmid=32140140}}</ref>''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.''<ref>{{Cite web |date=2015-08-31 |title=NIH Human Microbiome Project defines normal bacterial makeup of the body |url=https://www.nih.gov/news-events/news-releases/nih-human-microbiome-project-defines-normal-bacterial-makeup-body |access-date=2022-04-17 |website=National Institutes of Health (NIH) |language=EN}}</ref>''According to research, this microbiome is crucial in regulating our physiology by altering our metabolism, immune system, and more.<ref>{{Cite journal |last=Devaraj |first=Sridevi |last2=Hemarajata |first2=Peera |last3=Versalovic |first3=James |date=April 2013 |title=The Human Gut Microbiome and Body Metabolism: Implications for Obesity and Diabetes |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974587/ |journal=Clinical chemistry |volume=59 |issue=4 |pages=617–628 |doi=10.1373/clinchem.2012.187617 |issn=0009-9147 |pmc=3974587 |pmid=23401286}}</ref>'' ''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.''<ref>{{Cite journal |last=Petrosino |first=Joseph F. |date=2018-02-22 |title=The microbiome in precision medicine: the way forward |url=https://doi.org/10.1186/s13073-018-0525-6 |journal=Genome Medicine |volume=10 |issue=1 |pages=12 |doi=10.1186/s13073-018-0525-6 |issn=1756-994X |pmc=5824491 |pmid=29471863}}</ref> ''Evidence shows that the microbiome is essential as it may increase the effectiveness of specific cancer therapeutic treatments.'' <ref>{{Citation |title=User:Johnny British/sandbox |date=2022-04-16 |url=https://en.wikipedia.org/w/index.php?title=User:Johnny_British/sandbox&oldid=1082973783 |work=Wikipedia |language=en |access-date=2022-04-17}}</ref>''Therefore, scientists can identify the imbalance of the microbiome community within a patient and act upon it to enhance the success rate of treatments.
+In recent years, the interest in microbiome research has been rising and has become one of the critical components in precision medicine.''<ref>{{Cite journal |last1=Cullen |first1=Chad M. |last2=Aneja |first2=Kawalpreet K. |last3=Beyhan |first3=Sinem |last4=Cho |first4=Clara E. |last5=Woloszynek |first5=Stephen |last6=Convertino |first6=Matteo |last7=McCoy |first7=Sophie J. |last8=Zhang |first8=Yanyan |last9=Anderson |first9=Matthew Z. |last10=Alvarez-Ponce |first10=David |last11=Smirnova |first11=Ekaterina |date=2020 |title=Emerging Priorities for Microbiome Research |journal=Frontiers in Microbiology |volume=11 |doi=10.3389/fmicb.2020.00136 |issn=1664-302X |pmc=7042322 |pmid=32140140|doi-access=free }}</ref>''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.''<ref>{{Cite web |date=2015-08-31 |title=NIH Human Microbiome Project defines normal bacterial makeup of the body |url=https://www.nih.gov/news-events/news-releases/nih-human-microbiome-project-defines-normal-bacterial-makeup-body |access-date=2022-04-17 |website=National Institutes of Health (NIH) |language=EN}}</ref>''According to research, this microbiome is crucial in regulating our physiology by altering our metabolism, immune system, and more.<ref>{{Cite journal |last1=Devaraj |first1=Sridevi |last2=Hemarajata |first2=Peera |last3=Versalovic |first3=James |date=April 2013 |title=The Human Gut Microbiome and Body Metabolism: Implications for Obesity and Diabetes |journal=Clinical Chemistry |volume=59 |issue=4 |pages=617–628 |doi=10.1373/clinchem.2012.187617 |issn=0009-9147 |pmc=3974587 |pmid=23401286}}</ref>'' ''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.''<ref>{{Cite journal |last=Petrosino |first=Joseph F. |date=2018-02-22 |title=The microbiome in precision medicine: the way forward |url=https://doi.org/10.1186/s13073-018-0525-6 |journal=Genome Medicine |volume=10 |issue=1 |pages=12 |doi=10.1186/s13073-018-0525-6 |issn=1756-994X |pmc=5824491 |pmid=29471863}}</ref> ''Evidence shows that the microbiome is essential as it may increase the effectiveness of specific cancer therapeutic treatments.'' <ref>{{Citation |title=User:Johnny British/sandbox |date=2022-04-16 |url=https://en.wikipedia.org/w/index.php?title=User:Johnny_British/sandbox&oldid=1082973783 |work=Wikipedia |language=en |access-date=2022-04-17}}</ref>''Therefore, scientists can identify the imbalance of the microbiome community within a patient and act upon it to enhance the success rate of treatments.
 ==Diagnostics in Specific Disease Conditions==
@@ Line 35: / Line 35: @@
 ==== Genomic Sequencing In Lymphoma Diagnostics ====
-With recent advancements in [[genome sequencing]] and identification of [[mutation]]s linking toward diagnosing [[lymphoma]], more effect has been put in identifying key [[mutation]]s and genetic aberrations to aid precision diagnostics for Lymphoma patients. Most lymphoma identities may be characterized by [[Chromosomal translocation|chromosome translocations]], for example, [[Follicular lymphoma|follicular lymphoma (FL)]] t(14;18), [[Diffuse large B-cell lymphoma|diffuse large B cell lymphoma (DLBCL)]] t(8;14), and [[Anaplastic large-cell lymphoma|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, [[Follicular lymphoma|FL]] and [[Diffuse large B-cell lymphoma|DLBCL]] share translations of the 8th and 14th [[chromosome]]s. To address this problem, low-throughput and low-resolution methods such as [[Sanger sequencing|Sanger sequencing and fluorescence in situ hybridization (FISH)]] are used alongside commercial probes to detect [[Chromosomal translocation|translocation]] on desired [[chromosome]]s. 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|Next-generation sequencing (NGS)]] revealed several essential mutations for [[T-cell lymphoma|T cell-associated lymphoma]]: [[Tet methylcytosine dioxygenase 2|TET2]], [[IDH2]], and [[Transforming protein RhoA|RHOA]] [[mutation]]s are commonly observed in [[Peripheral T cell lymphoma|peripheral T cell lymphomas (PTCL)]], while [[STAT3]] and [[STAT5B]] [[mutation]]s are unique to [[Large granular lymphocytic leukemia|large granular lymphocytic (LGL)]] leukemia. Furthermore, transcriptomics analysis and visualization techniques has revealed key [[Receptor (biochemistry)|cellular receptors]] and pathways to specify diagnostics further. [[Notch signaling pathway|NOTCH signaling pathway]], [[T-cell receptor|T-cell Receptor (TCR)]] signaling pathways, and [[T cell|T-cell]] associated genes (''[[Tet methylcytosine dioxygenase 2|Tet2]]'', ''[[DNMT3B|Dmnt3]]'') were found to be prominent in [[T cell]], and [[B cell]]-related lymphomas and helped to diagnose subtypes of [[Peripheral T cell lymphoma|PTCL]]. On the other hand, subtypes of [[Diffuse large B-cell lymphoma|DLBCL]] and display [[mutation]]s associated with [[B cell]]s change [[B-cell receptor|B cell receptor (BcR)]], [[Notch signaling pathway|NOTCH signalling pathway]], [[Toll-like receptor|Toll-like receptor (TLR)]], and [[NF-κB |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.<ref>{{cite journal |last1=Mansouri |first1=Larry |last2=Thorvaldsdottir |first2=Birna |last3=Laidou |first3=Stamatia |last4=Stamatopoulos |first4=Kostas |last5=Rosenquist |first5=Richard |title=Precision diagnostics in lymphomas – Recent developments and future directions |journal=Seminars in Cancer Biology |date=23 October 2021 |doi=10.1016/j.semcancer.2021.10.007 |url=https://www.sciencedirect.com/science/article/pii/S1044579X21002650 |access-date=30 March 2022}}</ref>
+With recent advancements in [[genome sequencing]] and identification of [[mutation]]s linking toward diagnosing [[lymphoma]], more effect has been put in identifying key [[mutation]]s and genetic aberrations to aid precision diagnostics for Lymphoma patients. Most lymphoma identities may be characterized by [[Chromosomal translocation|chromosome translocations]], for example, [[Follicular lymphoma|follicular lymphoma (FL)]] t(14;18), [[Diffuse large B-cell lymphoma|diffuse large B cell lymphoma (DLBCL)]] t(8;14), and [[Anaplastic large-cell lymphoma|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, [[Follicular lymphoma|FL]] and [[Diffuse large B-cell lymphoma|DLBCL]] share translations of the 8th and 14th [[chromosome]]s. To address this problem, low-throughput and low-resolution methods such as [[Sanger sequencing|Sanger sequencing and fluorescence in situ hybridization (FISH)]] are used alongside commercial probes to detect [[Chromosomal translocation|translocation]] on desired [[chromosome]]s. 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|Next-generation sequencing (NGS)]] revealed several essential mutations for [[T-cell lymphoma|T cell-associated lymphoma]]: [[Tet methylcytosine dioxygenase 2|TET2]], [[IDH2]], and [[Transforming protein RhoA|RHOA]] [[mutation]]s are commonly observed in [[Peripheral T cell lymphoma|peripheral T cell lymphomas (PTCL)]], while [[STAT3]] and [[STAT5B]] [[mutation]]s are unique to [[Large granular lymphocytic leukemia|large granular lymphocytic (LGL)]] leukemia. Furthermore, transcriptomics analysis and visualization techniques has revealed key [[Receptor (biochemistry)|cellular receptors]] and pathways to specify diagnostics further. [[Notch signaling pathway|NOTCH signaling pathway]], [[T-cell receptor|T-cell Receptor (TCR)]] signaling pathways, and [[T cell|T-cell]] associated genes (''[[Tet methylcytosine dioxygenase 2|Tet2]]'', ''[[DNMT3B|Dmnt3]]'') were found to be prominent in [[T cell]], and [[B cell]]-related lymphomas and helped to diagnose subtypes of [[Peripheral T cell lymphoma|PTCL]]. On the other hand, subtypes of [[Diffuse large B-cell lymphoma|DLBCL]] and display [[mutation]]s associated with [[B cell]]s change [[B-cell receptor|B cell receptor (BcR)]], [[Notch signaling pathway|NOTCH signalling pathway]], [[Toll-like receptor|Toll-like receptor (TLR)]], and [[NF-κB |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.<ref>{{cite journal |last1=Mansouri |first1=Larry |last2=Thorvaldsdottir |first2=Birna |last3=Laidou |first3=Stamatia |last4=Stamatopoulos |first4=Kostas |last5=Rosenquist |first5=Richard |title=Precision diagnostics in lymphomas – Recent developments and future directions |journal=Seminars in Cancer Biology |date=23 October 2021 |doi=10.1016/j.semcancer.2021.10.007 |pmid=34699973 |s2cid=239936766 |url=https://www.sciencedirect.com/science/article/pii/S1044579X21002650 |access-date=30 March 2022}}</ref>
 ==== Molecular Analysis In Cancer Diagnostics ====
 [[Sampling (medicine)|Tumor sampling]] and [[Molecular diagnostics|molecular analysis]] is a common ways to determine the properties of cancers as well as cancer progression and [[immune response|host immune response]]. [[cancer|Cancers of unknown origin]] claim a small portion of all [[cancer]]s globally. Previously unknown [[primary tumor]]s were discovered from [[Programmed cell death protein 1|PD-1]] [[mutation]]s and [[Amplification of DNA|amplifications]] thanks to [[gene expression profiling|high dimension molecular profiling]]. A suspected [[cancer|carcinoma]] or poorly differentiated one may also be justified to apply to medical care. Newer technologies such as [[needle aspiration biopsy|endobronchial ultrasound-guided transbronchial needle aspiration biopsy (EBUS-TBNA)]] are currently used in [[lung cancer]] diagnostics with 95% [[Sensitivity and specificity|sensitivity]] and over 95% [[Sensitivity and specificity|specificity]]. This [[Minimally invasive procedure|minimally invasive method]] collects samples for [[Morphology (biology)|morphological diagnosis]] and [[Immunohistochemistry|IHC/ISH characterization]] to determine [[cancer]] subtype and corresponding drug for treatment. [[blood smear|Whole smear slides (WSI)]] also show potential for newer [[Molecular diagnostics|molecular analysis]]. Able to create a digital library of [[digital pathology|whole slide images]] from [[cytology|cytology data]], clinicians can have more information at diagnosis in Rapid on-site evaluation.
-Conventionally, treatment of cancers has been reliant on the [[Morphology (biology)|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 tumor]]s further away from the site of discovery. Upon using recent, [[DNA sequencing|high dimensional complete molecular sequencing]], diagnostics results may also include [[mutation]]s observed in tumours to better understand cancer types and aid future treatment plans. An extreme example of group of cancer, [[Esophageal cancer|oesophageal adenocarcinomas]], which are hardly distunguishable by [[Morphology (biology)|morphology]], makes [[Morphology (biology)|morphological diagnosis]] extremely difficult. This is due to the fact the nearly all [[Esophageal cancer|oesophageal adenocarcinomas]] arise from the [[Barrett's esophagus|Barrett's mucosa]]. Using [[DNA microarray|cDNA microarrays]], the genetic variations of subtypes of [[Esophageal cancer|oesophageal adenocarcinomas]] is profiled and prognosis of [[Invasive cancer|invasive hot cancers]] of this category is greatly improved.<ref>{{cite journal |last1=Sharma |first1=Sowmya |last2=George |first2=Peter |last3=Waddell |first3=Nicola |title=Precision diagnostics: integration of tissue pathology and genomics in cancer |journal=Pathology |date=December 2021 |volume=53 |issue=7 |pages=809–817 |doi=10.1016/j.pathol.2021.08.003 |url=https://pubmed.ncbi.nlm.nih.gov/34635323/ |issn=1465-3931}}</ref>
+Conventionally, treatment of cancers has been reliant on the [[Morphology (biology)|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 tumor]]s further away from the site of discovery. Upon using recent, [[DNA sequencing|high dimensional complete molecular sequencing]], diagnostics results may also include [[mutation]]s observed in tumours to better understand cancer types and aid future treatment plans. An extreme example of group of cancer, [[Esophageal cancer|oesophageal adenocarcinomas]], which are hardly distunguishable by [[Morphology (biology)|morphology]], makes [[Morphology (biology)|morphological diagnosis]] extremely difficult. This is due to the fact the nearly all [[Esophageal cancer|oesophageal adenocarcinomas]] arise from the [[Barrett's esophagus|Barrett's mucosa]]. Using [[DNA microarray|cDNA microarrays]], the genetic variations of subtypes of [[Esophageal cancer|oesophageal adenocarcinomas]] is profiled and prognosis of [[Invasive cancer|invasive hot cancers]] of this category is greatly improved.<ref>{{cite journal |last1=Sharma |first1=Sowmya |last2=George |first2=Peter |last3=Waddell |first3=Nicola |title=Precision diagnostics: integration of tissue pathology and genomics in cancer |journal=Pathology |date=December 2021 |volume=53 |issue=7 |pages=809–817 |doi=10.1016/j.pathol.2021.08.003 |pmid=34635323 |s2cid=238637655 |url=https://pubmed.ncbi.nlm.nih.gov/34635323/ |issn=1465-3931}}</ref>
 == Evaluation of precision medicine ==
@@ Line 47: / Line 47: @@
 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 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 [[Disease prevention|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%,<ref>{{Cite journal |last=Eiff |first=M. von |last2=Roos |first2=N. |last3=Schulten |first3=R. |last4=Hesse |first4=M. |last5=Zühlsdorf |first5=M. |last6=Loo |first6=J. van de |date=1995 |title=Pulmonary Aspergillosis: Early Diagnosis Improves Survival |url=https://www.karger.com/Article/FullText/196477 |journal=Respiration |language=english |volume=62 |issue=6 |pages=341–347 |doi=10.1159/000196477 |issn=0025-7931 |pmid=8552866}}</ref> reinforcing the importance of early diagnosis.
+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 [[Disease prevention|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%,<ref>{{Cite journal |last1=Eiff |first1=M. von |last2=Roos |first2=N. |last3=Schulten |first3=R. |last4=Hesse |first4=M. |last5=Zühlsdorf |first5=M. |last6=Loo |first6=J. van de |date=1995 |title=Pulmonary Aspergillosis: Early Diagnosis Improves Survival |url=https://www.karger.com/Article/FullText/196477 |journal=Respiration |language=english |volume=62 |issue=6 |pages=341–347 |doi=10.1159/000196477 |issn=0025-7931 |pmid=8552866}}</ref> 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 [https://www.researchgate.net/publication/265190765_71_Principles_of_patient_management patient management] by at least 35 percent.<ref>{{Cite news |date=2018-12-07 |title=Precision Medicine Could Have a Major Impact on Healthcare Outcomes and Costs - SPONSOR CONTENT FROM SIEMENS HEALTHINEERS |work=Harvard Business Review |url=https://hbr.org/sponsored/2018/12/precision-medicine-could-have-a-major-impact-on-healthcare-outcomes-and-costs |access-date=2022-03-29 |issn=0017-8012}}</ref> 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.<ref>{{Cite journal |last=Khoury |first=Muin J. |last2=Gwinn |first2=Marta L. |last3=Glasgow |first3=Russell E. |last4=Kramer |first4=Barnett S. |date=2012-06-01 |title=A Population Approach to Precision Medicine |url=https://www.ajpmonline.org/article/S0749-3797(12)00134-1/abstract |journal=American Journal of Preventive Medicine |language=English |volume=42 |issue=6 |pages=639–645 |doi=10.1016/j.amepre.2012.02.012 |issn=0749-3797 |pmc=3629731 |pmid=22608383}}</ref>
+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 [https://www.researchgate.net/publication/265190765_71_Principles_of_patient_management patient management] by at least 35 percent.<ref>{{Cite news |date=2018-12-07 |title=Precision Medicine Could Have a Major Impact on Healthcare Outcomes and Costs - SPONSOR CONTENT FROM SIEMENS HEALTHINEERS |work=Harvard Business Review |url=https://hbr.org/sponsored/2018/12/precision-medicine-could-have-a-major-impact-on-healthcare-outcomes-and-costs |access-date=2022-03-29 |issn=0017-8012}}</ref> 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.<ref>{{Cite journal |last1=Khoury |first1=Muin J. |last2=Gwinn |first2=Marta L. |last3=Glasgow |first3=Russell E. |last4=Kramer |first4=Barnett S. |date=2012-06-01 |title=A Population Approach to Precision Medicine |url=https://www.ajpmonline.org/article/S0749-3797(12)00134-1/abstract |journal=American Journal of Preventive Medicine |language=English |volume=42 |issue=6 |pages=639–645 |doi=10.1016/j.amepre.2012.02.012 |issn=0749-3797 |pmc=3629731 |pmid=22608383}}</ref>
 ==== 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 [https://journals.sagepub.com/doi/pdf/10.1177/00207314211041234 economic inequality] in the health system.<ref>{{Cite journal |last=Hekim |first=Nezih |last2=Coşkun |first2=Yavuz |last3=Sınav |first3=Ahmet |last4=Abou-Zeid |first4=Alaa H. |last5=Ağırbaşlı |first5=Mehmet |last6=Akintola |first6=Simisola O. |last7=Aynacıoğlu |first7=Şükrü |last8=Bayram |first8=Mustafa |last9=Bragazzi |first9=Nicola Luigi |last10=Dandara |first10=Collet |last11=Dereli |first11=Türkay |date=2014-07-01 |title=Translating Biotechnology to Knowledge-Based Innovation, Peace, and Development? Deploy a Science Peace Corps—An Open Letter to World Leaders |url=https://www.liebertpub.com/doi/10.1089/omi.2014.0079 |journal=OMICS: A Journal of Integrative Biology |volume=18 |issue=7 |pages=415–420 |doi=10.1089/omi.2014.0079 |pmc=4086476 |pmid=24955641}}</ref> This will limit precision medicine to an individual's benefit instead of improving the healthcare system as a collective benefit.
+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 [https://journals.sagepub.com/doi/pdf/10.1177/00207314211041234 economic inequality] in the health system.<ref>{{Cite journal |last1=Hekim |first1=Nezih |last2=Coşkun |first2=Yavuz |last3=Sınav |first3=Ahmet |last4=Abou-Zeid |first4=Alaa H. |last5=Ağırbaşlı |first5=Mehmet |last6=Akintola |first6=Simisola O. |last7=Aynacıoğlu |first7=Şükrü |last8=Bayram |first8=Mustafa |last9=Bragazzi |first9=Nicola Luigi |last10=Dandara |first10=Collet |last11=Dereli |first11=Türkay |date=2014-07-01 |title=Translating Biotechnology to Knowledge-Based Innovation, Peace, and Development? Deploy a Science Peace Corps—An Open Letter to World Leaders |journal=OMICS: A Journal of Integrative Biology |volume=18 |issue=7 |pages=415–420 |doi=10.1089/omi.2014.0079 |pmc=4086476 |pmid=24955641}}</ref> 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.<ref>{{Cite journal |last=Ferkol |first=Thomas |last2=Quinton |first2=Paul |date=2015-09-15 |title=Precision Medicine: At What Price? |url=https://www.atsjournals.org/doi/full/10.1164/rccm.201507-1428ED |journal=American Journal of Respiratory and Critical Care Medicine |volume=192 |issue=6 |pages=658–659 |doi=10.1164/rccm.201507-1428ED |issn=1073-449X}}</ref> Hence, data collected in precision medicine may introduce unfair treatment between different subgroups of patients.
+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.<ref>{{Cite journal |last1=Ferkol |first1=Thomas |last2=Quinton |first2=Paul |date=2015-09-15 |title=Precision Medicine: At What Price? |url=https://www.atsjournals.org/doi/full/10.1164/rccm.201507-1428ED |journal=American Journal of Respiratory and Critical Care Medicine |volume=192 |issue=6 |pages=658–659 |doi=10.1164/rccm.201507-1428ED |pmid=26207804 |issn=1073-449X}}</ref> Hence, data collected in precision medicine may introduce unfair treatment between different subgroups of patients.
-Not to mention, precision medicine requires the storing of patients’ information in a vast database. This begs to question the data privacy issue. As genetic information is very personal and sensitive insights into a person's life, privacy concerns need to be addressed. Even though there is legislation protecting patients from [https://www.ncbi.nlm.nih.gov/books/NBK236546/ data privacy], it does not necessarily prevent attackers from hacking the database. It might introduce genetic discrimination where people are being treated differently because of their genome information.<ref>{{Cite journal |last=Azencott |first=C.-A. |date=2018-09-13 |title=Machine learning and genomics: precision medicine versus patient privacy |url=https://royalsocietypublishing.org/doi/10.1098/rsta.2017.0350 |journal=Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |volume=376 |issue=2128 |pages=20170350 |doi=10.1098/rsta.2017.0350}}</ref>
+Not to mention, precision medicine requires the storing of patients’ information in a vast database. This begs to question the data privacy issue. As genetic information is very personal and sensitive insights into a person's life, privacy concerns need to be addressed. Even though there is legislation protecting patients from [https://www.ncbi.nlm.nih.gov/books/NBK236546/ data privacy], it does not necessarily prevent attackers from hacking the database. It might introduce genetic discrimination where people are being treated differently because of their genome information.<ref>{{Cite journal |last=Azencott |first=C.-A. |date=2018-09-13 |title=Machine learning and genomics: precision medicine versus patient privacy |url=https://royalsocietypublishing.org/doi/10.1098/rsta.2017.0350 |journal=Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |volume=376 |issue=2128 |pages=20170350 |doi=10.1098/rsta.2017.0350|s2cid=3699997 }}</ref>
 == Future prospects ==
 With the help of advanced technology and data collected in precision medicine, it improves [[Clinical decision making|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 [[Biosensor|wearable biosensor]]. By utilising [[Artificial intelligence in healthcare|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.<ref>{{Cite journal |last=Fernandez-Luque |first=Luis |last2=Al Herbish |first2=Abdullah |last3=Al Shammari |first3=Riyad |last4=Argente |first4=Jesús |last5=Bin-Abbas |first5=Bassam |last6=Deeb |first6=Asma |last7=Dixon |first7=David |last8=Zary |first8=Nabil |last9=Koledova |first9=Ekaterina |last10=Savage |first10=Martin O. |date=2021 |title=Digital Health for Supporting Precision Medicine in Pediatric Endocrine Disorders: Opportunities for Improved Patient Care |url=https://www.frontiersin.org/article/10.3389/fped.2021.715705 |journal=Frontiers in Pediatrics |volume=9 |doi=10.3389/fped.2021.715705 |issn=2296-2360 |pmc=8358399 |pmid=34395347}}</ref>
+Another potential prospect would be health apps which can be digital diagnostics devices in the form of a [[Biosensor|wearable biosensor]]. By utilising [[Artificial intelligence in healthcare|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.<ref>{{Cite journal |last1=Fernandez-Luque |first1=Luis |last2=Al Herbish |first2=Abdullah |last3=Al Shammari |first3=Riyad |last4=Argente |first4=Jesús |last5=Bin-Abbas |first5=Bassam |last6=Deeb |first6=Asma |last7=Dixon |first7=David |last8=Zary |first8=Nabil |last9=Koledova |first9=Ekaterina |last10=Savage |first10=Martin O. |date=2021 |title=Digital Health for Supporting Precision Medicine in Pediatric Endocrine Disorders: Opportunities for Improved Patient Care |journal=Frontiers in Pediatrics |volume=9 |doi=10.3389/fped.2021.715705 |issn=2296-2360 |pmc=8358399 |pmid=34395347|doi-access=free }}</ref>
-Besides from obtaining genome information, there is an on ‘[https://www.sciencedirect.com/science/article/pii/S2444340917001030#:~:text=As%20defined%20by%20the%20World,data%20on%20particular%20molecules%20(e.%20g.%2C 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.<ref>{{Cite journal |last=Wang |first=Edwin |last2=Cho |first2=William C. S. |last3=Wong |first3=S. C. Cesar |last4=Liu |first4=Siqi |date=2017-04-01 |title=Disease Biomarkers for Precision Medicine: Challenges and Future Opportunities |url=https://www.sciencedirect.com/science/article/pii/S1672022917300505 |journal=Genomics, Proteomics & Bioinformatics |series=Biomarkers for Human Diseases and Translational Medicine |language=en |volume=15 |issue=2 |pages=57–58 |doi=10.1016/j.gpb.2017.04.001 |issn=1672-0229 |pmc=5414969 |pmid=28392478}}</ref> This can be beneficial in future precision medicine as it makes obtaining patients’ health condition more cost-effective and gather more data.
+Besides from obtaining genome information, there is an on ‘[https://www.sciencedirect.com/science/article/pii/S2444340917001030#:~:text=As%20defined%20by%20the%20World,data%20on%20particular%20molecules%20(e.%20g.%2C 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.<ref>{{Cite journal |last1=Wang |first1=Edwin |last2=Cho |first2=William C. S. |last3=Wong |first3=S. C. Cesar |last4=Liu |first4=Siqi |date=2017-04-01 |title=Disease Biomarkers for Precision Medicine: Challenges and Future Opportunities |journal=Genomics, Proteomics & Bioinformatics |series=Biomarkers for Human Diseases and Translational Medicine |language=en |volume=15 |issue=2 |pages=57–58 |doi=10.1016/j.gpb.2017.04.001 |issn=1672-0229 |pmc=5414969 |pmid=28392478}}</ref> This can be beneficial in future precision medicine as it makes obtaining patients’ health condition more cost-effective and gather more data.
 == References ==