Precision diagnostics

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Precision diagnostics is a branch of precision medicine that involves precisely managing a patient's healthcare model and diagnosing specific diseases based on customized omics data analytics. [1]

Contents

The U.S. announced federal funding for precision medicine research efforts in 2015 with the Precision Medicine Initiative. A year later, the Human Personal Omics Profiling study was established to develop integrative multi-omics approaches for use in precision diagnostics. [2]

Diseases are diagnosed early in individuals based on their variability in DNA, environment, and lifestyle. This is made possible by recent technological advancements in the acquisition of data from genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiome studies. By accurately monitoring collateral molecular layers, a comprehensive understanding of an individual's personal molecular profile can be attained in an impartial manner.

Furthermore, contemporary computational algorithms improve the analysis of the omics data generated and digital technologies enhance data management. In addition, advancements in artificial intelligence, particularly convolutional neural networks and advanced data analysis, are utilized to predict the relationship between genotype and phenotype, potentially improving the sensitivity and specificity of precision diagnoses.

The advancement of Next Generation Sequencing (NGS) has improved cancer diagnostics. NGS provides a more comprehensive view of the genome than other single-gene assays. NGS-based molecular diagnostics offer genomic information about tumor-related variants and cancer-causing structural changes, enabling highly accurate diagnoses and the use of complementary targeted therapies. NGS samples can be collected using a buccal swab, peripheral blood or tissue-specific biopsy, and DNA is used to screen for single nucleotide variants, gene insertions/deletions, and copy number variants, while RNA is used to measure gene expression.

Precision diagnostics techniques

DNA sequencing

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 is used extensively for cancer patients. [3] It is used to help give further genetic information about the patient's background as well as their eligibility for clinical trials that may be beneficial to them. [4] [5] 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. [6] Furthermore, using germline data, clinical may evaluate cancer predisposition and pharmacogenomics information for earlier cancer identification and treatment. [7] 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. [8] The importance of fast access to the high-dimensional output of genomic data is growing. [9]

Example workflow of whole genome sequencing Whole genome sequencing process.jpg
Example workflow of whole genome sequencing

RNA sequencing

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 into cancer subtypes and patient backgrounds. [11] As a more advanced version of whole genome sequencing, RNA sequencing gives additional information when creating an individual patient's treatment plan. The importance of RNA sequencing in the diagnostics of malignant disorders, such as leukoplakia, is increasing. Transcriptome analysis may also reveal disease progression in pro-malignant conditions. [12] [13] Such analysis allows for an individualized prognosis for each patient. [14] The utilities for the sequencing of blood, bone marrow, or other bodily systems are becoming increasingly obvious. Using the database, clinicians may become more informed of the patient's situation. [15]

Proteomics

Proteomics is the study of proteins. Proteins that are translated from messenger RNA go through post-transcriptional modifications that include phosphorylation, ubiquitination, methylation, acetylation, glycosylation, etc. [16] Previously, immunoassay methods were used to study proteins, but mass spectrometry is now mainly used as a proteomic analyzing tool. [17] In mass spectrometry analysis, proteins/peptides are fragmented. Then, peptides are ionized through either electrospray ionization 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 analyzers include ion trap, time-of-flight, quadrupole, and Fourier transform ion cyclotron. Lastly, using computational bioinformatics tools and algorithms, collected proteomics data can be further analyzed and used for protein profiling. [18]

Microbiome

In recent years, the interest in microbiome research has been rising and has become one of the critical components in precision medicine. [19] Microbiome research refers to the studying of microorganisms' interaction within and outside of 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. [20] According to research, this microbiome is crucial in regulating our physiology by altering our metabolism, immune system, and more. [21] Hence, the changes in the microbial community can provide insights into the health condition of the specific host and patient. In precision medicine, patients' gut microbiome is often profiled in order to determine which treatment offers the most therapeutic value to them. [22] Evidence shows that the microbiome is essential as it may increase the effectiveness of specific cancer treatments.

Diagnostics in specific disease conditions

Genomic sequencing in lymphoma diagnostics

With recent[ when? ] advancements in genome sequencing and the identification of mutations linking toward diagnosing lymphoma, more effect has been put into 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 for identifying 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) is 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 have 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 signaling pathway, Toll-like receptor (TLR), and NF-κB signaling cascade. Simply put, the increasing knowledge of genetic aberration in lymphoma provides more information to design precision diagnostic tests for major and subtype lymphomas. [23]

Molecular analysis in cancer diagnostics

Tumor sampling and molecular analysis are 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 the 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, the 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 tumors to better understand cancer types and aid future treatment plans. An extreme example of a group of cancer, Esophageal adenocarcinomas, which are hardly distinguishable by morphology, makes morphological diagnosis extremely difficult. This is because nearly all oesophageal adenocarcinomas arise from Barrett's mucosa. Using cDNA microarrays, the genetic variations of subtypes of oesophageal adenocarcinomas are profiled and the prognosis of invasive hot cancers of this category is greatly improved. [24]

Evaluation of precision medicine

Advantages

As mentioned above, precision medicine brings unique insights into personalized treatments based on genetic information. Compared to conventional healthcare technology, precision medicine has several short and long-term advantages. Namely, healthcare professionals can use genetic data collected from 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.[ citation needed ]

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 developing the conditions. For instance, the underlying causes of disease may include environmental and lifestyle reasons. When identified early, 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%, [25] reinforcing the importance of early diagnosis.

Moreover, information gained from precision medicine may lead to reduced costs 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. [26] The healthcare professional can figure out the best possible treatment with detailed patients' genetic information. The comprehensive information about the patients can be used to avoid unnecessary diagnostic testing and scanning, which reduces the cost of healthcare. [27]

Limitations

Despite the benefits of precision medicine, it has several limitations and pitfalls for 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 consultations, 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, the improved quality of healthcare does not mean it is more cost-effective; it may further drive economic inequality in the health system. [28] 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. [29] Hence, data collected in precision medicine may introduce unfair treatment between different subgroups of patients.

Precision medicine requires the storing of patients’ sensitive health information in a database. Genetic information obtained through sequencing platforms is unique to the patient and is considered protected health information under the Health Insurance Portability and Accountability Act. [30] While this regulates how genetic information is stored to protect patient 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. [31]

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 used for digital diagnostic devices in the form of a wearable biosensor. By utilizing AI technology, patients can obtain essential information such as any physiological data. The data obtained from these health apps can be used by medical professionals to evaluate the information and determine the best possible treatment. [32]

Besides obtaining genome information, there is an ‘Omics’-based biomarkers that could be one of the prospects in future precision medicine. The omics-based test is considered a form of biomarker that helps capture information to understand patients’ lives. The recent development in Omic-based biomarkers has improved the complexity of information obtained from patients and also reduced the cost of the process. [33] This can be beneficial in future precision medicine as it makes obtaining patients’ health conditions more cost-effective and enables the gathering of more data.

Related Research Articles

<span class="mw-page-title-main">Omics</span> Suffix in biology

The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics, such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms.

<span class="mw-page-title-main">Single-nucleotide polymorphism</span> Single nucleotide in genomic DNA at which different sequence alternatives exist

In genetics and bioinformatics, a single-nucleotide polymorphism is a germline substitution of a single nucleotide at a specific position in the genome. Although certain definitions require the substitution to be present in a sufficiently large fraction of the population, many publications do not apply such a frequency threshold.

Immunodeficiency, also known as immunocompromisation, is a state in which the immune system's ability to fight infectious diseases and cancer is compromised or entirely absent. Most cases are acquired ("secondary") due to extrinsic factors that affect the patient's immune system. Examples of these extrinsic factors include HIV infection and environmental factors, such as nutrition. Immunocompromisation may also be due to genetic diseases/flaws such as SCID.

<span class="mw-page-title-main">Pharmacogenomics</span> Study of the role of the genome in drug response

Pharmacogenomics, often abbreviated "PGx," is the study of the role of the genome in drug response. Its name reflects its combining of pharmacology and genomics. Pharmacogenomics analyzes how the genetic makeup of a patient affects their response to drugs. It deals with the influence of acquired and inherited genetic variation on drug response, by correlating DNA mutations with pharmacokinetic, pharmacodynamic, and/or immunogenic endpoints.

<span class="mw-page-title-main">Personalized medicine</span> Medical model that tailors medical practices to the individual patient

Personalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept, though some authors and organizations differentiate between these expressions based on particular nuances. P4 is short for "predictive, preventive, personalized and participatory".

The Cancer Genome Project is part of the cancer, aging, and somatic mutation research based at the Wellcome Trust Sanger Institute in the United Kingdom. It aims to identify sequence variants/mutations critical in the development of human cancers. Like The Cancer Genome Atlas project within the United States, the Cancer Genome Project represents an effort in the War on Cancer to improve cancer diagnosis, treatment, and prevention through a better understanding of the molecular basis of the disease. The Cancer Genome Project was launched by Michael Stratton in 2000, and Peter Campbell is now the group leader of the project. The project works to combine knowledge of the human genome sequence with high throughput mutation detection techniques.

Personal genomics or consumer genetics is the branch of genomics concerned with the sequencing, analysis and interpretation of the genome of an individual. The genotyping stage employs different techniques, including single-nucleotide polymorphism (SNP) analysis chips, or partial or full genome sequencing. Once the genotypes are known, the individual's variations can be compared with the published literature to determine likelihood of trait expression, ancestry inference and disease risk.

The exome is composed of all of the exons within the genome, the sequences which, when transcribed, remain within the mature RNA after introns are removed by RNA splicing. This includes untranslated regions of messenger RNA (mRNA), and coding regions. Exome sequencing has proven to be an efficient method of determining the genetic basis of more than two dozen Mendelian or single gene disorders.

Cancer genome sequencing is the whole genome sequencing of a single, homogeneous or heterogeneous group of cancer cells. It is a biochemical laboratory method for the characterization and identification of the DNA or RNA sequences of cancer cell(s).

<span class="mw-page-title-main">Proteogenomics</span>

Proteogenomics is a field of biological research that utilizes a combination of proteomics, genomics, and transcriptomics to aid in the discovery and identification of peptides. Proteogenomics is used to identify new peptides by comparing MS/MS spectra against a protein database that has been derived from genomic and transcriptomic information. Proteogenomics often refers to studies that use proteomic information, often derived from mass spectrometry, to improve gene annotations. The utilization of both proteomics and genomics data alongside advances in the availability and power of spectrographic and chromatographic technology led to the emergence of proteogenomics as its own field in 2004.

Dr Vinod Scaria FRSB, FRSPH is an Indian biologist, medical researcher pioneering in Precision Medicine and Clinical Genomics in India. He is best known for sequencing the first Indian genome. He was also instrumental in the sequencing of The first Sri Lankan Genome, analysis of the first Malaysian Genome sequencing and analysis of the Wild-type strain of Zebrafish and the IndiGen programme on Genomics for Public Health in India.

<span class="mw-page-title-main">Molecular diagnostics</span> Collection of techniques used to analyze biological markers in the genome and proteome

Molecular diagnostics is a collection of techniques used to analyze biological markers in the genome and proteome, and how their cells express their genes as proteins, applying molecular biology to medical testing. In medicine the technique is used to diagnose and monitor disease, detect risk, and decide which therapies will work best for individual patients, and in agricultural biosecurity similarly to monitor crop- and livestock disease, estimate risk, and decide what quarantine measures must be taken.

<span class="mw-page-title-main">Circulating tumor DNA</span> Tumor-derived fragmented DNA in the bloodstream

Circulating tumor DNA (ctDNA) is tumor-derived fragmented DNA in the bloodstream that is not associated with cells. ctDNA should not be confused with cell-free DNA (cfDNA), a broader term which describes DNA that is freely circulating in the bloodstream, but is not necessarily of tumor origin. Because ctDNA may reflect the entire tumor genome, it has gained traction for its potential clinical utility; "liquid biopsies" in the form of blood draws may be taken at various time points to monitor tumor progression throughout the treatment regimen.

<span class="mw-page-title-main">Multiomics</span> Biological analysis approach

Multiomics, multi-omics, integrative omics, "panomics" or "pan-omics" is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome ; in other words, the use of multiple omics technologies to study life in a concerted way. By combining these "omes", scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association. The OmicTools service lists more than 99 softwares related to multiomic data analysis, as well as more than 99 databases on the topic.

CAPP-Seq is a next-generation sequencing based method used to quantify circulating DNA in cancer (ctDNA). The method was introduced in 2014 by Ash Alizadeh and Maximilian Diehn’s laboratories at Stanford, as a tool for measuring Cell-free tumor DNA which is released from dead tumor cells into the blood and thus may reflect the entire tumor genome. This method can be generalized for any cancer type that is known to have recurrent mutations. CAPP-Seq can detect one molecule of mutant DNA in 10,000 molecules of healthy DNA. The original method was further refined in 2016 for ultra sensitive detection through integration of multiple error suppression strategies, termed integrated Digital Error Suppression (iDES). The use of ctDNA in this technique should not be confused with circulating tumor cells (CTCs); these are two different entities.

Elective genetic and genomic testing are DNA tests performed for an individual who does not have an indication for testing. An elective genetic test analyzes selected sites in the human genome while an elective genomic test analyzes the entire human genome. Some elective genetic and genomic tests require a physician to order the test to ensure that individuals understand the risks and benefits of testing as well as the results. Other DNA-based tests, such as a genealogical DNA test do not require a physician's order. Elective testing is generally not paid for by health insurance companies. With the advent of personalized medicine, also called precision medicine, an increasing number of individuals are undertaking elective genetic and genomic testing.

Personalized onco-genomics (POG) is the field of oncology and genomics that is focused on using whole genome analysis to make personalized clinical treatment decisions. The program was devised at British Columbia's BC Cancer Agency and is currently being led by Marco Marra and Janessa Laskin. Genome instability has been identified as one of the underlying hallmarks of cancer. The genetic diversity of cancer cells promotes multiple other cancer hallmark functions that help them survive in their microenvironment and eventually metastasise. The pronounced genomic heterogeneity of tumours has led researchers to develop an approach that assesses each individual's cancer to identify targeted therapies that can halt cancer growth. Identification of these "drivers" and corresponding medications used to possibly halt these pathways are important in cancer treatment.

Clinical metagenomic next-generation sequencing (mNGS) is the comprehensive analysis of microbial and host genetic material in clinical samples from patients by next-generation sequencing. It uses the techniques of metagenomics to identify and characterize the genome of bacteria, fungi, parasites, and viruses without the need for a prior knowledge of a specific pathogen directly from clinical specimens. The capacity to detect all the potential pathogens in a sample makes metagenomic next generation sequencing a potent tool in the diagnosis of infectious disease especially when other more directed assays, such as PCR, fail. Its limitations include clinical utility, laboratory validity, sense and sensitivity, cost and regulatory considerations.

<span class="mw-page-title-main">Cancer pharmacogenomics</span>

Cancer pharmacogenomics is the study of how variances in the genome influences an individual’s response to different cancer drug treatments. It is a subset of the broader field of pharmacogenomics, which is the area of study aimed at understanding how genetic variants influence drug efficacy and toxicity.

Personalized genomics is the human genetics-derived study of analyzing and interpreting individualized genetic information by genome sequencing to identify genetic variations compared to the library of known sequences. International genetics communities have spared no effort from the past and have gradually cooperated to prosecute research projects to determine DNA sequences of the human genome using DNA sequencing techniques. The methods that are the most commonly used are whole exome sequencing and whole genome sequencing. Both approaches are used to identify genetic variations. Genome sequencing became more cost-effective over time, and made it applicable in the medical field, allowing scientists to understand which genes are attributed to specific diseases.

References

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