Precision diagnostics

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 [10]

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 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]

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.

With recent 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]

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]

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.

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]

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.

Not to mention, precision medicine requires the storing of patients’ information in a vast database. This begs the question of the data privacy issue. As genetic information is a very personal and sensitive insight into a person's life, privacy concerns need to be addressed. Even though there is legislation protecting patients from data privacy, it does not necessarily prevent attackers from hacking the database. It might introduce genetic discrimination where people are being treated differently because of their genome information.[30]