Abstract
Genomic medicine, which integrates genomics and bioinformatics into clinical care and diagnostics, is transforming healthcare by enabling personalized treatment approaches. Advances in technologies such as DNA sequencing, proteomics, and computational power have laid the foundation for individualized therapies that account for genetic variations influencing disease risk, progression, and treatment response. This review explores the historical milestones leading to current applications of genomic medicine, such as targeted therapies, gene therapies, and precision medicine, in fields including cardiovascular diseases, oncology, and rare genetic disorders. It highlights the use of next-generation sequencing and third-generation sequencing to improve diagnostic accuracy and treatment outcomes, emphasizing the role of genomic data in advancing personalized treatments. Furthermore, emerging therapies such as CRISPR/Cas-based genome editing and adeno-associated viral vectors showcase the potential of gene therapy in addressing complex diseases, including rare genetic disorders. Despite promising advancements, challenges remain in fully integrating genomic medicine into routine clinical practice, including cost barriers, data interpretation complexities, and the need for widespread genomic literacy among healthcare professionals. The future of genomic medicine holds transformative potential for revolutionizing the diagnosis, treatment, and management of both common and rare diseases.
Keywords: cardiology, genomic medicine, genomics
Genomic medicine refers to genomics and bioinformatics in the context of clinical care and diagnostics[1]. The Human Genome Project was an international collaboration with respect to research that attempted to study the entirety of what is known as the human genome. The human genome is roughly 6 billion DNA base pairs in size and to put it succinctly, contains all the code needed to create what we can call a human being. The fact that different DNA variants being dispersed throughout this genome is what makes an individual different from the rest. Likewise, these DNA variants might also be responsible for causing pathologies that manifest during ones lifetime. Some of these variants can be directly responsible for these pathologies while other variants might be an indicator of how an individuals body will react to a certain treatment. This is where personalized medicine tries to enhance the current scope of medicine[2]. In 1953, Watson and Crick published their first paper on the double helix structure of DNA[3]. In the same year, the sequencing of a biological molecule was completed for the first time via a refined partition chromatography method[4]. At the end of the 1960s, RNA sequencing was still ahead of DNA sequencing[5]. By 1979, the idea of shotgun sequencing was proposed which uses bacterial vectors to clone fragments of a DNA molecule, a procedure allowing sequencing of longer DNA molecules in less time[6]. In 1984, the genome of the Epstein-Barr B95-8 strain was determined[7]. Here-on, a myriad of full-genome screening project was launched and succeeded[5]. GenBank, the US National Institute of Health (NIH) sequence database, was founded in 1982[5]. Advancements in microfabrication, imaging, and computational power led to new sequencing methods. These involve preparing a DNA library by fragmenting DNA, attaching adapters, amplifying it, and then sequencing on a flow cell using massive parallel sequencing[8]. Beginning in the 2010s, third-generation sequencing emerged with the ability to sequence single DNA molecules without amplification. These technologies now produce much longer reads than next-generation sequencing (NGS), ranging from several to hundreds of kilobase pairs[5]. Personalized medicine tailors treatment based on individual patient data, such as genomic and biochemical information, due to significant inter-individual variations. Advances in technologies like DNA sequencing and proteomics have highlighted the need for this approach. Future challenges include enhancing the efficiency of patient characterization and developing effective personalized treatments, although universally effective drugs may still be sought but harder to find[9]. The purpose and objective of this review is to explore the role of genomic medicine in advancing personalized treatment and to assess its current applications, benefits, and challenges.
Genomic medicine integrates genomics and bioinformatics into clinical care and diagnostics, ushering in the era of personalized medicine.
With roots in the groundbreaking discoveries of Watson and Crick in 1953 and the Human Genome Project, advancements in DNA sequencing have revolutionized our understanding of human genetic variation and its role in health and disease.
From early RNA sequencing methods to cutting-edge third-generation sequencing, these innovations have enabled longer and more accurate DNA reads, paving the way for tailored treatments. Personalized medicine leverages genomic and biochemical data to address inter-individual variations, optimizing therapeutic outcomes.
Despite its promise, challenges remain in improving patient characterization and creating effective, individualized treatments, highlighting the ongoing need for innovation in genomic medicine. This review evaluates the transformative impact, current applications, and challenges of genomic medicine in advancing personalized care.
DNA, genes, and genomes constitute the fundamental structural components of an organisms biological framework. DNA double helix with structural base pairing is the most widely recognized DNA structure. It is evident from this structure that DNA is structurally dynamic and capable of adopting alternative secondary structures[3]. A genome is an organisms complete set of DNA sequences. Although people in this world may look different, all human genomes are highly similar[10]. It includes all of an organisms genes and non-coding sequences. Most genomes consist of a linear polymer of DNA wrapped around octameric histone protein complexes to generate a chromatin structure resembling beads on a string[11].
Genetic variations are the changes in the DNA sequences that range from single nucleotide changes to large structural alterations. Some human genetic variations are closely related to certain diseases or individual patient responses to certain medications[12], signifying the need of specific treatment options. These variations could be single nucleotide polymorphisms (SNPs); the simplest form of DNA variation which may influence promoter activity (gene expression), messenger RNA (mRNA) conformation (stability), and subcellular localization of mRNAs and/or proteins and hence may produce disease[13], short insertions and deletions (INDELs); the second most common type of genetic variations[14] characterized by addition and removal of small nucleotide sequences within the genome, copy number variations (CNVs) that arise from genomic rearrangements, primarily owing to deletion, duplication, insertion, and unbalanced translocation events[15] etc. Some human genetic variations are closely related to certain diseases or individual patient responses to certain medications[12], which makes it possible to opt for the treatment that brings the best outcome for the patients. For example, in precision medicine, physicians can choose different medications to help their patients quit smoking by examining the patients speed of nicotine metabolization[16]. Recent studies on genetic variation have moved from examining genes tied to rare single-gene disorders such as cystic fibrosis to investigating genes involved in multifactorial diseases such as cancer and cardiovascular disorders. Therefore, studying genetic variations is not only enriching our knowledge of different disease mechanisms but is also modifying our diagnostic and therapeutic approaches.
It is a slow process yet advancement in knowledge is increasing the use of genomic data and genomic medicine in clinical care[17]. Advancement in genetics brings genetic medicine and genetic data into clinical practice improving the diagnosis of rare diseases, illness related risk improvement, and treatment efficiency through advanced measurement and methods[18]. Next-generation sequencing (NGS) has changed the genomics and not only improve the method but also lowering the costs, can perform rapid genome sequencing and has several medical uses[19].
Genetic testing is important for the detection of inherited and acquired disorders, and also for treatment responses. Multiple genetic tests are used including targeted single-gene assays, gene panels, whole-exome sequencing, and whole-genome sequencing. Chromosomal testing use for detecting changes in chromosomes like additional or missing copies and any large segment modifications[20]. Exome sequencing improves genetic diagnosis and aid in the prenatal identification of structural abnormalities or genetic disorder. Combining copy number variant and single nucleotide variant analyses increases accuracy, whereas low-pass genome sequencing provides higher resolution[21]. Combining copy number variant sequencing and karyotyping improves the identification of prenatal pathogenic chromosomal abnormalities, enhancing the accuracy of prenatal diagnosis[22].
Fluorescence in situ hybridization (FISH) used for detection of tumor-specific genetic variations, enhancing diagnosis and treatment[23]. Genetic testing for prostate cancer, especially in metastatic patients, reveals up to 15% of germline mutations. Pre-test counseling covers inherited risk, diagnostic scope, results, and management options, enhancing personalized care with precision medicine[24]. Myeloid neoplasms and acute leukemias resulting from somatic mutations are helped by enhanced genomic testing, like whole-genome sequencing, for accurate diagnosis and evaluation of risk, thus improving personalized treatment and clinical decision-making[25].
Adequate care for epilepsy is difficult due to numerous syndromes and unique responses, however current genetic discoveries have found abnormalities in ion channels and neurotransmitter receptors in many individuals, whole-exome and whole-genome sequencing methods have enhanced our knowledge and led to precision treatment for particular diseases, like Dravet syndrome, pyroxidine-dependent epilepsy, and glucose transporter 1 deficiency[26].
Gene diagnosis in cardiovascular diseases is gaining attention, especially monogenic cardiovascular diseases. These are the diseases that have cardiovascular damage as their phenotype, e.g., cardiomyopathies, cardiac ion channel disease (long QT syndrome, Brugada syndrome, PVTs), inherited hypertension, inherited aortic diseases[27].
There has been established a causal link between risk of DNA methylation at cpg site and various subtypes of CVD, prior MI, atherosclerotic disease in a recent epigenome wide association study (EWAS)[28]. Selenium supplementation has been known to inhibit DNMT2 mediated DNA methylation of glutathione peroxidase 1 gene promoter in cardiomyocytes reducing the reactive oxygen species and toxicity to cardiomyocytes, and thus protecting the heart during its failure[29].
Genetics play an important role in cardiomyopathies. Pathogenic variants in MYH7 gene, MYBPC3 gene are the most common in encoding abnormal sarcomeric proteins causing Hypertrophic cardiomyopathy. TTN gene, LMNA gene are the most commonly implicated genes in dilated cardiomyopathy. DSC2, DSG2 genes are implicated in arrhythmogenic right ventricular cardiomyopathy. Pathological variant genes testing is implicated to improve prognosis via early screening. Screening of first-degree relatives is also implicated via serial ECGs and echocardiography[30].
Familial hypercholesterolemia is associated with genes such as LDLR, APOB, PCSK9, and APOE. Most of them have autosomal dominant variants and increase the risk of coronary artery disease, atherosclerotic disease, peripheral arterial disease. Early genetic detection can modify the course of disease by early interventions like lifestyle modifications, exercise, blood pressure control, early ignition of statins, and PCSK9 inhibitors [31]. Rather than genome sequencing or exome sequencing, capillary electrophoresis sequencing or next-generation sequencing targeted to known FH gene variants provide a more comprehensive result[32].
Molecular pathophysiology of cardiac diseases can encourage preclinical gene therapy. Adeno-associated viral vector helps in introducing therapeutic genes in heart. Sarcoplasmic reticulum Ca2+ ATPase protein delivery has shown promising result in phase 1 trials to improve cardiac function in heart failure. Crispr/Cas based genome engineering has gained wide recognition for treating cardiovascular disease[33].
Ultrasound targeted micro-bubble (UTM) strategy has gained recognition. Particularly, lipid micro-bubble carrying VEGF and stem cell factor has shown to improve myocardial perfusion and ventricular function in patients with MI[34].
Hypertrophy of ventricles has been shown to reverse with UTM mediated delivery of miR-133 in cardiomyocytes[35]. Anterograde arterial infusion has been indicated in patients with unstable and advanced heart failure, retrograde infusion in patients with impaired coronary artery circulation and limited potential for re-vascularization and direct intramyocardial infusion for focal arrhythmia therapy. Intra-coronary delivery of Ad vector encoding beta2 AR percutaneously, in rabbits, has shown to improve global ventricular systolic and contractility performance[33].
Over the past decade recent advances in genomic medicine has enhanced diagnosis and management of neoplastic diseases by knowing underlying molecular process. Cancer genomic profiling has shown to detect gene amplification, gene deletion mutation, gene fusion of the target genes. These results are interpreted extensively and reflected on treatments. Examples of genomic profiling tests are OncoGuide NCC Oncopanel System, FoundationOne CDx Cancer Genomic Profile, Todai OncoPanel, Oncomine Target Test System. The system used for these tests also functions as a companion diagnostic[36].
Targeted therapies and immunotherapies in oncology like monoclonal antibody against HER 2 which is overexpressed in HER2 positive breast cancer has revolutionized treatment[37]. Osmertinib targeted against EGFR-mutated non-small cell lung cancer has drastically improved disease-free survival[38]. In advanced melanoma, Ipilimumab, a monoclonal antibody directed against cytotoxic T-lymphocyte antigen (CTLA-4) has improved survival[39].
Denosumab targeted against nuclear factor -B ligand RANK-L, inhibits osteoclastic activation and prevents further growth in giant cell bone tumor[40]. Avelumab, a PD-1 inhibitor, is successfully used in Merkel cell carcinoma, Urothelial carcinoma and Renal cell carcinoma[41].
Targeted gene therapy can help destroy tumor without being aggressive with therapies. A case report on 56years old man with lung adenocarcinoma.
Patients PD-L1 TPS was 70% and patient was started on pembrolizumab but recurrence was evidenced after 6th cycle[42]. Other therapies also failed and led to side effects. Thereafter, the patient was enrolled in clinical study conducted by Japanese advanced medical treatment system and was found to be positive EGFR L858R-K860I doublet mutation. Treatment with oral osimertinib led to partial remission in just one month. Patient tolerated this drug and no side effects were noted[43].
Sometimes targeted therapy can lead to new gene activation and neoplastic transformation. Targeted therapy against triple negative breast cancer can lead to the development of metastatic malignant melanoma[44]. Secondary tumors and T-cell lymphoma can occur after CAR T-cell therapy. For patients who received axicabtagene ciloleucel therapy for diffuse large b cell lymphoma developed lethal T cell lymphoma[45]. Furthermore, In recent studies rigid extracellular matrix of cells (ECM) has demonstrated increased tumorigenesis. Targeting ECM stiffness, can lead to collagen depletion and has emerged as potential cancer therapy[46].
There are a wide variety of rare unexplained genetic disorders and developmental anomalies. Their apt diagnosis can help the patients understand their condition better. Recent advancements with NGS, which includes whole genome sequencing, whole exam sequencing, whole mtDNA sequencing, targeted exam sequencing and RNA sequencing, has countered the limitations of more traditional methods like Sanger method, karyotyping, and chromosomal arrays for rare genetic diseases[46].
Whole exome sequencing was done on~500000 individuals in UK Biobank that identified about 564 distinct genes that had significant trait associations, e.g., CHD2 with chronic lymphocytic leukemias of b-cell type, COL1A1 with bone disorders, SERPINC1 with coagulation defects, etc.[47,48].
Many Mitochondrial disorders have been linked with mutations in mitochondrial DNA or nuclear DNA using next-generation sequencing, e.g., MT-TL1, MT-TN mutation causing progressive external ophthalmoplegia, KearnsSayre syndrome (KSS) caused by single large-scale deletion[49].
A whole exome sequencing (WES) analysis was conducted in Lebanon for neurological diseases in consanguineous families[50]. Thirty-three gene variations were identified among the pre-screened consanguineous families with neurogenic disorders. Most common mutation was miss-sense mutation[51].
Rare genetic disorders affect millions of people across the globe either causing premature deaths or leaving them with prolonged co-morbidities. Genetic therapies are our first line revolutionary treatment options. Classic example is AAV (Aden-associated virus) gene therapy for Cystic Fibrosis[52].
X-linked Retinitis Pigmentosa, which occurs due to mutation in RPGR gene, is another target for gene therapy. Retina is excellent for non-invasive procedures and it limits the immunological systemic spread[53]. AAV vector mediated gene transfer in Hemophilia A and B can be used as a one-time treatment with factors level lasting for years[54].
Use of more advance lentiviral vectors as gene therapy for Primary immunodeficiency, SCID, Wiskot-Aldrich and other Leukodystrophies has improved the biosafety. There have been promising results for the use of Autologous T-cells as an alternate strategy for Primary immunodeficiencies[55]. Stem cell gene therapy for Fanconis Anemia is another genetic approach that uses corrected stem cells to rapidly improve entire hematopoiesis of patient[56]. SMN2 gene splicing modifiers like Nusinersen and Risdiplam, SMN1gene replacement therapy with Zongelsma is used for Spinal muscular atrophy[46].
CRISPR/CAS therapy is the gene therapy of future. It is going to be the therapy of choice for rare genetic diseases in the next 1520years. The technology is being tested for Thalassemia and Sickle cell Anemia, and is showing great potential[57]. Thus, the use of genomic medicine, specifically the gene therapy is going to revolutionize the way we clinically diagnose and manage rare genetic disorders.
Pharmacogenomics leverages genomic biomarkers to predict individual responses to drug efficacy and toxicity. While factors like disease severity, diet, and other medications also influence drug responses, genetic differences significantly impact drug metabolism and action. Despite the growing body of research, replicating findings remains a challenge. Genome-wide association studies (GWAS) have identified genetic variations associated with psychiatric disorders and drug responses, but most findings lack consistent replication. The FDA includes pharmacogenomics information in drug labels, highlighting its growing recognition. The Clinical Pharmacogenetics Implementation Consortium (CPIC) aims to translate genetic data into clinical practice, providing guidelines for genome-informed prescribing of antidepressants and antipsychotics[58].
Genetic factors significantly influence the metabolism of lamotrigine (LTG), an antiepileptic drug metabolized mainly by UGT enzymes, particularly UGT1A4. Polymorphisms in these enzymes, such as UGT1A4 and UGT2B7, can affect the drugs plasma concentration and efficacy. Additionally, genetic variations in transporters like OCT1 and ABCG2 also play a role in LTG pharmacokinetics, potentially necessitating dosage adjustments for effective treatment. Further research is needed to fully understand these genetic impacts and to optimize individual treatment plans[59].
A 55-year-old clinical molecular geneticist became a patient after a tumor was detected, leading to a diagnosis of estrogen-receptor positive breast cancer. Initially prescribed tamoxifen, she requested CYP2D6 testing due to concerns about genetic factors affecting the drugs efficacy. The test indicated an intermediate metabolizer status, prompting a switch to anastrozole, in line with CPIC guidelines[60].
P2Y12 inhibitors like clopidogrel and prasugrel are metabolized into active forms by CYP enzymes, notably CYP2C19, which affects their efficacy. Carriers of CYP2C19*2 or 3 alleles, which reduce enzyme function, show decreased drug effectiveness and higher cardiovascular risks. Conversely, the CYP2C1917 allele increases enzyme activity, enhancing drug efficacy and sometimes bleeding risk. Other genetic variants, such as ABCB1 c.3435C>T and CES1, also influence drug metabolism but are not routinely tested[61].
This represents substantial improvements in customized care by offering tailored methods according to the specific genetic characteristics of everyone. One of the main advantages is increased diagnostic accuracy. Comprehending genetics differences allows clinicians to properly diagnose disorders that might otherwise be missed using conventional approaches, resulting in more accurate and earlier disease identification[62]. Enhanced therapeutic efficacy and safety are also significant advantages. Personalized plans based on genomic data can assist in selecting the best medications and dosages, lowering adverse drug reactions and enhancing therapeutic success rates[10]. This personalized approach ensures that therapies are both effective and safe for each patient. Furthermore, enhanced patient results and well-being are significant advantages. individualized. Personalized treatments frequently result in better illness management and prognosis, which can benefit overall patient health and longevity[12]. Patients benefit from therapies that are carefully designed to their unique genetics, resulting in speedier recovery times and an and a higher standard of living. However, putting genomic medicine to use offers its own set of obstacles. These include the requirement for vast genetic data, the complexities of interpreting genetic data and moral questions on genetic privacy and prejudice. Despite these difficulties that genomic medicine seems to be a promising topic regarding the future of individualized therapy.
Incorporating genomics into therapeutic practice requires a dependable bioinformatics infrastructure to manage and interpret vast datasets. This involves developing standardized procedures for the purpose of genome sequencing, analysis, and ensuring compatibility with existing electronic health records[63]. Additionally, there is an urgent requirement for medical personnel to receive comprehensive training in genomics to effectively utilize these perspectives on patient care, bridging the gap between advanced technology and practical application[64].
High price of genome sequencing and related technology is a major obstacle to its broad use, even though sequencing costs have dropped over time, it remains prohibitive for many healthcare systems and patients, particularly in low- and middle-income countries[65].
However, ensuring equitable access to these treatments necessitates significant monetary commitment and supportive policies to subsidize costs and integrate genomic medicine into public healthcare systems.
The gathering, storing, and use of genetic information raise substantial privacy concerns. Making sure patient data are securely stored and used in an ethical manner critical to maintaining public trust in genomic medicine[66]. The ethical implications encompass preventing genetic discrimination and managing the potential psychological impact on patients who discover their risks. Rules and regulations must be established to protect peoples genetic information and discuss the ethical ramifications of using genomic data[67].
Regulating and storing genomic data presents tremendous privacy and security concerns since peoples genetic information is considered sensitive. There is adequate legal protection for genomic data for clinical use, especially where the Health Information Portability and Accountability Act (HIPAA) applies. HIPAA outlines the degree of protection provided to such data and restricts access to only personnel in the clinical field. Some states have additional protections, but these vary from state to state, leading to disparities in privacy levels[68].
Genomic data require robust protection from breaches, necessitating strong methods in their storage and transfer. Access to these data must be highly controlled, with monitoring of everyone who seeks access and logging of all actions on the data to properly identify violators. Preventive measures for the protection of genomic data are of utmost importance due to the severe consequences individuals may face from the misuse of their information[68].
Two critical aspects of handling genomic data are consent and confidentiality, aligning with patients concerns about the privacy of their genetic sequences and potential misuse. The Genetic Information Nondiscrimination Act (GINA), signed into law in 2008, addresses discrimination in insurance and employment based on genetic characteristics but does not cover life, disability, or long-term care insurance.
Privacy is paramount; each patient must know how their genetic details will be utilized, where they will be stored, and who will be allowed access. The HIPAA Privacy Rule generally restricts the disclosure of genetic data without the patients consent, though there may be exceptional cases requiring the disclosure to at-risk relatives based on ethical principles. For instance, physicians might encourage patients to disclose genetic risks to their families while respecting patient privacy and legal guidelines[68].
Lack of equal access to genomic testing and personalized treatments is rampant, with minorities, women, rural patients, uninsured/underinsured patients and those with low education and income levels being most affected. For instance, in the case of breast cancer, non-Hispanic Black women receive low rates of BRCA testing compared to non-Hispanic White women. This is partly because there are fewer conversations about genetic testing with healthcare providers and fewer referrals to genetic counselors among minority-serving physicians. Therefore, these disparities also translate to preventive measures, such as lower risk-reducing surgeries among Black women and fewer cases of cascade screening among Black families with BRCA variants. Furthermore, the underserved population, including racial and ethnic minorities, low-income groups, and women, have barriers in accessing treatments such as PCSK9 inhibitors for FH leading to poor cholesterol control and poor health outcomes[69].
In order to improve health equity for genomic medicine it is necessary to engage participants from non-European decent and other deprived population backgrounds. Increasing rates of utilization of genomic services depends on the ability to make such tests accessible and have acceptable coverage in various settings such as community hospitals or primary care physicians offices. Training of the workforce and infrastructure improvement in MSIs aids in improving culturally sensitive care and research. Sparking collaboration with the local communities and the healthcare providers ensures a mutual understanding between the two that will make genomic research to reflect their perception. Furthermore, the financing of the research facilities in other than academic institutions and in underprivileged regions contributes to a wider deployments and participants integration. Together, these strategies seek to address health disparity and guarantee the equitable improvement of all people through genomic medicine[70].
Genetic discrimination focuses on prejudice against people with specific genetic characteristics which exposes them to serious threats such as loss of insurance, inability to secure a job and social exclusion. The following risks have however been regulated by law especially by the Genetic Information Non-discrimination Act of 2008. GINA offers certain federal anti-discrimination provisions for genetic tests whether from the states and health insurers or employers perspectives as they ban such entities from obtaining or using genetic information for underwriting purposes or employment respectively[71]. However, GINA does not cover life, disability or long-term care insurance and for this void state laws try to provide a solution[71].
Some recommendations to address discrimination in genomic medicine are having general statutes such as the Genetic Information Non-discrimination Act, enacted with an aim of preventing discrimination of individuals based on genetic information. In employment and health insurance decisions, GINA is enforced; however, health insurers cannot use genetic information to underwrite life, disability, or long-term care insurance. Furthermore, there are intentions to increase the consciousness about the protection against genetic non-discrimination and to remove the general distrust thereby impeding genomic research due to apprehension for discrimination. Furthermore, the Affordable Care Act (ACA) has also sought to fill gaps by extending provisions that ban the health insurance status discrimination based on pre-existing conditions to include genetic information; thereby supporting GINAs provisions aimed at addressing employment discrimination. Enlarging such protections to encompass all sorts of insurance and ensuring people advocate for a similar system that combines risk might help to avoid discrimination and promote the proper usage of genomic medicine further[72].
Since the Human Genome Projects completion in 2003, DNA sequencing technologies have advanced significantly to fill previously existing gaps[73]. There are primarily two types of DNA sequencing technologies: short-read sequencing and long-read sequencing. Short-read sequencing methods, such as sequence molecule fluorescent sequencing and single-molecule nanopore base sequencing, generate genetic information in 100300 base pairs per read[19]. They are efficient and cost-effective but often miss repetitive regions, duplicated sequences, and complex structural variants, leaving gaps in the data. While long-read sequencing provides better resolution of complex regions and structural variants, it is typically more expensive and has higher error rates. Combining both approaches can enhance genomic analysis[73].
Advancements in pharmacogenomics and the integration of sequenced genomes with medical records, expression profiles, and imaging studies necessitate robust data storage solutions like cloud computing. It is crucial to manage these data while ensuring both accessibility and confidentiality. In the realm of AI, such comprehensive data can significantly enhance the development of genomics and improve outcomes. However, it is vital to apply this knowledge and data judiciously in clinical settings to ensure its effectiveness and ethical use[74,75].
Multi-omics refers to the use of multiple biological omes such as genome, proteome, transcriptome, epigenome, metabolome, radiomics, and microbiome to provide data to achieve a holistic understanding of biological systems and enhance personalized medical treatments[76]. Multi-omics can provide the missing link of information in the study of genomics and help uncover the pathophysiology underlying a disease which will help provide a new approach to its detection, treatment, and prevention[77].This new approach will pave the way for personalized medicine and optimize its clinical outcome based on the uniqueness of an individual[9].
Multi-omics approaches can fill critical gaps in genomic research by providing comprehensive insights into the underlying mechanisms of diseases. By integrating various types of omics data, such as genomics, proteomics, and metabolomics, researchers can better understand disease pathophysiology. This enhanced understanding enables the development of novel strategies for disease detection, treatment, and prevention[77]. The application of these strategies will support the advancement of personalized medicine, which aims to tailor medical interventions to the unique characteristics of each individual. Ultimately, this personalized approach will optimize clinical outcomes and improve patient care by addressing the specific needs and conditions of each patient, leading to more effective and targeted treatments[9].
Precision medicine can categorize individuals based on their clinical features, treatment responses, and prognostic factors[78]. By leveraging multi-omics studies in diseases such as inflammatory bowel disease, various cancers, and lifestyle-related conditions like diabetes, personalized medicine aims to tailor treatments to each persons unique profile. Since pharmacokinetics is closely linked to genetic variations, personalized medicine has the potential to revolutionize genomics and drive the development of new therapies. This approach integrates comprehensive omics data to refine treatment strategies, enhancing their effectiveness and leading to more targeted, individualized healthcare solutions. Ultimately, this method supports more precise and effective management of diverse health conditions, contributing to advancements in medical science and patient care[9].
Artificial intelligence excels at processing multidimentional clinical and biological data, which is critical for precision medicine. It assists in discovering biomarkers via genetic sequencing and other data sources, turning complex data into meaningful insights for tailored treatment strategies[79]. Artificial intelligence algorithms, such as machine learning and deep learning, improve disease daignosis and early detection . This is especially visible in diciplines like oncology and cardiovascular care, where AI helps anticipate disease risk and stratify indiviaduals based on their unique traits[80]. AI gives clinicians additional insight by combining data from many sources, such as electronic health records (EHR), imaging data, and omics data. This integration aids clinical decision-making by increasingthe accuracy of diagnoses and the efficacy of tretmenr strategies[81]. AI is widely employed in oncology for tasks such as tumor identification, therapy planing, and prognosis prediction . It assists in identifying new biomarkers and understanding tumor heterogeneity, resulting in more accurate and effective cancer treatments[82]. AI helps in diagnosis and forecast the prognosis of cardiovascular illnesses. It employs several machine learining models to assess data from EHR, imaging and omics thereby boosting the accuracy of risk prediction and treatment planing[83]. AI has the potential to predict the risks and outcomes of neurodevelopmental diseases by examining genomic variants and other biological markers. However the compexity and variability of these illnesses provide substantial hurdles that AI continues to solve[84].
Personalized medicine in oncology is adapting treatment to individual patient features, especially genomic and molecular markers. This strategy seeks to give the right treatment for the right person at the right time by using genetic information to guide therapeutic decisions[85].
The MINDACT study investigated the use of a 70-gene signature to inform chemotheraphy decisions in early-stage breast cancer.A decision-analytic modeling technique indicates that fewer women may benefit from genomic testing and treatment than previously indicated by the trial, underlining the necessity for personalized decision-making based on genomic risk[86].
The PROMISE study finds that concentrations of hs-cTn and IL-6 were associated with coronary artery disease (CAD) characteristics and major adverse cardiovascular events (MACEs), indicating that myocardial injury and inflamation play a role in CAD pathophysiology. This association was strongest in partipants with non-obstructive CAD, highlighting and opportunity to tailor treatment for this at-risk group[87].
Intensive blood pressure management in older hypertensive persons with sarcopenia was related with a lower risk of cardiovascular disease (CVD) without an increased risk for adverse events, suggesting potential for indivudualized treatment techniques targeted to this at-risk group[88].
STK11/LKB1 mutations were discovered to be a prominent cause of primary resistance to PD-1 inhibitors in KRAS-mutant lung adenocarcinoma (LUAC). This resistance was demonstrated in numerous clinical cohorts, with different response to PD-1 blocking among LUAC subtypes. These findings suggest that STK11/LKB1 mutations can be employed as a predictive biomarker for PD-1 inhibitor efficacy potentially informing customized treatment options for KRAS-mutant patients[89].
KRAS codon G12 mutations have been identified as biomarkers of resistance to trifluidine/tipiracil (FTD/TPI) chemotherapy in metastatic colorectal cancer (mCRC), with patients carrying these mutations showing significantly reduced overall survival benefit from treatment, implying that genomics-based precision medicine could inform chemotherapy selection and improve outcomes for mCRC[90].
The potential of genomics-based tailored treatment, demonstrating that magnesium spplementation can regulate DNA methylation in the TMPRSS2 gene, which is critical for SARS-CoV-2 viral entry. Adjusting magnesium levels in individuals with specific calcium-to-magnesium intake ratios suggests a novel gene-environment interaction that could be leveraged for personalized prevention strategies and treatment of early COVID-19, potentially altering viral susceptibility based on individual genetic and nutritional factors.[91]
Genomic medicine has increased our knowledge of genetic variations, resulting more accurate diagnosis and personalized treatment[18]. DNA sequencing technology, genetic data integration into clinical care, and the use of multi-omics techniques are among the most significant developments.
Future research should focus on increasing access to genetic technology, tackling ethical challenges, and enhancing bioinformatics facilities. Clinical practice needs to change to include these developments, providing fair and efficient individual treatment.
As genomic medicine growing, it will play an increasingly significant part in transforming healthcare. Addressing existing challenges will be important for achieving its full assurance, leading to more customized, precise, and efficient treatments that improves outcomes for patients.
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Published online 13 February 2025
Adil Khan, Email: dradilkhan17@gmail.com.
Anchal Ramesh Barapatre, Email: anchalbara15@gmail.com.
Nadir Babar, Email: nadirbabar@gmail.com.
Joy Doshi, Email: joydoshi10@gmail.com.
Mohamd Ghaly, Email: Ghalymohamed587@gmail.com.
Kirtan Ghanshyam Patel, Email: pkirtan099@gmail.com.
Shayan Nawaz, Email: shayan7788@outlook.com.
Uswa Hasana, Email: uswah.2501@gmail.com.
Swara Punit Khatri, Email: khatriswara8@gmail.com.
Shilpa Pathange, Email: Shilpa.pathange27@gmail.com.
Abhinya Reddy Pesaru, Email: abhinya2000@gmail.com.
Chaitanya Swaroop Puvvada, Email: chaitanyaswaroop17@gmail.com.
Marium Billoo, Email: Dr.mariumbilloo@hotmail.com.
Usama Jamil, Email: jamilusama719@gmail.com.
Ethical approval was not required for this review.
Informed consent was not required for this review.
None.
A.K.: conception and design of the study, drafting the manuscript, critical revision of the article for important intellectual content, and final approval of the version to be published. A.R.B.: acquisition of data, analysis and interpretation of data, drafting sections of the manuscript, and revising it critically for important intellectual content. N.B. and C.S.P.: acquisition of data, drafting sections of the manuscript, revising it critically for important intellectual content, and providing final approval of the version to be published. J.D.: assistance in data collection, drafting sections of the manuscript, and revising it critically for important intellectual content. M.G. and S.P.: data interpretation, drafting sections of the manuscript, and revising it critically for important intellectual content. K.G. and M.B.: analysis and interpretation of data, drafting sections of the manuscript, and revising it critically for important intellectual content. S.N.: data collection and interpretation, drafting sections of the manuscript, and revising it critically for important intellectual content. U.H.: assistance in data collection, drafting sections of the manuscript, and revising it critically for important intellectual content. S.P.K. and A.R.P: analysis and interpretation of data, drafting sections of the manuscript, and revising it critically for important intellectual content. U.J.: data collection, drafting sections of the manuscript, and revising it critically for important intellectual content.
The authors declare no conflicts of interest.
None.
Usama Jamil.
None.
None.
This section collects any data citations, data availability statements, or supplementary materials included in this article.
None.
Articles from Annals of Medicine and Surgery are provided here courtesy of Wolters Kluwer Health
See the original post:
Genomic medicine and personalized treatment: a narrative review