Advances in personalized medicine: translating genomic insights into targeted therapies for cancer treatment

Introduction

Background

Personalized medicine has emerged as a revolutionary approach in modern healthcare, particularly in cancer treatment (1). Unlike traditional methods that rely on generalized treatment plans, personalized medicine leverages genomic insights to tailor therapies to an individual’s genetic profile (1). This paradigm shift has been made possible by advances in genomic technologies, enabling more precise diagnoses and targeted therapeutic strategies that improve patient outcomes while minimizing adverse effects (2).

Cancer is a highly heterogeneous disease characterized by complex genetic and molecular variations among patients (3). The advent of next-generation sequencing (NGS) and other genomic profiling techniques has provided invaluable insights into the genetic mutations and alterations that drive tumor growth and progression (4). These advancements have paved the way for targeted therapies, which selectively inhibit specific molecular pathways involved in cancer development. By identifying actionable mutations, clinicians can prescribe treatments that are more effective for individual patients, thereby enhancing therapeutic efficacy and reducing unnecessary side effects (2).

Rationale and knowledge gap

The integration of genomic technologies into oncology has led to significant improvements in patient care (5). Targeted therapies, such as tyrosine kinase inhibitors (TKIs) and monoclonal antibodies, have demonstrated superior efficacy in treating cancers with known genetic drivers (6,7). For example, epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) can be effectively targeted using EGFR inhibitors (8), while HER2-positive breast cancers respond well to HER2-targeted therapies (9). Similarly, the identification of BRAF mutations in melanoma and KRAS mutations in colorectal cancer has enabled the development of highly specific therapeutic agents that significantly improve patient survival rates (10,11).

Beyond targeted therapies, emerging technologies such as clustered regularly interspaced short palindromic repeats (CRISPR) gene editing and AI are further refining the landscape of personalized medicine (12,13). CRISPR technology offers the potential to correct genetic mutations at the DNA level, opening new avenues for precision cancer treatment (12). AI and machine learning (ML) algorithms, on the other hand, are enhancing the interpretation of complex genomic data, facilitating the identification of novel biomarkers and optimizing treatment selection (13). These technological innovations are driving the transition from a one-size-fits-all approach to a more individualized treatment paradigm.

Objective

In this review, we discuss the role of genomic profiling in cancer treatment, highlighting its impact on therapy selection and patient outcomes. We also examine the challenges associated with accessibility, costs, and the need for robust bioinformatics infrastructure. Additionally, we explore emerging solutions such as AI and gene-editing technologies and their role in shaping the evolving landscape of precision medicine. Finally, we outline the future directions of genomic-guided cancer therapy, emphasizing the ongoing advancements and collaborative efforts required to fully realize the potential of personalized medicine in oncology. We present this article in accordance with the PRISMA-ScR reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-25-34/rc).

Methods

We conducted a literature search in PubMed on February 9, 2025, using a combination of MeSH terms and free-text keywords to identify original studies investigating genomic profiling with clinical relevance in cancer patients. The full search strategy is detailed in Table 1, and Figure 1 illustrates the study screening and selection process. The search covered the period from January 1, 1950 to February 9, 2025, with no language restrictions. Our PubMed search identified 238 studies. After screening titles and abstracts, 155 studies were excluded, leaving 83 studies for full-text review. Among these, 65 studies did not meet the inclusion criteria, and ultimately, 18 studies with clinical relevance were included in the review. The main findings of the 18 eligible studies are summarized in Table 2. Additionally, the risk of bias for the included studies was assessed using the Newcastle-Ottawa Scale (NOS).

Figure 1 PRISMA flow diagram depicting study screening and selection process.

Studies were included if they met the following criteria: (I) original research articles involving cancer patients undergoing genomic profiling and targeted therapies; (II) studies must examine genomic technologies, targeted therapies, tumor heterogeneity, resistance mechanisms, or emerging technologies such as CRISPR and AI; (III) studies must report patient outcomes, including survival or mortality.

Studies were excluded if they met any of the following criteria: (I) non-original research articles, including reviews, commentaries, editorials, case reports, or opinion pieces; (II) studies conducted on non-cancer patient populations; (III) preclinical research lacking direct clinical relevance or studies that did not report patient outcomes; (IV) studies that did not assess genomic technologies in conjunction with targeted therapy. Both authors reviewed the retrieved studies to assess their relevance. Discrepancies were resolved through discussion.

Results

Genomic technologies and the role of bioinformatics in data analysis

Epigenomics and phenomics data along with genomic technologies have become cornerstone tools in advancing personalized medicine. They have revolutionized our understanding and treatment of diseases, especially cancer (1,32). By enabling detailed analysis of an individual’s genetic makeup, these technologies provide critical insights into the molecular underpinnings of health and disease. Through high-throughput sequencing methods such as NGS, including targeted sequencing panels (TSP), whole exome sequencing (WES), and whole genome sequencing (WGS), researchers can identify specific genetic mutations and alterations that drive tumor development and progression (Figure 2) (4).

Figure 2 Overview of the key sequencing technologies utilized in personalized medicine. TSP, WES, and WGS are next-generation sequencing techniques with specific workflows. They involve DNA extraction, library preparation, DNA enrichment (regions of interest, exomes, or complete DNA), sequencing, and data analysis to identify regions with SNVs, MSI, CNVs, insertions, or deletions. These methods differ in coverage, accuracy, turnaround time, sequencing depth, and costs. CNVs, copy number variants; Ex, exon; MSI, microsatellite instability; ROI, region of interest; SNVs, single nucleotide variants; TSP, targeted sequencing panels; WES, whole exome sequencing; WGS, whole genome sequencing.

This wealth of genomic data empowers clinicians to tailor treatment strategies to each patient’s unique genetic profile, enhancing therapeutic efficacy while minimizing adverse effects. As a result, genomic technologies not only facilitate more precise diagnoses but also pave the way for innovative targeted therapies that hold great promise in transforming patient care within the realm of personalized medicine. In the following, we will provide a brief overview of these different sequencing techniques, highlighting their principles, applications, and significance in advancing personalized medicine. Additionally, we will mention some significant examples that illustrate how these technologies have been successfully applied in clinical settings to improve patient outcomes.

NGS has revolutionized genomic research by enabling rapid and cost-effective sequencing of DNA. Unlike traditional Sanger sequencing, which processes one sequence at a time, NGS can analyze millions of DNA fragments simultaneously (33). This parallel processing capability significantly enhances throughput and reduces costs, making it an invaluable tool for cancer genomics. The applications of NGS in oncology are vast. It facilitates comprehensive tumor profiling, enabling researchers to identify mutations, copy number variations, and other genomic alterations that drive cancer progression and clinical judgment in patients (34). Different platforms exist within the NGS landscape, such as Illumina’s sequencing technology and Ion Torrent’s semiconductor-based approach (35). These platforms have been instrumental in deciphering complex cancer genomes and guiding personalized treatment strategies. In particular, standardization in NGS workflow including template preparation (library preparation and amplification), sequencing, imaging, and data analysis has helped to increase the accuracy in data assembly and quality. However, the development of robust state-of-the-art bioinformatics pipelines to extract meaningful biological insights is still a significant topic (35).

TS panels are specialized tools used in genomics to analyze specific regions of DNA, allowing researchers to focus on a selected number of genes or genomic areas of interest for diagnosis, prognosis, and treatment monitoring (36). These panels contain a curated set of probes designed to capture and enrich targeted sequences (regions of interest), making it easier to identify variations such as single nucleotide polymorphisms (SNPs), insertions, deletions, and other genetic alterations associated with diseases. By concentrating on particular targets, these panels enhance the efficiency and accuracy of sequencing workflows while reducing costs and data complexity compared to whole-genome sequencing (37-39). They are widely used in clinical diagnostics, cancer research, and personalized medicine.

WES focuses specifically on the exons, which make up approximately 1.5% of the human genome but contain around 85% of known monogenic, disease-related variants (40). WES is particularly advantageous for identifying unbiased clinically relevant mutations and copy number alterations associated with various cancers (41). One notable example is its application in hereditary breast and ovarian cancer syndrome research, where WES has successfully identified pathogenic variants in genes such as BRCA1 and BRCA2. By concentrating on exonic regions, WES provides a cost-effective means to uncover significant genetic alterations that can inform therapeutic decisions (42).

WGS offers a more comprehensive analysis by sequencing the entire genome, including both coding and non-coding regions. The unique capabilities of WGS allow for the detection of structural variants, copy number variations, and regulatory elements that may play critical roles in tumor behavior. While WGS generates a vast amount of data compared to WES or TSP, its sensitivity enables researchers to uncover hidden complexities within cancer genomes. For instance, studies have demonstrated how WGS combined with specialized bioinformatics pipelines can detect novel fusion genes or previously unrecognized mutations that contribute to drug resistance or tumor recurrence (43,44). The breadth of information obtained from WGS positions it as a powerful tool in understanding cancer biology at an unprecedented level. Furthermore, combining the comprehensive genomic data of specific tumor types with detailed clinical information will create a groundbreaking research platform. This will enable a deeper understanding of the mechanisms behind treatment response, resistance development, and treatment failure. Ultimately, this approach will help identify unique characteristics of individual cancer genomes and facilitate the development of targeted cancer therapies (45,46).

It is evident that bioinformatics plays a pivotal role in the realm of personalized medicine, serving as the bridge between complex genomic data and actionable clinical insights. As advancements in sequencing technologies continue to evolve and produce vast amounts of ‘big data’, bioinformatics provides the necessary tools and methodologies to analyze, interpret, and integrate this information effectively (47). The primary objective is to translate raw genetic information into meaningful knowledge that can guide clinical personalized decision-making and improve patient outcomes (48). An exemplar Bioinformatics Multidrug Combination Protocol for Personalized Precision Medicine was developed for breast cancer patients. This protocol enables personalized drug combinations extracted from hundreds of drugs and thousands of drug combinations, offering more effective treatment options (49). The protocol integrates an individual pattern of perturbed gene expression for each patient, similar to a fingerprint, containing gene expression profiles different from healthy individuals. The algorithm can then extract an accurate personalized drug treatment, producing a more beneficial therapeutic effect in the respective patient (49).

At the heart of bioinformatics lies the data analysis pipeline, which includes several critical steps from sequencing to interpretation (Figure 3). Initially, raw sequencing data undergo quality control measures to filter out low-quality reads that could introduce errors into subsequent analyses (46,50). Following this step, the cleaned data is aligned against reference genomes using sophisticated pipelines such as the Burrows-Wheeler Aligner (BWA), Bowtie2, HISAT, or LongStrain (51-53). This alignment process is crucial for identifying genetic variants like SNPs, insertions, or deletions that may play a significant role in disease development. Variant calling is then performed using software like the Genome Analytic Tool Kit (GATK) or SAMtools to accurately identify these genetic alterations (54,55).

Figure 3 Integration of bioinformatics pipelines in personalized medicine. This diagram shows the bioinformatics pipelines that convert raw sequencing data into actionable clinical insights for decision-making in personalized medicine. Key steps include quality control to remove low-quality reads, aligning cleaned data to reference genomes, variant calling to identify genetic alterations, and annotation to provide context to variants using databases like COSMIC, dbSNP, and tools like SIFT, PolyPhen, and others for predicting functional impact. Additionally, the figure demonstrates how integrative genomics combines genomics, transcriptomics, and proteomics to offer a holistic view of tumor biology. Bioinformatics is crucial in interpreting complex genomic data and guiding personalized therapeutic decisions in cancer treatment by combining this data with patient-specific clinical and electronic health records. EHR, electronic health record(s); NGS, next-generation sequencing; SIFT, Sorting Intolerant From Tolerant.

Once variants are identified, bioinformatics tools facilitate their annotation and interpretation through cross-referencing with established databases like the Catalogue of Somatic Mutations in Cancer (56) for cancer-related mutations or dbSNP for known polymorphisms. These resources provide context regarding whether specific mutations have been previously associated with clinical outcomes or biological functions related to cancer pathogenesis. Functional impact prediction tools such as Sorting Intolerant From Tolerant (57) or Polymorphism Phenotyping (58) further aid in assessing whether specific mutations are likely to affect protein function, thereby guiding therapeutic decisions.

Moreover, bioinformatics enables integrative genomics, by combing genomic data with other omics layers, such as transcriptomics and proteomics, to provide a comprehensive view of tumor biology. This approach allows researchers to uncover intricate interactions between genetic alterations and gene expression profiles influenced by environmental factors or treatment modalities. For example, understanding how specific mutations correlate with differential gene expression patterns can lead to more targeted therapeutic strategies customized for individual patients (45,46).

ML and AI are making significant progress in bioinformatics, providing innovative solutions for analyzing high-dimensional datasets produced by modern sequencing technologies and influencing clinical decision-making (13,59). These advanced computational techniques can detect patterns within genomic data that traditional analytical methods may overlook. By utilizing ML algorithms, researchers can forecast patient responses using their individual genomic profiles and uncover new biomarkers linked to treatment effectiveness or resistance. However, despite its tremendous potential benefits, bioinformatics faces several challenges that must be addressed to fully realize its promise in personalized medicine. Issues surrounding data storage capacity requirements arise due to the massive volumes of genomic data generated daily, thus necessitating robust infrastructure capable of supporting efficient access and processing capabilities. Additionally, ensuring reproducibility and accuracy throughout analyses conducted across varying contexts remains paramount for establishing trust in bioinformatic findings. Moreover, although AI has rapidly revolutionized diagnosis, prognosis, treatment, and prevention in oncology and related fields, there are also concerns about biases inherent in algorithms, reflecting the unconscious biases of their creators (60). Nevertheless, bioinformatics serves as an essential pillar supporting personalized medicine by transforming complex genomic data into actionable insights that inform clinical practice and pave new pathways toward improved patient care experiences overall.

Looking ahead, future directions in bioinformatics will focus on developing user-friendly interfaces and automating routine tasks to minimize barriers for clinicians seeking expertise in genomic analysis. Efforts aimed at enhancing collaboration between computational scientists and healthcare professionals will foster an environment conducive to translating genomic insights into real-world applications ultimately benefiting patients navigating complex diseases like cancer.

Key genomic alterations and targeted therapies in cancer

Advances in NGS and biomarker-driven approaches have led to improved clinical outcomes in multiple malignancies. Table 3 summarizes key genomic alterations identified through genomic profiling, their associated cancer types, Food and Drug Administration (FDA)-approved or investigational targeted therapies, and their clinical significance.

Certain genomic alterations serve as crucial therapeutic targets across multiple malignancies. In NSCLC, EGFR mutations (L858R, exon 19 deletions, T790M) drive tumor growth through aberrant activation of the EGFR signaling pathway, making them prime candidates for EGFR TKIs (17,30). Similarly, ALK rearrangements and ROS1 fusions in NSCLC (62) define subsets of patients who benefit from ALK and ROS1 inhibitors, respectively (16,63).

In colorectal, melanoma, and thyroid cancers, BRAF V600E mutations lead to constitutive activation of the MAPK pathway, rendering tumors susceptible to BRAF/MEK inhibition (66,86). KRAS G12C mutations, once deemed undruggable, have become actionable with recent advancements in KRAS inhibitors, although other KRAS variants remain under investigation (64,65).

In breast and gastric cancers, HER2 (ERBB2) amplification is a well-characterized oncogenic driver, guiding the use of HER2-targeted therapies (71,72). PIK3CA mutations, particularly in hormone receptor-positive (HR+)/HER2-negative breast cancer, are associated with PI3K/AKT pathway activation and response to PI3K inhibitors (31,64,69,87,88).

Another emerging class of actionable alterations includes tumors with high microsatellite instability (MSI-H) and deficient mismatch repair (dMMR) (67,72). These tumors, found in colorectal, endometrial, and other malignancies, exhibit high mutation burdens and respond exceptionally well to immune checkpoint inhibitors, leading to tumor-agnostic FDA approvals (72,80). In hematologic cancers, mutations in JAK2 (p.V617F) and JAK3 drive myeloproliferative neoplasms, with JAK inhibitors demonstrating significant clinical benefit (89,90). Similarly, MET amplification and exon 14 skipping mutations in NSCLC have emerged as critical therapeutic targets (91). Rare but clinically significant alterations, such as NTRK gene fusions, occur across various solid tumors and have led to the development of tissue-agnostic therapies with durable response rates (84).

While genomic profiling has significantly improved treatment stratification, not all mutations translate into therapeutic success. TP53 mutations, prevalent in multiple cancers, remain challenging due to the lack of direct targeting strategies. However, they provide valuable prognostic information and guide treatment selection (72,75-79).

Clinical significance of genomic profiling in oncology

Genomic profiling has revolutionized patient outcomes by enabling personalized therapy selection and identifying prognostic biomarkers. Tables 2,3 summarize the clinical significance of various genomic profiling methods, including NGS, comprehensive genomic profiling (CGP), WES, and circulating tumor DNA (ctDNA) analysis.

Multiple studies confirm that genomic profiling facilitates the identification of actionable mutations, leading to improved treatment responses. For instance, Tsimberidou et al. (14) demonstrated that molecularly targeted therapy (MTT) based on CGP led to significantly longer failure-free survival (3.4 vs. 2.9 months; P=0.002) and overall survival (OS) (8.4 vs. 7.3 months; P=0.04). Similarly, Prager et al. (24) showed that NGS-based molecular profiling extended progression-free survival (PFS) from 61 to 112 days (P=0.002), reinforcing the role of personalized treatment selection.

In NSCLC, Hughes et al. (16) reported that patients who received targeted therapy had a dramatically improved median OS of 28.7 months compared to 6.6 months for those without targeted options (P<0.001). These findings highlight the necessity of early genomic testing to ensure that eligible patients benefit from precision medicine.

On these grounds, ctDNA analysis offers a minimally invasive alternative for tumor profiling, particularly for patients with insufficient tissue samples. Kim et al. (17) demonstrated that ctDNA had prognostic value in NSCLC, with high cell-free DNA (cfDNA) concentrations at diagnosis correlating with poorer survival. Similarly, Okamura et al. (18) showed that ctDNA profiling in biliary tract cancer led to significantly longer PFS (HR: 0.60, P=0.047) and a higher disease control rate (61% vs. 35%, P=0.04) when using molecularly matched therapy. However, despite its promise, liquid biopsy faces challenges. Riedl et al. (20) reported that matched treatment based on ctDNA findings did not result in meaningful PFS improvement, with a median PFS ratio of <1.2 in all cases. Similarly, Gouton et al. (28) found no significant survival difference between patients who received molecularly matched therapy and those who did not (PFS: 2.66 vs. 3.81 months, P=0.17). These findings suggest that while ctDNA profiling is valuable, its predictive accuracy and clinical application need refinement.

Beyond DNA mutation analysis, incorporating RNA expression data has been shown to enhance treatment stratification. Pleasance et al. (21) found that whole-genome and transcriptome analysis (WGTA) identified clinically actionable targets in 83% of advanced cancer patients, with 37% receiving WGTA-informed treatments. RNA expression data contributed to 67% of treatment decisions, highlighting its added value over DNA-based methods alone.

Neoantigen-based therapies also benefit from genomic profiling. Ding et al. (22) investigated whole-exome sequencing and RNA sequencing to guide neoantigen vaccine development in lung cancer, reporting a 25% objective response rate, 75% disease control rate, and a median OS of 7.9 months. These findings suggest that integrating genomic profiling into immunotherapy selection could further personalize treatment approaches. TMB has emerged as a potential biomarker for therapy response. Lin et al. (30) demonstrated that in EGFR-mutant NSCLC, patients with low TMB had significantly better PFS (16.4 vs. 9.0 vs. 7.4 months, P=0.006) on first-generation EGFR-TKIs. Similarly, in EGFR/ALK wild-type NSCLC patients treated with chemotherapy, those with low TMB had a superior objective response rate (53.8% vs. 23% vs. 8.3%, P=0.037), though PFS improvement was not statistically significant. These findings suggest that TMB could serve as a biomarker to refine treatment selection in lung cancer.

Challenges in real-world implementation

Despite the transformative role of genomic profiling in oncology, several real-world challenges hinder its widespread implementation. In particular, access to genomic testing remains highly variable across regions and healthcare systems (19,76,92). In low- and middle-income countries (LMICs), limited infrastructure, high costs, and absent reimbursement policies significantly restrict the availability of molecular diagnostics. Even in high-income countries, disparities exist between well-equipped academic centers and community hospitals with fewer resources. Martínez-Herrera et al. (27) emphasized this inequity by showing that 32.3% of EGFR-negative NSCLC patients in underserved areas harbored other actionable mutations that would have gone undetected without access to comprehensive NGS.

Germline profiling, though potentially valuable in predicting treatment response, remains underutilized. Dalens et al. (26) reported that certain germline SNPs in immunity-related genes negatively impacted survival in patients receiving EGFR TKI therapy. However, ethical concerns, unclear regulatory frameworks, and minimal integration into routine clinical workflows have limited its application. Standardized guidelines and further research are necessary to incorporate germline data effectively into precision oncology.

Interpreting complex genomic data is another major barrier for both oncologists and patients. Thavaneswaran et al. (93) found that while most oncologists referred patients for CGP, their confidence in interpreting and communicating these results was moderate, and significantly lower for germline findings. Similarly, Bylstra et al. (94) noted that patients often struggled to understand genomic information, while clinicians cited time constraints as a barrier to effective communication. These findings underscore the need for enhanced clinical training, robust decision-support tools, and accessible patient education materials to bridge the genomic literacy gap.

Furthermore, not all genomic findings translate into improved clinical outcomes. Dang et al. (25) observed that although 53.6% of patients had actionable alterations, only 11 received molecularly guided therapy, highlighting a significant gap between molecular insights and treatment implementation. Similarly, in pediatric oncology, Harttrampf et al. (19) found that while 60.9% of refractory pediatric tumors had targetable alterations, only a small number of cases were able to access appropriate therapies due to availability and access issues.

High drug prices, lack of insurance coverage, and systemic inefficiencies further limit the practical application of precision oncology. In many LMICs, targeted therapies remain prohibitively expensive, and healthcare systems lack the capacity to support advanced diagnostics. Even where therapies exist, regulatory restrictions and fragmented care models often delay or prevent their use. Addressing these barriers will require comprehensive policy reforms, tiered pricing strategies, and global health initiatives aimed at expanding equitable access.

To improve the clinical utility of genomic profiling, more integrated approaches are needed. The use of molecular tumor boards (MTBs) has shown promise in supporting personalized treatment decisions. For instance, Canino et al. (31) reported that NGS influenced therapy decisions in 9% of metastatic breast cancer cases. Expanding real-time genomic profiling in clinical trials can also help validate emerging biomarkers and inform treatment pathways.

Moreover, multi-omic approaches that integrate genomic, transcriptomic, and proteomic data are poised to refine precision oncology further. Pleasance et al. (21) demonstrated that combining RNA and DNA data enhances treatment selection, supporting a shift toward broader molecular characterization. However, realizing the full benefits of these advances will require coordinated efforts to address systemic barriers through targeted clinical training, patient education, standardized workflows, and supportive policy frameworks ensuring equitable implementation of precision oncology.

Proposed conceptual model

To address these real-world limitations, we propose a conceptual model (Figure 4) that integrates advanced genomic technologies, collaborative expertise, and adaptive clinical workflows into a patient-centered framework for personalized oncology. This model emphasizes the seamless incorporation of CGP, sophisticated bioinformatics interpretation, and multidisciplinary decision-making to guide individualized treatment strategies.

Figure 4 Conceptual model for personalized cancer therapy. The proposed conceptual framework for personalized cancer care starts with the patient undergoing standard diagnostics, including clinical assessment, imaging, and histopathological confirmation, before moving on to comprehensive genomic profiling. This step involves testing of the tumor (and germline of necessary) using targeted sequencing panels, whole exome or whole genome sequencing, or ctDNA analysis to capture actionable mutations. Subsequently, data interpretation and bioinformatic analysis using robust computational pipelines, AI-driven tools, and curated databases are used to highlight clinically significant genomic variation. These findings are then evaluated by a multidisciplinary MTB, where oncologists, pathologists, geneticists, bioinformaticians, and other specialists interpret the results within the patient’s overall clinical context. This phase results in the selection and implementation of a targeted therapy. Such treatment plans are continually optimized through monitoring, response assessment and re-evaluation, integrating imaging, biomarker monitoring, and, where indicated, repeat genomic profiling to detect emerging resistance. A final feedback loop then refines the therapy, incorporating new data, clinical trial updates, and patient-specific factors for iterative improvement. AI, artificial intelligence; ctDNA, circulating tumor DNA; MTB, molecular tumor board.

The patient journey begins with a standard clinical workup, including imaging, risk assessment, and histopathological evaluation, followed by in-depth genomic testing—ranging from targeted panels to exome or whole-genome sequencing—to detect actionable molecular alterations. These data are processed through validated bioinformatics pipelines that prioritize clinically relevant mutations and potential therapeutic options.

A multidisciplinary MTB—including oncologists, pathologists, geneticists, bioinformaticians, and other relevant specialists—reviews the molecular findings in conjunction with the patient’s clinical context to determine a personalized treatment plan. This plan is not static; it evolves through ongoing monitoring using imaging, biomarkers, and repeat genomic testing such as ctDNA, allowing real-time adaptation of therapy in response to treatment response or disease progression.

Based on this model, the proposed clinical guidelines highlight key implementation steps: clearly defined eligibility criteria for genomic testing, robust informed consent procedures, and selection of appropriate sequencing platforms tailored to clinical need. Standardized bioinformatics workflows are essential to ensure reproducibility, accurate variant calling, and functional annotation (Table 4).

MTBs play a central role in interpreting complex genomic data and addressing ethical and legal issues such as incidental germline findings and patient data privacy. Therapeutic decisions prioritize FDA-approved targeted agents matched to the tumor’s molecular profile, which may include combination therapies or investigational approaches, like CRISPR-based gene-editing strategies, especially in cases of acquired resistance.

Regular outcome assessment through imaging, molecular biomarkers, and adaptive treatment adjustments ensures that care remains responsive to each patient’s evolving disease trajectory. The model also emphasizes the importance of healthcare infrastructure, ongoing training for clinical staff, and policy development to support equitable access to genomic testing and the appropriate use of AI-driven decision-support systems.

In summary, this conceptual framework offers a practical, iterative approach to precision oncology by connecting cutting-edge genomic science with real-world clinical application. By placing the patient at the center of an integrated, data-driven ecosystem, it aims to maximize therapeutic efficacy, support dynamic care planning, and promote equitable implementation of personalized cancer treatment worldwide.

Discussion

Key findings

The conceptual model proposed in this review underscores the significance of integrating genomic insights, real-time data analytics, and multidisciplinary care into a cohesive framework for personalized oncology. Two transformative technologies, CRISPR gene editing and AI, are leading the way in this innovation, each making unique contributions to the advancement of cancer care. These technologies not only support but also enhance the fundamental pillars of the personalized oncology model, providing new opportunities to enhance diagnostic accuracy, therapeutic precision, and clinical outcomes.

CRISPR technology and gene editing in personalized cancer therapy

CRISPR technology and gene editing have revolutionized personalized medicine, especially in cancer treatment (95). By allowing precise editing of specific genetic sequences involved in tumorigenesis, CRISPR enables researchers and clinicians to create targeted therapies tailored to an individual’s unique mutational profile. This capability represents a significant departure from traditional “one-size-fits-all” treatments, which are often associated with limited efficacy and adverse effects, and towards more refined, mutation-specific interventions (96,97).

CRISPR-Cas9, a widely used gene-editing system, uses RNA-guided endonucleases to introduce double-strand breaks at specific genomic locations (Figure 5) (95). These breaks activate the cell’s natural repair mechanisms, allowing for either gene disruption or the insertion of desired genetic material. This precision editing opens the door for innovative therapeutic approaches, such as silencing oncogenes or reactivating tumor suppressor genes, with a level of specificity that surpasses pervious gene-editing tools.

Figure 5 Overview of CRISPR technology in personalized medicine for cancer treatment. This figure explains the mechanism, applications, advances, challenges, and limitations of CRISPR/Cas9 technology in personalized cancer therapies. The left section illustrates the CRISPR technology, showing how RNA-guided endonucleases bind to specific DNA sequences, create double-strand breaks, and facilitate gene editing through the cell’s natural repair processes. Applications in cancer treatment include correcting oncogenic mutations (e.g., TP53, KRAS), enhancing immune responses with engineered T-cells for CAR-T therapy, and using multiplexed targeting strategies to address tumor heterogeneity. CRISPR technology offers advantages over traditional methods for improving personalized medicine, but also presents challenges and limitations that may be addressed through integration of AI and ML to analyze large genomic data sets and predict patient outcomes. AI, artificial intelligence; CAR, chimeric antigen receptor; CRISPR, clustered regularly interspaced short palindromic repeats; ML, machine learning.

A significant application of CRISPR technology lies in its potential for screening and correcting mutations that drive cancer development (98). Many cancers are fueled by specific genetic aberrations that confer growth advantages. Preclinical studies have demonstrated the feasibility of correcting such mutations in genes like TP53, PTEN, KRAS, and ARID1A using CRISPR-Cas9 systems, restoring their tumor-suppressive functions or altering oncogenic signaling pathways (99,100).

CRISPR also enables combinatorial targeting of multiple genes within a signaling network, addressing the complexity of cancer biology and drug resistance (98). By designing multiplexed CRISPR strategies, researchers can disrupt several genes simultaneously, which may help overcome tumor heterogeneity and reduce the likelihood of therapeutic escape mechanisms.

A pivotal moment in the evolution of genome editing was recently marked by regulatory approval. In 2023, the FDA approved CASGEVY^TM, the first CRISPR/Cas9-based gene therapy. This marks a major milestone in clinical gene editing. CASGEVY is a one-time, cell-based therapy for patients aged 12 years and older with sickle cell disease and recurrent vaso-occlusive crises (101). The treatment involves editing the patient’s hematopoietic stem cells to reduce the expression of the BCL11A gene, a repressor of γ-globin. This results in increased fetal hemoglobin production, effectively mitigating disease symptoms (102). The success of CASGEVY^TM demonstrates the therapeutic viability of CRISPR-Cas9 and is likely to inspire future gene-editing-based treatments for other genetic diseases, including many forms of cancer.

In addition to correcting mutations, CRISPR technology is actively being explored to enhance immunotherapy, a transformative field in cancer treatment. While immune checkpoint inhibitors have shown remarkable efficacy against certain malignancies (103), responses can be inconsistent due to variability within tumor microenvironments. Researchers are investigating how they can use CRISPR tools not only to enhance anti-tumor immunity but also engineer immune cells (e.g., T-cells) so they better recognize and eliminate malignant cells based on their unique genetic signatures (104).

One example is chimeric antigen receptor (CAR)-T cell therapy, where a patient’s T-cells are modified ex vivo using viral vectors to target tumor-specific antigens like CD19, found in B-cell malignancies such as acute lymphoblastic leukemia (ALL) (105,106). However, the use of viral vectors carries risks of off-target effects and presents logistical challenges in clinical production (107). CRISPR/Cas9 offers a more precise and scalable alternative, potentially increasing safety and expanding applicability across a wider range of solid and hematologic tumors.

Despite the remarkable progress, challenges remain. Ethical concerns surrounding human genome manipulating must be addressed within robust regulatory frameworks before clinical implementation (108). Equitable access to CRISPR-based therapies and diverse representation in clinical trials are also critical to ensure that innovations benefit all populations.

Another area requiring continued focus is the safe and efficient delivery of CRISPR components to target tissues or cells without affecting healthy adjacent tissue. Current delivery methods, such as liposomes, nanoparticles, and plasmid DNA constructs, rely heavily on traditional transfection techniques, which may be inadequate given biological complexities (95). Future advancements may stem from integrating emerging technologies like nanotechnology, biomaterials, and principles from synthetic biology, aimed at optimizing targeting efficiency while minimizing off-target effects and unintended consequences (95,109).

In conclusion, the convergence of genome editing, biotechnology, and personalized medicine principles is reshaping the future of cancer care. Tools like CRISPR not only provide unmatched precision in modifying disease-driving genes but also support broader strategies involving immune modulation and combination therapies. The approval of CASGEVY^TM, the first CRISPR therapy, illustrates that we are entering a new era where gene editing can revolutionize treatment approaches. Despite ongoing challenges related to ethics, access, safety, and clinical translation, continued interdisciplinary collaboration will be crucial in realizing the full potential of CRISPR-based personalized therapies and enhancing outcomes for cancer patients globally.

Artificial intelligence (AI) and ML in personalized cancer therapy

The integration of AI and ML into personalized medicine is rapidly transforming treatment selection in cancer care (110). As the volume of genomic and clinical data continues to grow, AI algorithms offer a powerful means of extracting meaningful patterns and insights that may be difficult or impossible for human researchers to discern (47). By leveraging predictive analytics, AI can assist clinicians in identifying the most effective, individualized treatment options based on a patient’s unique genetic profile, tumor characteristics, and clinical history.

One of the most significant advantages of AI and ML lies in their capacity to process and analyze vast, heterogeneous datasets at unprecedented speed and scale (47). Cancer research generates diverse data types, including genomic and transcriptomic sequences, proteomic signatures, clinical trial outcomes, imaging data, and demographic information. Advanced AI techniques are capable of integrating and analyzing this multimodal data to provide a more holistic understanding of each patient’s condition.

Moreover, AI-driven platforms are revolutionizing biomarker discovery, a critical step in developing targeted therapies (111). ML algorithms can sift through large-scale datasets, derived from clinical trials, biobanks, or real-world evidence, to identify novel biomarkers associated with specific cancer subtypes or therapeutic responses. These insights enable clinicians to stratify patients more precisely and select therapies with higher probabilities of success based on individual molecular features.

Another exciting application of AL in oncology is in the optimizing combination therapies (49), a strategy commonly employed to overcome tumor resistance. AI models trained on historical treatment outcomes can predict synergistic interactions between therapeutic agents, allowing the development of personalized drug regimens that maximize efficacy while minimizing toxicity. In addition, AI-powered survival analysis models are being used to predict patient outcomes by incorporating factors such as genetic mutations, treatment history, comorbidities, and demographic variables (112). These tools support risk stratification and aid in tailoring treatment intensity, surveillance strategies, and follow-up care to patient-specific prognostic profiles.

Despite the clear potential benefits of AI and ML, several challenges must be addressed to facilitate seamless integration into existing workflows. Issues related to data interoperability, standardization, and system compatibility must be overcome to ensure the reliability and scalability of AI applications in healthcare settings (113,114). Equally important are concerns surrounding data privacy and cybersecurity, given the sensitive nature of genomic and health-related information. Adherence to stringent regulatory frameworks is essential to protect patient confidentiality while enabling responsible data sharing for innovation.

As discussed, ethical implications of AI applications in clinical decision-making require ongoing scrutiny. Transparency and accountability in algorithmic predictions are crucial for building trust among patients and providers. Ensuring fairness and mitigating biases within AI models are necessary to prevent disparities in care and to promote equitable access to advanced technologies.

As AI continues to evolve, interdisciplinary collaboration is vital for ensuring its responsible and effective application (115,116). Engaging experts from diverse fields, including bioinformatics, oncology, computational biology, ethics, law, and patient advocacy, can guide the development of AI solutions that are both scientifically sound and socially responsible. Incorporating stakeholder feedback throughout the design and implementation process also fosters the creation of user-friendly, accessible interfaces that promote adoption across varied clinical settings.

In conclusion, the convergence of AI and ML with advances in genomics and biotechnology heralds a new era of personalized cancer therapy. Intelligent systems are poised to enhance decision-making, improve treatment precision, and significantly extend survival rates by aligning therapeutic strategies with individual patient needs. As these technologies mature, ensuring ethical, equitable, and interdisciplinary integration will be key to realizing their full potential in transforming cancer care worldwide.

Strengths and limitations

This review highlights significant advancements in personalized medicine, particularly in the context of cancer treatment, by synthesizing current knowledge on genomic profiling and targeted therapies. Its strengths lie in the comprehensive overview of emerging technologies such as CRISPR and AI, which are reshaping treatment paradigms and enhancing the precision of therapeutic strategies. However, limitations include the potential for selection bias in included studies and the challenge of generalizing findings across diverse patient populations due to variations in access to genomic testing and targeted therapies. Additionally, while the review discusses technological innovations, it may not fully address the ethical implications and practical challenges associated with implementing these advancements in clinical practice. Finally, economic aspects are not discussed which might be a limitation, specifically in low-income countries.

Comparison with similar research

Several recent reviews have documented significant advancements in personalized medicine within oncology (117-120). Building upon this growing body of literature, our scoping review offers a comprehensive and up-to-date synthesis of recent innovations, with a particular emphasis on advances in genomic profiling and targeted therapies for cancer treatment.

What distinguishes our review from previous works is its holistic approach, which not only synthesizes established practices but also integrates insights from emerging technologies that are shaping the future of precision oncology. Specifically, we highlight the transformative roles of CRISPR gene editing and AI, exploring how these innovations are being incorporated into clinical decision-making to support more accurate, individualized treatment strategies.

Our analysis further addresses critical implementation challenges, including issues related to data interpretation, accessibility, and integration into routine clinical workflows, areas that are often underrepresented in prior reviews. By contextualizing these barriers within the broader framework of precision oncology, we provide practical insights that may inform future research, policy development, and clinical application.

Moreover, our review introduces an up-to-date conceptual model and practice-oriented recommendations for integrating personalized medicine into oncology care. This framework is designed to guide clinicians, researchers, and healthcare systems in navigating the complexities of translating genomic insights into actionable, patient-centered therapies.

Explanations of findings

The findings of this review emphasize the transformative impact of genomic profiling on personalized medicine, particularly in cancer treatment. By identifying specific genetic alterations that drive tumor growth, clinicians can tailor targeted therapies for individual patients, enhancing treatment effectiveness and overall outcomes. For example, identifying actionable mutations like EGFR or BRAF alterations allows the selection of treatments that target the molecular pathways involved in cancer progression. Additionally, advancements in technologies such as NGS have significantly improved our ability to conduct comprehensive genomic analyses, providing deeper insights into tumor heterogeneity and resistance mechanisms. The integration of AI and ML into bioinformatics further enhances this process by enabling more accurate interpretation of complex genomic data and facilitating optimal treatment selection based on patient-specific profiles. Together, these points illustrate how leveraging genomic insights not only guides clinical decision-making but also paves the way for innovative therapeutic strategies that show promise for enhancing patient care in oncology moving forward.

Implications and actions needed

The implications of this review are profound, as they highlight the necessity for a paradigm shift in cancer treatment towards a more personalized approach rooted in genomic insights. To fully realize the potential of personalized medicine, several actions are needed. First, there must be an increased emphasis on expanding access to genomic testing and targeted therapies across diverse populations to ensure equitable healthcare delivery. This includes addressing barriers such as cost, availability of testing facilities, and training for healthcare professionals in interpreting genomic data effectively. Additionally, fostering interdisciplinary collaboration among oncologists, geneticists, bioinformaticians, and policymakers is crucial to develop comprehensive frameworks that integrate genomic profiling into routine clinical practice. Furthermore, ongoing research should focus on refining bioinformatics tools and developing robust guidelines for the ethical application of emerging technologies like CRISPR and AI in patient care. By taking these steps, the medical community can enhance treatment personalization and improve outcomes for cancer patients globally.

Conclusions

Personalized medicine is transforming the field of oncology, with genomic profiling emerging as a key factor in customizing cancer treatments for individual patients. The integration of advanced technologies like NGS, CRISPR-based gene editing, and AI has sped up the progress of identifying actionable mutations, predicting treatment outcomes, and creating more precise therapeutic regimens. However, despite these advancements, the path to fully integrated precision oncology remains intricate. Looking forward, a practical vision for personalized cancer care involves tackling various crucial challenges. These challenges include ensuring fair access to genomic testing, improving healthcare infrastructure, enhancing clinician education, and establishing solid ethical and regulatory frameworks to govern the use of genomic data. Moreover, the transition of research breakthroughs into everyday clinical practice requires collaboration across disciplines, consistent funding, and strategies that prioritize the need of patients. To truly redefine oncology care, future initiatives must prioritize not only innovation but also inclusivity, scalability, and sustainability. Personalized medicine will not be a one-size-fits-all solution; it will require flexible models that can adapt alongside scientific advancements and healthcare system capabilities. By aligning technological progress with practical and ethical considerations, we can move closer to a future where cancer treatment is not only more effective, but also more equitable, accessible, and responsive to the diverse needs of patients worldwide.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA-ScR reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-25-34/rc

Peer Review File: Available at https://atm.amegroups.com/article/view/10.21037/atm-25-34/prf

Funding: This work was supported by grants from the German Research Foundation (project WE2554/17-1 to R.W.), the Deutsche Krebshilfe (grant 70115581 to R.W.), and a grant from the Interdisciplinary Centre for Clinical Research within the Faculty of Medicine at the RWTH Aachen University (grant PTD 1-5 to R.W.).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-25-34/coif). R.W. serves as an unpaid editorial board member of Annals of Translational Medicine from August 2024 to July 2026. The other author has no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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