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This report is written by MaltSci based on the latest literature and research findings

How does precision oncology work? ​

Abstract ​

Precision oncology is a transformative approach to cancer treatment that tailors therapies to the individual genetic and molecular profiles of patients' tumors. Unlike traditional cancer therapies that employ a uniform treatment strategy, precision oncology acknowledges the heterogeneous nature of cancer, characterized by distinct genetic alterations among patients. Recent advancements in genomic sequencing technologies and bioinformatics have enabled the identification of actionable mutations, paving the way for targeted therapies that enhance treatment efficacy while minimizing adverse effects. This report explores the fundamentals of precision oncology, including its definition, historical evolution, and the role of genomic profiling in cancer treatment. Techniques such as next-generation sequencing and liquid biopsies are highlighted for their ability to monitor tumor evolution and treatment response in real time. The report also discusses various targeted therapies, including small molecule inhibitors, monoclonal antibodies, and immunotherapies, which are designed to disrupt specific molecular pathways driving cancer progression. Additionally, the integration of artificial intelligence in genomic data analysis and predictive modeling is examined, showcasing its potential to refine treatment strategies and improve patient outcomes. Despite the promising advancements, challenges such as the identification of robust biomarkers and the need for comprehensive data integration remain. The report concludes with a forward-looking perspective on the future directions of precision oncology, emphasizing the importance of continued research and innovation to enhance its application in clinical practice. By personalizing cancer treatment based on individual tumor characteristics, precision oncology holds the potential to revolutionize cancer care and significantly improve patient survival rates.

Outline ​

This report will discuss the following questions.

  • 1 Introduction
  • 2 The Fundamentals of Precision Oncology
    • 2.1 Definition and Scope
    • 2.2 Historical Context and Evolution
  • 3 Genomic Profiling in Cancer Treatment
    • 3.1 Techniques for Genomic Sequencing
    • 3.2 Interpretation of Genetic Data
  • 4 Targeted Therapies and Their Mechanisms
    • 4.1 Types of Targeted Therapies
    • 4.2 Case Studies and Clinical Applications
  • 5 The Role of Artificial Intelligence in Precision Oncology
    • 5.1 AI in Genomic Data Analysis
    • 5.2 Predictive Modeling for Treatment Outcomes
  • 6 Challenges and Future Directions
    • 6.1 Ethical Considerations
    • 6.2 Integration into Clinical Practice
  • 7 Summary

1 Introduction ​

Precision oncology represents a paradigm shift in cancer treatment, aiming to tailor therapeutic strategies based on the individual genetic profile of a patient's tumor. Unlike traditional cancer therapies that often adopt a one-size-fits-all approach, precision oncology recognizes the heterogeneous nature of cancer, which is characterized by distinct genetic alterations and variations across different patients. The advancement of genomic sequencing technologies and bioinformatics has significantly enhanced our ability to identify specific mutations and alterations in tumor DNA, thereby paving the way for targeted therapies that not only improve treatment efficacy but also minimize adverse effects on healthy tissues [1][2].

The significance of precision oncology lies in its potential to optimize treatment outcomes and reshape the landscape of cancer care. By integrating multidisciplinary teams—including oncologists, geneticists, and data scientists—precision oncology fosters the development of comprehensive treatment plans that take into account the patient's genetic makeup, tumor characteristics, and responses to previous treatments [3]. This approach is particularly vital in an era where cancer is increasingly understood as a collection of diseases, each requiring a unique therapeutic strategy [4]. The ability to match patients with the most effective treatments based on their specific genetic profiles is crucial for improving survival rates and enhancing the quality of life for cancer patients [5].

Current research in precision oncology has led to the emergence of various genomic profiling techniques that facilitate the identification of actionable mutations and therapeutic targets. Techniques such as next-generation sequencing (NGS) and liquid biopsies have become instrumental in this process, allowing for real-time monitoring of tumor evolution and treatment response [6][7]. These advancements not only provide insights into the genetic landscape of tumors but also enable the development of targeted therapies that can specifically inhibit the growth of cancer cells harboring particular mutations [8]. However, despite these promising developments, challenges remain, including the identification of robust biomarkers for many therapies and the need for comprehensive data integration to guide clinical decision-making [9].

The structure of this report is organized to provide a comprehensive overview of precision oncology. We will begin with an exploration of the fundamentals of precision oncology, including its definition, scope, and historical evolution. Following this, we will delve into the role of genomic profiling in cancer treatment, examining the techniques employed for genomic sequencing and the interpretation of genetic data. Next, we will discuss targeted therapies and their mechanisms, supported by case studies and clinical applications. The report will also highlight the role of artificial intelligence in precision oncology, particularly in genomic data analysis and predictive modeling for treatment outcomes. Finally, we will address the challenges faced in the implementation of precision oncology and outline future directions for research and clinical practice.

By elucidating these components, this report aims to enhance our understanding of how precision oncology works and its potential to revolutionize cancer treatment. As we continue to advance our knowledge and technologies in this field, the promise of personalized medicine becomes increasingly attainable, offering hope for improved patient outcomes and a more effective approach to cancer care.

2 The Fundamentals of Precision Oncology ​

2.1 Definition and Scope ​

Precision oncology, also known as personalized oncology or precision medicine, is a sophisticated approach to cancer treatment that tailors therapies to individual patients based on the specific genetic, molecular, and other unique characteristics of their tumors. This strategy recognizes the high heterogeneity of cancer, wherein each patient's tumor possesses distinct genetic variations. The primary objective of precision oncology is to enhance the effectiveness of cancer treatments while minimizing associated toxicities and improving patient outcomes [1].

At the core of precision oncology lies the hypothesis that cancer treatment can be significantly improved if therapies are guided by the genomic alterations present in a tumor. This hypothesis has led to substantial initiatives that focus on whole-genome and exome sequencing, the creation of extensive databases, and the development of analytical tools to identify actionable genetic alterations. Such alterations serve as molecular targets for therapy [4]. The identification of critical gene mutations, such as BRCA1/2, has been instrumental in advancing precision diagnosis in cancer, facilitating the development of targeted therapies that directly address these mutations [2].

Precision oncology operates on the principle of matching the most effective treatments to individual patients based on their tumor-specific molecular features. This includes identifying "druggable" genomic alterations, understanding the downstream activities of cancer-signaling pathways, and analyzing the expression of specific genes involved in tumorigenesis and cancer progression [3]. The ultimate aim is to deliver the right drug to the right patient at the right time, maximizing treatment efficacy while minimizing exposure to ineffective therapies and reducing co-morbidity-related side effects [3].

Despite its promise, precision oncology faces several challenges. Many existing therapies lack clear predictive biomarkers, which can hinder their application and effectiveness [1]. Additionally, the emergence of drug resistance and the incomplete understanding of cancer pathogenesis complicate the implementation of precision oncology strategies [5]. Nevertheless, advancements in genomic analysis, drug development, and clinical trial design are expected to propel precision oncology toward becoming the standard of care in cancer therapeutics [2].

In summary, precision oncology represents a paradigm shift in cancer treatment, focusing on the individualization of therapy based on the unique molecular profile of each patient's tumor. This approach not only enhances the potential for improved outcomes but also addresses the complexities inherent in cancer treatment, striving to create a more effective and personalized therapeutic landscape.

2.2 Historical Context and Evolution ​

Precision oncology operates on the premise of tailoring cancer treatment to the individual characteristics of each patient's tumor, thereby optimizing therapeutic efficacy while minimizing adverse effects. This approach is fundamentally rooted in the understanding that cancer is not a single disease but a collection of highly heterogeneous conditions, each defined by unique genetic, molecular, and phenotypic characteristics. The historical evolution of precision oncology has been marked by significant advancements in genomic analysis and the development of targeted therapies.

At its core, precision oncology seeks to match patients with the most effective treatments based on specific genetic alterations found in their tumors. This methodology is driven by the hypothesis that therapies guided by a tumor's genomic profile will lead to better treatment outcomes compared to traditional, one-size-fits-all approaches. Major initiatives have emerged, focusing on whole-genome and exome sequencing, which have led to the creation of extensive databases and tools for statistical analysis aimed at identifying actionable alterations and molecular targets in patients' tumors (Nussinov et al., 2019) [4].

The implementation of precision oncology has resulted in the emergence of "haves" and "have nots" within the realm of cancer therapies. While numerous targeted therapies with approved companion diagnostics are available—enabling predictive biomarkers to guide clinical decision-making—many treatments still lack clear biomarkers, which complicates the application of precision oncology (Beecher et al., 2025) [1]. The disparity between these two categories underscores the challenges that persist in fully realizing the potential of precision medicine in oncology.

The historical context of precision oncology can be traced back to the identification of key gene mutations, such as BRCA1 and BRCA2, which have significantly influenced cancer diagnosis and treatment paradigms. These findings have paved the way for the development of targeted therapies that directly address specific genetic alterations, thereby improving patient outcomes (Shin et al., 2017) [2]. The evolution of treatment paradigms has also shifted from the use of nonspecific cytotoxic drugs to more personalized approaches that consider the unique molecular features of each patient's tumor, thus maximizing treatment efficacy and minimizing unnecessary side effects (DasGupta et al., 2023) [3].

Furthermore, the integration of advanced technologies such as next-generation sequencing and machine learning has facilitated the identification of effective therapeutic regimens. These technologies enable the analysis of vast amounts of genomic data, allowing for the identification of patterns and mutations that inform treatment decisions. For instance, machine learning methods have been employed to predict drug efficacy based on genome-scale omics data, enhancing the ability to match patients with suitable therapies, regardless of whether the drugs are molecularly targeted or conventional chemotherapeutics (Ding et al., 2018) [10].

In summary, precision oncology represents a paradigm shift in cancer treatment, characterized by its focus on individualized therapy based on the distinct molecular landscape of each patient's tumor. The historical development of this field has been marked by significant advancements in genomic technologies, the identification of critical biomarkers, and the evolution of treatment strategies that prioritize patient-specific characteristics. Despite the progress made, challenges remain in the form of incomplete biomarker identification and the need for further research to enhance the efficacy and applicability of precision oncology across diverse cancer types.

3 Genomic Profiling in Cancer Treatment ​

3.1 Techniques for Genomic Sequencing ​

Precision oncology operates by utilizing genomic profiling to tailor cancer treatment to the individual characteristics of each patient's tumor. This approach is grounded in the identification of specific genetic mutations and alterations within cancer cells that can influence treatment decisions. The fundamental goal is to enhance therapeutic efficacy while minimizing adverse effects by matching the most effective treatments to the unique molecular makeup of each tumor.

Genomic profiling involves several advanced techniques, primarily next-generation sequencing (NGS), which has significantly transformed the landscape of cancer diagnostics and therapeutics. NGS allows for the comprehensive analysis of a tumor's genetic information by generating vast amounts of sequencing data from tumor samples. This includes whole-exome sequencing, which focuses on the protein-coding regions of the genome, and whole-genome sequencing, which examines the entire genetic blueprint of the tumor. Additionally, RNA sequencing (RNA-seq) provides insights into gene expression profiles, further informing treatment decisions[11].

The integration of these genomic technologies enables the identification of actionable mutations, such as those in genes like KRAS, BRAF, and PIK3CA, which are crucial in regulating cell signaling pathways related to proliferation and survival. This information is pivotal for selecting targeted therapies that specifically inhibit the aberrant pathways activated by these mutations, thus optimizing treatment effectiveness[12].

Moreover, precision oncology leverages molecular profiling techniques beyond traditional DNA sequencing. Approaches such as transcriptomics, immunophenotyping, and epigenetic profiling are increasingly utilized to provide a more comprehensive view of the tumor's molecular landscape. Liquid biopsies, which analyze circulating tumor DNA (ctDNA) and other biomarkers in the blood, have emerged as a non-invasive method for monitoring tumor dynamics and treatment response[13].

Despite the promise of precision oncology, several challenges persist. These include logistical and regulatory hurdles, the high costs associated with genomic testing, and the need for standardized protocols across clinical settings. Furthermore, tumor heterogeneity and the complexity of the tumor microenvironment complicate treatment strategies, as these factors can lead to variations in treatment response and drug resistance[14].

To address these challenges, ongoing research is focusing on the development of more effective genomic testing frameworks, the integration of artificial intelligence for data interpretation, and the establishment of adaptive clinical trial designs that can accommodate the evolving landscape of precision medicine[15]. As these advancements continue, precision oncology is expected to become a cornerstone of cancer care, offering tailored therapeutic strategies that improve patient outcomes and enhance the quality of life for individuals battling cancer.

3.2 Interpretation of Genetic Data ​

Precision oncology operates by tailoring cancer treatment to individual patients based on the specific genetic, molecular, and other unique characteristics of their tumors. This approach aims to optimize treatment effectiveness while minimizing toxicities and improving patient outcomes. The foundational principle of precision oncology is the use of genomic profiling, which involves analyzing the genetic makeup of tumors to identify specific mutations and alterations that drive cancer progression.

Genomic profiling utilizes advanced technologies such as next-generation sequencing (NGS) to obtain comprehensive insights into the genetic landscape of a patient's cancer. By sequencing specific panels of genes, exomes, or even the entire genome, clinicians can identify critical mutations in genes such as KRAS, BRAF, and PIK3CA. These mutations are often linked to key signaling pathways that regulate cell proliferation, apoptosis, and differentiation. This information is crucial as it enables oncologists to select targeted therapies that specifically inhibit the molecular pathways altered by these mutations, thereby maximizing treatment efficacy and minimizing adverse effects (Muhar et al. 2025; Yoon & Lee 2021).

In addition to genomic data, precision oncology increasingly incorporates other forms of data, such as epigenomic and transcriptomic information, to create a more holistic understanding of the tumor biology. The integration of these diverse data types can reveal complex interactions within the tumor microenvironment and identify potential therapeutic vulnerabilities. This multifaceted approach enhances the ability to predict patient responses to specific treatments and guides the development of personalized treatment regimens (Asada et al. 2021; Konda et al. 2023).

However, the interpretation of genetic data poses significant challenges. The identification of driver mutations and their implications for treatment must be communicated effectively to clinicians who may not have specialized training in genomics. Moreover, while genomic tests can provide insights into cancer risk, prognosis, and therapeutic options, their clinical utility is often underutilized due to difficulties in interpreting results and a lack of standardization in clinical practice (Casolino et al. 2024). Practical guidelines are essential to aid clinicians in understanding and integrating genomic information into their decision-making processes (Shin et al. 2017).

Furthermore, the rapid evolution of precision oncology is facilitated by advancements in machine learning and artificial intelligence, which are employed to analyze vast amounts of genomic data. These technologies can identify predictive biomarkers and therapeutic targets, thereby expanding the potential of precision oncology beyond traditional targeted therapies. For instance, machine learning frameworks have been developed to predict responses to specific cancer drugs based on genomic signatures, significantly enhancing the personalization of treatment (Ding et al. 2018; Gunturu et al. 2024).

In summary, precision oncology is a dynamic and evolving field that leverages genomic profiling and advanced data analytics to customize cancer treatment. By integrating genetic, epigenetic, and clinical data, precision oncology aims to improve therapeutic outcomes for patients, although challenges in data interpretation and clinical application remain significant hurdles to widespread implementation.

4 Targeted Therapies and Their Mechanisms ​

4.1 Types of Targeted Therapies ​

Precision oncology operates on the principle of tailoring cancer treatment to the individual characteristics of each patient's tumor. This approach is grounded in the understanding that cancer is not a uniform disease; rather, it exhibits significant heterogeneity at the genetic and molecular levels. By leveraging the unique genetic, molecular, and other characteristics of a tumor, precision oncology aims to optimize treatment effectiveness while minimizing adverse effects.

Targeted therapies are a cornerstone of precision oncology, designed to interfere with specific molecules involved in tumor growth and progression. These therapies can be categorized into several types based on their mechanisms of action:

  1. Small Molecule Inhibitors: These drugs typically target specific proteins or enzymes that play a crucial role in cancer cell proliferation and survival. For example, tyrosine kinase inhibitors (TKIs) block the action of enzymes that can promote cancer cell growth. This type of targeted therapy is particularly effective against tumors with specific mutations that drive their growth, such as those found in non-small cell lung cancer (NSCLC) or chronic myeloid leukemia (CML).

  2. Monoclonal Antibodies: These are engineered proteins that can specifically bind to antigens on the surface of cancer cells. By binding to these targets, monoclonal antibodies can block signaling pathways that promote tumor growth or mark cancer cells for destruction by the immune system. For instance, trastuzumab (Herceptin) targets the HER2 receptor, which is overexpressed in some breast cancers, thereby inhibiting tumor growth.

  3. Immunotherapies: This class of targeted therapies harnesses the body’s immune system to fight cancer. Immune checkpoint inhibitors, such as pembrolizumab (Keytruda) and nivolumab (Opdivo), work by blocking proteins that inhibit immune responses, thereby allowing T cells to recognize and attack cancer cells more effectively. This approach has transformed the treatment landscape for various cancers, including melanoma and lung cancer.

  4. Hormonal Therapies: These therapies are used primarily in cancers that are hormone-sensitive, such as breast and prostate cancers. For example, aromatase inhibitors and selective estrogen receptor modulators (SERMs) are employed in the treatment of estrogen receptor-positive breast cancer by blocking the effects of estrogen on tumor growth.

  5. Gene Therapy: This innovative approach aims to correct or replace faulty genes responsible for cancer development. By introducing genetic material into a patient's cells, gene therapy can restore normal function or enhance the immune response against cancer.

  6. Organelle-targeted therapies: Recent advancements have introduced strategies that specifically target cellular organelles to induce cancer cell death. These therapies utilize the unique characteristics of organelles, such as mitochondria or lysosomes, to deliver cytotoxic agents more precisely, thereby reducing side effects and enhancing therapeutic efficacy [16].

Each of these targeted therapies relies on the identification of specific biomarkers that indicate which patients are likely to benefit from the treatment. Biomarkers can include genetic mutations, protein expressions, or other molecular characteristics of the tumor. The successful application of targeted therapies often hinges on the ability to accurately profile these biomarkers, which is increasingly facilitated by advancements in genomic sequencing and bioinformatics [9].

In summary, precision oncology utilizes a range of targeted therapies that are designed to exploit the unique molecular and genetic features of individual tumors. By focusing on specific pathways and targets, these therapies offer the potential for more effective and personalized cancer treatment, ultimately improving patient outcomes.

4.2 Case Studies and Clinical Applications ​

Precision oncology operates on the principle of tailoring cancer treatment to individual patients based on the unique genetic, molecular, and other characteristics of their tumors. This personalized approach aims to optimize treatment effectiveness while minimizing side effects, recognizing the heterogeneous nature of cancer and the distinct genetic diversity of each patient's tumor. Precision oncology encompasses several methodologies, including the use of targeted therapies, genomic profiling, and the integration of advanced technologies.

Targeted therapies are designed to specifically target genetic alterations within cancer cells. For instance, advancements in "omics" techniques, such as next-generation sequencing, have enabled the identification of specific genetic mutations and pathways involved in tumorigenesis. These targeted therapies can include small molecules, monoclonal antibodies, and immunotherapies that directly inhibit the function of specific proteins or pathways that drive cancer progression. The goal is to disrupt the growth and survival of cancer cells while sparing normal cells, thereby improving patient outcomes and reducing toxicity associated with conventional chemotherapy [1].

Clinical applications of precision oncology have demonstrated significant promise. For example, in breast cancer, precision medicine is evolving to ensure individualized treatment regimens based on a thorough assessment of tumor biology, risk of recurrence, and response to therapy [9]. The implementation of precision oncology in pediatric cancer is also gaining momentum, with the FDA approving targeted drugs for pediatric indications, thereby expanding treatment options for high-risk patients [17].

Case studies illustrate the practical applications of precision oncology. For instance, the use of functional precision medicine involves directly testing live tumor cells from patients with various drugs to identify vulnerabilities and determine the most effective therapeutic combinations. This approach contrasts with traditional methods that rely solely on static tumor features, providing real-time insights into tumor behavior and drug response [18]. Additionally, machine learning techniques applied to genomic data can predict drug efficacy across various cancer cell lines, enhancing the ability to match patients with the most suitable treatments [10].

Challenges remain in the broader implementation of precision oncology. Many first-line chemotherapy drugs lack clear biomarkers, complicating treatment decisions. Moreover, the need for robust bioinformatics tools and standardized frameworks for patient management is critical for the successful integration of precision therapies into clinical practice [19].

In summary, precision oncology leverages a combination of targeted therapies, genomic profiling, and innovative technologies to provide personalized cancer treatment. The ongoing development of these strategies aims to improve therapeutic outcomes, reduce adverse effects, and ultimately transform cancer care into a more individualized and effective approach.

5 The Role of Artificial Intelligence in Precision Oncology ​

5.1 AI in Genomic Data Analysis ​

Precision oncology operates on the principle of tailoring cancer treatment to the individual characteristics of each patient, particularly focusing on the genetic and molecular profiles of their tumors. This approach is greatly enhanced by the integration of artificial intelligence (AI) technologies, which facilitate the analysis of complex genomic data and the extraction of clinically actionable insights.

The advent of next-generation sequencing (NGS) technologies has significantly expanded the availability of genetic information, marking the beginning of the big-data era in cancer genomics. AI approaches, including machine learning, deep learning, and natural language processing (NLP), are being employed to manage the challenges associated with the scalability and high dimensionality of genomic data. These technologies transform vast datasets into clinically relevant knowledge, which is foundational for precision medicine (Xu et al., 2019) [20].

AI's role in genomic data analysis encompasses several key applications. For instance, AI models are used for the computational prediction of pathogenic variants in cancer susceptibility genes, mutational analysis, and the identification of gene targets in cancer through text mining (Lin et al., 2022) [21]. The integration of AI with clinical patient data—such as electronic medical records, physiological measurements, and imaging—enables a comprehensive understanding of a patient's cancer, allowing for more precise treatment strategies (Fountzilas et al., 2025) [22].

Furthermore, AI technologies facilitate the analysis of multi-dimensional data, including multiomic and spatial pathology data, which enhances the understanding of molecular pathways involved in cancer. This capability aids in identifying critical nodes within tumor biology, optimizing treatment selection, and ultimately improving patient outcomes (Fountzilas et al., 2025) [22]. The ability to generate synthetic data, such as digital twins, also plays a crucial role in designing and expediting clinical trials, thus enhancing the efficiency of the precision oncology workflow (Fountzilas et al., 2025) [22].

In the context of breast cancer management, AI has demonstrated significant effectiveness in predicting treatment responses and patient survival outcomes. A systematic review indicated that various deep learning methods and machine learning algorithms achieved high accuracy in breast cancer classification, screening, and prognosis, with average accuracy metrics ranging from 90% to 96% (Sohrabei et al., 2024) [23]. These advancements highlight the potential of AI to uncover hidden patterns in complex omics and genetic data, thereby facilitating more personalized treatment approaches.

In summary, precision oncology is fundamentally about customizing cancer care based on individual patient data, with AI serving as a pivotal tool in genomic data analysis. By leveraging AI's capabilities, clinicians can enhance diagnostic accuracy, predict treatment outcomes, and ultimately improve the quality of care for cancer patients, thereby revolutionizing the landscape of oncology (Zhang & Wei, 2023) [24].

5.2 Predictive Modeling for Treatment Outcomes ​

Precision oncology is a tailored approach to cancer treatment that focuses on the individual characteristics of each patient's cancer, including genetic, molecular, and clinical factors. The goal is to provide the most effective treatment for each patient, thereby improving outcomes and minimizing unnecessary side effects. The integration of artificial intelligence (AI) into precision oncology has emerged as a transformative force, enhancing the ability to predict treatment outcomes through advanced predictive modeling techniques.

AI leverages vast amounts of data from various sources, including genomic sequencing, clinical records, and imaging studies, to identify patterns that may not be apparent to human analysts. This data-driven approach enables the development of predictive models that can forecast how individual patients will respond to specific therapies. For instance, studies have shown that AI models can achieve an average area under the curve (AUC) of 0.91, indicating high accuracy in predicting treatment responses [23].

The application of machine learning and deep learning techniques is particularly significant in this context. Various algorithms, such as support vector machines (SVM), random forests (RF), and neural networks, have been employed to analyze complex datasets. In a systematic review, it was found that 26 studies utilizing machine learning methods improved breast cancer classification, screening, diagnosis, and prognosis, with reported accuracy, sensitivity, specificity, and precision values ranging from 90% to 96% [23].

Moreover, AI has been instrumental in identifying prognostic biomarkers and optimizing therapeutic strategies. By analyzing multi-omics data, AI can facilitate the classification of tumors based on their molecular profiles, thereby aiding in the selection of targeted therapies that are most likely to be effective for a particular patient. For example, AI tools have been shown to improve the accuracy and efficiency of tumor detection and classification, as well as to predict treatment responses [25].

In the context of breast cancer, a novel AI pipeline was developed to predict neoadjuvant chemotherapy responses using hematoxylin and eosin-stained tissue images. This approach combined multiple machine learning models to capture different aspects of cancer atypia, achieving a remarkable prediction accuracy of 95.15% [26]. Such advancements illustrate how AI can refine treatment selection and personalize therapy based on individual tumor characteristics.

The convergence of AI technologies with precision oncology not only enhances predictive modeling for treatment outcomes but also addresses challenges associated with cancer heterogeneity. By uncovering hidden patterns in complex omics and genetic data, AI can help clinicians make informed decisions regarding treatment plans, ultimately leading to improved patient survival rates and quality of life [27].

In conclusion, precision oncology, empowered by AI, represents a significant shift towards personalized cancer care. The use of predictive modeling to assess treatment outcomes is central to this paradigm, enabling healthcare providers to tailor interventions that are most likely to succeed for each patient based on their unique cancer profile. The ongoing evolution of AI in this field promises to further enhance the effectiveness of cancer therapies and improve overall patient outcomes.

6 Challenges and Future Directions ​

6.1 Ethical Considerations ​

Precision oncology is a transformative approach in cancer treatment that tailors therapeutic strategies to the unique genetic, molecular, and environmental characteristics of individual patients. This personalized medicine paradigm leverages advancements in next-generation sequencing (NGS), molecular profiling, and biomarker-driven approaches to enhance diagnostic accuracy and optimize treatment selection, thereby improving patient outcomes while minimizing adverse effects associated with conventional treatments[14].

However, the implementation of precision oncology is not without its challenges. Tumor heterogeneity poses a significant obstacle, as the complex interactions within the tumor microenvironment can drive disease progression and treatment resistance[14]. This heterogeneity complicates the translatability of genetic profiling, particularly in cancers such as ovarian cancer, where obtaining adequate and representative tissue samples remains a barrier. Additionally, the development of standardized screening methods and effective data management systems is crucial for successful implementation[28].

Ethical considerations also play a pivotal role in the evolution of precision oncology. As clinical trial designs shift towards smaller, more exclusive early-phase trials, there is a heightened need to address ethical challenges associated with patient consent, data privacy, and health equity. The intricacies of genetic data handling and the potential disparities in access to precision oncology necessitate ongoing dialogue and regulation to protect patients and ensure equitable healthcare delivery[29].

Future directions in precision oncology include the integration of artificial intelligence and multi-omics approaches, which promise to refine diagnostic and therapeutic strategies further. These advancements aim to overcome current challenges and enhance the applicability of precision medicine across diverse cancer types. As research continues to evolve, the goal remains to make tailored treatments more accessible and effective, thereby redefining cancer care and improving patient outcomes on a broader scale[14].

In summary, precision oncology represents a significant shift in cancer treatment, focusing on individualized therapies informed by detailed molecular insights. While it offers promising avenues for improved care, addressing the challenges of tumor heterogeneity, ethical considerations, and the need for ongoing research and innovation is essential for its successful implementation and future growth in clinical practice.

6.2 Integration into Clinical Practice ​

Precision oncology is an innovative approach to cancer treatment that tailors therapies to the individual characteristics of each patient's tumor, based on genetic, molecular, and environmental factors. This paradigm shift from traditional one-size-fits-all treatments aims to enhance therapeutic efficacy while minimizing adverse effects. The foundation of precision oncology lies in advanced technologies such as next-generation sequencing (NGS), molecular profiling, and biomarker-driven approaches, which have significantly improved diagnostic accuracy and optimized treatment selection [14].

However, the integration of precision oncology into clinical practice faces several challenges. One of the most significant obstacles is tumor heterogeneity, which complicates the treatment landscape as different tumor cells may respond differently to therapies. The dynamic interactions within the tumor microenvironment further exacerbate this issue, influencing disease progression and treatment resistance [14]. Additionally, high costs associated with precision medicine, disparities in access to these advanced treatments, and the need for standardization in diagnostic and therapeutic protocols pose further hurdles to widespread adoption [15].

Despite these challenges, there are promising directions for the future of precision oncology. Advances in artificial intelligence and machine learning are expected to enhance data analysis and improve patient stratification. The integration of multi-omics approaches, which combine genomic, transcriptomic, and proteomic data, holds the potential to provide a more comprehensive understanding of individual tumors [30]. Furthermore, innovative clinical trial designs, such as adaptive trials, can facilitate the rapid evaluation of new therapies and enhance the incorporation of real-world evidence into clinical practice [30].

Efforts to overcome barriers to the implementation of precision oncology include developing multicentric networks that facilitate collaboration among healthcare providers, regulatory agencies, and research institutions [30]. These networks can support the establishment of a robust ecosystem necessary for translating precision oncology innovations into clinical benefits for patients. Moreover, the establishment of payer policies that ensure coverage for comprehensive genomic testing will be crucial in promoting the broader application of precision medicine [31].

In conclusion, while precision oncology represents a transformative approach to cancer care, its successful integration into clinical practice requires addressing significant challenges. Continued research, technological advancements, and collaborative efforts among stakeholders will be essential in refining precision oncology and expanding its accessibility to diverse patient populations [1].

7 Conclusion ​

Precision oncology represents a revolutionary shift in cancer treatment, emphasizing the customization of therapeutic strategies based on the unique genetic and molecular profiles of individual tumors. This approach not only enhances treatment efficacy but also minimizes adverse effects, thereby improving patient outcomes. Key findings indicate that genomic profiling, advanced technologies, and targeted therapies are central to the success of precision oncology. However, challenges such as tumor heterogeneity, ethical considerations, and the need for standardized protocols must be addressed to fully realize the potential of this innovative approach. Future research directions include the integration of artificial intelligence and multi-omics strategies to refine diagnostic and therapeutic frameworks, alongside efforts to enhance accessibility and equity in cancer care. The ongoing evolution of precision oncology holds the promise of transforming cancer treatment into a more personalized and effective endeavor, ultimately leading to improved survival rates and quality of life for patients.

References ​

  • [1] Kate Beecher;Tivya Kulasegaran;Sunil R Lakhani;Amy E McCart Reed. Genomic Predictive Biomarkers in Breast Cancer: The Haves and Have Nots.. International journal of molecular sciences(IF=4.9). 2025. PMID:40806432. DOI: 10.3390/ijms26157300.
  • [2] Seung Ho Shin;Ann M Bode;Zigang Dong. Addressing the challenges of applying precision oncology.. NPJ precision oncology(IF=8.0). 2017. PMID:29872710. DOI: 10.1038/s41698-017-0032-z.
  • [3] Ramanuj DasGupta;Aixin Yap;Elena Yong Yaqing;Shumei Chia. Evolution of precision oncology-guided treatment paradigms.. WIREs mechanisms of disease(IF=4.9). 2023. PMID:36168283. DOI: 10.1002/wsbm.1585.
  • [4] Ruth Nussinov;Hyunbum Jang;Chung-Jung Tsai;Feixiong Cheng. Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.. PLoS computational biology(IF=3.6). 2019. PMID:30921324. DOI: 10.1371/journal.pcbi.1006658.
  • [5] Jiaqian Luo;David B Solit. Leveraging real-world data to advance biomarker discovery and precision oncology.. Cancer cell(IF=44.5). 2025. PMID:40154486. DOI: 10.1016/j.ccell.2025.03.012.
  • [6] Ellen Heitzer;Samantha Perakis;Jochen B Geigl;Michael R Speicher. The potential of liquid biopsies for the early detection of cancer.. NPJ precision oncology(IF=8.0). 2017. PMID:29872715. DOI: 10.1038/s41698-017-0039-5.
  • [7] Hyunho Yoon;Sanghoon Lee. Integration of Genomic Profiling and Organoid Development in Precision Oncology.. International journal of molecular sciences(IF=4.9). 2021. PMID:35008642. DOI: 10.3390/ijms23010216.
  • [8] Zannel Blanchard;Elisabeth A Brown;Arevik Ghazaryan;Alana L Welm. PDX models for functional precision oncology and discovery science.. Nature reviews. Cancer(IF=66.8). 2025. PMID:39681638. DOI: 10.1038/s41568-024-00779-3.
  • [9] Eleanor E R Harris. Precision Medicine for Breast Cancer: The Paths to Truly Individualized Diagnosis and Treatment.. International journal of breast cancer(IF=3.0). 2018. PMID:29862084. DOI: 10.1155/2018/4809183.
  • [10] Michael Q Ding;Lujia Chen;Gregory F Cooper;Jonathan D Young;Xinghua Lu. Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.. Molecular cancer research : MCR(IF=4.7). 2018. PMID:29133589. DOI: 10.1158/1541-7786.MCR-17-0378.
  • [11] Ahad Amer Alsaiari. Recent advances in the methods and clinical applications of next-generation sequencing in genomic profiling and precision cancer therapy.. EXCLI journal(IF=4.9). 2025. PMID:39967910. DOI: 10.17179/excli2024-7594.
  • [12] Adi Muradi Muhar;Adrian Joshua Velaro;Arya Tjipta Prananda;Sony Eka Nugraha;Princella Halim;Rony Abdi Syahputra. Precision medicine in colorectal cancer: genomics profiling and targeted treatment.. Frontiers in pharmacology(IF=4.8). 2025. PMID:40083375. DOI: 10.3389/fphar.2025.1532971.
  • [13] Eoghan R Malone;Marc Oliva;Peter J B Sabatini;Tracy L Stockley;Lillian L Siu. Molecular profiling for precision cancer therapies.. Genome medicine(IF=11.2). 2020. PMID:31937368. DOI: 10.1186/s13073-019-0703-1.
  • [14] Rituraj;Rashmi Saxena Pal;Jenny Wahlang;Yogendra Pal;Mvnl Chaitanya;Sumedha Saxena. Precision oncology: transforming cancer care through personalized medicine.. Medical oncology (Northwood, London, England)(IF=3.5). 2025. PMID:40488843. DOI: 10.1007/s12032-025-02817-y.
  • [15] Rita Khoury;Chris Raffoul;Christina Khater;Colette Hanna. Precision Medicine in Hematologic Malignancies: Evolving Concepts and Clinical Applications.. Biomedicines(IF=3.9). 2025. PMID:40722725. DOI: 10.3390/biomedicines13071654.
  • [16] Jingjing Yang;Anthony Griffin;Zhe Qiang;Jie Ren. Organelle-targeted therapies: a comprehensive review on system design for enabling precision oncology.. Signal transduction and targeted therapy(IF=52.7). 2022. PMID:36402753. DOI: 10.1038/s41392-022-01243-0.
  • [17] Mark Marshall;Jennifer Ivanovich;Morgan Schmitt;Amy Helvie;Lisa Langsford;Jennifer Casterline;Michael Ferguson. Pediatric precision oncology: "better three hours too soon than a minute too late".. Frontiers in oncology(IF=3.3). 2023. PMID:38023209. DOI: 10.3389/fonc.2023.1279953.
  • [18] Anthony Letai;Patrick Bhola;Alana L Welm. Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations.. Cancer cell(IF=44.5). 2022. PMID:34951956. DOI: 10.1016/j.ccell.2021.12.004.
  • [19] Young Kwang Chae;Alan P Pan;Andrew A Davis;Sandip P Patel;Benedito A Carneiro;Razelle Kurzrock;Francis J Giles. Path toward Precision Oncology: Review of Targeted Therapy Studies and Tools to Aid in Defining "Actionability" of a Molecular Lesion and Patient Management Support.. Molecular cancer therapeutics(IF=5.5). 2017. PMID:29203694. DOI: 10.1158/1535-7163.MCT-17-0597.
  • [20] Jia Xu;Pengwei Yang;Shang Xue;Bhuvan Sharma;Marta Sanchez-Martin;Fang Wang;Kirk A Beaty;Elinor Dehan;Baiju Parikh. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.. Human genetics(IF=3.6). 2019. PMID:30671672. DOI: 10.1007/s00439-019-01970-5.
  • [21] Peng-Chan Lin;Yi-Shan Tsai;Yu-Min Yeh;Meng-Ru Shen. Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care.. Biomolecules(IF=4.8). 2022. PMID:36009026. DOI: 10.3390/biom12081133.
  • [22] Elena Fountzilas;Tillman Pearce;Mehmet A Baysal;Abhijit Chakraborty;Apostolia M Tsimberidou. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.. NPJ digital medicine(IF=15.1). 2025. PMID:39890986. DOI: 10.1038/s41746-025-01471-y.
  • [23] Solmaz Sohrabei;Hamid Moghaddasi;Azamossadat Hosseini;Seyed Jafar Ehsanzadeh. Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.. BMC cancer(IF=3.4). 2024. PMID:39026174. DOI: 10.1186/s12885-024-12575-1.
  • [24] Zhe Zhang;Xiawei Wei. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy.. Seminars in cancer biology(IF=15.7). 2023. PMID:36796530. DOI: 10.1016/j.semcancer.2023.02.005.
  • [25] A Marra;S Morganti;F Pareja;G Campanella;F Bibeau;T Fuchs;M Loda;A Parwani;A Scarpa;J S Reis-Filho;G Curigliano;C Marchiò;J N Kather. Artificial intelligence entering the pathology arena in oncology: current applications and future perspectives.. Annals of oncology : official journal of the European Society for Medical Oncology(IF=65.4). 2025. PMID:40307127. DOI: 10.1016/j.annonc.2025.03.006.
  • [26] Bin Shen;Akira Saito;Ai Ueda;Koji Fujita;Yui Nagamatsu;Mikihiro Hashimoto;Masaharu Kobayashi;Aashiq H Mirza;Hans Peter Graf;Eric Cosatto;Shoichi Hazama;Hiroaki Nagano;Eiichi Sato;Jun Matsubayashi;Toshitaka Nagao;Esther Cheng;Syed Af Hoda;Takashi Ishikawa;Masahiko Kuroda. Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.. The journal of pathology. Clinical research(IF=3.7). 2023. PMID:36896856. DOI: 10.1002/cjp2.314.
  • [27] Hasan Hashem;Iyad Sultan. Revolutionizing precision oncology: the role of artificial intelligence in personalized pediatric cancer care.. Frontiers in medicine(IF=3.0). 2025. PMID:40458648. DOI: 10.3389/fmed.2025.1555893.
  • [28] Laura R Moffitt;Nazanin Karimnia;Amy L Wilson;Andrew N Stephens;Gwo-Yaw Ho;Maree Bilandzic. Challenges in Implementing Comprehensive Precision Medicine Screening for Ovarian Cancer.. Current oncology (Toronto, Ont.)(IF=3.4). 2024. PMID:39727715. DOI: 10.3390/curroncol31120592.
  • [29] Saksham Gupta;Timothy R Smith;Marike L Broekman. Ethical considerations of neuro-oncology trial design in the era of precision medicine.. Journal of neuro-oncology(IF=3.1). 2017. PMID:28555425. DOI: 10.1007/s11060-017-2502-0.
  • [30] Anders Edsjö;Louise Holmquist;Birgit Geoerger;Frédérique Nowak;Georgy Gomon;Catherine Alix-Panabières;Carolin Ploeger;Ulrik Lassen;Christophe Le Tourneau;Janne Lehtiö;Patrick A Ott;Andreas von Deimling;Stefan Fröhling;Emile Voest;Frederick Klauschen;Rodrigo Dienstmann;Aisha Alshibany;Lillian L Siu;Albrecht Stenzinger. Precision cancer medicine: Concepts, current practice, and future developments.. Journal of internal medicine(IF=9.2). 2023. PMID:37641393. DOI: 10.1111/joim.13709.
  • [31] Lincoln D Nadauld;James M Ford;Daryl Pritchard;Thomas Brown. Strategies For Clinical Implementation: Precision Oncology At Three Distinct Institutions.. Health affairs (Project Hope)(IF=8.1). 2018. PMID:29733728. DOI: 10.1377/hlthaff.2017.1575.

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