Cancer Genetics and Epigenetics 2024, Vol.12 http://medscipublisher.com/index.php/cge © 2024 MedSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Publisher
Cancer Genetics and Epigenetics 2024, Vol.12 http://medscipublisher.com/index.php/cge © 2024 MedSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. MedSci Publisher is an international Open Access publisher specializing in cancer genetics, cancer epigenetics, clinical pharmacology, cancer biology at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. Publisher MedSci Publisher Editedby Editorial Team of Cancer Genetics and Epigenetics Email: edit@cge.medscipublisher.com Website: http://www.medscipublisher.com/index.php/cge Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Cancer Genetics and Epigenetics (ISSN 2369-2995) is an open access, peer reviewed journal published online by MedSci Publisher. The journal is aimed to publish all works in the areas that with quality and originality, with a scope that spans the areas of cancer genetics and cancer epigenetics. It is archived in LAC (Library and Archives Canada) and deposited in CrossRef. The journal has been indexed by ProQuest as well, expected to be indexed by PubMed and other datebases in near future. All the articles published in Cancer Genetics and Epigenetics are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MedSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
Cancer Genetics and Epigenetics (online), 2024, Vol. 12, No. 2 ISSN 2369-2995 http://www.medscipublisher.com/index.php/cge © 2024 MedSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content The Application of Genomics in Personalized Cancer Therapy Jiayao Zhou Cancer Genetics and Epigenetics, 2024, Vol. 12, No. 2, 70-78 Advances and Prospects in Whole-Genome Sequencing Studies of Prostate Cancer Liqin Guo, Jiayi Wu Cancer Genetics and Epigenetics, 2024, Vol. 12, No. 2, 79-87 Review of DNA Methylation in Early Detection of Breast Cancer MinLi Cancer Genetics and Epigenetics, 2024, Vol. 12, No. 2, 88-96 The Role of Genetic Markers in Early Screening of Prostate Cancer Liting Wang Cancer Genetics and Epigenetics, 2024, Vol. 12, No. 2, 97-105 Personalized and Precise Treatment of Cancer JieWang Cancer Genetics and Epigenetics, 2024, Vol. 12, No. 2, 106-114
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 70 Research Report Open Access The Application of Genomics in Personalized Cancer Therapy Jiayao Zhou Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding email: 2013478397@qq.com Cancer Genetics and Epigenetics, 2024, Vol.12, No.2 doi: 10.5376/cge.2024.12.0009 Received: 25 Jan., 2024 Accepted: 28 Feb., 2024 Published: 11 Mar., 2024 Copyright © 2024 Zhou, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhou J.Y., 2024, The application of genomics in personalized cancer therapy, Cancer Genetics and Epigenetics, 12(2): 70-78 (doi: 10.5376/cge.2024.12.0009) Abstract Genomics provides precise tools and methods for personalized cancer treatment by revealing the genomic characteristics of cancer. Through the practical application of typical cases, it demonstrates its great potential in improving treatment efficacy and patient survival rate, indicating that genomics will play a more important role in cancer treatment in the future. This review summarizes the genomic characteristics of cancer, including its variations, mutations, and roles in the occurrence, development, and metastasis of cancer, emphasizing the guiding significance of genomics for cancer treatment. It elaborates on the basic principles of personalized cancer treatment, and further analyzes the practical application effect and patient survival rate of genomics in personalized cancer treatment through specific cases. It discusses the development trend of genomics treatment technologies and methods, as well as its application prospects in personalized cancer treatment. This manuscript aims to reveal its key role in improving treatment efficacy and patient survival rate, and explore future development directions, with a view to providing new treatment strategies and methods for the field of cancer treatment. Keywords Genomics; Cancer; Personalized therapy; Precision medicine; Genetic sequencing Cancer, a disease that often strikes fear into the hearts of many, has long been a formidable challenge in the medical field. In recent years, with the rapid advancements in genomics, our understanding of cancer has moved from a superficial level to a molecular and genetic one, paving the way for personalized treatment options. The significance of genomics in cancer treatment cannot be overstated. Each individual's genome is unique, and cancer is driven by specific mutations within these genomes. By thoroughly studying these mutations, we can better understand the mechanisms of cancer initiation, progression, and metastasis, thereby tailoring the most effective treatment plans for each patient (Besser et al., 2018). Personalized medicine, a direct application of genomics in cancer therapy, is fundamentally changing the paradigm of cancer treatment. The traditional "one-size-fits-all" treatment approach is gradually being replaced by precision therapies based on patient-specific genomic information (Mook et al., 2018). Genomics provides essential tools for personalized cancer treatment. The development of high-throughput sequencing technologies allows for the rapid acquisition of a patient's complete genomic profile, while powerful bioinformatics tools help interpret this data and identify gene mutations closely linked to cancer development. Based on this information, the most appropriate targeted drugs or immunotherapy options can be selected for patients, maximizing the effectiveness of the treatment (Gwinn et al., 2019). This paper aims to provide a comprehensive overview of the current status and trends in the application of genomics in personalized cancer treatment. It discusses the genomic characteristics of cancer and the basic principles and implementation methods of personalized cancer therapy. Through specific case studies, it examines the practical effectiveness of genomics in personalized treatment and explores the future directions of genomic therapy techniques and methods. It is hoped that this paper will serve as a valuable reference for researchers and clinicians in the field of cancer treatment, promoting the broader application of genomics in personalized cancer therapy.
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 71 1 Genomic Characteristics of Cancer 1.1 Genomic variations and mutations in cancer Genomic variations and mutations are crucial in the initiation, progression, and metastasis of cancer. These changes are not only alterations in single genes but involve multiple genes, signaling pathways, and complex network interactions. They can occur in the coding regions, regulatory areas, or non-coding regions of genes, leading to altered protein functions, abnormal gene expression, and uncontrolled cell growth and division (Preethi et al., 2021). Common types of genomic variations in cancer include point mutations, insertions, and deletions, which can result in the loss or gain of protein functions. Additionally, changes in chromosomal structures such as translocations, inversions, and deletions can affect the expression and regulation of multiple genes. The accumulation and interaction of these mutations and variations in cancer cells confer malignant phenotypes such as limitless proliferation, evasion of immune surveillance, and resistance to apoptosis. 1.2 Role of genomics in cancer initiation, progression, and metastasis Cancer often begins with minor genomic variations that may arise from environmental factors, genetic predispositions, or random errors. Over time, these variations accumulate and expand, leading to uncontrolled cell growth and division, ultimately forming visible tumors. Genomics plays a critical role in the initiation, development, and spread of cancer. During cancer progression, genomics reveals how cancer cells evade the immune system, resist apoptosis, and acquire enhanced growth and invasive capabilities. In-depth genomic studies have identified mutations in various cancer-related genes, such as the inactivation of tumor suppressor genes and activation of oncogenes, playing key roles in cancer advancement (Yuan et al., 2019). Cancer metastasis, the spread of cancer cells from the primary site to other parts of the body, involves multiple complex steps. Genomics has shown how cancer cells modify their adhesiveness, motility, and invasiveness to facilitate this process. Additionally, it reveals how cancer cells interact with the host environment to exploit host cells for nutrients and support, and how they evade host immune attacks. 1.3 Implications of genomic research for cancer treatment In-depth study of the genomic characteristics of cancer allows for a more accurate understanding of the nature and mechanisms of cancer, providing more precise and personalized guidance for treatment strategies. Genomic research has identified key gene mutations and signaling pathway abnormalities in cancer, offering direct targets for targeted therapy and immunotherapy. For instance, specific gene mutations can be targeted with drugs that precisely attack cancer cells while minimizing toxicity to normal cells. Similarly, genomic studies have uncovered molecular mechanisms related to cancer immune evasion, leading to the development of immunotherapies that activate the patient's immune system to attack cancer cells (Aiello et al., 2018). Furthermore, genomic research can predict cancer prognosis and the risk of recurrence, providing more personalized treatment and management plans for patients. Comprehensive analysis of the cancer genome enables assessment of tumor type, stage, molecular characteristics, and more, predicting responses and efficacy to different treatment regimens. This helps doctors select the most suitable treatment options for patients and adjust strategies in time to improve treatment outcomes. 2 Principles of Personalized Cancer Treatment 2.1 Concept and principles of personalized medicine Personalized medicine, also known as precision medicine or tailored medicine, is an emerging medical model that emphasizes creating and optimizing treatment plans based on the unique physiological, genetic, and environmental characteristics of each patient. The concept of personalized medicine is founded on a profound understanding of the human genome and biodiversity, challenging the traditional “one-size-fits-all” treatment approach in conventional medical practice. It proposes more refined and individualized methods for disease prevention, diagnosis, and treatment (Guthrie et al., 2019).
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 72 The principles of personalized medicine primarily rely on advancements in two key areas: genomics and bioinformatics. Genomics provides the techniques and methods to study human genes, gene variations, and gene expressions, while bioinformatics is responsible for analyzing these extensive genomic data to extract crucial information related to disease onset and progression. By integrating knowledge and technology from these domains, physicians can perform in-depth analyses of a patient’s genome, identify gene mutations related to diseases, predict disease risks and progression, and tailor the most appropriate treatment plans for patients. Personalized medicine not only improves treatment outcomes and reduces unnecessary medical expenses but also enhances patients' quality of life. By precisely selecting drugs and treatment methods, ineffective treatments and the side effects and complications of overtreatment can be avoided. Furthermore, personalized medicine encourages patients to actively participate in their medical decision-making process, enhancing communication and trust between doctors and patients. 2.2 Utilizing genomic technologies for personalized cancer treatment Utilizing genomic technologies for personalized cancer treatment represents a revolutionary medical model that formulates customized treatment plans based on the unique genetic backgrounds and disease characteristics of patients. In personalized cancer treatment, genomic technologies play a crucial role (Hill et al., 2018). High-throughput sequencing and other genomic technologies enable researchers to obtain comprehensive genomic information from patients, including gene sequences, expression levels, and variations. This data provides physicians with a rich source of information, allowing them to accurately understand the type and progression of a patient’s disease and thereby choose the most suitable treatment plans. Based on genomic data, physicians can predict patients' responses to specific drugs and their therapeutic effects, thus avoiding ineffective treatments and their associated side effects and complications. For instance, certain cancer patients may have specific gene mutations that cause resistance to some drugs. Through genomic testing, doctors can quickly identify these mutations and select alternative, more effective medications (Yamaguchi et al., 2018). Moreover, genomic technologies also help researchers discover new therapeutic targets and develop novel drugs. By thoroughly studying cancer genomes, scientists can identify key genes and signaling pathways related to cancer development and progression, thereby developing targeted drugs or immunotherapies aimed at these targets. These new treatments offer higher selectivity and efficacy, providing cancer patients with better survival chances and quality of life. 2.3 Processes and methods of personalized treatment 2.3.1 Genomic sequencing and analysis Genomic sequencing and analysis are the core components of the field of genomics, providing deep insights into an organism’s genome. Genomic sequencing involves using high-throughput sequencing technology to sequence the entire genome of an organism, thereby obtaining complete genomic information. During this process, researchers use advanced sequencing equipment and reagents to convert the genetic material of an organism into digital signals, thus obtaining genomic sequence data (Satta et al., 2018). After sequencing, the next step is genomic analysis, which includes quality control of sequencing data, alignment to reference genomes, identification of gene variants, and analysis of gene expression, among other steps. By employing bioinformatics tools and algorithms, researchers can deeply mine genomic data to reveal the genomic structure, gene functions, regulatory mechanisms, and key mutations related to disease onset and progression (Figure 1). 2.3.2 Selection of molecular targeted drugs The selection of molecular targeted drugs is a significant advancement in the field of cancer treatment, based on genomic research findings. This approach precisely targets specific molecular abnormalities in cancer. Initially, it involves determining the genomic characteristics of a cancer patient, typically through sequencing and expression analysis of tumor samples. Through these analyses, researchers can identify gene mutations and abnormal signaling pathways closely related to cancer development and progression.
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 73 Figure 1 Typical pathogen genomics workflow (Gregory et al., 2019) Based on the patient's genomic characteristics, physicians select appropriate molecular targeted drugs. These drugs typically target specific proteins or signaling pathways, inhibiting or activating these molecules to block cancer growth and spread. For example, some targeted drugs may focus on specific receptors on cancer cell surfaces, preventing their interaction with ligands and thus inhibiting cancer cell growth; others may target intracellular signaling pathways, blocking cancer cell proliferation and survival. When selecting molecular targeted drugs, considerations also include the patient’s overall health, previous treatment responses, and the side effects of the drugs. As each patient's genomic characteristics are unique, the selection of molecular targeted drugs must be individualized. Physicians need to weigh the pros and cons according to the specific circumstances of the patient, devising the most suitable treatment plan.
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 74 2.3.3 Application of immunotherapy and immune checkpoint inhibitors The application of immunotherapy and immune checkpoint inhibitors has brought revolutionary breakthroughs in cancer treatment. Immunotherapy leverages the human body’s own immune system, activating or restoring the function of immune cells to attack cancer cells. This method maximizes the use of the body’s natural defenses, making treatment more precise and with relatively fewer side effects. Immune checkpoint inhibitors are a crucial class of drugs in immunotherapy, capable of suppressing immune checkpoint molecules on cancer cells, thereby releasing the inhibition of immune cells against cancer cells. This reactivates immune cells to recognize and attack cancer cells, restoring anti-tumor immune responses. Immune checkpoint inhibitors, such as PD-1 inhibitors and CTLA-4 inhibitors, have achieved significant clinical success in treating various cancers, significantly extending patients' survival periods and improving their quality of life. 3 The Application of Genomics in Personalized Cancer Treatment 3.1 Typical cases of genomics in personalized cancer treatment CRISPR genome editing can be used for personalized cancer treatment. A study published by "Nature" magazine on November 10, 2022, reported significant advances in modified cells and their clinical trials in humans. This research, conducted by researchers at the University of California and the cell therapy company PACT Pharma, developed a method using the CRISPR-Cas9 genome editing system to insert cancer-specific T-cell receptors into the T cells of cancer patients, thereby generating personalized anti-cancer immune cells. Leveraging the power of the human immune system to treat cancer is an attractive goal. T-cell receptors on the surface (a key part of the immune system involved in recognizing specific antigens and responding) can detect cancer cells because single mutations in the cancer cell genome alter surface proteins (MacCannell, 2019). Isolating these T-cell receptors that can detect cancer cells and using them to generate therapeutic T cells might pave a new way for treating intractable cancers (Figure 2). Figure 2 Gene-edited T cells for the treatment of cancer (Photo source: https://www.sohu.com/a/606549401_120554400) 3.2 Analysis of treatment effects and patient survival rates in cases In a Phase I clinical trial, the University of California and the cell therapy company PACT Pharma treated 16 patients with metastatic solid tumors (mostly colorectal cancer) who were unresponsive to standard therapies using genetically engineered T cells. These T cells expressed personalized T-cell receptors targeting individual cancer mutations. Among the 16 participants, the therapy stabilized the disease in 5 patients, while the condition of the other 11 patients progressed. Only 2 patients experienced adverse reactions due to T-cell therapy, whereas all patients suffered expected adverse reactions related to concurrent chemotherapy.
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 75 Although this method has limitations, such as characterizing potential antigens and the time required to isolate, clone, and test T-cell receptors, and the variable affinity of patient-specific T-cell receptors to their corresponding antigens, the clinical response benefits were limited. However, some processes were optimized during the trial, and there is room for further improvements. This research also demonstrated the potential viability of this treatment strategy. 3.3 Application and challenges of genomics in various types of cancer Genomics is widely and deeply applied in various types of cancer, but it also faces many challenges. In terms of application, genomics provides a crucial foundation for precision medicine by deeply analyzing gene mutations in cancer cells. For instance, in lung cancer, genomics helps doctors identify mutations in key genes such as EGFR and KRAS, guiding the selection of targeted drugs. In breast cancer, the amplification status of the HER2 gene becomes an important indicator for assessing whether a patient is suitable for specific treatments. In gastrointestinal cancer, in-depth research on the genome has revealed multiple gene mutations related to tumor occurrence and development, providing clues for developing new treatment strategies. However, genomics also faces challenges in cancer applications. Different types of cancers have distinct genomic characteristics, making the interpretation and analysis of genomic data complex. Even within the same type of cancer, genomic variations among different patients are significant, requiring doctors to have a high level of expertise and skills in formulating personalized treatment plans. The acquisition and analysis of genomic data require expensive equipment and skilled personnel, which somewhat limits its application in primary healthcare facilities. Additionally, the privacy protection of genomic data is an urgent issue to address; ensuring that patients' genetic information is not misused or leaked poses significant ethical and legal challenges. Genomics still faces challenges in drug development and clinical trials. Although some drugs based on genomic discoveries have entered clinical use, many potential therapeutic targets still require further validation and research. Moreover, effectively integrating genomic data with clinical trial results to guide clinical practice remains a critical task. 4 Trends in Genomics Technologies and Methods for Treatment 4.1 Trends and future directions in genomics technologies Genomics, as a core area of modern biology, is undergoing rapid development with limitless potential for the future. With continuous innovations in sequencing technologies, third-generation methods such as single-molecule sequencing and nanopore sequencing are becoming mature. These advancements are set to enhance the accuracy and speed of sequencing, reducing the costs and time required for whole-genome sequencing. This progress will significantly promote the large-scale production and application of genomic data (Huang et al., 2018). The application of artificial intelligence and machine learning in the analysis of genomic data is becoming increasingly widespread. As the volume of genomic data grows exponentially, traditional data analysis methods are becoming inadequate. Using artificial intelligence and machine learning technologies can efficiently parse these data, extracting more biological information and identifying potential therapeutic targets. The integration of genomics with other omics technologies will also be a significant future direction (Gregory et al., 2019). For example, the combination of genomics with transcriptomics, proteomics, and metabolomics can reveal the molecular mechanisms of organisms under various physiological and pathological states more comprehensively, providing more precise guidance for disease diagnosis and treatment. 4.2 New technologies and methods in personalized cancer treatment In recent years, a variety of new technologies and methods have emerged in the field of personalized cancer treatment, enhancing treatment efficacy and significantly reducing side effects, thus offering new hope to cancer patients. Among these, genomic technologies are undoubtedly one of the most representative methods in personalized cancer treatment. By performing whole-genome sequencing on a patient's tumor tissue, doctors can precisely
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 76 understand the genetic mutations present, allowing them to tailor personalized treatment plans. For instance, specific gene mutations may guide the selection of targeted drugs that act directly on cancer cells, achieving precise treatment effects. Besides genomic technologies, immunotherapy has brought breakthroughs to personalized cancer treatment. By activating the patient's immune system to attack cancer cells, immunotherapy has become a lifesaver for many cancer patients. Particularly, therapies targeting specific tumor markers, such as CAR-T cell therapy and PD-1 inhibitors, have shown remarkable results in clinical trials. 4.3 Prospects of gene editing and gene repair technologies in personalized cancer treatment The prospects for applying gene editing and gene repair technologies in personalized cancer treatment are broad and promising. These technologies, especially the CRISPR-Cas9 gene-editing system, have revolutionized cancer treatment (Mary et al., 2020). Gene editing can precisely target and modify specific genes within a patient’s body, correcting or eliminating mutations that cause cancer. For example, editing oncogenes or tumor suppressor genes can restore normal cell function and prevent cancer progression. In personalized cancer treatment, gene editing technologies can tailor treatment plans based on a patient's genomic information. Editing a patient's own immune cells to more effectively recognize and attack cancer cells strengthens immunotherapy as a powerful weapon. Additionally, gene editing can be used to develop new cell therapies, such as CAR-T cell therapy, by modifying T cells to express specific cancer-fighting receptors. Gene repair technologies focus on repairing damaged DNA, preventing or reversing the onset of cancer. In cancer treatment, gene repair can address hereditary diseases caused by genetic mutations, reducing cancer risk. Moreover, repairing DNA damage in tumor cells can inhibit their proliferation and spread, offering a new strategy for cancer treatment. Although gene editing and gene repair technologies are still in the early stages of application in cancer treatment, continued improvements and in-depth clinical research are expected to bring more breakthroughs and progress in the future. These technologies hold the potential to be key in curing cancer, improving the quality of life and extending the lifespan of cancer patients. 5 Conclusion and Outlook As technology continues to advance, genomics is gradually unveiling the profound mysteries of life, particularly in the field of cancer treatment, where its application prospects and significance are increasingly prominent. Personalized medicine, also known as precision medicine, is becoming an important direction in modern medical development. It emphasizes creating unique treatment plans based on the specific biological characteristics of each patient, aimed at improving treatment outcomes, reducing side effects, and enhancing the quality of life for patients (Janet et al., 2020). Genomic technologies, especially whole-genome sequencing, provide strong support for the precise diagnosis and treatment of cancer. By conducting in-depth analysis of the genomic data from a patient's tumor tissue, doctors can accurately understand the genetic mutations within the patient, thus developing personalized treatment plans. This genomics-based precision treatment strategy not only improves treatment outcomes but also significantly reduces unnecessary side effects, bringing greater hope for survival to patients. Additionally, genomic technology offers new ideas for optimizing drug development and immunotherapy. By studying the functions of specific genetic mutations in depth, scientists can develop more precise and effective targeted drugs. The rise of immunotherapy has brought revolutionary changes to cancer treatment. By editing or regulating the patient's immune system, genomic technologies are expected to further enhance the effectiveness of immunotherapy, benefiting more patients. However, despite significant achievements in personalized cancer treatment using genomics, numerous challenges remain. Further optimization of technology, enhanced integration and analysis of data, proper handling of ethical and privacy issues, and strengthened international cooperation are all critical directions for
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 70-78 http://medscipublisher.com/index.php/cge 77 future research and development. Looking ahead, as genomic technologies continue to evolve and improve, personalized cancer treatment is expected to bring hope to more patients. We anticipate more breakthroughs and innovations in this field and believe that humanity will ultimately conquer this life-threatening adversary. In the journey towards this goal, genomics will continue to play an irreplaceable role, bringing a brighter future to cancer patients. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Aiello N.M., Maddipati R., Norgard R.J., Balli D.,Li J., and Yuan S., 2018, EMT subtype influences epithelial plasticity and mode of cell migration, Dev Cell, 45: 681-684. https://doi.org/10.1016/j.devcel.2018.05.027 PMid:29920274 PMCid:PMC6014628 Besser J., Carleton H.A., Gerner-Smidt P., Lindsey R.L., and Trees E., 2018, Next-generation sequencing technologies and their application to the study and control of bacterial infections, Clin Microbiol Infect, 24: 335-341. https://doi.org/10.1016/j.cmi.2017.10.013 PMid:29074157 PMCid:PMC5857210 Gregory L.A., Duncan R.M., Jill T., Heather A., Carleton M.P.H., Elizabeth B.N., Richard S.B., James E.P., and Marta G., 2019, Pathogen genomics in public health, N Engl J Med., 381: 2569-2580. https://doi.org/10.1056/NEJMsr1813907 PMid:31881145 PMCid:PMC7008580 Guthrie J.L., Strudwick L., and Roberts B., 2019, Whole genome sequencing for improved understanding of Mycobacterium tuberculosis transmission in a remote circumpolar region, Epidemiol Infect, 147: 188-188. https://doi.org/10.1017/S0950268819000670 PMid:31364521 PMCid:PMC6518594 Gwinn M., MacCannell D., and Armstrong G.L., 2019, Next-generation sequencing of infectious pathogens, JAMA, 321: 893-894. https://doi.org/10.1001/jama.2018.21669 PMid:30763433 PMCid:PMC6682455 Hill M.A., Alexander W.B., Guo B., Kato Y., Patra K., and O'Dell M.R., 2018, Kras and Tp53 mutations cause cholangiocyte- and hepatocyte-derived cholangiocarcinoma, Cancer Res., 78: 4445-4451. https://doi.org/10.1158/0008-5472.CAN-17-1123 PMid:29871934 PMCid:PMC6097629 Huang S.J., Cai N.G., Pedro P.P., Shavira N., Wang Y., and Xu W.Y., 2018, Applications of support vector machine (SVM) learning in cancer genomics, Cancer Genomics & Proteomics, 15(1): 41-51. https://doi.org/10.21873/cgp.20063 PMCid:PMC5822181 Janet P., Juan M.R.A., Josep S.P., Francesco R., Emilio C., Ferran S., and Laura I.F., 2020, The DisGeNET knowledge platform for disease genomics: 2019 update, Nucleic Acids Research, 48(1): 845-855. MacCannell D., 2019, Platforms and analytical tools used in nucleic acid sequence-based microbial genotyping procedures, Microbiol Spectr, 7(1): 5. https://doi.org/10.1128/microbiolspec.AME-0005-2018 PMid:30737915 Mary J.G., Brian C., Mim H., Kristupas R., Fran M., Akhil K., Ayan B., Yunhai L., Dave R., Angela N.B., Zhu J.C., and David H., 2020, Visualizing and interpreting cancer genomics data via the Xena platform, Nature Biotechnology, 38: 675-678. https://doi.org/10.1038/s41587-020-0546-8 PMid:32444850 PMCid:PMC7386072 Mook P., Gardiner D., and Verlander N.Q., 2018, Operational burden of implementing salmonella enteritidis and typhimurium cluster detection using whole genome sequencing surveillance data in England: a retrospective assessment, Epidemiol Infect, 146: 1452-1460. https://doi.org/10.1017/S0950268818001589 PMid:29961436 PMCid:PMC9133683 Preethi K.A., Lakshmanan G., and Sekar D., 2021, Antagomir technology in the treatment of different types of cancer, Epigenomics, 13(7): 481-484. https://doi.org/10.2217/epi-2020-0439 PMid:33719531 Satta G., Lipman M., Smith G.P., Arnold C., Kon O.M., and McHugh T.D., 2018, Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential? Clin Microbiol Infect., 24: 604-609. https://doi.org/10.1016/j.cmi.2017.10.030 PMid:29108952
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Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 79-87 http://medscipublisher.com/index.php/cge 79 Systematic Review Open Access Advances and Prospects in Whole-Genome Sequencing Studies of Prostate Cancer Liqin Guo, Jiayi Wu Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: jiayi.wu@cuixi.org Cancer Genetics and Epigenetics, 2024, Vol.12, No.2 doi: 10.5376/cge.2024.12.0010 Received: 01 Feb., 2024 Accepted: 05 Mar., 2024 Published: 21 Mar., 2024 Copyright © 2024 Guo and Wu, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Guo L.Q., and Wu J.Y., 2024, Advances and prospects in whole-genome sequencing studies of prostate cancer, Cancer Genetics and Epigenetics, 12(2): 79-87 (doi: 10.5376/cge.2024.12.0010) Abstract Prostate cancer remains a significant clinical challenge due to its complex genetic landscape and the difficulty in predicting disease progression and treatment response. Recent advances in whole-genome sequencing (WGS) have provided deeper insights into the genetic alterations driving prostate cancer, offering new avenues for personalized treatment strategies. This study synthesizes findings from multiple studies that have employed WGS to identify novel driver mutations, potential therapeutic targets, and biomarkers for treatment response in prostate cancer. Key discoveries include the identification of new putative driver genes, such as NEAT1 and FOXA1, and the elucidation of the role of noncoding mutations in disease progression. Integrative high-throughput sequencing has demonstrated the feasibility of identifying clinically actionable mutations within a clinically relevant timeframe, facilitating biomarker-driven clinical trials. Studies have also highlighted the genomic heterogeneity of prostate cancer, with frequent alterations in genes such as AR, TP53, and PTEN, and the presence of actionable mutations in a significant proportion of cases. Additionally, the use of liquid biopsies for WGS has emerged as a promising non-invasive approach to monitor metastatic castration-resistant prostate cancer (mCRPC) and guide personalized treatment. This study underscores the potential of WGS to transform the clinical management of prostate cancer by enabling precision medicine approaches tailored to the genetic profile of individual tumors. Keywords Whole-genome sequencing; Prostate cancer; Driver mutations; Biomarkers; Personalized treatment 1 Introduction Prostate cancer (PCa) is one of the most prevalent malignancies affecting men worldwide and is a leading cause of cancer-related mortality, particularly in Western countries (Gudmundsson et al., 2012). The disease exhibits significant clinical heterogeneity, ranging from indolent tumors that may not require immediate treatment to aggressive forms that can be fatal (Baca and Garraway, 2012). Genetic factors play a crucial role in the development and progression of PCa, with both germline and somatic mutations contributing to its pathogenesis (Nakagawa, 2013). Family history is a well-known risk factor, and recent studies have identified numerous genetic variants associated with increased susceptibility to PCa (Schaid et al., 2020). Whole-genome sequencing (WGS) has emerged as a powerful tool for uncovering the genetic underpinnings of various cancers, including PCa. By providing a comprehensive view of the entire genome, WGS allows for the identification of both common and rare genetic alterations that drive cancer development and progression (Ren et al., 2017; Jaratlerdsiri et al., 2018). High-throughput sequencing technologies have facilitated the discovery of novel mutations, gene fusions, and other genomic aberrations that are critical for understanding the molecular mechanisms of PCa (Roychowdhury and Chinnaiyan, 2013; Liu et al., 2019). These insights have the potential to inform the development of precision medicine approaches, enabling more accurate diagnosis, prognosis, and targeted therapies (Roychowdhury and Chinnaiyan, 2013). This study synthesizes the latest advancements in prostate cancer (PCa) whole-genome sequencing (WGS) research, highlighting key genetic alterations and their impact on disease progression and treatment. By examining the results of multiple WGS studies, the research provides a comprehensive overview of the genomic landscape of PCa, identifies potential biomarkers for clinical use, and discusses future research prospects in the field. The study
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 79-87 http://medscipublisher.com/index.php/cge 80 also explores the genetic diversity of PCa among different populations, emphasizing the importance of including diverse racial groups in genomic research to uncover population-specific genetic drivers of the disease. 2 Advances in Whole-Genome Sequencing of Prostate Cancer 2.1 Identification of genetic mutations Whole-genome sequencing (WGS) has significantly advanced our understanding of the genetic mutations associated with prostate cancer. Key mutations identified through WGS include alterations in genes such as CHD1, BRCA2, and androgen receptor (AR) upstream activator genes. For instance, a study on Chinese prostate cancer patients revealed a high frequency of CHD1 deletions and mutations in androgen receptor upstream activator genes, which are associated with disease progression (Ren et al., 2017). Additionally, WGS has uncovered 22 previously unidentified putative driver genes, including NEAT1 and FOXA1, which act as drivers through noncoding mutations (Wedge et al., 2018). These findings highlight the complexity and diversity of genetic alterations in prostate cancer, providing new insights into the molecular mechanisms driving the disease. 2.2 Insights into tumor heterogeneity WGS has also provided valuable insights into the heterogeneity of prostate tumors. Intra-tumor and inter-tumor heterogeneity have been extensively studied, revealing significant variations in genetic alterations within and between tumors. For example, a study using targeted next-generation sequencing of advanced prostate cancer identified substantial heterogeneity in genomic alterations, including AR copy number gain, TMPRSS2-ERG fusion, and PTEN loss (Beltran et al., 2013; Bewicke-Copley et al., 2019). This heterogeneity has important implications for treatment and prognosis, as it suggests that personalized treatment strategies may be necessary to effectively target the diverse genetic landscape of prostate cancer. 2.3 Discovery of new biomarkers The discovery of new biomarkers through WGS has opened up new avenues for early detection and prognosis of prostate cancer. Biomarkers such as PCDH9 and PLXNA1 have been identified as potential prognostic indicators. PCDH9, which is deleted or lost in approximately 23% of tumors, functions as a novel tumor suppressor gene with prognostic potential (Ren et al., 2017). Similarly, the gain/amplification of the PLXNA1 gene, observed in approximately 17% of tumors, has been shown to promote prostate tumor growth and predict poor survival outcomes (Ren et al., 2017). These biomarkers have potential clinical applications in improving the diagnosis, prognosis, and treatment of prostate cancer. 3 Clinical Applications and Implications 3.1 Personalized medicine 3.1.1 Role of WGS in developing personalized treatment plans Whole-genome sequencing has significantly advanced the field of personalized medicine by enabling the identification of unique genetic alterations in individual prostate cancer patients. This allows for the development of tailored treatment plans that target specific mutations and pathways involved in the patient's cancer. For instance, WGS has been used to identify mutations in DNA damage response genes, PI3K, MAPK, and Wnt pathways, which can inform the use of targeted therapies such as PARP inhibitors and immunotherapy (Ciccarese et al., 2017; Crumbaker et al., 2020). Additionally, the integration of WGS with other sequencing methods, such as whole-exome sequencing (WES) and RNA sequencing (RNAseq), has been shown to provide a comprehensive mutational landscape that can guide clinical decision-making (Roychowdhury et al., 2011; Nauseef et al., 2023). 3.1.2 Case studies and clinical trials Several case studies and clinical trials have demonstrated the feasibility and clinical utility of WGS in personalized medicine for prostate cancer. For example, a study involving 34 patients with advanced cancers utilized WGS to identify therapeutically relevant targets, leading to genomic-directed treatments in 10 patients, with preliminary clinical efficacy observed in four patients (Borad et al., 2013). Another study highlighted the use of WGS in a patient with advanced prostate cancer, where the identification of an SPOP mutation and androgen-receptor dependency informed a successful personalized treatment approach (Figure 1) (Armstrong et
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 79-87 http://medscipublisher.com/index.php/cge 81 al., 2021). These examples underscore the potential of WGS to revolutionize personalized medicine by providing actionable insights into the genetic underpinnings of prostate cancer. Figure 1 Summary of the patient case and outcomes (Adopted from Armstrong et al., 2021) 3.2 Targeted therapies 3.2.1 Identification of targets for drug development WGS has been instrumental in identifying novel targets for drug development in prostate cancer. By analyzing the genetic alterations present in prostate cancer samples, researchers have discovered new candidate driver mutations and potential drug targets. For instance, a comprehensive sequencing study identified 22 previously unidentified putative driver genes and several targets of approved and investigational drugs (Wedge et al., 2018). This highlights the potential of WGS to uncover new therapeutic targets that can be exploited for drug development. 3.2.2 Examples of successful targeted therapies The application of WGS in identifying actionable mutations has led to the development and implementation of successful targeted therapies. For example, the identification of BRCA2 mutations in prostate cancer patients has informed the use of PARP inhibitors, which have shown efficacy in treating these patients (Ciccarese et al., 2017; Crumbaker et al., 2020). Additionally, the discovery of specific gene fusions and amplifications, such as TMPRSS2-ERG and FGFR1, has guided the use of targeted therapies that inhibit these pathways (Roychowdhury et al., 2011; Nauseef et al., 2023). These examples demonstrate the clinical impact of WGS in enabling the development and application of targeted therapies for prostate cancer. 3.3 Predictive and prognostic value 3.3.1 Use of genetic information for predicting disease progression WGS provides valuable genetic information that can be used to predict disease progression in prostate cancer patients. By identifying specific genetic alterations associated with different stages of cancer development, WGS can help predict the likelihood of disease progression and inform treatment decisions. For instance, the loss of CHD1 and BRCA2 has been identified as early events in the development of ETS fusion-negative prostate cancers, providing insights into disease progression (Wedge et al., 2018). Additionally, the presence of mutations in DNA repair genes has been associated with sensitivity to certain therapies, which can be used to predict treatment response and disease outcomes (Ciccarese et al., 2017; Crumbaker et al., 2020). 3.3.2 Studies demonstrating prognostic significance Several studies have demonstrated the prognostic significance of genetic alterations identified through WGS. For example, a retrospective analysis of metastatic prostate cancer patients revealed that most cases harbored
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 79-87 http://medscipublisher.com/index.php/cge 82 therapeutically relevant alterations, including those associated with PARP inhibitor sensitivity and resistance to androgen pathway targeting agents (Figure 2) (Crumbaker et al., 2020). Another study showed that the integration of WGS with clinical data could identify molecular signatures associated with homologous recombination deficiency and mismatch repair deficiency, which have prognostic implications for treatment response and disease progression (Nauseef et al., 2023). These findings highlight the potential of WGS to provide prognostic insights that can guide clinical management and improve patient outcomes. Figure 2 Summary of genomic alterations in primary prostate samples with synchronous lymph node metastases (Cases 19011, 19260 and 19145) (Adopted from Crumbaker et al., 2020) In conclusion, WGS has emerged as a powerful tool in the clinical management of prostate cancer, offering significant advances in personalized medicine, targeted therapies, and predictive and prognostic value. The continued integration of WGS into clinical practice holds promise for improving the diagnosis, treatment, and outcomes of prostate cancer patients. 4 Challenges and Limitations 4.1 Technical and analytical challenges Whole-genome sequencing (WGS) of prostate cancer presents several technical and analytical challenges. One significant issue is the difficulty in obtaining sufficient high-quality DNA from metastatic tissues, which are often limited in availability and quality. This is particularly problematic for advanced prostate cancer, where metastatic tissue is crucial for comprehensive genomic analysis (Beltran et al., 2013; Lohr et al., 2014). Additionally, the isolation and sequencing of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) require overcoming the challenges of isolating rare cells and sequencing low-input material, which can lead to incomplete or biased genomic data (Lohr et al., 2014; Sumanasuriya et al., 2021). Furthermore, the complexity of prostate cancer genomes, characterized by a high degree of heterogeneity and the presence of both coding and non-coding mutations, complicates the identification and interpretation of driver mutations and other clinically relevant alterations (Robinson et al., 2015; Ren et al, 2017; Wedge et al., 2018). 4.2 Clinical and ethical considerations The clinical application of WGS in prostate cancer is fraught with ethical and practical considerations. One major concern is the interpretation and communication of incidental findings, which may have significant implications for patients and their families. The identification of germline mutations, for instance, can reveal hereditary cancer syndromes that necessitate genetic counseling and potential testing of family members (Abida et al., 2017). Additionally, the clinical utility of WGS data is still under investigation, and there is a need for robust clinical trials to validate the prognostic and therapeutic relevance of identified genomic alterations (Beltran et al., 2015). Ethical considerations also include the potential for genetic discrimination and the need for informed consent processes that adequately explain the risks and benefits of WGS to patients (Beltran et al., 2015; Abida et al., 2017).
Cancer Genetics and Epigenetics 2024, Vol.12, No.2, 79-87 http://medscipublisher.com/index.php/cge 83 4.3 Cost and accessibility The high cost of WGS remains a significant barrier to its widespread adoption in clinical practice. Despite advances in sequencing technologies that have reduced costs, WGS is still expensive compared to other genomic testing methods, such as targeted sequencing or panel-based approaches (Beltran et al., 2013; Imieliński et al., 2017). This cost factor limits accessibility, particularly in resource-limited settings, and raises questions about the cost-effectiveness of WGS in routine clinical care. Additionally, the infrastructure required for WGS, including bioinformatics support and data storage, adds to the overall expense and complexity of implementing WGS in clinical settings (Abida et al., 2017; Imieliński et al., 2017). Efforts to reduce costs and improve the efficiency of WGS are essential to make this technology more accessible and practical for widespread clinical use. 5 Future Prospects and Research Directions 5.1 Technological advancements 5.1.1 Emerging technologies in genome sequencing The field of whole-genome sequencing is rapidly evolving, with significant advancements in next-generation sequencing (NGS) technologies. These advancements have enabled more comprehensive and detailed analyses of cancer genomes, including prostate cancer. Emerging technologies such as deep WGS and liquid biopsy techniques are particularly promising. For instance, deep WGS of circulating tumor DNA (ctDNA) has shown potential in revealing the clonal architecture and evolution of treatment-resistant prostate cancer, providing insights into the heterogeneity of ctDNA populations compared to metastatic tissue (Herberts et al., 2022; Weiss et al., 2022). Additionally, the development of scalable NGS systems, such as the Oncomine Comprehensive Panel, has facilitated the detection of relevant somatic variants in solid tumors, including prostate cancer, using minimal DNA/RNA from formalin-fixed paraffin-embedded (FFPE) tissues (Hovelson et al., 2015; Liang et al., 2019). 5.1.2 Potential improvements in WGS accuracy and efficiency Despite the progress, there are still substantial improvements to be made in sequencing technologies, informatics, and computational resources to enhance the accuracy and efficiency of WGS. Current WGS platforms are considered primitive, and there is a need for better integration of multi-omics data, functional data, and clinical-pathological data to fully interpret the diverse cancer genomes and phenotypes (Nakagawa et al., 2015; Nakagawa and Fujita, 2018). Moreover, the feasibility of WGS in routine clinical practice has been demonstrated, but challenges such as low tumor purity and the need for fresh-frozen samples instead of FFPE samples need to be addressed to improve the success rate and turnaround time of WGS (Samsom et al., 2022). 5.2 Integrative approaches 5.2.1 Combining WGS with other omics technologies Integrative approaches that combine WGS with other omics technologies, such as transcriptomics, epigenomics, and immunogenomics, are essential for a comprehensive understanding of cancer biology. For example, integrating WGS data with RNA-Seq, epigenomics, and clinical-pathological information can help elucidate the functional or clinical implications of unexplored genomic regions and mutational signatures in cancer genomes (Nakagawa and Fujita, 2018; Rubin and Demichelis, 2018). Additionally, ctDNA nucleosome footprinting has been used to infer mRNA abundance in metastatic lesions, demonstrating the potential of combining WGS with transcriptomic data to understand the transcriptomic patterns in prostate cancer (Herberts et al., 2022). 5.2.2 Benefits of integrative cancer genomics The benefits of integrative cancer genomics are manifold. By combining WGS with other omics data, researchers can identify new genomic alterations and clinically actionable aberrations that could impact treatment decisions. For instance, a multi-institutional clinical sequencing infrastructure for metastatic castration-resistant prostate cancer (mCRPC) identified frequent aberrations in AR, ETS genes, TP53, and PTEN, as well as new genomic alterations in PIK3CA/B, BRAF/RAF1, and other cancer-related genes (Robinson et al., 2015). Such integrative approaches can provide a more comprehensive understanding of the genomic landscape of prostate cancer and facilitate the development of precision medicine frameworks.
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