Precision Oncology: Revolutionizing Cancer Treatment

Ohtake Takaaki^*
*Correspondence: Ohtake Takaaki, Department of Gastroenterology, International University of Health and Welfare School of Medicine, Narita, Japan, Email:

Introduction

Cancer is a devastating disease that affects millions of people worldwide, causing significant morbidity and mortality. Over the years, researchers and healthcare professionals have made tremendous strides in understanding cancer and developing treatments. One ground-breaking approach that has emerged in recent years is precision oncology. Precision oncology, also known as personalized medicine or targeted therapy, aims to tailor cancer treatment to the specific genetic characteristics of an individual's tumor. This article will delve into the concept of precision oncology, its key components, advancements, challenges, and its potential to revolutionize cancer treatment.

Precision oncology is based on the premise that each cancer is unique, and treatment decisions should be made by considering the individual characteristics of the tumor and the patient. Traditional cancer treatments such as chemotherapy and radiation therapy have a broad spectrum of action and can cause significant side effects. In contrast, precision oncology focuses on identifying the specific genetic alterations or biomarkers driving the growth and progression of the tumor. By understanding the genetic landscape of a tumor, targeted therapies can be developed to selectively inhibit the molecular pathways that promote cancer growth [1].

Genomic profiling involves the analysis of a tumor's DNA or RNA to identify genetic alterations. Techniques such as next-generation sequencing (NGS) allow researchers to comprehensively examine the entire genome or specific gene panels for mutations, copy number alterations, gene fusions, and other genomic abnormalities. Genomic profiling helps identify specific alterations that can be targeted with precision therapies. Biomarkers are measurable indicators that can predict a patient's response to a particular treatment. In precision oncology, biomarkers play a crucial role in identifying patients who are most likely to benefit from targeted therapies. Examples of biomarkers include mutations in genes such as EGFR in lung cancer or HER2 in breast cancer, which can guide the use of specific targeted drugs. Once genetic alterations or biomarkers have been identified, targeted therapies can be employed. Targeted therapies are designed to block specific molecular pathways involved in tumor growth and survival. These therapies can be small molecules that inhibit specific proteins or monoclonal antibodies that bind to receptors on cancer cells. Examples of targeted therapies include tyrosine kinase inhibitors (TKIs) such as imatinib for chronic myeloid leukemia (CML) and monoclonal antibodies like trastuzumab for HER2-positive breast cancer [2].

Liquid biopsies are a non-invasive method of obtaining tumor-derived genetic information from blood samples or other bodily fluids. They allow for the detection of genetic alterations and monitoring of tumor dynamics over time. Liquid biopsies have the potential to overcome the limitations of tissue biopsies, providing a more comprehensive and real-time view of the tumor's genomic profile. While precision oncology is often associated with targeted therapies, it also encompasses immunotherapy, a rapidly evolving field in cancer treatment. Immunotherapy harnesses the body's immune system to recognize and attack cancer cells. Checkpoint inhibitors, such as pembrolizumab and nivolumab, have shown remarkable success in treating various types of cancer, including melanoma, lung cancer, and bladder cancer. Precision oncology aims to identify biomarkers that predict response to immunotherapy, optimizing patient selection and improving outcomes [3].

Precision oncology has also explored the use of combination therapies, where multiple targeted therapies or targeted therapies with traditional treatments are used simultaneously or sequentially. This approach aims to overcome tumor heterogeneity and the development of resistance by attacking multiple molecular targets simultaneously. Combination therapies have shown promise in several cancer types, including lung cancer, melanoma, and colorectal cancer.

Tumors are genetically heterogeneous, meaning they can consist of multiple subpopulations of cancer cells with distinct genetic alterations. It can be challenging to capture the entire genomic landscape of a tumor through a single biopsy, as the genetic profile can evolve over time. Additionally, metastatic tumors may have different genetic alterations compared to the primary tumor, requiring repeated biopsies for accurate profiling. While biomarkers play a crucial role in guiding treatment decisions, identifying clinically relevant biomarkers can be challenging. Some genetic alterations may be rare or have uncertain clinical significance. The availability of targeted therapies may be limited to specific biomarkers, making treatment options narrow for patients without actionable alterations. Cancer cells can develop resistance to targeted therapies over time, leading to treatment failure and disease progression. Resistance mechanisms can arise from the emergence of new genetic alterations or activation of alternative signalling pathways. Developing strategies to overcome resistance and prevent relapse remains a significant challenge in precision oncology.

Description

Precision oncology relies heavily on advanced genomic profiling techniques and targeted therapies, which can be costly. The accessibility of these technologies and therapies can vary across different healthcare settings, leading to disparities in access to precision oncology approaches. Widespread adoption of precision oncology requires addressing cost-related challenges and ensuring equitable access for all patients. Despite the challenges, precision oncology holds immense promise for the future of cancer treatment. The integration of genomics, bioinformatics, and clinical data will continue to refine treatment strategies and improve patient outcomes. AI and machine learning algorithms have the potential to enhance the interpretation of complex genomic data, aid in biomarker discovery, and predict treatment response. AI-driven platforms can analyze large-scale genomic and clinical datasets to identify patterns, correlations, and potential therapeutic targets.

Patient-derived organoids (PDOs) and xenograft models are threedimensional cell culture systems that recapitulate the genetic and molecular characteristics of individual tumors. These models provide a platform for testing drug sensitivity, identifying personalized treatment strategies, and studying tumor biology in a more clinically relevant setting. Precision oncology's potential extends beyond treatment to early detection and prevention. By identifying highrisk individuals through genetic profiling and monitoring for early signs of cancer, interventions can be initiated at the earliest stages, improving survival rates and reducing the burden of advanced disease. Collaboration and data sharing among researchers, clinicians, and institutions are crucial for advancing precision oncology. Establishing global networks and databases that integrate genomic, clinical, and treatment data will enable comprehensive analysis, facilitate the discovery of novel biomarkers, and enhance knowledge sharing across the field [4].

Precision oncology represents a paradigm shift in cancer treatment, leveraging the power of genomics and targeted therapies to tailor treatments to individual patients. Advances in genomic profiling, biomarker identification, and targeted therapies have laid the foundation for a more precise and personalized approach to cancer care. While challenges remain, ongoing research, technological advancements, and collaborations hold the promise of overcoming these obstacles and improving patient outcomes. Precision oncology has the potential to revolutionize cancer treatment by enabling more effective therapies, reducing side effects, and ultimately leading to better survival rates and quality of life for cancer patients. Moreover, the integration of precision oncology with other fields such as immunotherapy, radiomics, and AI-driven approaches further enhances its potential. By combining different modalities, clinicians can develop comprehensive treatment strategies that address the complexities of cancer and improve patient outcomes.

One area where precision oncology has shown remarkable success is in the treatment of certain types of cancers with specific actionable mutations. For example, the use of tyrosine kinase inhibitors (TKIs) has transformed the prognosis for patients with chronic myeloid leukemia (CML) and non-small cell lung cancer (NSCLC) harboring specific genetic alterations such as BCR-ABL and EGFR mutations, respectively. These targeted therapies have demonstrated higher response rates and improved survival compared to traditional chemotherapy approaches. Furthermore, the application of precision oncology extends beyond solid tumors to hematologic malignancies such as lymphomas and leukemias. In these cases, genomic profiling and the identification of specific gene mutations or translocations have enabled the development of targeted therapies, such as Bruton tyrosine kinase (BTK) inhibitors for patients with B-cell malignancies [5].

Precision oncology also plays a critical role in pediatric oncology. Childhood cancers often have distinct genetic alterations compared to adult cancers, and targeted therapies specifically designed for pediatric patients can improve treatment outcomes while minimizing long-term side effects. The impact of precision oncology is not limited to treatment decisions alone. It also has implications for clinical trial design and drug development. By selecting patients based on specific genetic alterations or biomarkers, clinical trials can be more focused, efficient, and have a higher likelihood of success. This personalized approach allows researchers to assess the effectiveness of targeted therapies in specific patient populations, leading to the accelerated development of new drugs and treatment strategies. Despite its tremendous potential, precision oncology faces several challenges that need to be addressed for widespread adoption and implementation. One of the primary challenges is the complexity of tumor heterogeneity.

Conclusion

As mentioned earlier, tumors consist of multiple subpopulations of cancer cells with varying genetic alterations. Therefore, a single biopsy may not capture the full genomic landscape of the tumor. Repeat biopsies or the use of liquid biopsies can help overcome this challenge, allowing for a more comprehensive understanding of the tumor's genetic profile. Another challenge is the identification and validation of clinically relevant biomarkers. While there are well-established biomarkers in certain cancer types, there are still many tumors for which actionable biomarkers have not been identified. The discovery and validation of new biomarkers require extensive research and collaboration between academia, industry, and regulatory bodies.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Sung, Hyuna, Jacques Ferlay, Rebecca L. Siegel and Mathieu Laversanne, et al. "Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries."CA: Cancer J Clin 71 (2021): 209-249.

Google Scholar, Crossref, Indexed at

2. Piendl, Wolfgang, Christa Eichenseer, Petra Viel and Josef Altenbuchner, et al. "Analysis of putative DNA amplification genes in the element AUD1 of Streptomyces lividans 66."Mol Genet Gen MGG244 (1994): 439-443.

Google Scholar, Crossref, Indexed at

3. Tong, Christy WS, Mingxia Wu, William CS Cho and Kenneth KW. "Recent advances in the treatment of breast cancer."Front Oncol 8 (2018): 227.

Google Scholar, Crossref, Indexed at

4. Hsu, Sheng-Kai, Chia-Yang Li, I-Ling Lin and Wun-Jyun Syue, et al. "Inflammation-related pyroptosis, a novel programmed cell death pathway, and its crosstalk with immune therapy in cancer treatment."Theranostics11 (2021): 8813.

Google Scholar, Crossref, Indexed at

5. Al-Hajj, Muhammad, Max S. Wicha, Adalberto Benito-Hernandez and Sean J. Morrison, et al. "Prospective identification of tumorigenic breast cancer cells."Proc Natl Acad Sci100 (2003): 3983-3988.

Google Scholar, Crossref, Indexed at