Recent Advancements, Limitations, and Future Perspectives of the use of Personalized Medicine in Treatment of Colon Cancer

Dey, Amit; Mitra, Abhijit; Pathak, Surajit; Prasad, Suhanya Veronica; Zhang, Alexander Sun; Zhang, Hong; Sun, Xiao‐Feng; Banerjee, Antara

doi:10.1177/15330338231178403

Cited by 20 publications

(8 citation statements)

References 194 publications

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“…The intricate nature of cancer manifests in the variability of responses to therapies among patients owing to the individualized genetic makeup and biological characteristics of their tumors. This necessitates tailored treatment approaches to optimize the chances of successful outcomes [142]. To achieve personalized treatment, CAP is emerging as a promising approach, as the parameters of the plasma device can be fine-tuned during the treatment to suit the patient's specific dosage.…”

Section: Discussionmentioning

confidence: 99%

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

Ramaswamy,

Keidar

2023

Applied Sciences

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Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species and electromagnetic emissions generated through gas plasma jets have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets or approaches within machine learning, such as reinforcement learning and deep learning. This review emphasizes the significant application of advanced algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of machine learning techniques in plasma medicine requires research efforts, data sharing, and interdisciplinary collaborations. Unraveling the complex mechanisms, developing real-time diagnostics, and optimizing advanced models will be crucial to harnessing the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.

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Section: Discussionmentioning

confidence: 99%

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

Ramaswamy,

Keidar

2023

Applied Sciences

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“…This longitudinal approach is particularly valuable in colorectal cancer, where clonal evolution and the development of resistance mechanisms can occur. By periodically assessing the tumor’s genetic profile, clinicians can adapt treatment strategies as needed, ensuring that patients receive the most effective therapies at each stage of their disease …”

Section: Challenges and Considerations In Precision Medicine Implemen...mentioning

confidence: 99%

“…By periodically assessing the tumor's genetic profile, clinicians can adapt treatment strategies as needed, ensuring that patients receive the most effective therapies at each stage of their disease. 168…”

Section: Tumor Heterogeneity and Clonal Evolutionmentioning

confidence: 99%

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Kiran,

Yashaswini,

Maheshwari

et al. 2024

ACS Pharmacol. Transl. Sci.

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Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.

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“…Although surgery is the only curative treatment for locally resectable CC, even in the presence of high-risk factors, adjuvant postsurgery chemotherapy is required to prevent recurrence and prolong patient survival 5 . Despite advances in various treatments, including targeted therapy and immunotherapy, oxaliplatin-based chemotherapy remains the main adjuvant treatment for stage II–III CC 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

Optimal duration of oxaliplatin-based adjuvant chemotherapy in patients with different risk factors for stage II–III colon cancer: a meta-analysis

Kuang,

Wang,

Liu

et al. 2024

International Journal of Surgery

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Background: The duration of oxaliplatin–based chemotherapy in high–risk stage II, low–risk stage III, and high–risk stage III colon cancer patients is controversial. To reduce the risk of adverse events (AEs) without compromising efficacy while improving chemotherapy compliance is crucial. Methods: We searched Cochrane, Embase, Pubmed, and Web of Science databases for articles from inception to 2023.8.8, the main outcomes were disease–free survival, overall survival, chemotherapy completion rates, and AE frequency. Results: Six randomized controlled trials involving 10,332 patients were included. Disease–free survival analysis revealed that only the high–risk stage III colon cancer patients experienced better results with the six–month FOLFOX regimen when compared with the 3–month regimen (Hazard ratio [HR]: 1.32, 95% CI: 1.15–1.51, P<0.0001). Overall survival analysis revealed that extending the use of FOLFOX and CAPEOX regimens did not provide survival benefits for stage III colon cancer patients (HR: 1.16, 95% CI: 0.9–1.49, and HR: 0.89, 95% CI: 0.67–1.18, P=0.40). The completion rate of the three–month oxaliplatin–based adjuvant chemotherapy regimen was significantly higher than that of the six–month regimen (Relative risk [RR]: 1.16, 95% CI: 1.06–1.27, P=0.002). Moreover, the three–month regimen had significantly lower AE rates than the six–month regimen (RR: 0.62, 95% CI: 0.57–0.68, P<0.00001), with differences mainly concentrated in grade 3/4 neutropenia (RR: 0.70, 95% CI: 0.59–0.85, P=0.0002), peripheral sensory neuropathy at≥grade 2 (RR: 0.45, 95% CI: 0.38–0.53, P<0.00001), and hand–foot syndrome at≥grade 2 (RR: 0.36, 95% CI: 0.17–0.77, P=0.009). Conclusions: The six–month FOLFOX regimen should only be recommended for high–risk stage III colon cancer, while the three–month regimen can be recommended for other stages. A three–month CAPEOX regimen can be recommended for stage II–III colon cancer.

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Recent Advancements, Limitations, and Future Perspectives of the use of Personalized Medicine in Treatment of Colon Cancer

Cited by 20 publications

References 194 publications

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Optimal duration of oxaliplatin-based adjuvant chemotherapy in patients with different risk factors for stage II–III colon cancer: a meta-analysis

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