Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy.

IF 3.5 3区 医学 Q2 ONCOLOGY Frontiers in Oncology Pub Date : 2024-08-19 eCollection Date: 2024-01-01 DOI:10.3389/fonc.2024.1419599
Matheus Correia Casotti, Débora Dummer Meira, Aléxia Stefani Siqueira Zetum, Camilly Victória Campanharo, Danielle Ribeiro Campos da Silva, Giulia Maria Giacinti, Iris Moreira da Silva, João Augusto Diniz Moura, Karen Ruth Michio Barbosa, Lorena Souza Castro Altoé, Lorena Souza Rittberg Mauricio, Luíza Santa Brígida de Barros Góes, Lyvia Neves Rebello Alves, Sarah Sophia Guedes Linhares, Vinícius do Prado Ventorim, Yasmin Moreto Guaitolini, Eldamária de Vargas Wolfgramm Dos Santos, Flavia Imbroisi Valle Errera, Sonia Groisman, Elizeu Fagundes de Carvalho, Flavia de Paula, Marcelo Victor Pires de Sousa, Pierre Basílio Almeida Fechine, Iuri Drumond Louro
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Abstract

Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.

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整合前沿:通过系统生物学和多学科协同攻克癌症的整体、量子和进化方法。
以体细胞突变、精准肿瘤学和海量大数据为核心的各种技术和研究工作,使癌症治疗面临着日益严峻的挑战。尽管信息如此丰富,但治疗癌症的努力往往显得更加遥不可及,"抗癌战争 "尚未取得最终胜利。一个尤为紧迫的问题是肿瘤治疗耐药性的产生,这凸显了对创新方法的迫切需求。进化生物学、量子生物学和系统生物学为推进癌症实验研究提供了一个前景广阔的框架。通过整合理论研究、转化方法和灵活的多学科临床研究,有可能加强目前的治疗策略,改善癌症患者的预后。在进化论、量子论、熵和混沌原理与肿瘤学之间建立更紧密的联系,可以利用对肿瘤进化动态的了解,找到更有效的治疗方法,为控制和缓解癌症的新方法铺平道路。要实现这些目标,就必须致力于多学科和跨专业合作,将其作为肿瘤学研究和临床工作的核心。这就需要打破学科之间的隔阂,鼓励开放式交流和数据共享,并从研究项目一开始就整合不同的观点和专业知识。接受新的科学发现并及时了解患者对治疗的反应也至关重要。这些策略是使肿瘤学领域始终处于有效治疗癌症的前沿,确保患者得到最个性化和最有效治疗的关键。归根结底,这种方法旨在推动对癌症的认识,将癌症作为一种可控的慢性病来治疗,延长患者的预期寿命,提高患者的生活质量。
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来源期刊
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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