Mathematical oncology: A new frontier in cancer biology and clinical decision making: Comment on "Improving cancer treatments via dynamical biophysical models" by M. Kuznetsov, J. Clairambault & V. Volpert.
Mathematical oncology: A new frontier in cancer biology and clinical decision making: Comment on "Improving cancer treatments via dynamical biophysical models" by M. Kuznetsov, J. Clairambault & V. Volpert.
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DOI：
10.1016/j.plrev.2021.11.005
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发表时间：
2022-03
影响因子：
11.7
通讯作者：
Enderling, Heiko
中科院分区：
文献类型：
--
作者：
Enderling, Heiko
在他们的综合评论中，Kuznetsov、Claiambault 和 Volpert 对癌症生物学和癌症治疗以及过去几十年中开发的不同数学建模方法进行了精彩的介绍 [1]。许多科学学科的许多研究人员花费了数十年的时间试图破译驱动癌发生、肿瘤生长和进展、治疗反应和结果的生物学机制。从历史上看，实验学家、临床医生和定量科学家并没有完全接受跨学科研究，可能是因为缺乏机会，也可能是因为缺乏对其他领域的理解或同情心。癌症是一个非线性自适应动态系统，为了充分解读这种复杂性，我们可能需要超越分子还原论[2]，转向物理学、数据科学、机械建模和其他传统上在生物医学研究中未提及的新兴学科的整合。库兹涅佐夫、克莱兰博和沃尔珀特认为，数学和计算方法可以很好地帮助解决癌症难题。作者全面介绍了不同生物尺度的不同技术，并讨论了数学生物学工作，这些工作有助于获得从基础癌症生物学到优化临床肿瘤学的新见解和新假设。通过数学生物学与常规统计方法以及令人兴奋的新机器学习和人工智能技术之间的密切对话，对疾病动力学的关注可能有助于确定疾病进展和治疗反应的机制，并可能产生新的生物标志物和治疗适应的触发因素。我们之前假设，以放射肿瘤学为例，在体外、体内和临床上以不同的顺序和所有可能的时间，在有或没有越来越多的治疗药物的情况下，详尽地评估每种可能的剂量和剂量分割是难以捉摸的[3]。数学建模可以提供必要的工具来机械地理解许多生物参与者及其相互作用。放射生物学和放射肿瘤学有着悠久的融合历史
In their comprehensive review, Kuznetsov, Claiambault and Volpert give a wonderful introduction into cancer biology and cancer therapy and the different mathematical modeling approaches that have been developed over the past decades [1]. Many investigators in numerous scientific disciplines have spent decades trying to decipher the mechanisms that drive the biology of carcinogenesis, tumor growth and progression, treatment response, and outcomes. Historically, experimentalists, clinicians, and quantitative scientists have not fully embraced interdisciplinary research, maybe due to lack of opportunity, maybe due to lack of understanding or compassion for the other fields. Cancer is a non-linear adaptive dynamic system, and to fully decipher this complexity we may need to look beyond molecular reductionism [2] towards the integration of physics, data science, mechanistic modeling and other emerging disciplines that have traditionally not been consulted in biomedical research. Kuznetsov, Clairambault and Volpert posit that mathematical and computational approaches are well-positioned to help detangle the cancer conundrum. The authors thoroughly introduce the different techniques for the different biological scales and discuss mathematical biology work that has helped gain new insights and generate new hypotheses from basic cancer biology to optimizing clinical oncology. A focus on disease dynamics, in a close dialogue between mathematical biology with routine statistical approaches and exciting new machine learning and artificial intelligence techniques, may help identify mechanisms that underly disease progression and treatment responses, and could yield new biomarkers and triggers for treatment adaptations.We have previously postulated that, for radiation oncology as an example, it is elusive to exhaustively evaluate every possible dose and dose fractionation, with and without the growing number of therapeutic agents, in different sequences and at all possible timings in vitro, in vivo, and clinically [3]. Mathematical modeling may provide the necessary tools to provide a mechanistic understanding of the many biological players and their interactions. Radiation biology and radiation oncology have had a long history of integrating
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影响因子：
--
作者：
Zahid MU;Mohamed ASR;Caudell JJ;Harrison LB;Fuller CD;Moros EG;Enderling H
通讯作者：
Enderling H
影响因子：
2.1
作者：
Parsai, Shireen;Qiu, Richard L. J.;Scott, Jacob G.
通讯作者：
Scott, Jacob G.
DOI：
10.1093/imammb/dqw013
发表时间：
2017-12-01
影响因子：
1.1
作者：
Stocks, Theresa;Hillen, Thomas;Burger, Martin
通讯作者：
Burger, Martin
影响因子：
64.5
作者：
Leder K;Pitter K;LaPlant Q;Hambardzumyan D;Ross BD;Chan TA;Holland EC;Michor F
通讯作者：
Michor F
影响因子：
3.5
作者：
WEBB, S;NAHUM, AE
通讯作者：
NAHUM, AE