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Computational and systems biology of cancer 癌症的计算和系统生物学
Pub Date : 2020-09-11 DOI: 10.1002/cso2.1005
David Dingli MD, PhD

Ever since the war on cancer was declared in 1971, there has been an explosion in our understanding of this diverse group of diseases. The application of molecular genetics and molecular biology technologies have enabled a deep understanding of the genetic, epigenetic, signaling cascades, survival pathways, and invasive mechanisms that underlie the cancer phenotype [1, 2]. Concomitantly this has translated in the development of ever more effective and safe medications that work through different mechanisms of action and target fundamental aspects of the biology of the tumor. The paradigm has been chronic myeloid leukemia where the discovery of the Philadelphia chromosome [3] ultimately led to the identification of the BCR-ABL oncogene and the development of tyrosine kinase inhibitors such as imatinib, nilotinib, dasatinib, and so on led to rapid, deep, and long-lasting remissions in this disease [4-6]. Another success story has been acute promyelocytic leukemia with the vast majority of patients now being cured of the disease without the need for any classical chemotherapy [7].

The rapid development of deep sequencing technologies has enabled the discovery of multiple mutations and a deeper understanding of the complex “structure” of the tumor as being composed of multiple subclones that are competing with each other for resources [8-15]. The subclones are being selected for or against by therapy [16]. Principles from evolutionary biology have been applied to understand the dynamics of how these clones change in time [16, 17]. It appears that in the absence of therapy, neutral evolution is very important for the development of the tumor [18], but in the presence of therapy, the potential fitness advantage of resistant clones dominates. The identification of a specific tumor sequence also enables monitoring of patients using simple blood tests (liquid biopsy) [19] for the presence of disease and its burden and perhaps will be used in the future to screen people for premalignant or early malignant processes.

Naturally over the years, a major focus has been on the tumor cells themselves leading to major advances in understanding of signaling pathways that are critical for tumor cell replication, growth, survival, and cell cycle regulation. This leads to the discovery of important pathways such as the Janus kinases/signal transducer and activator of transcription proteins (JAK/STAT), phosphoinositide-3 (PI-3) kinase, Protein kinase B (AKT), and receptor tyrosine kinases (RTK) [1, 2]. All of this knowledge has been translated into effective therapies for a wide variety of tumors including myeloproliferative neoplasms, hepatocellular carcinoma, renal cell carcinoma, nonsmall cell lung cancer, and so on. The discovery of potent anti-apoptotic mechanisms that are overexpressed in tumor cells has led to th

自从1971年向癌症宣战以来,我们对这类不同疾病的理解有了爆炸式的增长。分子遗传学和分子生物学技术的应用使人们能够深入了解癌症表型背后的遗传、表观遗传、信号级联、生存途径和侵袭机制[1,2]。与此同时,这已经转化为更有效和安全的药物的发展,这些药物通过不同的作用机制起作用,并针对肿瘤生物学的基本方面。以慢性髓系白血病为例,费城染色体的发现[3]最终导致了BCR-ABL癌基因的鉴定,酪氨酸激酶抑制剂如伊马替尼、尼罗替尼、达沙替尼等的开发导致了这种疾病的快速、深度和持久的缓解[4-6]。另一个成功案例是急性早幼粒细胞白血病,目前绝大多数患者无需任何经典化疗即可治愈该病[7]。深度测序技术的快速发展使得人们能够发现多种突变,并对肿瘤复杂的“结构”有了更深入的了解,肿瘤是由多个相互竞争资源的亚克隆组成的[8-15]。这些亚克隆是通过治疗来选择的[16]。进化生物学的原理已被应用于理解这些克隆如何随时间变化的动力学[16,17]。似乎在没有治疗的情况下,中性进化对肿瘤的发展非常重要[18],但在有治疗的情况下,抗性克隆的潜在适应度优势占主导地位。特定肿瘤序列的识别还可以通过简单的血液检查(液体活检)对患者进行监测[19],以了解疾病的存在及其负担,并可能在未来用于筛查人们的癌前或早期恶性过程。自然,多年来,主要的焦点一直放在肿瘤细胞本身上,导致对肿瘤细胞复制、生长、存活和细胞周期调节的关键信号通路的理解取得重大进展。这导致了重要途径的发现,如Janus激酶/转录蛋白信号换能器和激活因子(JAK/STAT)、磷酸肌醇-3 (PI-3)激酶、蛋白激酶B (AKT)和受体酪氨酸激酶(RTK)[1,2]。所有这些知识已经转化为各种肿瘤的有效治疗方法,包括骨髓增生性肿瘤、肝细胞癌、肾细胞癌、非小细胞肺癌等。肿瘤细胞中过度表达的有效抗凋亡机制的发现导致了目前针对BCL2的有效治疗方法的发展,但其他靶向MCL-1和其他分子正在研究中。“组学”革命使研究肿瘤的基因组、表观基因组、代谢组和蛋白质组成为可能。一个新的认识水平可能是相当意想不到的,涉及到代谢在肿瘤中的重要性。糖酵解和三羧酸循环的改变与异柠檬酸脱氢酶(IDH) 1和2的突变已被首先在脑肿瘤中发现[20],随后在髓系肿瘤中发现[21],为开发IDH1和IDH2抑制剂等药物提供了合理的靶点,这些药物已转化为改善患者的预后。肿瘤细胞对糖酵解的异常依赖性(Warburg效应)是基于18f -氟脱氧葡萄糖的PET成像的基础,该成像提供了更好的肿瘤负荷量化,监测治疗反应,并经常用于各种疾病的预后。增强的代谢需要持续的资源供应,这与肿瘤内血管生成的证据非常吻合[22],这种方法也被转化为特定肿瘤的治疗方法,特别是胃肠道和肺部的肿瘤。多年来,肿瘤中免疫细胞的存在被认为是一种附带现象,直到在某些肿瘤中,免疫细胞的存在与预后的改善有关[23]。从那时起,免疫肿瘤学领域掀起了癌症界的风暴。免疫检查点的发现以及随后PD-1、PDL1和CTLA-4抑制剂的开发以及免疫突触的有效治疗[24]改善了许多癌症患者的预后。针对多种肿瘤抗原的单克隆抗体的开发以及抗体药物偶联物的产生也提供了有效的新疗法。 最近,随着靶向表达CD19或BCMA的肿瘤的重组嵌合抗原受体t细胞疗法的发展,该领域获得了额外的推动[25],但随着更多肿瘤特异性抗原在临床试验中研究并随后转化为实践,该领域有望取得重大进展。这些发现有效地改变了我们对肿瘤的看法。肿瘤不仅由恶性细胞组成,而且有相当多的间充质细胞、血管、细胞外基质和免疫细胞的支持,所有这些都有助于肿瘤群体的生长。在某些肿瘤中,恶性细胞群甚至是少数(例如,经典霍奇金淋巴瘤)。在这方面,癌症是一种在体内进化的器官,可以威胁到个人的生命。癌症的发生与体内存在的大量细胞、微小但不可避免的突变率[26]、人类预期寿命的增加、可增加突变率的环境因素以及免疫系统未能根除早期突变克隆[27-29]有关。癌症是一个多细胞的问题,长期以来,大型生物已经发展出降低患癌症风险的机制,包括将突变积累和保留的风险降至最低的特定组织结构[30]。这种肿瘤的整体观点需要一个系统的方法来理解和发展这些疾病的治疗方法。我们生活在大数据时代[31,32]。如今,在诊断时对肿瘤进行测序几乎已成为常规。来自同一细胞但不同患者的肿瘤内的基因组多样性是明确的,需要识别每个患者肿瘤的特定驱动突变,在这种情况下,平均值不够好[33]。同样,我们对药物基因组学的理解也在迅速增加,希望在不久的将来,我们将能够为患有特定肿瘤的正确患者确定正确的药物或药物组合。这将有望在为患者提供真正个性化治疗的同时,最大限度地提高反应和减少毒性。患者的高分辨率成像捕获肿瘤负荷,未来使用特定成像探针识别治疗靶点将成为常规[34]。人工智能辅助病理标本分析将有助于进一步对肿瘤进行亚分类,梳理出新的诊断标志物,并提高检测灵敏度[35]。因此,未来对癌症患者的护理将更多地由数据驱动,用培根的话来说,需要理解数据,以便将其转化为可以应用于患者护理的知识(智慧)。随着时间相关数据的引入,从物理、化学、工程和数学的角度更加强调肿瘤的物理方面,癌症研究也转变为系统科学[36-39]。癌症数学模型的发展有着悠久的历史,因为我们已经看到了进化和进化博弈论的应用,以了解肿瘤的起源和发展以及对治疗的抵抗[40-43]。几个肿瘤中心物理科学的资助和癌症系统生物学联盟的后续发展(https://www.cancer.gov/about-nci/organization/dcb/research-programs/csbc)承诺促进更多的癌症跨学科研究。鉴于数据产生的爆炸式增长,很明显需要一本专门针对癌症领域中至关重要的计算和系统方法的期刊。基于这个原因,我们的新期刊《计算与系统肿瘤学》将于今年发行。该杂志成功地吸引了一个涵盖所有相关领域的国际编辑委员会,包括信息学、计算和理论生物学、人工智能、图像分析、数学建模、进化动力学和博弈论、免疫遗传学和物理生物学。本刊的总体目标是提供一个传播技术和应用的平台,以促进从“系统方法”理解癌症。我们也处于大数据时代,癌症提供了一个非常成熟的领域,可以使用包含许多生活史的大型数据集来梳理出哪些疗法可能有效,哪些无效。因此,该杂志欢迎应用于肿瘤基因组学、蛋白质组学、代谢组学、人工智能、数据科学、肿瘤免疫学和免疫遗传学、治疗学、分子成像、进化动力学和博弈论等领域的数学和计算方法的稿件。 我们鼓励开放共享作者为快速传播信息而开发的计算和系统工具,以使其在肿瘤学研究和实践中快速和广泛的应用。感谢您考虑将《计算与系统肿瘤学》作为下一期出版物。
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引用次数: 0
Computational and systems biology of cancer 癌症的计算和系统生物学
Pub Date : 2020-07-28 DOI: 10.22541/au.159592715.53358895
D. Dingli
Ever since the war on cancer was declared in 1971, there has been an explosion in our understanding of this diverse group of diseases. The application of molecular genetics and molecular biology technologies have enabled a deep understanding of the genetic, epigenetic, signaling cascades, survival pathways, and invasive mechanisms that underlie the cancer phenotype. Concomitantly this has translated in the development of ever more effective and safe medications that work through different mechanisms of action and target fundamental aspects of the biology of the tumor. The paradigm has been chronic myeloid leukemia where the discovery of the Philadelphia chromosome, ultimately led to the identification of the BCR-ABL oncogene and the development of tyrosine kinase inhibitors such as imatinib, nilotinib, dasatinib and others and lead to rapid, deep and long-lasting remissions in this disease. Another success story has been acute promyelocytic leukemia with the vast majority of patients now being cured of the disease without the need for any classical chemotherapy.
自从1971年向癌症宣战以来,我们对这类不同疾病的理解有了爆炸式的增长。分子遗传学和分子生物学技术的应用使人们能够深入了解癌症表型背后的遗传、表观遗传、信号级联、生存途径和侵袭机制。与此同时,这已经转化为更有效和安全的药物的发展,这些药物通过不同的作用机制起作用,并针对肿瘤生物学的基本方面。范例是慢性髓性白血病,其中费城染色体的发现最终导致BCR-ABL癌基因的鉴定和酪氨酸激酶抑制剂如伊马替尼、尼罗替尼、达沙替尼等的开发,并导致该疾病的快速、深度和持久缓解。另一个成功案例是急性早幼粒细胞白血病,绝大多数患者现在都治愈了,不需要任何传统的化疗。
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引用次数: 0
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Computational and systems oncology
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