Benzekry Sebastian, Tuszynski Jack A, Rietman Edward A, Lakka Klement Giannoula
Inria team MC2, Institut de Mathématiques de Bordeaux, Bordeaux, France.
UMR CNRS 5251, University of Bordeaux, 351 cours de la Libération, Talence, Cedex, 33405, France.
Biol Direct. 2015 May 28;10:32. doi: 10.1186/s13062-015-0058-5.
The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity.
We observed a linear correlation of R = -0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target.
We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger potential for survival benefit. We hope that the use of advanced mathematics in medicine will provide timely information about the best drug combination for patients, and avoid the expense associated with an unsuccessful clinical trial, where drug(s) did not show a survival benefit.
在线生物信息学数据的不断扩展，不仅为探索这些数据的可视化提供了新方法，还能应用新颖的数学方法来提取有意义的信息，用于临床相关的通路分析和治疗决策。用于计算不同空间分辨率下空间拓扑特征的方法之一是持久同调。这一概念也可应用于网络理论，更具体地说，可应用于蛋白质 - 蛋白质相互作用网络，其中单个癌症网络中环的数量代表了一种复杂性度量。
我们观察到持久同调与多种癌症患者的5年生存率之间存在R = -0.55的线性相关性。这种关系被用于预测蛋白质 - 蛋白质相互作用网络中对癌症进展影响最大的蛋白质。通过在计算上从蛋白质 - 蛋白质相互作用网络中移除单个节点（蛋白质）后重新计算持久同调，我们能够评估这种抑制是否会导致患者生存率的提高。这种方法的强大之处在于其能够识别多种蛋白质抑制的效果，以及能够揭示单一抑制的效果是否可能被其他蛋白质的抑制所放大。更重要的是，我们展示了持久同调计算的具体例子，这些例子正确地预测了在使用已鉴定分子靶点抑制剂的临床试验中观察到的生存获益效果。
我们提出，诸如持久同调这样的计算方法未来可能会用于临床分子治疗的选择。该技术使用一种数学算法来评估抑制后能最大程度降低网络复杂性的节点（蛋白质）。持久同调下降得越多，网络复杂性降低得就越多，因此生存获益的潜力就越大。我们希望医学中先进数学的应用能为患者提供关于最佳药物组合的及时信息，并避免与不成功的临床试验相关的费用，即在临床试验中药物未显示出生存获益。
