Clinica Chirurgica Generale 2, Department of Oncological and Surgical Sciences, University of Padova, Padova, Italy.
PLoS One. 2010 Aug 10;5(8):e11965. doi: 10.1371/journal.pone.0011965.
The efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically untested and therefore do not translate into a therapeutic benefit for patients.
To present a computational method aimed to comprehensively exploit the scientific knowledge in order to foster the development of personalized cancer treatment by matching the patient's molecular profile with the available evidence on targeted therapy.
To this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for novel therapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data were manually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapy tested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochrane databases were searched.
We created a manually annotated database (Targeted Therapy Database, TTD) where the relevant data are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up for the identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatments based on the molecular profile of individual patients. In this essay we describe the principles and computational algorithms of an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal of fruitfully driving both preclinical and clinical research on anticancer targeted therapy. In the light of its theoretical nature, the prediction performance of this model must be validated before it can be implemented in the clinical setting.
目前的抗癌治疗效果远不能令人满意，许多患者仍死于该疾病。尽管在临床实践中还处于起步阶段，但人们普遍认为迫切需要开发针对特定分子的治疗方法。事实上，尽管有大量的临床前研究解决了这些问题，但在临床领域测试每种靶向治疗假说的困难是一个内在的障碍。因此，我们看到了一种矛盾的情况，即大多数关于癌症的分子和细胞生物学的假说在临床上仍未得到验证，因此不能为患者带来治疗上的益处。
提出一种计算方法，旨在全面利用科学知识，通过将患者的分子谱与针对特定疗法的现有证据相匹配，促进个性化癌症治疗的发展。
为此，我们专注于黑色素瘤，这是一种越来越常见的恶性肿瘤，由于在晚期没有有效的治疗方法，因此迫切需要新的治疗方法。从描述任何类型的抗黑色素瘤靶向治疗在任何类型的实验或临床模型中进行测试的同行评审全文原始文章中手动提取了相关数据。为此，搜索了 Medline、Embase、Cancerlit 和 Cochrane 数据库。
我们创建了一个手动注释数据库（靶向治疗数据库，TTD），其中将相关数据收集在可进行计算分析的正式表示中。为基于现有证据确定流行的治疗假说以及基于个体患者的分子谱对治疗方法进行排名，制定了专门的算法。在本文中，我们描述了一种原始方法的原理和计算算法，该方法旨在充分利用癌症生物学的现有知识，最终目标是为抗癌靶向治疗的临床前和临床研究提供有力指导。鉴于其理论性质，在将该模型应用于临床环境之前，必须验证其预测性能。
