Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Warwick Cancer Research Centre, University of Warwick, Coventry, UK.
Sci Rep. 2023 May 22;13(1):8255. doi: 10.1038/s41598-023-34853-x.
Personalised approaches to cancer therapeutics primarily involve identification of patient sub-populations most likely to benefit from targeted drugs. Such a stratification has led to plethora of designs of clinical trials that are often too complex due to the need for incorporating biomarkers and tissue types. Many statistical methods have been developed to address these issues; however, by the time such methodology is available research in cancer has moved on to new challenges and therefore in order to avoid playing catch-up it is necessary to develop new analytic tools alongside. One of the challenges facing cancer therapy is to effectively and appropriately target multiple therapies for sensitive patient population based on a panel of biomarkers across multiple cancer types, and matched future trial designs. We present novel geometric methods (mathematical theory of hypersurfaces) to visualise complex cancer therapeutics data as multidimensional, as well as geometric representation of oncology trial design space in higher dimensions. The hypersurfaces are used to describe master protocols, with application to a specific example of a basket trial design for melanoma, and thus setup a framework for further incorporating multi-omics data as multidimensional therapeutics.
个性化癌症治疗方法主要涉及确定最有可能从靶向药物中受益的患者亚群。这种分层导致了大量临床试验设计，由于需要纳入生物标志物和组织类型，这些设计往往过于复杂。已经开发了许多统计方法来解决这些问题；然而，当这种方法可用时，癌症研究已经进入了新的挑战，因此为了避免追赶，有必要同时开发新的分析工具。癌症治疗面临的挑战之一是如何针对多种疗法进行有效和适当的靶向治疗，这些疗法针对的是基于多个癌症类型的生物标志物面板的敏感患者群体，并匹配未来的试验设计。我们提出了新的几何方法（超曲面数学理论），将复杂的癌症治疗数据可视化多维，以及肿瘤学试验设计空间在更高维度的几何表示。超曲面用于描述主方案，应用于黑色素瘤篮子试验设计的特定示例，从而为进一步将多组学数据作为多维治疗方法纳入建立了一个框架。
