Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Clin Cancer Res. 2020 Jan 1;26(1):82-92. doi: 10.1158/1078-0432.CCR-19-1467. Epub 2019 Nov 21.
Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption.
We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema.
We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX.
The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.
近年来，胰腺癌的分子亚型分类取得了重大进展，这有助于优化现有的治疗方法，改善胰腺癌的临床结局。随着治疗组合和选择的进步，确定如何让患者尽早接受最佳治疗方法变得越来越重要。尽管已经提出了各种胰腺癌的分子亚型分类系统，但对于所提出的亚型及其相对临床实用性，仍缺乏共识，这是广泛采用的一个自然障碍。
我们在两项临床试验的结果以及公开表达数据的荟萃分析的背景下，评估了三种主要的亚型分类方案，以评估亚型稳健性和整体临床相关性的统计标准。然后，我们基于最稳健和可重复的方案，使用惩罚逻辑回归开发了一种单样本分类器（SSC）。
我们证明了肿瘤内在的两亚型分类方案是最稳健、可重复和具有临床相关性的。我们开发了 PurIST（Purity Independent Subtyping of Tumors），这是一种基于广泛平台和样本类型具有稳健和高度可重复性能的 SSC。我们表明，PurIST 亚型与患者预后有显著的关联，并对 FOLIFIRNOX 的治疗反应有重要影响。
PurIST 在肿瘤活检等低输入样本上的灵活性和实用性使其能够在诊断时用于为胰腺导管腺癌患者选择有效的治疗方法，并应在未来的临床试验中考虑。
