Pancreatic cancer subtypes: a roadmap for precision medicine

胰腺癌亚型:精准医疗路线图

阅读:1

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is projected to become the second cause of cancer-related deaths by 2020. Although it has traditionally been approached as a disease, accumulated evidences point to the clinical heterogeneity of this disease, which translate into disparity in outcomes among the patients. Much emphasis has been put into patient classification introducing a platform for more tailored therapies. In the last 10 years, there have been important advances in the understanding of the molecular pathogenesis of PDAC, which has culminated with a comprehensive integrated genomic analysis from RNA expression profiles. Bailey et al. defined four subtypes and the different transcriptional networks underlying them. Firstly, we briefly describe and compare different subtyping approaches, which are mostly based on tissue mRNA expression analysis. We propose that these latest approaches to disease classification embrace not only those patients that are surgically resectable (20%), but even patients ineligible for surgery. Such considerations will include possible reclassification of these specific subtypes, enabling more personalized diagnosis and individualized treatment. The ultimate goal of this review is to identify current challenges in this area and summarize current efforts in developing clinical modalities that can effectively identify these subtypes in order to advance Precision Medicine. KEY MESSAGES   • Pancreatic cancer can no longer be considered as one disease. • The heterogeneity underlying pancreatic cancer patients makes therapeutic options based on one-size-fits-all approach ineffective.   • Identifying patients that could benefit from a specific treatment would help to avoid futile therapy approaches and to improve outcomes and quality of life of those whose long-term survival is unpromising.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。