A network-based approach to overcome BCR::ABL1-independent resistance in chronic myeloid leukemia

一种基于网络的方法克服慢性粒细胞白血病中 BCR::ABL1 非依赖性耐药性

阅读:17
作者:Valeria Bica #,Veronica Venafra #,Giorgia Massacci,Simone Graziosi,Sara Gualdi,Gessica Minnella,Federica Sorà,Patrizia Chiusolo,Maria Elsa Brunetti,Gennaro Napolitano,Massimo Breccia,Dimitrios Mougiakakos,Martin Böttcher,Thomas Fischer,Livia Perfetto,Francesca Sacco

Abstract

Background: About 40% of relapsed or non-responder tumors exhibit therapeutic resistance in the absence of a clear genetic cause, suggesting a pivotal role of intracellular communication. A deeper understanding of signaling pathways rewiring occurring in resistant cells is crucial to propose alternative effective strategies for cancer patients. Methods: To achieve this goal, we developed a novel multi-step strategy, which integrates high sensitive mass spectrometry-based phosphoproteomics with network-based analysis. This strategy builds context-specific networks recapitulating the signaling rewiring upon drug treatment in therapy-resistant and sensitive cells. Results: We applied this strategy to elucidate the BCR::ABL1-independent mechanisms that drive relapse upon therapy discontinuation in chronic myeloid leukemia (CML) patients. We built a signaling map, detailing - from receptor to key phenotypes - the molecular mechanisms implicated in the control of proliferation, DNA damage response and inflammation of therapy-resistant cells. In-depth analysis of this map uncovered novel therapeutic vulnerabilities. Functional validation in patient-derived leukemic stem cells revealed a crucial role of acquired FLT3-dependency and its underlying molecular mechanism. Conclusions: In conclusion, our study presents a novel generally applicable strategy and the reposition of FLT3, one of the most frequently mutated drivers of acute leukemia, as a potential therapeutic target for CML relapsed patients.

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。