Pathway potency in methylation altered network reveals lung cancer branching evolution and hallmarks
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
Summary The inability to profile the dynamic evolution and heterogeneity of cancer cells presents a significant barrier to effective early screening for lung cancer. By mapping pathway interactions within and beyond gene systems, one can decode their synergistic interactions underlying molecular stability and state transitions. To this end, we propose an efficient approach that derives pathway potency characterization from methylation altered sample specific networks. Applying it to lung adenocarcinoma samples uncovers a seven-state trajectory across three branching points. We show that while patient survival tends to decline within each state, it improves when the cancer transitions to a new state at branching points, which offers a more dynamic view of progression. Our approach also identifies the top feature pathways that distinguish different cancer states. Given the marked heterogeneity in pathway interplay patterns, we propose this method holds promise for decoding cancer complexity and eventually resolving critical issues in early screening. abs0010 Graphical abstract http://www.w3.org/1999/xlink float portrait ga1.jpg undfig1 anchor portrait graphical abs0015 Highlights • MASS-Path assesses pathway potency from methylation altered sample specific networks u0010 • MASS-Path captures lung cancer’s branching evolution via tumor trajectory inference u0015 • MASS-Path’s discrimination power is validated by top feature pathways between states u0020 • Heterogeneity and plast
Abstract
Summary The inability to profile the dynamic evolution and heterogeneity of cancer cells presents a significant barrier to effective early screening for lung cancer. By mapping pathway interactions within and beyond gene systems, one can decode their synergistic interactions underlying molecular stability and state transitions. To this end, we propose an efficient approach that derives pathway potency characterization from methylation altered sample specific networks. Applying it to lung adenocarcinoma samples uncovers a seven-state trajectory across three branching points. We show that while patient survival tends to decline within each state, it improves when the cancer transitions to a new state at branching points, which offers a more dynamic view of progression. Our approach also identifies the top feature pathways that distinguish different cancer states. Given the marked heterogeneity in pathway interplay patterns, we propose this method holds promise for decoding cancer complexity and eventually resolving critical issues in early screening. abs0010 Graphical abstract http://www.w3.org/1999/xlink float portrait ga1.jpg undfig1 anchor portrait graphical abs0015 Highlights • MASS-Path assesses pathway potency from methylation altered sample specific networks u0010 • MASS-Path captures lung cancer’s branching evolution via tumor trajectory inference u0015 • MASS-Path’s discrimination power is validated by top feature pathways between states u0020 • Heterogeneity and plasticity in pathway interplay patterns inform cancer hallmarks u0025 simple ulist0010 author-highlights abs0020 Bioinformatics; Systems biology; Cancer teaser abs0025