Nongenetic Evolution Drives Lung Adenocarcinoma Spatial Heterogeneity and Progression.
Daniele TavernariElena BattistelloElie DheillyAaron S PetruzzellaMarco MinaJessica Sordet-DessimozSolange PetersThorsten KruegerDavid GfellerNicolo RiggiElisa OricchioIgor LetovanecGiovanni CirielloPublished in: Cancer discovery (2021)
Cancer evolution determines molecular and morphologic intratumor heterogeneity and challenges the design of effective treatments. In lung adenocarcinoma, disease progression and prognosis are associated with the appearance of morphologically diverse tumor regions, termed histologic patterns. However, the link between molecular and histologic features remains elusive. Here, we generated multiomics and spatially resolved molecular profiles of histologic patterns from primary lung adenocarcinoma, which we integrated with molecular data from >2,000 patients. The transition from indolent to aggressive patterns was not driven by genetic alterations but by epigenetic and transcriptional reprogramming reshaping cancer cell identity. A signature quantifying this transition was an independent predictor of patient prognosis in multiple human cohorts. Within individual tumors, highly multiplexed protein spatial profiling revealed coexistence of immune desert, inflamed, and excluded regions, which matched histologic pattern composition. Our results provide a detailed molecular map of lung adenocarcinoma intratumor spatial heterogeneity, tracing nongenetic routes of cancer evolution. SIGNIFICANCE: Lung adenocarcinomas are classified based on histologic pattern prevalence. However, individual tumors exhibit multiple patterns with unknown molecular features. We characterized nongenetic mechanisms underlying intratumor patterns and molecular markers predicting patient prognosis. Intratumor patterns determined diverse immune microenvironments, warranting their study in the context of current immunotherapies.This article is highlighted in the In This Issue feature, p. 1307.
Keyphrases
- single cell
- single molecule
- machine learning
- case report
- endothelial cells
- newly diagnosed
- genome wide
- squamous cell
- ejection fraction
- oxidative stress
- patient reported outcomes
- electronic health record
- big data
- prognostic factors
- chronic kidney disease
- pluripotent stem cells
- childhood cancer
- neural network
- heat shock