bioRxivCancer BiologyPreprint
Opposing lineage specifiers induce a pro-tumor hybrid-identity state in lung adenocarcinoma
Gabriela Fort, Henry Arnold, Soledad A. Camolotto, Kayla T. O’TooleiD, Rushmeen Tariq, Anna K. Waters, Katherine GillisiD, Eric L. SnyderiD
Abstract
The ability of cancer cells to alter their identity, known as lineage plasticity, is crucial for tumor progression and therapy resistance. In lung adenocarcinoma (LUAD), tumor progression is characterized by a gradual loss of lineage fidelity and the emergence of non-pulmonary identity programs. This can lead to hybrid-identity (hybrid-ID) states in which developmentally incompatible identity programs are co-activated within individual cells. However, the molecular mechanisms underlying these identity shifts remain incompletely understood. Here, we identify the gastrointestinal (GI) transcriptional regulator HNF4 as a critical driver of tumor growth and proliferation in KRAS-driven LUAD. In LUAD cells that express the lung lineage specifier NKX2-1, HNF4 can induce a GI/liver-like state by directly binding and activating its canonical targets. HNF4 also forms an aberrant protein complex with NKX2-1, which disrupts NKX2-1 localization and dampens pulmonary identity within hybrid-ID LUAD. Sustained signaling through the RAS/MEK pathway is critical for maintaining the hybrid-ID state. Moreover, RAS/MEK inhibition augments NKX2-1 chromatin binding at pulmonary-specific genes and induces resistance-associated pulmonary signatures. Finally, we demonstrate that HNF4 depletion enhances sensitivity to pharmacologic KRASG12D inhibition. Collectively, our data show that co-expression of opposing lineage specifiers leads to a hybrid identity state that can drive tumor progression and dictate response to targeted therapy in LUAD.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 12
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