Identifying a gene signature of metastatic potential by linking pre-metastatic state to ultimate metastatic fate.
Jesse S HandlerZijie LiRachel K DveirinWeixiang FangHani GoodarziElana J FertigReza KalhorPublished in: bioRxiv : the preprint server for biology (2024)
Identifying the key molecular pathways that enable metastasis by analyzing the eventual metastatic tumor is challenging because the state of the founder subclone likely changes following metastatic colonization. To address this challenge, we labeled primary mouse pancreatic ductal adenocarcinoma (PDAC) subclones with DNA barcodes to characterize their pre-metastatic state using ATAC-seq and RNA-seq and determine their relative in vivo metastatic potential prospectively. We identified a gene signature separating metastasis-high and metastasis-low subclones orthogonal to the normal-to-PDAC and classical-to-basal axes. The metastasis-high subclones feature activation of IL-1 pathway genes and high NF-κB and Zeb/Snail family activity and the metastasis-low subclones feature activation of neuroendocrine, motility, and Wnt pathway genes and high CDX2 and HOXA13 activity. In a functional screen, we validated novel mediators of PDAC metastasis in the IL-1 pathway, including the NF-κB targets Fos and Il23a , and beyond the IL-1 pathway including Myo1b and Tmem40 . We scored human PDAC tumors for our signature of metastatic potential from mouse and found that metastases have higher scores than primary tumors. Moreover, primary tumors with higher scores are associated with worse prognosis. We also found that our metastatic potential signature is enriched in other human carcinomas, suggesting that it is conserved across epithelial malignancies. This work establishes a strategy for linking cancer cell state to future behavior, reveals novel functional regulators of PDAC metastasis, and establishes a method for scoring human carcinomas based on metastatic potential.
Keyphrases
- squamous cell carcinoma
- small cell lung cancer
- rna seq
- endothelial cells
- genome wide
- stem cells
- signaling pathway
- oxidative stress
- machine learning
- single cell
- immune response
- cystic fibrosis
- risk assessment
- deep learning
- transcription factor
- dna methylation
- gene expression
- computed tomography
- cell proliferation
- long non coding rna
- pseudomonas aeruginosa
- copy number
- inflammatory response
- pi k akt
- induced pluripotent stem cells
- current status
- nuclear factor
- genome wide analysis
- pluripotent stem cells