Back

Spatially-resolved transcriptomics analyses of invasive fronts in solid tumors

Wu, L.; Yan, J.; Bai, Y.; Chen, F.; Xu, J.; Zou, X.; Huang, A.; Hou, L.; Zhong, Y.; Jing, Z.; Zhou, X.; Sun, H.; Cheng, M.; Ji, Y.; Luo, R.; Li, Q.; Wu, L.; Wang, P.; Guo, D.; Huang, W.; Lei, J.; Liao, S.; Li, Y.; Jiang, Z.; Yao, N.; Yu, Y.; Li, Y.; Liu, F.; Zhang, M.; Yang, H.; Yang, S.; Xu, X.; Liu, L.; Wang, X.; Wang, J.; Fan, J.; Liu, S.; Yang, X.; Chen, A.; Zhou, J.

2021-10-22 cancer biology
10.1101/2021.10.21.465135 bioRxiv
Show abstract

Solid tumors are complex ecosystems, and heterogeneity is the major challenge for overcoming tumor relapse and metastasis. Uncovering the spatial heterogeneity of cell types and functional states in tumors is essential for developing effective treatment, especially in invasive fronts of tumor, the most active region for tumor cells infiltration and invasion. We firstly used SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq) with a nanoscale resolution to characterize the tumor microenvironment of intrahepatic cholangiocarcinoma (ICC). Enrichment of distinctive immune cells, suppressive immune microenvironment and metabolic reprogramming of tumor cells were identified in the 500{micro}m-wide zone centered bilaterally on the tumor boundary, namely invasive fronts of tumor. Furthermore, we found the damaged states of hepatocytes with overexpression of Serum Amyloid A (SAA) in invasive fronts, recruiting macrophages for facilitating further tumor invasion, and thus resulting in a worse prognosis. We also confirmed these findings in hepatocellular carcinoma and other liver metastatic cancers. Our work highlights the remarkable potential of high-resolution-spatially resolved transcriptomic approaches to provide meaningful biological insights for comprehensively dissecting the tumor ecosystem and guiding the development of novel therapeutic strategies for solid tumors.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.