Back

Spatiotemporal lineage mapping of tumor immune escape with eTRACER

Yang, J.; Hou, L.; Wang, X.; Zhang, N.; Bian, Y.; Lu, Z.; Chen, Y.; Xie, D.; Fang, Y.; Wang, K.; Wan, R.; Jin, Y.; Chen, Y.; Cai, X.; Lee, L. T. O.; Hu, Z.; Ji, H.

2025-08-09 cancer biology
10.1101/2025.08.06.668639 bioRxiv
Show abstract

Deciphering spatiotemporal cell lineage dynamics remains a fundamental yet unresolved challenge. Here we introduce eTRACER, a novel CRISPR-Cas9 lineage tracer that targets neutral 3UTR of high-expression endogenous genes, enabling efficient recovery of static and evolving barcodes from single-cell and spatial transcriptomics. By optimizing gradient editing efficacy and avoiding large disruptive deletions, eTRACER reconstructs high-fidelity and high-resolution single-cell phylogenies. Applied to EGFR-mutant lung adenocarcinoma (LUAD) under CD8+ T cell cytotoxicity, eTRACER reveals directional state transitions from Hypoxic and Proliferative states to Epithelial-Mesenchymal Transition state during immune evasion. Spatially-resolved lineage mapping unveils layered stratification of distinct tumor states and location-primed cell migration and state transitions. Lineage-coupled single-cell multiomic analysis uncovers cooperative mechanism between tumor cell-intrinsic AP-1 transcriptional program and spatially restricted macrophage-tumor cell interaction leading to immune evasion. Collectively, we develop a powerful spatiotemporal lineage tracer and uncover microenvironment-primed cellular evolution underlying immune evasion of EGFR-mutant LUAD, with important implication for efficient immunotherapy.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 5%
25.9%
Similar papers in this journal
2
Cell Reports
○ 1498 papers in training set
Top 3%
7.7%
Similar papers in this journal
3
Science Advances
● 1243 papers in training set
Top 5%
5.4%
Similar papers in this journal
4
Nature
○ 645 papers in training set
Top 4%
4.3%
Similar papers in this journal
5
Cancer Discovery
● 66 papers in training set
Top 0.7%
3.9%
Similar papers in this journal
6
Cancer Cell
○ 42 papers in training set
Top 0.3%
3.4%
50% of probability mass above
7
Advanced Science
○ 286 papers in training set
Top 2%
3.3%
8
Cell
○ 431 papers in training set
Top 3%
3.2%
9
eLife
● 5828 papers in training set
Top 37%
3.2%
10
Nature Cell Biology
○ 118 papers in training set
Top 1%
3.1%
11
Molecular Cell
○ 350 papers in training set
Top 3%
2.1%
12
Nature Methods
○ 385 papers in training set
Top 4%
2.1%
13
Nature Cancer
○ 39 papers in training set
Top 0.8%
1.9%
14
Science
● 477 papers in training set
Top 5%
1.7%
15
Communications Biology
○ 993 papers in training set
Top 15%
1.7%
16
Cell Research
○ 51 papers in training set
Top 0.6%
1.6%
17
Cell Genomics
○ 172 papers in training set
Top 3%
1.4%
18
Cancer Immunology Research
● 35 papers in training set
Top 0.6%
1.4%
19
Genome Medicine
○ 183 papers in training set
Top 3%
1.3%
20
Genome Biology
○ 637 papers in training set
Top 7%
1.3%
21
Nature Genetics
○ 286 papers in training set
Top 4%
1.3%
22
Cell Stem Cell
○ 62 papers in training set
Top 1%
1.1%
23
iScience
○ 1154 papers in training set
Top 28%
1.1%
24
Cancer Research
● 130 papers in training set
Top 3%
1.1%
25
Nature Biotechnology
○ 172 papers in training set
Top 4%
1.0%
26
Developmental Cell
○ 196 papers in training set
Top 4%
0.8%
27
Nucleic Acids Research
◐ 1281 papers in training set
Top 14%
0.8%
28
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 42%
0.8%
29
Cell Reports Medicine
○ 153 papers in training set
Top 5%
0.8%

"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.