Back

Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics

Wang, J.; Seo, J. W.; Kare, A. J.; Schneider, M.; Tumbale, S. K.; Wu, B.; Raie, M. N.; Pandrala, M.; Iagaru, A.; Brunsing, R. L.; Charville, G. W.; Park, W. G.; Ferrara, K. W.

2024-01-08 bioengineering
10.1101/2024.01.07.574209 bioRxiv
Show abstract

We apply spatial transcriptomics and proteomics to select pancreatic cancer surface receptor targets for molecular imaging and theranostics using an approach that can be applied to many cancers. Selected cancer surfaceome epithelial markers were spatially correlated and provided specific cancer localization, whereas the spatial correlation between cancer markers and immune- cell or fibroblast markers was low. While molecular imaging of cancer-associated fibroblasts and integrins has been proposed for pancreatic cancer, our data point to the tight junction protein claudin-4 as a theranostic target. Claudin-4 expression increased [~]16 fold in cancer as compared with normal pancreas, and the tight junction localization conferred low background for imaging in normal tissue. We developed a peptide-based molecular imaging agent targeted to claudin-4 with accumulation to [~]25% injected activity per cc (IA/cc) in metastases and [~]18% IA/cc in tumors. Our work motivates a new approach for data-driven selection of molecular targets.

Matching journals

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

1
Nature Communications
○ 5641 papers in training set
Top 3%
31.2%
Similar papers in this journal
2
Science Advances
● 1243 papers in training set
Top 1%
9.8%
Similar papers in this journal
3
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.1%
9.7%
Similar papers in this journal
50% of probability mass above
4
Cancer Research
● 130 papers in training set
Top 0.4%
6.3%
Similar papers in this journal
5
Nature Metabolism
○ 69 papers in training set
Top 0.6%
3.2%
Similar papers in this journal
6
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.8%
7
eLife
● 5828 papers in training set
Top 40%
2.7%
8
Communications Biology
○ 993 papers in training set
Top 7%
2.7%
9
Cell Reports
○ 1498 papers in training set
Top 15%
2.5%
10
Advanced Science
○ 286 papers in training set
Top 4%
2.1%
11
Cell
○ 431 papers in training set
Top 5%
2.1%
12
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 25%
1.9%
13
Cell Reports Medicine
○ 153 papers in training set
Top 2%
1.7%
14
Nature Genetics
○ 286 papers in training set
Top 4%
1.3%
15
Nature Methods
○ 385 papers in training set
Top 5%
1.1%
16
Theranostics
● 37 papers in training set
Top 1%
0.8%
17
Cell Systems
○ 201 papers in training set
Top 4%
0.8%
18
npj Precision Oncology
○ 53 papers in training set
Top 2%
0.8%
19
Nature Nanotechnology
○ 32 papers in training set
Top 0.6%
0.8%
20
Scientific Reports
○ 3612 papers in training set
Top 76%
0.6%

"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.