Back

A new clustering approach identifies tumor-specific common TCRs with pan-cancer reactivity

Hennig, S.; Gennermann, K.; Elezkurtaj, S.; Seitz, V.; Hirsch, B.; Droege, A.; Schaper, S.; Bents, D.; Eggeling, S.; Beushausen, C.; Herbst, H.; Genzel, N.; Gloekler, J.; Lennerz, V.; Woelfel, C.; Doppler, C.; Hammer, R.

2025-04-30 immunology
10.1101/2025.04.26.650660 bioRxiv
Show abstract

Tumor-specific T-cells are key in combating cancer as shown in adoptive cell therapy with tumor infiltrating lymphocytes (TILs) and checkpoint inhibitor therapy. Studies in many types of cancer have shown that preexisting tumor reactive T-cells are not only tumor-but typically also patient-specific, requiring personalized treatment options. For viral infections, public T-cell receptors (TCRs) with substantial sequence homologies suggest shared immune-dominant targets in human leucocyte antigen (HLA)-matched individuals. We hypothesized that also in the complex TCR repertoires of tumors subsets of tumor-specific TCRs exist that can be found in different patients with identical or near identical TCRs. This paper presents a TCR-V(D)J-sequence clustering approach identifying clusters of tumor-specific common TCRs mainly from TILs of non-small cell lung cancer (NSCLC) patients. Using two TCR-clusters as examples, we show that T-cells engineered genetically to only express those common TCRs recognized HLA-matched allogeneic tumor cell lines in a cluster typical manner. Recognition of allogeneic tumors was dependent on the HLA allele inferred by the cluster and could be blocked by HLA antibodies. In addition to NSCLC, TCR repertoire analyses in pancreatic ductal adenocarcinoma and a smaller number of breast and colorectal cancer samples revealed TCRs highly homologous or even identical to NSCLC cluster TCRs. TCR-T cells expressing TCRs from these tumors assigned to a specific cluster recognized allogeneic tumor lines in the expected cluster-typical manner. These findings suggest a pan-cancer therapeutic potential of tumor-specific common TCRs.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
European Journal of Immunology
60 papers in training set
Top 0.1%
22.3%
2
Frontiers in Immunology
638 papers in training set
Top 0.6%
15.4%
3
PLOS ONE
5266 papers in training set
Top 24%
6.9%
4
Scientific Reports
3612 papers in training set
Top 25%
4.1%
5
Cells
249 papers in training set
Top 0.6%
4.1%
50% of probability mass above
6
Frontiers in Oncology
103 papers in training set
Top 1%
3.3%
7
OncoImmunology
24 papers in training set
Top 0.2%
2.8%
8
International Journal of Molecular Sciences
494 papers in training set
Top 4%
2.7%
9
Cancers
213 papers in training set
Top 2%
2.4%
10
International Journal of Cancer
49 papers in training set
Top 0.5%
2.2%
11
Molecular Immunology
14 papers in training set
Top 0.1%
2.2%
12
Journal for ImmunoTherapy of Cancer
75 papers in training set
Top 1%
1.8%
13
Vaccines
198 papers in training set
Top 2%
1.8%
14
Immunogenetics
11 papers in training set
Top 0.1%
1.8%
15
Biomedicines
67 papers in training set
Top 0.9%
1.8%
16
Cytotherapy
15 papers in training set
Top 0.1%
1.7%
17
Genomics, Proteomics & Bioinformatics
172 papers in training set
Top 1%
1.7%
18
Journal of Experimental & Clinical Cancer Research
25 papers in training set
Top 0.4%
1.4%
19
Immunology
28 papers in training set
Top 0.6%
0.9%
20
Biomolecules
100 papers in training set
Top 3%
0.9%
21
Life
29 papers in training set
Top 0.8%
0.9%
22
Journal of Immunological Methods
24 papers in training set
Top 0.3%
0.9%
23
Computational and Structural Biotechnology Journal
242 papers in training set
Top 6%
0.9%
24
Immunity, Inflammation and Disease
10 papers in training set
Top 0.5%
0.6%
25
Frontiers in Cell and Developmental Biology
233 papers in training set
Top 6%
0.6%
26
Molecular Oncology
55 papers in training set
Top 1%
0.6%
27
Blood Advances
62 papers in training set
Top 1%
0.6%
28
International Immunopharmacology
15 papers in training set
Top 0.4%
0.6%
29
Cytometry Part A
33 papers in training set
Top 0.4%
0.6%