Back

Mapping the immune landscape in small cell lung cancer unveils a distinct tumor-reactive CD8+ T cell molecular signature

Khinvasara, K.; Diken, E.; Gerbracht, J. V.; Huduti, E.; D'Rozario, J.; Omokoko, T.; Newrzela, S.; Akilli, O.; Lang, F.; Schroers, B.; Hoepker, K.; Stanganello, E.; Schork, M.; Gargano, A.; Al Alwash, A.; Weber, J.-P.; George, J.; Thomas, R. K.; Kuebler, A.; Diken, M.; Sahin, U.; Kolb, L.

2026-07-03 immunology
10.64898/2026.06.29.735200 bioRxiv
Show abstract

Small cell lung cancer (SCLC) is a highly aggressive malignancy with limited therapeutic advances. Unlike many other cancers, its immune landscape, particularly immune competence and T cell recognition, remains poorly characterized. Here, we generate a single-cell transcriptome atlas of the SCLC immune microenvironment with paired T cell receptor (TCR) sequencing. By linking T cell states with clonality and a multilayered functional screening, we identify 6 tumor-reactive TCRs that recognize and eradicate autologous SCLC cell lines. We delineate a novel SCLC-reactive CD8+ T cell signature (SCLC_TR), enabling the identification of 47 further SCLC-reactive TCRs. The SCLC_TR signature performs extremely well in pancreatic ductal adenocarcinoma (PDAC), another immune-cold tumor indication, and, most strikingly, patients with elevated SCLC_TR signature scores exhibited significantly improved survival, underlining its prognostic potential. Comparative cell-cell interaction analyses implicate several immunosuppressive mechanisms, with myeloid cells and CD4+ regulatory T cells possibly acting as counterbalances to effector T cell activity in SCLC. In summary, our study challenges the prevailing notion of SCLC as an immune-cold tumor type by providing direct evidence of tumor-reactive T cell responses and introduces the SCLC_TR signature as a tool to identify tumor-specific T cells and their microenvironmental restraints and escape mechanisms, ultimately shaping next-generation immunotherapeutic strategies.

Matching journals

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

1
Nature Immunology
79 papers in training set
Top 0.2%
11.8%
2
Journal for ImmunoTherapy of Cancer
75 papers in training set
Top 0.2%
11.8%
3
Nature Communications
5641 papers in training set
Top 17%
10.9%
4
Cancer Cell
42 papers in training set
Top 0.1%
10.6%
5
Cell Reports
1498 papers in training set
Top 6%
5.5%
50% of probability mass above
6
Science Immunology
88 papers in training set
Top 1.0%
2.8%
7
eLife
5828 papers in training set
Top 41%
2.4%
8
Science Advances
1243 papers in training set
Top 14%
2.4%
9
Immunity
67 papers in training set
Top 0.7%
2.4%
10
Cancer Letters
35 papers in training set
Top 0.4%
2.1%
11
Cancer Research
130 papers in training set
Top 2%
2.1%
12
Gut
40 papers in training set
Top 0.4%
2.1%
13
Cancer Discovery
66 papers in training set
Top 1%
1.7%
14
Cancer Immunology Research
35 papers in training set
Top 0.5%
1.7%
15
Cell Reports Medicine
153 papers in training set
Top 2%
1.7%
16
Frontiers in Immunology
638 papers in training set
Top 6%
1.5%
17
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 33%
1.3%
18
Signal Transduction and Targeted Therapy
30 papers in training set
Top 0.4%
1.3%
19
Gastroenterology
42 papers in training set
Top 0.8%
1.1%
20
Cell
431 papers in training set
Top 8%
1.1%
21
Communications Biology
993 papers in training set
Top 22%
1.1%
22
Genome Medicine
183 papers in training set
Top 4%
1.1%
23
JCI Insight
277 papers in training set
Top 7%
1.0%
24
npj Precision Oncology
53 papers in training set
Top 1%
1.0%
25
European Journal of Immunology
60 papers in training set
Top 1%
0.8%
26
eBioMedicine
183 papers in training set
Top 6%
0.8%
27
Journal of Experimental Medicine
119 papers in training set
Top 3%
0.8%
28
Oncogene
85 papers in training set
Top 2%
0.8%
29
iScience
1154 papers in training set
Top 35%
0.8%