Bulk and single-cell transcriptome profiling reveal tumor-specific T-based molecular classification, tumor microenvironment infiltration characterization relevant to combination therapies in lung adenocarcinoma

Abstract Background: In lung adenocarcinoma (LUAD), the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices. Methods: We defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups. Results: We identified six clusters of T cells as TSTs in LUAD, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in LUAD provides the theoretical basis for combined anti-angiogenic and immunotherapy. Lachnoclostridium affect Hyaluronidase and promotes immune infiltration in the high TSTS group through CXCL16-CXCR6 between myeloid and T cells. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy. Conclusions: The proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in LUAD patients for helping clinical treatment choices..

Medienart:

Preprint

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

ResearchSquare.com - (2022) vom: 03. Juni Zur Gesamtaufnahme - year:2022

Sprache:

Englisch

Beteiligte Personen:

Luo, Ziwei [VerfasserIn]
Liu, Xuefei [VerfasserIn]
Chen, Ying [VerfasserIn]
Shen, Lize [VerfasserIn]
Qin, Hui [VerfasserIn]
Zha, Qiongfang [VerfasserIn]
Hu, Feng [VerfasserIn]
Wang, Yali [VerfasserIn]

Links:

Volltext [kostenfrei]

doi:

10.21203/rs.3.rs-1710574/v1

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XRA036185051