Abstract 130: Resolving the spatial and cellular architecture of lung adenocarcinoma by multi-region single-cell sequencing

Author(s):  
Ansam Sinjab ◽  
Guangchun Han ◽  
Warapen Treekitkarnmongkol ◽  
Kieko Hara ◽  
Patrick Brennan ◽  
...  
2021 ◽  
pp. candisc.1285.2020
Author(s):  
Ansam Sinjab ◽  
Guangchun Han ◽  
Warapen Treekitkarnmongkol ◽  
Kieko Hara ◽  
Patrick M Brennan ◽  
...  

2020 ◽  
Author(s):  
Ansam Sinjab ◽  
Guangchun Han ◽  
Kieko Hara ◽  
Warapen Treekitkarnmongkol ◽  
Patrick Brennan ◽  
...  

ABSTRACTLittle is known of the geospatial architecture of individual cell populations in lung adenocarcinoma (LUAD) evolution. Here, we perform single-cell RNA sequencing of 186,916 cells from five early-stage LUADs and fourteen multi-region normal lung tissues of defined spatial proximities from the tumors. We show that cellular lineages, states, and transcriptomic features geospatially evolve across normal regions to the LUADs. LUADs exhibit pronounced intratumor cell heterogeneity within single sites and transcriptional lineage-plasticity programs driven by KRAS mutations. T regulatory cell phenotypes are increased in normal tissues with closer proximity to LUAD, in contrast to diminished signatures and fractions of cytotoxic CD8+ T cells, antigen-presenting macrophages and inflammatory dendritic cells. Further, the LUAD ecosystem harbors gain of ligand-receptor based interactions involving increased expression of CD24 antigen on epithelial cells and SIGLEC10 on myeloid subsets. These data provide a spatial atlas of LUAD evolution, and a resource for identification of targets for treatment.Statement of significanceThe geospatial ecosystem of the peripheral lung and early-stage LUAD is not known. Our multi-region single-cell sequencing analyses unravel cell populations, states, and phenotypes in the spatial and ecological evolution LUAD from the lung that comprise high-potential targets for early interception.


2019 ◽  
Vol 14 (10) ◽  
pp. S440-S441
Author(s):  
B.M. Ku ◽  
H.A. Jung ◽  
J. Sun ◽  
S. Lee ◽  
J.S. Ahn ◽  
...  

2021 ◽  
Vol 2 (2) ◽  
pp. 100583
Author(s):  
Isabella Del Priore ◽  
Sai Ma ◽  
Jonathan Strecker ◽  
Tyler Jacks ◽  
Lindsay M. LaFave ◽  
...  

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2278
Author(s):  
Afshin Derakhshani ◽  
Zeinab Rostami ◽  
Hossein Safarpour ◽  
Mahdi Abdoli Shadbad ◽  
Niloufar Sadat Nourbakhsh ◽  
...  

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.


Author(s):  
Xue Bai ◽  
Yuxuan Li ◽  
Xuemei Zeng ◽  
Qiang Zhao ◽  
Zhiwei Zhang

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