scholarly journals OA19.03 Identify Lung Adenocarcinoma in Situ among Pulmonary Micro-Nodules through Blood Gene Expression Profiles

2017 ◽  
Vol 12 (1) ◽  
pp. S319
Author(s):  
Baohui Han ◽  
Huiming Wang ◽  
Changming Cheng ◽  
Xueyan Zhang ◽  
Wenjia Yang ◽  
...  
2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A954-A955
Author(s):  
Jacob Kaufman ◽  
Doug Cress ◽  
Theresa Boyle ◽  
David Carbone ◽  
Neal Ready ◽  
...  

BackgroundLKB1 (STK11) is a commonly disrupted tumor suppressor in NSCLC. Its loss promotes an immune exclusion phenotype with evidence of low expression of interferon stimulated genes (ISG) and decreased microenvironment immune infiltration.1 2 Clinically, LKB1 loss induces primary immunotherapy resistance.3 LKB1 is a master regulator of a complex downstream kinase network and has pleiotropic effects on cell biology. Understanding the heterogeneous phenotypes associated with LKB1 loss and their influence on tumor-immune biology will help define and overcome mechanisms of immunotherapy resistance within this subset of lung cancer.MethodsWe applied multi-omic analyses across multiple lung adenocarcinoma datasets2 4–6 (>1000 tumors) to define transcriptional and genetic features enriched in LKB1-deficient lung cancer. Top scoring phenotypes exhibited heterogeneity across LKB1-loss tumors, and were further interrogated to determine association with increased or decreased markers of immune activity. Further, immune cell-types were estimated by Cibersort to identify effects of LKB1 loss on the immune microenvironment. Key conclusions were confirmed by blinded pathology review.ResultsWe show that LKB1 loss significantly affects differentiation patterns, with enrichment of ASCL1-expressing tumors with putative neuroendocrine differentiation. LKB1-deficient neuroendocrine tumors had lower expression of Interferon Stimulated Genes (ISG), MHC1 and MHC2 components, and immune infiltration compared to LKB1-WT and non-neuroendocrine LKB1-deficient tumors (figure 1).The abundances of 22 immune cell types assessed by Cibersort were compared between LKB1-deficient and LKB1-WT tumors. We observe skewing of immune microenvironmental composition by LKB1 loss, with lower abundance of dendritic cells, monocytes, and macrophages, and increased levels of neutrophils and plasma cells (table 1). These trends were most pronounced among tumors with neuroendocrine differentiation, and were concordant across three independent datasets. In a confirmatory subset of 20 tumors, plasma cell abundance was assessed by a blinded pathologist. Pathologist assessment was 100% concordant with Cibersort prediction, and association with LKB1 loss was confirmed (P=0.001).Abstract 909 Figure 1Immune-associated Gene Expression Profiles Affected by Neuroendocrine Differentiation within LKB1-Deficient Lung Adenocarcinomas. Gene expression profiles corresponding to five immune-associated phenotypes are shown with bars indicating average GEP scores for tumors grouped according to LKB1 and neuroendocrine status as indicated. P-values represent results from Student’s T-test between groups as indicated.Abstract 909 Table 1LKB1 Loss Affects Composition of Immune Microenvironment. Values indicate log10 P-values comparing LKB1-loss to LKB1-WT tumors. Positive (red) indicates increased abundance in LKB1 loss. Negative (blue) indicates decreased abundance.ConclusionsWe conclude that tumor differentiation patterns strongly influence the immune microenvironment and immune exclusion characteristics of LKB1-deficient tumors. Neuroendocrine differentiation is associated with the strongest immune exclusion characteristics and should be evaluated clinically for evidence of immunotherapy resistance. A novel observation of increased plasma cell abundance is observed across multiple datasets and confirmed by pathology. Causal mechanisms linking differentiation status to immune activity is not well understood, and the functional role of plasma cells in the immune biology of LKB1-deficient tumors is undefined. These questions warrant further study to inform precision immuno-oncology treatments for these patients.AcknowledgementsThis work was funded by SITC AZ Immunotherapy in Lung Cancer grant (SPS256666) and DOD Lung Cancer Research Program Concept Award (LC180633).ReferencesSkoulidis F, Byers LA, Diao L, et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov 2015;5:860–77.Schabath MB, Welsh EA, Fulp WJ, et al. Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene 2016;35:3209–16.Skoulidis F, Goldberg ME, Greenawalt DM, et al. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discovery 2018;8:822-835.Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014;511:543–50.Chitale D, Gong Y, Taylor BS, et al. An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors. Oncogene 2009;28:2773–83.Shedden K, Taylor JM, Enkemann SA, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 2008;14:822–7.


2020 ◽  
Author(s):  
Mizuki Honda ◽  
Shinya Oki ◽  
Akihito Harada ◽  
Kazumitsu Maehara ◽  
Kaori Tanaka ◽  
...  

ABSTRACTIn multicellular organisms, individual cells are characterized by their gene expression profiles and the spatial interactions among cells enable the elaboration of complex functions. Expression profiling in spatially defined regions is crucial to elucidate cell interactions and functions. Here, we established a transcriptome profiling method coupled with photo-isolation chemistry (PIC) that allows the determination of expression profiles specifically from photo-irradiated regions of whole tissues. PIC uses photo-caged oligodeoxynucleotides for in situ reverse transcription. After photo-irradiation of limited areas, gene expression was detected from at least 10 cells in the tissue sections. PIC transcriptome analysis detected genes specifically expressed in small distinct areas of the mouse embryo. Thus, PIC enables transcriptome profiles to be determined from limited regions at a spatial resolution up to the diffraction limit.


2019 ◽  
Vol 48 (3) ◽  
pp. 030006051989383 ◽  
Author(s):  
Xing Wu ◽  
Linlin Wang ◽  
Fan Feng ◽  
Suyan Tian

Objective To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications. Methods Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes. Results The five-gene diagnostic signature including keratin 5 ( KRT5), mucin 1 ( MUC1), triggering receptor expressed on myeloid cells 1 ( TREM1), complement C3 ( C3) and transmembrane serine protease 2 ( TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide ( ADH1C), alpha-2-glycoprotein 1, zinc-binding ( AZGP1), clusterin ( CLU), cyclin dependent kinase 1 ( CDK1) and paternally expressed 10 ( PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set. Conclusions Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.


PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e84002 ◽  
Author(s):  
Samantha E. Tangen ◽  
Darwin Tsinajinnie ◽  
Martha Nuñez ◽  
Gabriel Q. Shaibi ◽  
Lawrence J. Mandarino ◽  
...  

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