Therapeutic Efficacy of Zinc Oxide Nanoparticles Against Small Cell Lung Cancer in an Orthotopic Xenograft Model

Author(s):  
R. Tanino ◽  
Y. Amano ◽  
X. Tong ◽  
R. Sun ◽  
J. Umemoto ◽  
...  
2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A1013-A1013
Author(s):  
Stephanie Schmidt ◽  
Younghee Lee ◽  
Cheuk Leung ◽  
Lorenzo Federico ◽  
Heather Lin ◽  
...  

BackgroundHow neoadjuvant chemo-immunotherapy modulates tumor immune composition and response is not completely understood. We interrogate immunomodulation of neoadjuvant platinum-based chemotherapy (C), nivolumab (N), and N-plus-C (NC) and their connections to therapeutic efficacy in resected non-small cell lung cancer (NSCLC) by integrating immunomic data from the ImmunogenomiC PrOfiling of NSCLC (ICON) study and NEOSTAR trial cohorts.MethodsIn NEOSTAR (NCT03158129), patients with stage I-IIIA (single N2) resectable NSCLC (AJCC7th) received N (3 mg/kg IV, D1,15,29); patients with stage IB(≥4cm)-IIIA (single N2) resectable NSCLC received NC (N 360 mg IV plus C, D1,22,43 for 3 cycles, every 3 weeks) before surgery; major pathologic response (MPR) was the primary endpoint. In ICON, patients with stage IB(≥4cm)-IIIA resectable NSCLC received C before surgery. Surgically resected tumor samples underwent immune profiling via flow cytometry (n=16,13,9 for C,N,NC), immunohistochemistry (IHC;n=0,18,14), and multiplexed immunofluorescence (mIF;n=28,16,10). Treatment-associated immunomodulation and associations with therapeutic efficacy were analyzed using: 1) a shared nearest neighbors-based network we developed linking measurements across datasets; 2) MetaCyto, a specialized cytometry analysis method for identifying cell subsets by clustering.ResultsWe holistically explored the immunomic data by integration across cohorts. Through hierarchical regression of the integrated data, we determined the overall effect of a given treatment controlling for the presence or absence of the other treatment.We examined C’s effects across all cohorts controlling for N. Across all patients, regardless of MPR, C is associated with immunosuppression, increasing PD1+ T cell (CD45+CD3+) populations: regulatory (CD4+CD25+FOXP3+), helper (CD4+), and effector (CD8+) (effect size(ES):1.48,1.61,1.26;q<0.05). C also decreases proliferative (Ki67+) populations: helper and effector T cells as well as NK (CD45+CD3-CD56+) cells (ES:-1.27,-1.43;-1.36;q<0.05). In patients without MPR (i.e., non-responding patients), immunosuppression appears heightened by increased Ki67+ regulatory T cells (ES:1.86;q<0.05).Conversely, we examined N’s effects across all cohorts controlling for C. Across all patients, regardless of MPR, N is associated with immune activation, increasing ICOS+ T cell populations: regulatory, helper, and effector (ES:1.29,1.29,1.47;q<0.05). Comparing N and NC reveals that adding C may drive exhaustion by increasing TIM3+ regulatory, helper and effector T cells (ES:1.16,1.17,1.23;q<0.05), an effect more pronounced in non-responding patients (ES:1.31,1.33,1.35;q<0.05).ConclusionsWe report the first integrated examination of the immunomodulatory effect of neoadjuvant C and N. C is associated with immunosuppression while N with immune activation; together, N appears to lessen C’s suppressive effects. Incorporation of transcriptomics into this integrated network of flow cytometry, mIF, and IHC immune profiling data is ongoing to augment translational insights for neoadjuvant chemo/immunotherapies.


Cancers ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 282 ◽  
Author(s):  
Tatsuya Imabayashi ◽  
Junji Uchino ◽  
Hisayuki Osoreda ◽  
Keiko Tanimura ◽  
Yusuke Chihara ◽  
...  

Previously, we reported that nicotine reduces erlotinib sensitivity in a xenograft model of PC9, an epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI)-sensitive non-small-cell lung cancer cell line. The present study examined whether smoking induces erlotinib resistance in vitro. We assessed resistance to EGFR-TKIs by treating cancer cell lines with erlotinib, afatinib, or osimertinib, and serum collected from smokers within 30 min of smoking and that from a non-smoker as a control. We also assessed erlotinib resistance by treating PC9 cells exposed to serum from a smoker or a non-smoker, or serum from an erlotinib user. Treatment of the cancer cell lines with serum from smokers induced significant erlotinib resistance, compared with the control (p < 0.05). Furthermore, serum samples with a high concentration of cotinine (a nicotine exposure indicator) demonstrated stronger erlotinib resistance than those with low concentrations. Similar to the observations with erlotinib treatment of cell lines, the analysis of serum from erlotinib users revealed that smokers demonstrated significantly reduced sensitivity to erlotinib (p < 0.001). In conclusion, our present results support the hypothesis that smoking contributes to resistance to erlotinib therapy in non-small-cell lung cancer.


Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 740 ◽  
Author(s):  
Julia Schueler ◽  
Cordula Tschuch ◽  
Kerstin Klingner ◽  
Daniel Bug ◽  
Anne-Lise Peille ◽  
...  

In up to 30% of non-small cell lung cancer (NSCLC) patients, the oncogenic driver of tumor growth is a constitutively activated epidermal growth factor receptor (EGFR). Although these patients gain great benefit from treatment with EGFR tyrosine kinase inhibitors, the development of resistance is inevitable. To model the emergence of drug resistance, an EGFR-driven, patient-derived xenograft (PDX) NSCLC model was treated continuously with Gefitinib in vivo. Over a period of more than three months, three separate clones developed and were subsequently analyzed: Whole exome sequencing and reverse phase protein arrays (RPPAs) were performed to identify the mechanism of resistance. In total, 13 genes were identified, which were mutated in all three resistant lines. Amongst them the mutations in NOMO2, ARHGEF5 and SMTNL2 were predicted as deleterious. The 53 mutated genes specific for at least two of the resistant lines were mainly involved in cell cycle activities or the Fanconi anemia pathway. On a protein level, total EGFR, total Axl, phospho-NFκB, and phospho-Stat1 were upregulated. Stat1, Stat3, MEK1/2, and NFκB displayed enhanced activation in the resistant clones determined by the phosphorylated vs. total protein ratio. In summary, we developed an NSCLC PDX line modelling possible escape mechanism under EGFR treatment. We identified three genes that have not been described before to be involved in an acquired EGFR resistance. Further functional studies are needed to decipher the underlying pathway regulation.


2019 ◽  
Vol 133 (2) ◽  
pp. 381-392 ◽  
Author(s):  
Jianjun Jin ◽  
Huanqin Wang ◽  
Jiming Si ◽  
Ran Ni ◽  
Yuanhua Liu ◽  
...  

Abstract Long non-coding RNAs (lncRNAs) have been reported to play a vital role in non-small-cell lung cancer (NSCLC). ZEB1-AS1 overexpression predicts a poor prognosis in osteosarcoma and colorectal cancers. In the current study, we determined the clinical significance and prognostic value of ZEB1-AS1 in patients with NSCLC. The expression of ZEB1-AS1 and inhibitor of differentiation-1 (ID1) was measured using qRT-PCR and Western blot. Cell growth, migration, and invasion were determined using colony formation assays, Transwell assay, and flow cytometry, respectively. Tumor growth was determined with a xenograft model. ZEB1-AS1 was significantly up-regulated in NSCLC tissues compared with normal samples. ZEB1-AS1 overexpression was significantly associated with advanced tumor, lymph node, and metastases (TNM) stage and tumor size, as well as poorer overall survival. Moreover, ZEB1-AS1 knockdown inhibited NSCLC cell proliferation and migration, and promoted cell apoptosis. In addition, a chromatin immunoprecipitation assay revealed that ZEB1-AS1 interacted with STAT3, thereby repressing ID1 expression. Furthermore, rescue experiments indicated that ZEB1-AS1 functioned as an oncogene partly by repressing ID1 in NSCLC cells. Taken together, our findings indicate that ZEB1-AS1 serves as a promising therapeutic target to predict the prognosis of NSCLC.


2010 ◽  
Author(s):  
Syed H. Jafri ◽  
Molly Boyd ◽  
Shakeela Bahadur ◽  
Jonathan Glass ◽  
Runhua Shi ◽  
...  

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