scholarly journals Quantitative Assessment of CMTM6 in the Tumor Microenvironment and Association with Response to PD-1 Pathway Blockade in Advanced-Stage Non–Small Cell Lung Cancer

2019 ◽  
Vol 14 (12) ◽  
pp. 2084-2096 ◽  
Author(s):  
Jon Zugazagoitia ◽  
Yuting Liu ◽  
Maria Toki ◽  
John McGuire ◽  
Fahad Shabbir Ahmed ◽  
...  
2002 ◽  
Vol 29 (3 Suppl 12) ◽  
pp. 10-16 ◽  
Author(s):  
Angela Davies ◽  
David R. Gandara ◽  
Primo Lara ◽  
Zelanna Goldberg ◽  
Peter Roberts ◽  
...  

2017 ◽  
Vol 35 (5) ◽  
pp. 529-535 ◽  
Author(s):  
Cathy J. Bradley ◽  
K. Robin Yabroff ◽  
Angela B. Mariotto ◽  
Christopher Zeruto ◽  
Quyen Tran ◽  
...  

Purpose Multiple agents for advanced non–small-cell lung cancer (NSCLC) have been approved in the past decade, but little is known about their use and associated spending and survival. Methods We used SEER-Medicare data for elderly patients with a new diagnosis of advanced-stage NSCLC and were treated with antineoplastic agents between 2000 and 2011 (N = 22,163). We estimated the adjusted percentage of patients who received each agent, days while on treatment, survival, and spending in the 12 months after diagnosis. Results During the 12-year study period, a marked shift in treatment occurred along with a rapid adoption of pemetrexed (39.2%), erlotinib (20.3%), and bevacizumab (18.9%) and a decline in paclitaxel (38.7%), gemcitabine (17.0%), and vinorelbine (5.7%; all P < .05). The average total days on therapy increased by 5 days (from 103 to 108 days). Patients who received bevacizumab, erlotinib, or pemetrexed had the longest treatment durations on average (approximately 146 days v 75 days for those who did not receive these agents). Approximately 44% of patients received antineoplastic agents in the last 30 days of life throughout the study period. Acute inpatient spending declined (from $29,376 to $23,731), whereas outpatient spending increased 23% (from $37,931 to $46,642). Median survival gains of 1.5 months were observed. Conclusion Considerable shifts in the treatment of advanced-stage NSCLC occurred along with modest gains in survival and total Medicare spending. More precise outcome information is needed to inform value-based treatment decisions for advanced-stage NSCLC.


2006 ◽  
Vol 13 ◽  
pp. S243
Author(s):  
John Nemunaitis ◽  
Thierry Jahan ◽  
Helen Ross ◽  
Daniel Sterman ◽  
Donald Richards ◽  
...  

2002 ◽  
Vol 29 (3) ◽  
pp. 10-16 ◽  
Author(s):  
Angela Davies ◽  
David R. Gandara ◽  
Primo Lara ◽  
Zelanna Goldberg ◽  
Peter Roberts ◽  
...  

2018 ◽  
Vol Volume 10 ◽  
pp. 6555-6561 ◽  
Author(s):  
Ahmed Mohieldin ◽  
Ayman Rasmy ◽  
Mohamed Ashour ◽  
Muath Al-Nassar ◽  
Rola H Ali ◽  
...  

2009 ◽  
Vol 4 (9) ◽  
pp. 1075-1082 ◽  
Author(s):  
Yingwei Qi ◽  
Steven E. Schild ◽  
Sumithra J. Mandrekar ◽  
Angelina D. Tan ◽  
James E. Krook ◽  
...  

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A966-A966
Author(s):  
Hyung-Gyo Cho ◽  
Grace Lee ◽  
Hye Sung Kim ◽  
Sanghoon Song ◽  
Kyunghyun Paeng ◽  
...  

BackgroundThe phosphatidylinositol 3-kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) pathway plays a significant role in both tumorigenesis and progression of disease in non-small cell lung cancer (NSCLC).1 Increased activation of the pathway, whether in tumor or immune cells, results in an immunosuppressive tumor microenvironment.2 Therefore, we looked into how this pathway differs in three distinct NSCLC immune phenotypes.MethodsLunit SCOPE IO (Lunit, Seoul, Republic of Korea), a deep learning-based hematoxylin and eosin (H&E) image analytics tool, identifies lymphocytes and quantifies lymphocyte density within the cancer epithelium (CE-Lym), stroma (CS-Lym), and combined area (C-Lym). We applied Lunit-SCOPE IO to H&E-stained tissue images of 965 NSCLC samples from The Cancer Genome Atlas (TCGA). Tumors in the lowest tertile of C-Lym were labeled as immune-desert, and the remaining tumors were classified as inflamed and immune-excluded according to the median of the ratio of CE-Lym to CS-Lym.Utilizing RNA-sequencing data from TCGA, gene set enrichment analysis (GSEA) was conducted to analyze the differences in mTORC1 and PI3K/Akt/mTOR signaling between the subtypes.3 We obtained mutational data related to the PI3K/Akt/mTOR pathway from cBioPortal to compare the ratio of functional mutations between the immune phenotypes.4ResultsThe mTORC1 signaling gene set was consistently enriched in immune-excluded, whether compared to inflamed (padj < 0.01, normalized enrichment score [NES]: 2.3) or immune-desert (padj < 0.01, NES: 1.6). However, PI3K/Akt/mTOR signaling gene set enrichment did not show statistically significant differences between the immune phenotypes.Within the three immune phenotypes, we analyzed three functional mutations: PIK3CA, PTEN, and Akt1 (figure 1). Of the total 112 samples showing the functional mutations of the PI3K/Akt/mTOR pathway, 53 were immune-excluded, 31 inflamed, and 28 immune-desert. The relation between mutation frequency and the immune subtypes was significant (X2 (2) = 11.1979, p < .01). The immune-excluded was more likely than the other subtypes to have functional PI3K/Akt/mTOR mutations.Abstract 921 Figure 1The landscape of functional mutation and immune phenotypes regarding PI3K/Akt/mTOR pathwayConclusionsThe three tissue phenomic subtypes showed different PI3K/Akt/mTOR pathway profiles, with immune-excluded having the most mutation samples and the greatest enhancement of mTORC1 signaling gene set. Likewise, tissue H&E based tumor microenvironment classification by Lunit SCOPE IO can be applied to other hallmark pathways and tumor types, and such further investigation of the tumor microenvironment can provide insights into novel therapeutic avenues.ReferencesTan AC. Targeting the PI3K/Akt/mTOR pathway in non-small cell lung cancer (NSCLC). Thorac Cancer 2020;11(3):511–8.O’Donnell JS, Massi D, Teng MWL, Mandala M. PI3K-AKT-mTOR inhibition in cancer immunotherapy, redux. Semin Cancer Biol 2018;48:91–103.Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Systems 2015;1(6):417–25.Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2(5):401–4.


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