Chrome Extension
WeChat Mini Program
Use on ChatGLM

Survival and Mutational Differences Based on ESR1 and ESR2 Expression in Non-Small Cell Lung Cancer (NSCLC).

Journal of Clinical Oncology(2024)

Norris Comprehensive Cancer Center | University of Southern California | Caris Life Sciences | University of California | Barbara Ann Karmanos Cancer Institute | Sylvester Comprehensive Cancer Center | Fox Chase Cancer Center | Lombardi Comprehensive Cancer Center | Penn State Milton S. Hershey Medical Center | West Cancer Center and Research Institute

Cited 0|Views7
Abstract
8526 Background: Estrogen receptor (ER) can activate MAPK signaling but the contribution of the two classical receptors—ER-alpha ( ESR1) and ER-beta ( ESR2) is unclear. Past trials targeting ER and EGFR in NSCLC lacked efficacy. We evaluated the association of ESR1& 2 expression with the genomic landscape and overall survival (OS) in NSCLC. Methods: NSCLC tumors (N = 21603) were tested at Caris Life Sciences (Phoenix, AZ) with NextGen Sequencing of DNA (592-gene or whole exome) and RNA (whole transcriptome). Mutation prevalence (-Mt) was calculated for pathogenic SNVs/indels. ESR1& 2 expression was split into quartiles (transcripts per million, top (-H) and bottom (-L) quartiles were compared). A transcriptomic signature associated with MAPK activation (MPAS, arbitrary units: AU) was applied. The X2 test was applied, p-value was adjusted for multiple comparisons ( p < .05). Real-world OS was obtained from insurance claims and Kaplan-Meier estimates were calculated. Results: There was a greater proportion of females in ESR1-H (53%) versus (v) -L (45%, p < .05) but not in ESR2-H v -L (50% v 50%). There was a greater proportion of adenocarcinoma (AD) in ESR1-H (65%) v -L (44%) and a greater proportion of squamous (SCC) tumors in ESR2-H (27%) v -L (15%) ( p < .001 all). The prevalence of EGFR-Mt and KRAS-Mt was greater in ESR1-H v -L and smaller in ESR2-H v -L. (Table) Of all KRAS-Mt, there was a greater percent of KRAS G12C-Mt in ESR1-H (44%) v -L (39%, p = .001). In EGFR-Mt, ESR1-H/ ESR2-H had a higher MPAS (1.4 AU) than ESR1-H/ ESR2-L (.52), ESR1-L/ ESR2-H (.59) or ESR1-L/ ESR2-L (-1.6, p < .05). In KRAS-Mt, ESR1-H/ ESR2-H (1.7 AU) and ESR1-L/ ESR2-H (1.8) had a higher MPAS than ESR1-H/ ESR2-L (.7) or ESR1-L/ ESR2-L (-.7, p < .001). ESR1-H had longer median OS v -L (22 months (m) v 16 m, p < .001) as did ESR2-H v -L (23 m v 15 m p< .001). ESR1-H/ ESR2-H had the longest OS (25 m) followed by ESR1-H/ ESR2-L (18 m), ESR1-L/ ESR2-H (16 m) and ESR1-L/ ESR2-L (14 m, p < .001). In EGFR-Mt, a significant difference in survival since treatment (SST) with osimertinib was seen for ESR1-L/ ESR2-H (40 m, N = 13), ESR1-H/ ESR2-L (36 m, N = 62), ESR1-H/ ESR2-H (34 m, N = 161) and ESR1-L/ ESR2-L (30 m, N = 138, p = .03). In KRAS G12C-Mt, a significant difference in SST was seen with sotorasib; ESR1-H/ ESR2-H had the longest SST (median not reached, N = 17), followed by ESR1-H/ ESR2-L (17 m, N = 17), ESR1-L/ ESR2-L (13 m, N = 34) and ESR1-L/ ESR2-H (1 m, N = 1, p = .002). Conclusions: ESR1-H had a higher percentage of females, AD, and EGFR/ KRAS-mt v ESR1-L while ESR2-H had no sex difference, more SCC, and fewer EGFR/ KRAS-mt v ESR2-L. ESR1-H/ ESR2-H tumors had the highest MPAS and longest OS and there were SST differences with EGFR and KRAS G12C inhibition. ESR1& 2 may play key roles in activating the MAPK pathway and future trials could consider targeted therapy combined with ER inhibition based on ESR1&2 expression. [Table: see text]
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined