In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 205-205
Abstract:
Introduction: Genomic analyses have yielded a tremendous amount of data on the genetic changes in lung cancers, but translating these experiments into actionable information benefitting lung squamous cell carcinoma (SQLC) patients has proven more difficult. Studies by the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC), our group, and others have demonstrated that gene and protein expression show only moderate correlation, demonstrating limitations in explaining phenotypic changes from genomics alone. These findings indicate a clear need for integrative proteogenomics to better understand tumor biology, especially in a complex disease like SQLC. Experimental: We have assembled a comprehensive proteogenomic dataset including DNA copy number (Affymetrix CytoScan HD Assay), targeted exome sequencing (Agilent Comprehensive Cancer Panel), RNA-sequencing (Illumina NextSeq), and shotgun proteomics (Q Exactive LC-MS/MS) on 116 surgically resected SQLC tumor samples with extensive clinical and follow up data. Results: We have identified 6584 high confidence proteins from preliminary proteomic analysis. After quality control filtering, we utilized 5562 gene-protein pairs for further analysis. Clustering of patient RNA expression in this patient cohort has been unable to fully reproduce the molecular classification previously published for SQLC. Furthermore, proteomic results indicate yet another potential classification strategy selecting patient subgroups that differ at protein level. We observed a 0.29 median Spearman’s correlation of 5562 gene-protein pairs. There were 2781 highly correlated gene-protein pairs (greater than median) and 2781 poorly correlated gene-protein pairs (less than median) including 773 anti-correlated gene-protein pairs (less than 0). We hypothesized that poorly correlated gene-protein pairs could be functionally related in a pathway-dependent manner. Enrichment analysis of poorly correlated proteins identified pathways related to mRNA processing, growth factor signaling (EGFR, FGFR), and nonsense-mediated decay (NMD). Interestingly, there were 9 frequently mutated SQLC genes in the low correlation gene-protein pairs but only 3 in the highly correlated pairs. We found three distinct patient subgroups by clustering poorly correlated proteins. Analysis of these subgroups showed differentially expressed pathways related to mRNA processing, ubiquitination, and NMD. Conclusion: Differential modulation of the proteome outside of genomic regulation may suggest important regulatory mechanisms in cancer and give new insights into treating SQLC. Analysis of poorly correlated gene-protein pairs suggests certain pathways are dysregulated in cancer, and ongoing DNA analysis and future analyses involving miRNAs, RNA-binding proteins, and the ubiquitin proteome system will help elucidate our preliminary findings. Citation Format: Paul A. Stewart, Robbert J. Slebos, Eric A. Welsh, Ling Cen, Yonghong Zhang, Zhihua Chen, Chia-Ho Cheng, Fredrik Pettersson, Anders Berglund, Guolin Zhang, Bin Fang, Victoria Izumi, Sean Yoder, Katherine Fellows, Ann Chen, Jamie K. Teer, Steven A. Eschrich, John M. Koomen, Eric B. Haura. Underlying mechanisms of genome-proteome discordance in squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 205. doi:10.1158/1538-7445.AM2017-205
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2017-205
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2017
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
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