A02 Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Gillette, Michael A.; Satpathy, Shankha; Cao, Song; Dhanasekaran, Saravana M.; Vasaikar, Suhas; Krug, Karsten; Petralia, Francesca; Li, Y.; Liang, Wen-Wei; Reva, Boris; Hong, Runyu; Savage, Sara R.; Getz, Gad; Li, Q.K.; Zhang, B.; Rodriguez, Henry; Ruggles, Kelly V.; Robles, Ana I.; Clauser, Karl; Govindan, Ramaswamy; Wang, P.; Nesvizhskii, Alexey I.; Li, Ding; Mani, D. R.; Carr, Steven A.

doi:10.1016/j.jtho.2019.12.031

Cited by 48 publications

(93 citation statements)

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“…To study the regulation of protein activities of cancer cells, we compiled and standardised multiomics datasets made available by the CPTAC consortium (Fig 1A, Appendix Fig S1A; Materials and Methods). These datasets were comprised of cancer patient samples with matched somatic mutations, gene copy number variation (CNV), mRNA expression, protein abundance, phosphorylation and clinical data from nine tissues: breast (Cancer Genome Atlas Network, 2012b; Mertins et al , 2016), brain (Petralia et al , 2020), colorectal (Cancer Genome Atlas Network, 2012a; Zhang et al , 2014; Vasaikar et al , 2019), ovarian (Cancer Genome Atlas Research Network, 2011; Zhang et al , 2016), liver (Gao et al , 2019), kidney (Clark et al , 2019), uterus (Dou et al , 2020), lung (Gillette et al , 2020) and stomach (Mun et al , 2019). In addition, we collected data for breast (Lawrence et al , 2015; Lapek et al , 2017) and colorectal (Roumeliotis et al , 2017) cancer cell lines, for which multiomics data were available (Fig 1A, Appendix Fig S1A; Materials and Methods).…”

Section: Resultsmentioning

confidence: 99%

“…The mass spectrometry (MS)‐based protein and phosphosite quantifications (absolute [phospho]peptide intensities and ratios relative to controls) for the cancer samples of brain (Petralia et al , 2020), breast (Mertins et al , 2016), colorectal (Zhang et al , 2014), kidney (Clark et al , 2019), liver (Gao et al , 2019), lung (Gillette et al , 2020), ovarian (Zhang et al , 2016), stomach (Mun et al , 2019) and uterus (Dou et al , 2020) were downloaded from the CPTAC data portal (http://proteomics.cancer.gov/data-portal). For the colon cancer samples (Vasaikar et al , 2019), we downloaded the data from the linkedomics database (http://linkedomics.org/login.php).…”

Section: Methodsmentioning

confidence: 99%

“…The RNA‐seq data were obtained in the format of read counts and Fragments Per Kilobase of transcript per Million mapped reads (FPKM). The data for the tumour tissues of breast (Mertins et al , 2016), colorectal (Zhang et al , 2014), kidney (Clark et al , 2019), lung (Gillette et al , 2020), ovarian (Zhang et al , 2016) and uterus (Dou et al , 2020) were downloaded from the GDC portal (http://portal.gdc.cancer.gov/). The data for the brain cancer were compiled from the paediatric cBioPortal (http://pedcbioportal.kidsfirstdrc.org/); for the liver (Gao et al , 2019) from NODE (http://www.biosino.org/node/; accession ID: OEP000321); for the stomach (Mun et al , 2019) from GEO (http://ncbi.nlm.nih.gov/geo/; accession ID: GSE122401); and for the colon cancer (Vasaikar et al , 2019) from the authors.…”

Section: Methodsmentioning

confidence: 99%

“…The MAF file for the colon cancer samples (Vasaikar et al , 2019) was downloaded from the linkedomics database (http://linkedomics.org/login.php). Regarding the kidney (Clark et al , 2019), lung (Gillette et al , 2020) and uterus (Dou et al , 2020) cancers, we downloaded the MuTect2‐called and VEP‐annotated VCF files from the GDC data portal (http://portal.gdc.cancer.gov/). For the liver (Gao et al , 2019) and stomach cancers (Mun et al , 2019), we obtained the somatic mutations from the publication and authors, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The RNA-seq data were obtained in the format of read counts and Fragments Per Kilobase of transcript per Million mapped reads (FPKM). The data for the tumour tissues of breast (Mertins et al, 2016), colorectal (Zhang et al, 2014), kidney (Clark et al, 2019), lung (Gillette et al, 2020), ovarian (Zhang et al, 2016) and uterus (Dou et al, 2020) were downloaded from the GDC portal (portal.gdc.cancer.gov/). The data for the brain cancer were compiled from the paediatric cBioPortal (pedcbioportal.kidsfirstdrc.…”

Section: Transcriptomicsmentioning

confidence: 99%

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Pan‐Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Sousa

Dugourd

Memon

et al. 2023

Molecular Systems Biology

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Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours has been extensively characterized, the measurements of protein activities has been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Kinase activities are, on average, not strongly determined by protein abundance but rather by their phosphorylation state while the reverse is more common for TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. Loss-of-function mutation is not often associated with dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into dysregulation of protein activities in cancer and their contribution to disease severity.1 .

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Transcriptomicsmentioning

confidence: 99%

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Pan‐Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Sousa

Dugourd

Memon

et al. 2023

Molecular Systems Biology

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Transcriptional analysis of landmark molecular pathways in lung adenocarcinoma results in a clinically relevant classification with potential therapeutic implications

Hijazo‐Pechero,

Alay,

Cordero

et al. 2023

Molecular Oncology

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Lung adenocarcinoma (LUAD) is a molecularly heterogeneous disease. In addition to genomic alterations, cancer transcriptional profiling can be helpful to tailor cancer treatment and to estimate each patient's outcome. Transcriptional activity levels of 50 molecular pathways were inferred in 4,573 LUAD patients using Gene Set Variation Analysis (GSVA) method. Seven LUAD subtypes were defined and independently validated based on the combined behavior of the studied pathways: AD (adenocarcinoma subtype)1‐7. AD1, AD4 and AD5 subtypes were associated with better overall survival. AD1 and AD4 subtypes were enriched in epidermal growth factor receptor (EGFR) mutations, whereas AD2 and AD6 showed higher tumor protein p53 (TP53) alteration frequencies. AD2 and AD6 subtypes correlated with higher genome instability, proliferation‐related pathways expression and specific sensitivity to chemotherapy, based on data from LUAD cell lines. LUAD subtypes were able to predict immunotherapy response in addition to CD274 (PD‐L1) gene expression and tumor mutational burden (TMB). AD2 and AD4 subtypes were associated with potential resistance and response to immunotherapy, respectively. Thus, analysis of transcriptomic data could improve patient stratification beyond genomics and single biomarkers (i.e., PD‐L1, TMB) and may lay the foundation for more personalized treatment avenues, especially in driver‐negative LUAD.

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FBXL16 promotes cell growth and drug resistance in lung adenocarcinomas with KRAS mutation by stabilizing IRS1 and upregulating IRS1/AKT signaling

Morel,

Long

2024

Molecular Oncology

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Lung cancer is the leading cause of cancer‐related deaths worldwide. Lung adenocarcinomas (LUADs) are a major subtype of non‐small cell lung cancers (NSCLCs). About 25% of LUADs harbor GTPase KRAS mutations associated with poor prognosis and limited treatment options. While encouraging tumor response to novel covalent inhibitors specifically targeting KRASG12C have been shown in the clinic, either intrinsic resistance exists or acquired therapeutic resistance arises upon treatment. There is an unmet need to identify new therapeutic targets for treating LUADs with activating KRAS mutations, particularly those with resistance to KRASG12C inhibitor(s). In this study, we have revealed that F‐box/LRR‐repeat protein 16 (FBXL16) is selectively upregulated in LUAD with KRAS mutations. It promotes LUAD cell growth and transforms lung epithelial cells. Importantly, FBXL16 depletion greatly enhances sensitivity to the KRASG12C inhibitor (sotorasib) in resistant cells by downregulating phosphatidylinositol 3‐kinase (PI3K)/protein kinase B (PKB; also known as AKT) signaling. Mechanistically, FBXL16 upregulates insulin receptor substrate 1 (IRS1) protein stability, leading to an increase of IGF1/AKT signaling, thereby promoting cell growth and migration. Taken together, our study highlights the potential of FBXL16 as a therapeutic target for treating LUAD with KRAS activating mutations.

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A02 Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Cited by 48 publications

References 0 publications

Pan‐Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Pan‐Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Transcriptional analysis of landmark molecular pathways in lung adenocarcinoma results in a clinically relevant classification with potential therapeutic implications

FBXL16 promotes cell growth and drug resistance in lung adenocarcinomas with KRAS mutation by stabilizing IRS1 and upregulating IRS1/AKT signaling

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