Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma

Sharpnack, Michael F.; Ranbaduge, Nilini; Srivastava, Arunima; Cerciello, Ferdinando; Codreanu, Simona G.; Liebler, D.C.; Mascaux, Céline; Miles, Wayne; Morris, Robert T.; McDermott, Jason; Sharpnack, James; Amann, Joseph M.; Maher, Christopher A.; Machiraju, Raghu; Wysocki, Vicki H.; Govindan, R.; Mallick, Parag; Coombes, Kevin R.; Huang, Kun; Carbone, David P.

doi:10.1016/j.jtho.2018.06.025

Cited by 18 publications

(19 citation statements)

References 42 publications

(48 reference statements)

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“…The three CPTAC flagship reports on colorectal 23 , breast 24 , and ovarian 25 cancers, respectively, have provided a resource to the cancer research community, both by indicating which of the genomic and transcriptomic features associated with these cancers was recapitulated at the protein level, and by providing new insights into the substantial effects of post-translational modifications, specifically phosphorylation and acetylation, on the functional activities associated with DNA repair, proliferation, and survival. The proteogenomic approach has also been applied to other cancer studies based on tumor specimens, patient-derived xenograft (PDX) models, or cell lines 26–33 . There have been multiple reviews on the concept and methods of proteogenomics and its application to cancer research 34–42 .…”

Section: Proteogenomics-driven Target Discovery: Added Value Of Protementioning

confidence: 99%

Clinical potential of mass spectrometry-based proteogenomics

et al. 2018

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Cancer genomics research aims to advance personalized oncology by finding and targeting genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients based on the genomic sequence of their tumor. This personalized oncology approach has prolonged survival for subsets of cancer patients. However, many patients do not respond to the predicted therapies based on genomic profiles of their tumors. Recent studies pairing genomic and proteomic analyses in the same tumors have shown that the proteome encodes novel information that is not discerned through genomic analysis alone. This has led to the concept of “proteogenomics,” in which both types of data are leveraged for a more complete view of tumor biology that may more successfully match cancer patients to efficacious treatments. We discuss the added value of a combined proteogenomics approach over the current genome-centric approach in characterizing human cancers, and summarize current efforts to incorporate targeted proteomic measurements based on multiple reaction monitoring mass spectrometry (MRM) into the clinical laboratory to facilitate clinical proteogenomics.

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Section: Proteogenomics-driven Target Discovery: Added Value Of Protementioning

confidence: 99%

Clinical potential of mass spectrometry-based proteogenomics

et al. 2018

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“…Consequently, numerous additional prognostic or predictive factors [ 5 – 7 ], among image-derived parameters [ 8 – 12 ], have been investigated aiming at more differentiated outcome prediction and more differentiated management decisions. Among parameters from positron emission tomography/computed tomography with [ 18 F]fluorodeoxyglucose (FDG-PET/CT), asphericity (ASP) is a parameter that reflects shape irregularity of the primary tumor’s metabolic tumor volume (MTV), combining metric and metabolic features of the primary tumor.…”

Section: Introductionmentioning

confidence: 99%

Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

et al. 2020

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Background Asphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) in FDG-PET/CT is independently predictive for survival in patients with non-small cell lung cancer (NSCLC). However, comparability between PET systems may be limited. Therefore, reproducibility of ASP was evaluated at varying image reconstruction and acquisition times to assess feasibility of ASP assessment in multicenter studies. Methods This is a retrospective study of 50 patients with NSCLC (female 20; median age 69 years) undergoing pretherapeutic FDG-PET/CT (median 3.7 MBq/kg; 180 s/bed position). Reconstruction used OSEM with TOF4/16 (iterations 4; subsets 16; in-plane filter 2.0, 6.4 or 9.5 mm), TOF4/8 (4 it; 8 ss; filter 2.0/6.0/9.5 mm), PSF + TOF2/17 (2 it; 17 ss; filter 2.0/7.0/10.0 mm) or Bayesian-penalized likelihood (Q.Clear; beta, 600/1750/4000). Resulting reconstructed spatial resolution (FWHM) was determined from hot sphere inserts of a NEMA IEC phantom. Data with approx. 5-mm FWHM were retrospectively smoothed to achieve 7-mm FWHM. List mode data were rebinned for acquisition times of 120/90/60 s. Threshold-based delineation of primary tumor MTV was followed by evaluation of relative ASP/SUVmax/MTV differences between datasets and resulting proportions of discordantly classified cases. Results Reconstructed resolution for narrow/medium/wide in-plane filter (or low/medium/high beta) was approx. 5/7/9 mm FWHM. Comparing different pairs of reconstructed resolution between TOF4/8, PSF + TOF2/17, Q.Clear and the reference algorithm TOF4/16, ASP differences was lowest at FWHM of 7 versus 7 mm. Proportions of discordant cases (ASP > 19.5% vs. ≤ 19.5%) were also lowest at 7 mm (TOF4/8, 2%; PSF + TOF2/17, 4%; Q.Clear, 10%). Smoothing of 5-mm data to 7-mm FWHM significantly reduced discordant cases (TOF4/8, 38% reduced to 2%; PSF + TOF2/17, 12% to 4%; Q.Clear, 10% to 6%), resulting in proportions comparable to original 7-mm data. Shorter acquisition time only increased proportions of discordant cases at < 90 s. Conclusions ASP differences were mainly determined by reconstructed spatial resolution, and multicenter studies should aim at comparable FWHM (e.g., 7 mm; determined by in-plane filter width). This reduces discordant cases (high vs. low ASP) to an acceptable proportion for TOF and PSF + TOF of < 5% (Q.Clear: 10%). Data with better resolution (i.e., lower FWHM) could be retrospectively smoothed to the desired FWHM, resulting in a comparable number of discordant cases.

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“…Recently, proteogenomic analysis has become a powerful tool for cancer research and several studies on lung cancer have been reported. In a recent study, Sharpnack et al (2018) established a novel analysis about the co-relation between mRNA and proteins and predicted 51 potential biomarkers for lung cancer. Treue et al (2019) reported EGFRmutated NSCLC with whole exome sequencing data, phosphorylated protein data and computational models, and identified three potential biomarkers for therapy targets.…”

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confidence: 99%

Comparative proteogenomics profiling of non-small and small lung carcinoma cell lines using mass spectrometry

Zou

et al. 2020

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Background Evidences indicated that non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) might originate from the same cell type, which however ended up to be two different subtypes of lung carcinoma, requiring different therapeutic regimens. We aimed to identify the differences between these two subtypes of lung cancer by using integrated proteome and genome approaches. Methods and Materials Two representative cell lines for each lung cancer subtype were comparatively analysed by quantitative proteomics, and their corresponding transcriptomics data were obtained from the Gene Expression Omnibus database. The integrated analyses of proteogenomic data were performed to determine key differentially expressed proteins that were positively correlated between proteomic and transcriptomic data. Result The proteomics analysis revealed 147 differentially expressed proteins between SCLC and NSCLC from a total of 3,970 identified proteins. Combined with available transcriptomics data, we further confirmed 14 differentially expressed proteins including six known and eight new lung cancer related proteins that were positively correlated with their transcriptomics data. These proteins are mainly involved in cell migration, proliferation, and invasion. Conclusion The proteogenomic data on both NSCLC and SCLC cell lines presented in this manuscript is complementary to existing genomic and proteomic data related to lung cancers and will be crucial for a systems biology-level understanding of the molecular mechanism of lung cancers. The raw mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015270.

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Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma

Abstract: Our results suggest that mRNA and protein data possess independent biological and clinical importance, which can be leveraged to create higher-powered expression biomarkers.

Cited by 18 publications

References 42 publications

Clinical potential of mass spectrometry-based proteogenomics

Clinical potential of mass spectrometry-based proteogenomics

Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

Comparative proteogenomics profiling of non-small and small lung carcinoma cell lines using mass spectrometry

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