Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.

mRNAs, proteins and the emerging principles of gene expression control.

Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data.

Single-cell multiomics: technologies and data analysis methods.

Stress induced by the accumulation of unfolded proteins in the endoplasmic reticulum (ER) is observed in many human diseases, including cancers. Cellular adaptation to ER stress is mediated by the unfolded protein response (UPR), which aims at restoring ER homeostasis. The UPR has emerged as a major pathway in remodeling cancer gene expression, thereby either preventing cell transformation or providing an advantage to transformed cells. UPR sensors are highly regulated by the formation of dynamic protein scaffolds, leading to integrated reprogramming of the cells. Herein, we describe the regulatory mechanisms underlying UPR signaling upon cell intrinsic or extrinsic challenges, and how they engage cell transformation programs and/or provide advantages to cancer cells, leading to enhanced aggressiveness or chemoresistance. We discuss the emerging cross-talk between the UPR and related metabolic processes to ensure maintenance of protein homeostasis and its impact on cell transformation and tumor growth.

Significance: ER stress signaling is dysregulated in many forms of cancer and contributes to tumor growth as a survival factor, in addition to modulating other disease-associated processes, including cell migration, cell transformation, and angiogenesis. Evidence for targeting the ER stress signaling pathway as an anticancer strategy is compelling, and novel agents that selectively inhibit the UPR have demonstrated preliminary evidence of preclinical efficacy with an acceptable safety profile. Cancer Discov; 5(6); 586–97. ©2015 AACR .

/pdf/endoplasmic-reticulum-stress-activated-cell-reprogramming-in-1a4o326puh.pdf

Endoplasmic reticulum stress-activated cell reprogramming in oncogenesis.

Is early-onset cancer an emerging global epidemic? Current evidence and future implications

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics’ integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

/pdf/recent-advances-in-mass-spectrometry-based-clinical-523flrmn4j.pdf

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research.

Proteogenomic Characterization of Human Early-Onset Gastric Cancer

Since the emergence of proteomics methods, many proteins specific for renal cell carcinoma (RCC) have been identified. Despite their usefulness for the specific diagnosis of RCC, such proteins do not provide spatial information on the diseased tissue. Therefore, the identification of cancer-specific proteins that include information on their specific location is needed. Recently, matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) based imaging mass spectrometry (IMS) has emerged as a new tool for the analysis of spatial distribution as well as identification of either proteins or small molecules in tissues. In this report, surgical tissue sections of papillary RCC were analyzed using MALDI-IMS. Statistical analysis revealed several discriminative cancer-specific m/z-species between normal and diseased tissues. Among these m/z-species, two particular proteins, S100A11 and ferritin light chain, which are specific for papillary RCC cancer regions, were successfully identified using LC-MS/MS following protein extraction from independent RCC samples. The expressions of S100A11 and ferritin light chain were further validated by immunohistochemistry of human tissues and tissue microarrays (TMAs) of RCC. In conclusion, MALDI-IMS followed by LC-MS/MS analysis in human tissue identified that S100A11 and ferritin light chain are differentially expressed proteins in papillary RCC cancer regions.

Identification of Protein Markers Specific for Papillary Renal Cell Carcinoma Using Imaging Mass Spectrometry.

The molecular mechanism underlying microRNA (miR)-17 overexpression has not been clearly evaluated in gastric cancer. We aimed to evaluate the functional roles of miR-17 in gastric cancer and test its viability as a therapeutic target. We conducted comparative genomic hybridization and expression array analyses on human gastric cancer tissue samples, as well as evaluating the functional roles of miR-17 in gastric cancer cell lines and transgenic mice. miR-17 overexpression in gastric cancer patients was associated with copy number gain of proliferation-associated oncogenes such as MYC, CCNE1, ERBB2, and FGFR2. Copy number gain of MIR17HG gene (13q31.3) was rare, with an overall frequency of 2% in gastric cancers (1 of 51). miR-17 knockdown suppressed the monolayer and anchorage-independent growth of FGFR2-amplified KATO-III gastric cancer cells. mir-17-92 TG/TG mice overexpressing the mir-17-92 cluster under the villin promoter developed spontaneous benign tumors in the intestinal tract (log-rank P for tumor-free survival = 0.069). Taken together, miR-17 overexpression in gastric cancer was rarely associated with MIR17HG gene amplification, but correlated with proliferation-associated oncogene amplification. Therefore, miR-17-targeting approach may benefit patients with gastric cancers harboring proliferation-associated oncogene amplification.

Overexpression of miR-17 in gastric cancer is correlated with proliferation-associated oncogene amplification.

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Proteogenomic Characterization of Human Early-Onset Gastric Cancer

Identification of Protein Markers Specific for Papillary Renal Cell Carcinoma Using Imaging Mass Spectrometry.

Overexpression of miR-17 in gastric cancer is correlated with proliferation-associated oncogene amplification.