CD109 Overexpression in Pancreatic Cancer Identified by Cell-Surface Glycoprotein Capture

Defining the Cell Surface Cysteinome Using Two-Step Enrichment Proteomics

The plasma membrane proteome is a rich resource of functionally important and therapeutically relevant protein targets. Distinguished by high hydrophobicity, heavy glycosylation, disulfide-rich sequences, and low overall abundance, the cell surface proteome remains undersampled in established proteomic pipelines, including our own cysteine chemoproteomics platforms. Here, we paired cell surface glycoprotein capture with cysteine chemoproteomics to establish a two-stage enrichment method that enables chemoproteomic profiling of cell Surface Cysteinome. Our "Cys-Surf" platform captures >2,800 total membrane protein cysteines in 1,046 proteins, including 1,907 residues not previously captured by bulk proteomic analysis. By pairing Cys-Surf with an isotopic chemoproteomic readout, we uncovered 821 total ligandable cysteines, including known and novel sites. Cys-Surf also robustly delineates redox-sensitive cysteines, including cysteines prone to activation-dependent changes to cysteine oxidation state and residues sensitive to addition of exogenous reductants. Exemplifying the capacity of Cys-Surf to delineate functionally important cysteines, we identified a redox sensitive cysteine in the low-density lipoprotein receptor (LDLR) that impacts both the protein localization and uptake of low-density lipoprotein (LDL) particles. Taken together, the Cys-Surf platform, distinguished by its two-stage enrichment paradigm, represents a tailored approach to delineate the functional and therapeutic potential of the plasma membrane cysteinome.

Defining the Cell Surface Cysteinome using Two-step Enrichment Proteomics

The plasma membrane proteome is a rich resource of functional and therapeutically relevant protein targets. Distinguished by high hydrophobicity, heavy glycosylation, disulfide-rich sequences, and low overall abundance, the cell surface proteome remains undersampled in established proteomic pipelines, including our own cysteine chemoproteomics platforms. Here we paired cell surface glycoprotein capture with cysteine chemoproteomics to establish a two-stage enrichment method that enables chemoproteomic profiling of cell Surface Cysteinome. Our "Cys-Surf" platform captures >2,800 total membrane protein cysteines in 1,046 proteins, including 1,907 residues not previously captured by bulk proteomic analysis. By pairing Cys-Surf with an isotopic chemoproteomic readout, we uncovered 821 total ligandable cysteines, including known and novel sites. Cys-Surf also robustly delineates redox-sensitive cysteines, including cysteines prone to activation-dependent changes to cysteine oxidation state and residues sensitive to addition of exogenous reductants. Exemplifying the capacity of Cys-Surf to delineate functionally important cysteines, we identified a redox sensitive cysteine in the low-density lipoprotein receptor (LDLR) that impacts both the protein localization and uptake of LDL particles. Taken together, the Cys-Surf platform, distinguished by its two-stage enrichment paradigm, represents a tailored approach to delineate the functional and therapeutic potential of the plasma membrane cysteinome.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

Structural and functional properties of meprin β metalloproteinase with regard to cell signaling

The metalloproteinase meprin β plays an important role during collagen I deposition in the skin, mucus detachment in the small intestine and also regulates the abundance of different cell surface proteins such as the interleukin-6 receptor (IL-6R), the triggering receptor expressed on myeloid cells 2 (TREM2), the cluster of differentiation 99 (CD99), the amyloid precursor protein (APP) and the cluster of differentiation 109 (CD109). With that, regulatory mechanisms that control meprin β activity and regulate its release from the cell surface to enable access to distant substrates are increasingly important. Here, we will summarize factors that alternate meprin β activity and thereby regulate its proteolytic activity on the cell surface or in the supernatant. We will also discuss cleavage of the IL-6R and TREM2 on the cell surface and compare it to CD109. CD109, as a substrate of meprin β, is cleaved within the protein core, thereby releasing defined fragments from the cell surface. At last, we will also summarize the role of proteases in general and meprin β in particular in substrate release on extracellular vesicles.

Cell Surface Processing of CD109 by Meprin β Leads to the Release of Soluble Fragments and Reduced Expression on Extracellular Vesicles

Cluster of differentiation 109 (CD109) is a glycosylphosphatidylinositol (GPI)-anchored protein expressed on primitive hematopoietic stem cells, activated platelets, CD4+ and CD8+ T cells, and keratinocytes. In recent years, CD109 was also associated with different tumor entities and identified as a possible future diagnostic marker linked to reduced patient survival. Also, different cell signaling pathways were proposed as targets for CD109 interference including the TGFβ, JAK-STAT3, YAP/TAZ, and EGFR/AKT/mTOR pathways. Here, we identify the metalloproteinase meprin β to cleave CD109 at the cell surface and thereby induce the release of cleavage fragments of different size. Major cleavage was identified within the bait region of CD109 residing in the middle of the protein. To identify the structural localization of the bait region, homology modeling and single-particle analysis were applied, resulting in a molecular model of membrane-associated CD109, which allows for the localization of the newly identified cleavage sites for meprin β and the previously published cleavage sites for the metalloproteinase bone morphogenetic protein-1 (BMP-1). Full-length CD109 localized on extracellular vesicles (EVs) was also identified as a release mechanism, and we can show that proteolytic cleavage of CD109 at the cell surface reduces the amount of CD109 sorted to EVs. In summary, we identified meprin β as the first membrane-bound protease to cleave CD109 within the bait region, provide a first structural model for CD109, and show that cell surface proteolysis correlates negatively with CD109 released on EVs.

Elevation of CD109 promotes metastasis and drug resistance in lung cancer via activation of EGFR-AKT-mTOR signaling

Lung cancer is the most commonly diagnosed cancer worldwide, and metastasis in lung cancer is the leading cause of cancer‐related deaths. Thus, understanding the mechanism of lung cancer metastasis will improve the diagnosis and treatment of lung cancer patients. Herein, we found that expression of cluster of differentiation 109 (CD109) was correlated with the invasive and metastatic capacities of lung adenocarcinoma cells. CD109 is upregulated in tumorous tissues, and CD109 overexpression was associated with tumor progression, distant metastasis, and a poor prognosis in patient with lung adenocarcinoma. Mechanistically, expression of CD109 regulates protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling via its association with the epidermal growth factor receptor (EGFR). Inhibition of CD109 decreases EGFR phosphorylation, diminishes EGF‐elicited activation of AKT/mTOR, and sensitizes tumor cells to an EGFR inhibitor. Taken together, our results show that CD109 is a potential diagnostic and therapeutic target in lung cancer patients.

Development of novel monoclonal antibodies against CD109 overexpressed in human pancreatic cancer

Pancreatic cancer is one of the most aggressive and lethal types of cancer, and more effective therapeutic agents are urgently needed. Overexpressed cell surface antigens are ideal targets for therapy with monoclonal antibody (mAb)-based drugs, but none have been approved for the treatment of pancreatic cancer. Here, we report development of two novel mouse mAbs, KU42.33C and KU43.13A, against the human pancreatic cancer cell line BxPC-3. Using ELISA, flow cytometry, competitive assay and immunoprecipitation followed by mass spectrometry, we discovered that these two mAbs target two distinct epitopes on the external domain of CD109 that are overexpressed by varying amounts in human pancreatic cancer cell lines. Treatment with these two naked antibodies alone did not affect tumour cell growth or migration in vitro. Of the two mAbs, only KU42.33C was useful in determining the expression of CD109 in tumour cells by Western blot and immunohistochemistry. Interestingly, immunohistochemistry of human pancreatic carcinoma tissue arrays with mAb KU42.33C showed that 94% of the 65 human pancreatic adenocarcinoma cases were CD109 positive, with no expression in normal pancreatic tissues. Our results suggest that these two novel mAbs are excellent tools for determining the expression level of CD109 in the tumour specimens and sera of patients with a wide range of cancers, in particular pancreatic cancer, and for investigating its diagnostic, prognostic and predictive value. Further research is warranted and should aim to unravel the therapeutic potential of the humanised forms or conjugated versions of such antibodies in patients whose tumours overexpress CD109 antigen.

Controlling Directed Protein Interaction Networks in Cancer

Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma

Background

Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.

Methods

Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.

Results

In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10^-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.

Conclusions

Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

Biomarkers and Targeted Therapy in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) constitutes 90% of pancreatic cancers. PDAC is a complex and devastating disease with only 1%-3% survival rate in five years after the second stage. Treatment of PDAC is complicated due to the tumor microenvironment, changing cell behaviors to the mesenchymal type, altered drug delivery, and drug resistance. Considering that pancreatic cancer shows early invasion and metastasis, critical research is needed to explore different aspects of the disease, such as elaboration of biomarkers, specific signaling pathways, and gene aberration. In this review, we highlight the biomarkers, the fundamental signaling pathways, and their importance in targeted drug delivery for pancreatic cancers.

Microscopes and Mass Spectrometers

Proteomics is a relatively young discipline while pathology is one of the oldest forms of scientific inquiry. These two fields have different methods and aims, but have many areas of overlap and shared interests. Cultivation of synergistic projects between physicians who study static images of disease and biologists who study the dynamic environment that produces disease states will help further biomedical research providing new diagnostic, prognostic, and therapeutic approaches. Here, a pathologist and a proteomic scientist share their views on recent collaborations among the fields.

Bioorthogonal labeling cell-surface proteins expressed in pancreatic cancer cells to identify potential diagnostic/therapeutic biomarkers

To develop new diagnostic and therapeutic tools to specifically target pancreatic tumors, it is necessary to identify cell-surface proteins that may serve as potential tumor-specific targets. In this study we used an azido-labeled bioorthogonal chemical reporter to metabolically label N-linked glycoproteins on the surface of pancreatic cancer cell lines to identify potential targets that may be exploited for detection and/or treatment of pancreatic cancer. Labeled glycoproteins were tagged with biotin using click chemistry, purified by streptavidin-coupled magnetic beads, separated by gel electrophoresis, and identified by liquid chromatography-tandem mass spectrometry (MS). MS/MS analysis of peptides from 3 cell lines revealed 954 unique proteins enriched in the azido sugar samples relative to control sugar samples. A comparison of the proteins identified in each sample indicated 20% of these proteins were present in 2 cell lines (193 of 954) and 17 of the proteins were found in all 3 cell lines. Five of the 17 proteins identified in all 3 cell lines have not been previously reported to be expressed in pancreatic cancer; thus indicating that novel cell-surface proteins can be revealed through glycoprotein profiling. Western analysis of one of these glycoproteins, ecto-5'-nucleotidase (NT5E), revealed it is expressed in 8 out of 8 pancreatic cancer cell lines examined. Further, immunohistochemical analysis of human pancreatic tissues indicates NT5E is significantly overexpressed in pancreatic tumors compared to normal pancreas. Thus, we have demonstrated that metabolic labeling with bioorthogonal chemical reporters can be used to selectively enrich and identify novel cell-surface glycoproteins expressed in pancreatic ductal adenocarcinomas.