The Spatial Extracellular Proteomic Tumor Microenvironment Distinguishes Molecular Subtypes of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.

Novel Combined Enzymatic Approach to Analyze Nonsialylated N-Linked Glycans through MALDI Imaging Mass Spectrometry.

Alterations to N-glycan expression are relevant to the progression of various diseases, particularly cancer. In many cases, specific N-glycan structural features such as sialylation, fucosylation, and branching are of specific interest. A novel MALDI imaging mass spectrometry workflow has been recently developed to analyze these features of N-glycosylation through the utilization of endoglycosidase enzymes to cleave N-glycans from associated glycoproteins. Enzymes that have previously been utilized to cleave N-glycans include peptide-N-glycosidase F (PNGase F) to target N-glycans indiscriminately and endoglycosidase F3 (Endo F3) to target core fucosylated N-glycans. In addition to these endoglycosidases, additional N-glycan cleaving enzymes could be used to target specific structural features. Sialidases, also termed neuraminidases, are a family of enzymes that remove terminal sialic acids from glycoconjugates. This work aims to utilize sialidase, in conjunction with PNGase F/Endo F3, to enzymatically remove sialic acids from N-glycans in an effort to increase sensitivity for nonsialylated N-glycan MALDI-IMS peaks. Improving detection of nonsialylated N-glycans allows for a more thorough analysis of specific structural features such as fucosylation or branching, particularly of low abundant structures. Sialidase utilization in MALDI-IMS dramatically increases sensitivity and increases on-tissue endoglycosidase efficiency, making it a very useful companion technique to specifically detect nonsialylated N-glycans.

Beyond glyco-proteomics-Understanding the role of genetics in cancer biomarkers.

The development of robust cancer biomarkers is the most effective way to improve overall survival, as early detection and treatment leads to significantly better clinical outcomes. Many of the cancer biomarkers that have been identified and are clinically utilized are glycoproteins, oftentimes a specific glycoform. Aberrant glycosylation is a common theme in cancer, with dysregulated glycosylation driving tumor initiation and metastasis, and abnormal glycosylation can be detection both on the tissue surface and in serum. However, most cancer types are heterogeneous in regard to tumor genomics, and this heterogeneity extends to cancer glycomics. This limits the sensitivity of standalone glycan-based biomarkers, which has slowed their implementation clinically. However, if targeted biomarker development can take into account genomic tumor information, the development of complementary biomarkers that target unique cancer subgroups can be accomplished. This idea suggests the need for algorithm-based cancer biomarkers, which can utilize multiple biomarkers along with relevant demographic information. This concept has already been established in the detection of hepatocellular carcinoma with the GALAD score, and an algorithm-based approach would likely be effective in improving biomarker sensitivity for additional cancer types. In order to increase cancer diagnostic biomarker sensitivity, there must be more targeted biomarker development that considers tumor genomic, proteomic, metabolomic, and clinical data while identifying tumor biomarkers.

Analysis of N-linked Glycan Alterations in Tissue and Serum Reveals Promising Biomarkers for Intrahepatic Cholangiocarcinoma.

There is an urgent need for the identification of reliable prognostic biomarkers for patients with intrahepatic cholangiocarcinoma (iCCA) and alterations in N-glycosylation have demonstrated an immense potential to be used as diagnostic strategies for many cancers, including hepatocellular carcinoma (HCC). N-glycosylation is one of the most common post-translational modifications known to be altered based on the status of the cell. N-glycan structures on glycoproteins can be modified based on the addition or removal of specific N-glycan residues, some of which have been linked to liver diseases. However, little is known concerning the N-glycan alterations that are associated with iCCA. We characterized the N-glycan modifications quantitatively and qualitatively in three cohorts, consisting of two tissue cohorts: a discovery cohort (n = 104 cases) and a validation cohort (n = 75), and one independent serum cohort consisting of patients with iCCA, HCC, or benign chronic liver disease (n = 67). N-glycan analysis in situ was correlated to tumor regions annotated on histopathology and revealed that bisected fucosylated N-glycan structures were specific to iCCA tumor regions. These same N-glycan modifications were significantly upregulated in iCCA tissue and serum relative to HCC and bile duct disease, including primary sclerosing cholangitis (PSC) (P < 0.0001). N-glycan modifications identified in iCCA tissue and serum were used to generate an algorithm that could be used as a biomarker of iCCA. We demonstrate that this biomarker algorithm quadrupled the sensitivity (at 90% specificity) of iCCA detection as compared with carbohydrate antigen 19-9, the current "gold standard" biomarker of CCA. Significance: This work elucidates the N-glycan alterations that occur directly in iCCA tissue and utilizes this information to discover serum biomarkers that can be used for the noninvasive detection of iCCA.

N-Glycosylation Patterns Correlate with Hepatocellular Carcinoma Genetic Subtypes.

Hepatocellular carcinoma (HCC) is the second leading cause of cancer deaths globally, and the incidence rate in the United States is increasing. Studies have identified inter- and intratumor heterogeneity as histologic and/or molecular subtypes/variants associated with response to certain molecular targeted therapies. Spatial HCC tissue profiling of N-linked glycosylation by matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) may serve as a new method to evaluate the tumor heterogeneity. Previous work has identified significant changes in the N-linked glycosylation of HCC tumors but has not accounted for the heterogeneous genetic and molecular nature of HCC. To determine the correlation between HCC-specific N-glycosylation changes and genetic/molecular tumor features, we profiled HCC tissue samples with MALDI-IMS and correlated the spatial N-glycosylation with a widely used HCC molecular classification (Hoshida subtypes). MALDI-IMS data displayed trends that could approximately distinguish between subtypes, with subtype 1 demonstrating significantly dysregulated N-glycosylation versus adjacent nontumor tissue. Although there were no individual N-glycan structures that could identify specific subtypes, trends emerged regarding the correlation of branched glycan expression to HCC as a whole and fucosylated glycan expression to subtype 1 tumors specifically. IMPLICATIONS: Correlating N-glycosylation to specific subtypes offers the specific detection of subtypes of HCC, which could both enhance early HCC sensitivity and guide targeted clinical therapies.

Molecular Rectifiers on Silicon: High Performance by Enhancing Top-Electrode/Molecule Coupling.

One of the simplest molecular-scale electronic devices is the molecular rectifier. In spite of considerable efforts aimed at understanding structure-property relationships in these systems, devices with predictable and stable electronic properties are yet to be developed. Here, we demonstrate highly efficient current rectification in a new class of compounds that form self-assembled monolayers on silicon. We achieve this by exploiting the coupling of the molecules with the top electrode which, in turn, controls the position of the relevant molecular orbitals. The molecules consist of a silane anchoring group and a nitrogen-substituted benzene ring, separated by a propyl group and imine linkage, and result from a simple, robust, and high-yield synthetic procedure. We find that when incorporated in molecular diodes, these compounds can rectify current by as much as 3 orders of magnitude, depending on their structure, with a maximum rectification ratio of 2635 being obtained in ( E)-1-(4-cyanophenyl)- N-(3-(triethoxysilyl) propyl)methanimine (average Ravg = 1683 ± 458, at an applied voltage of 2 V). This performance is on par with that of the best molecular rectifiers obtained on metallic electrodes, but it has the advantage of lower cost and more efficient integration with current silicon technologies. The development of molecular rectifiers on silicon may yield hybrid systems that can expand the use of silicon toward novel functionalities governed by the molecular species grafted onto its surface.