Rewiring of the N-Glycome with prostate cancer progression and therapy resistance

Sialic acid blockade inhibits the metastatic spread of prostate cancer to bone

BACKGROUND

Bone metastasis is a common consequence of advanced prostate cancer. Bisphosphonates can be used to manage symptoms, but there are currently no curative treatments available. Altered tumour cell glycosylation is a hallmark of cancer and is an important driver of a malignant phenotype. In prostate cancer, the sialyltransferase ST6GAL1 is upregulated, and studies show ST6GAL1-mediated aberrant sialylation of N-glycans promotes prostate tumour growth and disease progression.

METHODS

Here, we monitor ST6GAL1 in tumour and serum samples from men with aggressive prostate cancer and using in vitro and in vivo models we investigate the role of ST6GAL1 in prostate cancer bone metastasis.

FINDINGS

ST6GAL1 is upregulated in patients with prostate cancer with tumours that have spread to the bone and can promote prostate cancer bone metastasis in vivo. The mechanisms involved are multi-faceted and involve modification of the pre-metastatic niche towards bone resorption to promote the vicious cycle, promoting the development of M2 like macrophages, and the regulation of immunosuppressive sialoglycans. Furthermore, using syngeneic mouse models, we show that inhibiting sialylation can block the spread of prostate tumours to bone.

INTERPRETATION

Our study identifies an important role for ST6GAL1 and α2-6 sialylated N-glycans in prostate cancer bone metastasis, provides proof-of-concept data to show that inhibiting sialylation can suppress the spread of prostate tumours to bone, and highlights sialic acid blockade as an exciting new strategy to develop new therapies for patients with advanced prostate cancer.

FUNDING

Prostate Cancer Research and the Mark Foundation For Cancer Research, the Medical Research Council and Prostate Cancer UK.

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

An N-glycome tissue atlas of 15 human normal and cancer tissue types determined by MALDI-imaging mass spectrometry

N-glycosylation is an abundant post-translational modification of most cell-surface proteins. N-glycans play a crucial role in cellular functions like protein folding, protein localization, cell-cell signaling, and immune detection. As different tissue types display different N-glycan profiles, changes in N-glycan compositions occur in tissue-specific ways with development of disease, like cancer. However, no comparative atlas resource exists for documenting N-glycome alterations across various human tissue types, particularly comparing normal and cancerous tissues. In order to study a broad range of human tissue N-glycomes, N-glycan targeted MALDI imaging mass spectrometry was applied to custom formalin-fixed paraffin-embedded tissue microarrays. These encompassed fifteen human tissue types including bladder, breast, cervix, colon, esophagus, gastric, kidney, liver, lung, pancreas, prostate, sarcoma, skin, thyroid, and uterus. Each array contained both normal and tumor cores from the same pathology block, selected by a pathologist, allowing more in-depth comparisons of the N-glycome differences between tumor and normal and across tissue types. Using established MALDI-IMS workflows and existing N-glycan databases, the N-glycans present in each tissue core were spatially profiled and peak intensity data compiled for comparative analyses. Further structural information was determined for core fucosylation using endoglycosidase F3, and differentiation of sialic acid linkages through stabilization chemistry. Glycan structural differences across the tissue types were compared for oligomannose levels, branching complexity, presence of bisecting N-acetylglucosamine, fucosylation, and sialylation. Collectively, our research identified the N-glycans that were significantly increased and/or decreased in relative abundance in cancer for each tissue type. This study offers valuable information on a wide scale for both normal and cancerous tissues, serving as a reference for future studies and potential diagnostic applications of MALDI-IMS.