Locoregional failure (LRF) in patients with breast cancer post-surgery and post-irradiation is linked to a dismal prognosis. In a refined new model, we identified ectonucleotide pyrophosphatase/phosphodiesterase 1/CD203a (ENPP1) to be closely associated with LRF. ENPP1hi circulating tumor cells (CTC) contribute to relapse by a self-seeding mechanism. This process requires the infiltration of polymorphonuclear myeloid-derived suppressor cells and neutrophil extracellular trap (NET) formation. Genetic and pharmacologic ENPP1 inhibition or NET blockade extends relapse-free survival. Furthermore, in combination with fractionated irradiation, ENPP1 abrogation obliterates LRF. Mechanistically, ENPP1-generated adenosinergic metabolites enhance haptoglobin (HP) expression. This inflammatory mediator elicits myeloid invasiveness and promotes NET formation. Accordingly, a significant increase in ENPP1 and NET formation is detected in relapsed human breast cancer tumors. Moreover, high ENPP1 or HP levels are associated with poor prognosis. These findings unveil the ENPP1/HP axis as an unanticipated mechanism exploited by tumor cells linking inflammation to immune remodeling favoring local relapse. Significance: CTC exploit the ENPP1/HP axis to promote local recurrence post-surgery and post-irradiation by subduing myeloid suppressor cells in breast tumors. Blocking this axis impairs tumor engraftment, impedes immunosuppression, and obliterates NET formation, unveiling new opportunities for therapeutic intervention to eradicate local relapse and ameliorate patient survival. This article is highlighted in the In This Issue feature, p. 1171

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (¹H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Chemoinformatics is a scientific discipline at the interface between chemistry and computer science, which nowadays is currently implemented in pharmaceutical companies as a part of the usual drug discovery pathway. Furthermore, taking into account the vast amount of experimental and computational data currently generated on drug discovery projects, the use of chemoinformatics tools has become increasingly necessary. Most of chemoinformatics projects are initiated from information stored in large databases of chemicals. The compounds are systematically characterized by numerical descriptors in order to manage this information and obtain some kind of chemical-biological relationships by similarity/diversity analysis, QSAR development for ADMET predictions, chemical space navigation for the selection of drug subspaces, drug-like and lead-like selection, or substructure searching. In this paper, we will review some of the more important chemical databases of small molecules and the descriptors that can be used to describe them, as well as their applications to the specific area of design of focused/targeted libraries.

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.