Cancer stem cells (CSCs) are critical for cancer progression and chemoresistance. How lipid metabolism regulates CSCs and chemoresistance remains elusive. Here, we demonstrate that JAK/STAT3 regulates lipid metabolism, which promotes breast CSCs (BCSCs) and cancer chemoresistance. Inhibiting JAK/STAT3 blocks BCSC self-renewal and expression of diverse lipid metabolic genes, including carnitine palmitoyltransferase 1B (CPT1B), which encodes the critical enzyme for fatty acid β-oxidation (FAO). Moreover, mammary-adipocyte-derived leptin upregulates STAT3-induced CPT1B expression and FAO activity in BCSCs. Human breast-cancer-derived data suggest that the STAT3-CPT1B-FAO pathway promotes cancer cell stemness and chemoresistance. Blocking FAO and/or leptin re-sensitizes them to chemotherapy and inhibits BCSCs in mouse breast tumors in vivo. We identify a critical pathway for BCSC maintenance and breast cancer chemoresistance.

Summary Durable antibody production after vaccination or infection is mediated by long-lived plasma cells (LLPCs). Pathways that specifically allow LLPCs to persist remain unknown. Through bioenergetic profiling, we found that human and mouse LLPCs could robustly engage pyruvate-dependent respiration whereas their short-lived counterparts could not. LLPCs took up more glucose than did short-lived plasma cells (SLPCs) in vivo, and this glucose was essential for the generation of pyruvate. Glucose was primarily used to glycosylate antibodies, but glycolysis could be promoted by stimuli such as low ATP levels and the resultant pyruvate used for respiration by LLPCs. Deletion of Mpc2, which encodes an essential component of the mitochondrial pyruvate carrier, led to a progressive loss of LLPCs and of vaccine-specific antibodies in vivo. Thus, glucose uptake and mitochondrial pyruvate import prevent bioenergetic crises and allow LLPCs to persist. Immunizations which maximize these plasma cell metabolic properties may thus provide enduring antibody-mediated immunity.

The authors declare the following potential conflicts of interest. Kristin Nieman and Ernst Lengyel hold a patent for treating ovarian cancer by inhibiting fatty acid binding proteins.

Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.

As one of the first studies, we comprehensively assessed differences in metabolic, lipidomic, and transcriptomic profiles between paired human VAT and SAT and their association with CRC tumor stage. We identified markers of inflammation in VAT, which supports prior evidence regarding the role of visceral adiposity and cancer.