The Achilles' Heel of Pancreatic Cancer: Targeting pancreatic cancer's unique immunologic characteristics and metabolic dependencies in clinical trials.
The Achilles' Heel of Pancreatic Cancer: Targeting pancreatic cancer's unique immunologic characteristics and metabolic dependencies in clinical trials.
Project description:Error-prone DNA repair pathways promote genomic instability which leads to the onset of cancer hallmarks by progressive genetic aberrations in tumor cells. The molecular mechanisms which foster this process remain mostly undefined, and breakthrough advancements are eagerly awaited. In this context, the alternative non-homologous end joining (Alt-NHEJ) pathway is considered a leading actor. Indeed, there is experimental evidence that up-regulation of major Alt-NHEJ components, such as LIG3, PolQ, and PARP1, occurs in different tumors, where they are often associated with disease progression and drug resistance. Moreover, the Alt-NHEJ addiction of cancer cells provides a promising target to be exploited by synthetic lethality approaches for the use of DNA damage response (DDR) inhibitors and even as a sensitizer to checkpoint-inhibitors immunotherapy by increasing the mutational load. In this review, we discuss recent findings highlighting the role of Alt-NHEJ as a promoter of genomic instability and, therefore, as new cancer's Achilles' heel to be therapeutically exploited in precision oncology.
Project description:Pancreatic ductal adenocarcinomas contain a subset of exclusively tumorigenic cancer stem cells (CSCs), which are capable of repopulating the entire heterogeneous cancer cell populations and are highly resistant to standard chemotherapy. Here we demonstrate that metformin selectively ablated pancreatic CSCs as evidenced by diminished expression of pluripotency-associated genes and CSC-associated surface markers. Subsequently, the ability of metformin-treated CSCs to clonally expand in vitro was irreversibly abrogated by inducing apoptosis. In contrast, non-CSCs preferentially responded by cell cycle arrest, but were not eliminated by metformin treatment. Mechanistically, metformin increased reactive oxygen species production in CSC and reduced their mitochondrial transmembrane potential. The subsequent induction of lethal energy crisis in CSCs was independent of AMPK/mTOR. Finally, in primary cancer tissue xenograft models metformin effectively reduced tumor burden and prevented disease progression; if combined with a stroma-targeting smoothened inhibitor for enhanced tissue penetration, while gemcitabine actually appeared dispensable.
Project description:Pancreatic ductal adenocarcinoma (PDA) is a highly lethal cancer with a long-term survival rate under 10%. Available cytotoxic chemotherapies have significant side effects, and only marginal therapeutic efficacy. FDA approved drugs currently used against PDA target DNA metabolism and DNA integrity. However, alternative metabolic targets beyond DNA may prove to be much more effective. PDA cells are forced to live within a particularly severe microenvironment characterized by relative hypovascularity, hypoxia, and nutrient deprivation. Thus, PDA cells must possess biochemical flexibility in order to adapt to austere conditions. A better understanding of the metabolic dependencies required by PDA to survive and thrive within a harsh metabolic milieu could reveal specific metabolic vulnerabilities. These molecular requirements can then be targeted therapeutically, and would likely be associated with a clinically significant therapeutic window since the normal tissue is so well-perfused with an abundant nutrient supply. Recent work has uncovered a number of promising therapeutic targets in the metabolic domain, and clinicians are already translating some of these discoveries to the clinic. In this review, we highlight mitochondria metabolism, non-canonical nutrient acquisition pathways (macropinocytosis and use of pancreatic stellate cell-derived alanine), and redox homeostasis as compelling therapeutic opportunities in the metabolic domain.
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with a 5-year survival rate of <10%. The tumour microenvironment (TME) of PDAC is characterized by excessive fibrosis and deposition of extracellular matrix, termed desmoplasia. This unique TME leads to high interstitial pressure, vascular collapse and low nutrient and oxygen diffusion. Together, these factors contribute to the unique biology and therapeutic resistance of this deadly tumour. To thrive in this hostile environment, PDAC cells adapt by using non-canonical metabolic pathways and rely on metabolic scavenging pathways such as autophagy and macropinocytosis. Here, we review the metabolic pathways that PDAC use to support their growth in the setting of an austere TME. Understanding how PDAC tumours rewire their metabolism and use scavenging pathways under environmental stressors might enable the identification of novel therapeutic approaches.
Project description:Relapse continues to limit survival for patients with B-cell acute lymphoblastic leukemia (B-ALL). Previous studies have independently implicated activation of B-cell developmental signaling pathways and increased glucose consumption with chemo-resistance and relapse risk. Here, we connect these observations, demonstrating that B-ALL cells with active signaling, defined by high expression of phosphorylated ribosomal protein S6 ("pS6+ cells"), are metabolically unique and glucose dependent. Isotope tracing and metabolic flux analysis confirm that pS6+ cells are highly glycolytic and notably sensitive to glucose deprivation, relying on glucose for de novo nucleotide synthesis. Uridine, but not purine or pyrimidine, rescues pS6+ cells from glucose deprivation, highlighting uridine is essential for their survival. Active signaling in pS6+ cells drives uridine production through activating phosphorylation of carbamoyl phosphate synthetase (CAD), the enzyme catalyzing the initial steps of uridine synthesis. Inhibition of signaling abolishes glucose dependency and CAD phosphorylation in pS6+ cells. Primary pS6+ cells demonstrate high expression of uridine synthesis proteins, including dihydroorotate dehydrogenase (DHODH), the rate-limiting catalyst of de novo uridine synthesis. Gene expression demonstrates that increased expression of DHODH is associated with relapse and inferior event-free survival after chemotherapy. Further, the majority of B-ALL genomic subtypes demonstrate activity of DHODH. Inhibiting DHODH using BAY2402232 effectively kills pS6+ cells in vitro, with its IC50 correlated with the strength of pS6 signaling across 14 B-ALL cell lines and patient-derived xenografts (PDX). In vivo DHODH inhibition prolongs survival and decreases leukemia burden in pS6+ B-ALL cell line and PDX models. These findings link active signaling to uridine dependency in B-ALL cells and an associated risk of relapse. Targeting uridine synthesis through DHODH inhibition offers a promising therapeutic strategy for chemo-resistant B-ALL as a novel therapeutic approach for resistant disease.
Project description:Pancreatic ductal adenocarcinoma (PDAC) cells use glutamine (Gln) to support proliferation and redox balance. Early attempts to inhibit Gln metabolism using glutaminase inhibitors resulted in rapid metabolic reprogramming and therapeutic resistance. Here, we demonstrated that treating PDAC cells with a Gln antagonist, 6-diazo-5-oxo-L-norleucine (DON), led to a metabolic crisis in vitro. In addition, we observed a profound decrease in tumor growth in several in vivo models using sirpiglenastat (DRP-104), a pro-drug version of DON that was designed to circumvent DON-associated toxicity. We found that extracellular signal-regulated kinase (ERK) signaling is increased as a compensatory mechanism. Combinatorial treatment with DRP-104 and trametinib led to a significant increase in survival in a syngeneic model of PDAC. These proof-of-concept studies suggested that broadly targeting Gln metabolism could provide a therapeutic avenue for PDAC. The combination with an ERK signaling pathway inhibitor could further improve the therapeutic outcome.
Project description:Mycobacterial pathogens adapt to environmental stresses such as nutrient deprivation by entering a non-replicative antibiotic-tolerant state of persistence. Using a biochemically-validated data-driven approach, we identified an adaptive metabolic network underlying the mycobacterial response to starvation in M. tuberculosis, M. bovis BCG and M. smegmatis. All three species show a strong Mg+2-dependence for surviving complete nutrient deprivation, accompanied by a broad phenotypic antibiotic resistance. Multivariate analysis of RNA-seq, metabolic phenotyping and biochemical data revealed substantial metabolic remodelling involving a shift to triacylglycerol utilization with adaptation to the consequent ketoacidosis by upregulation of cytochrome P450s. Paradoxically, the ketosis-driven P450 upregulation generated substantial levels of reactive oxygen species (ROS) yet conferred hypersensitivity to killing by hydrogen peroxide-induced inactivation of the P450s that reduced ROS levels. This emergent property of starvation-induced mycobacterial persistence represents a potentially exploitable vulnerability.
Project description:BackgroundThe assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.Main textHerein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice.ConclusionEfforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.