Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance

Interrogating Matrix Stiffness and Metabolomics in Pancreatic Ductal Carcinoma Using an Openable Microfluidic Tumor-on-a-Chip

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense fibrotic stroma that contributes to aggressive tumor biology and therapeutic resistance. Current in vitro PDAC models lack sufficient optical and physical access for fibrous network visualization, in situ mechanical stiffness measurement, and metabolomic profiling. Here, we describe an openable multilayer microfluidic PDAC-on-a-chip platform that consists of pancreatic tumor cells (PTCs) and pancreatic stellate cells (PSCs) embedded in a 3D collagen matrix that mimics the stroma. Our system allows fibrous network visualization via reflected light confocal (RLC) microscopy, in situ mechanical stiffness testing using atomic force microscopy (AFM), and compartmentalized hydrogel extraction for PSC metabolomic profiling via mass spectrometry (MS) analysis. In comparing cocultures of gel-embedded PSCs and PTCs with PSC-only monocultures, RLC microscopy identified a significant decrease in pore size and corresponding increase in fiber density. In situ AFM indicated significant increases in stiffness, and hallmark characteristics of PSC activation were observed using fluorescence microscopy. PSCs in coculture also demonstrated localized fiber alignment and densification as well as increased collagen production. Finally, an untargeted MS study putatively identified metabolic contributions consistent with in vivo PDAC studies. Taken together, this platform can potentially advance our understanding of tumor-stromal interactions toward the discovery of novel therapies.

Illuminating the immunological landscape: mitochondrial gene defects in pancreatic cancer through a multiomics lens

This comprehensive review delves into the complex interplay between mitochondrial gene defects and pancreatic cancer pathogenesis through a multiomics approach. By amalgamating data from genomic, transcriptomic, proteomic, and metabolomic studies, we dissected the mechanisms by which mitochondrial genetic variations dictate cancer progression. Emphasis has been placed on the roles of these genes in altering cellular metabolic processes, signal transduction pathways, and immune system interactions. We further explored how these findings could refine therapeutic interventions, with a particular focus on precision medicine applications. This analysis not only fills pivotal knowledge gaps about mitochondrial anomalies in pancreatic cancer but also paves the way for future investigations into personalized therapy options. This finding underscores the crucial nexus between mitochondrial genetics and oncological immunology, opening new avenues for targeted cancer treatment strategies.

Drug Targeting of Acyltransferases in the Triacylglyceride and 1-O-AcylCeramide Biosynthetic Pathways

Acyltransferase enzymes (EC 2.3.) are a large group of enzymes that transfer acyl groups to a variety of substrates. This review focuses on fatty acyltransferases involved in the biosynthetic pathways of glycerolipids and sphingolipids and how these enzymes have been pharmacologically targeted in their biologic context. Glycerolipids and sphingolipids, commonly treated independently in their regulation and biologic functions, are put together to emphasize the parallelism in their metabolism and bioactive roles. Furthermore, a newly considered signaling molecule, 1-O-acylceramide, resulting from the acylation of ceramide by DGAT2 enzyme, is discussed. Finally, the implications of DGAT2 as a putative ceramide acyltransferase (CAT) enzyme, with a putative dual role in TAG and 1-O-acylceramide generation, are explored. SIGNIFICANCE STATEMENT: This manuscript reviews the current status of drug development in lipid acyltransferases. These are current targets in metabolic syndrome and other diseases, including cancer. A novel function for a member in this group of lipids has been recently reported in cancer cells. The responsible enzyme and biological implications of this added member are discussed.

Unveiling the intricate causal nexus between pancreatic cancer and peripheral metabolites through a comprehensive bidirectional two-sample Mendelian randomization analysis

Aim: Pancreatic cancer (PC) is a devastating malignancy characterized by its aggressive nature and poor prognosis. However, the relationship of PC with peripheral metabolites remains not fully investigated. The study aimed to explore the causal linkage between PC and peripheral metabolite profiles. Methods: Employing publicly accessible genome-wide association studies (GWAS) data, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis. The primary analysis employed the inverse-variance weighted (IVW) method. To address potential concerns about horizontal pleiotropy, we also employed supplementary methods such as maximum likelihood, weighted median, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO). Results: We ascertained 20 genetically determined peripheral metabolites with causal linkages to PC while high-density lipoprotein (HDL) and very low-density lipoprotein (VLDL) particles accounted for the vast majority. Specifically, HDL particles exhibited an elevated PC risk while VLDL particles displayed an opposing pattern. The converse MR analysis underscored a notable alteration in 17 peripheral metabolites due to PC, including branch chain amino acids and derivatives of glycerophospholipid. Cross-referencing the bidirectional MR results revealed a reciprocal causation of PC and X-02269 which might form a self-perpetuating loop in PC development. Additionally, 1-arachidonoylglycerophosphocholine indicated a reduced PC risk and an increase under PC influence, possibly serving as a negative feedback regulator. Conclusion: Our findings suggest a complex interplay between pancreatic cancer and peripheral metabolites, with potential implications for understanding the etiology of pancreatic cancer and identifying novel early diagnosis and therapeutic targets. Moreover, X-02269 may hold a pivotal role in PC onset and progression.

Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells

In this work, we demonstrate the development and first application of nanocapillary sampling followed by analytical flow liquid chromatography-mass spectrometry for single-cell lipidomics. Around 260 lipids were tentatively identified in a single cell, demonstrating remarkable sensitivity. Human pancreatic ductal adenocarcinoma cells (PANC-1) treated with the chemotherapeutic drug gemcitabine can be distinguished from controls solely on the basis of their single-cell lipid profiles. Notably, the relative abundance of LPC(0:0/16:0) was significantly affected in gemcitabine-treated cells, in agreement with previous work in bulk. This work serves as a proof of concept that live cells can be sampled selectively and then characterized using automated and widely available analytical workflows, providing biologically relevant outputs.

Evaluating Metabolite-Based Biomarkers for Early Diagnosis of Pancreatic Cancer: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with five-year survival rates around 10%. The only curative option remains complete surgical resection, but due to the delay in diagnosis, less than 20% of patients are eligible for surgery. Therefore, discovering diagnostic biomarkers for early detection is crucial for improving clinical outcomes. Metabolomics has become a powerful technology for biomarker discovery, and several metabolomic-based panels have been proposed for PDAC diagnosis, but these advances have not yet been translated into the clinic. Therefore, this review focused on summarizing metabolites identified for the early diagnosis of PDAC in the last five years. Bibliographic searches were performed in the PubMed, Scopus and WOS databases, using the terms "Biomarkers, Tumor", "Pancreatic Neoplasms", "Early Diagnosis", "Metabolomics" and "Lipidome" (January 2018-March 2023), and resulted in the selection of fourteen original studies that compared PDAC patients with subjects with other pancreatic diseases. These investigations showed amino acid and lipid metabolic pathways as the most commonly altered, reflecting their potential for biomarker research. Furthermore, other relevant metabolites such as glucose and lactate were detected in the pancreas tissue and body fluids from PDAC patients. Our results suggest that the use of metabolomics remains a robust approach to improve the early diagnosis of PDAC. However, these studies showed heterogeneity with respect to the metabolomics techniques used and further studies will be needed to validate the clinical utility of these biomarkers.

Combining the antianginal drug perhexiline with chemotherapy induces complete pancreatic cancer regression in vivo

Pancreatic ductal adenocarcinoma (PDAC) remains one of the human cancers with the poorest prognosis. Interestingly, we found that mitochondrial respiration in primary human PDAC cells depends mainly on the fatty acid oxidation (FAO) to meet basic energy requirements. Therefore, we treated PDAC cells with perhexiline, a well-recognized FAO inhibitor used in cardiac diseases. Some PDAC cells respond efficiently to perhexiline, which acts synergistically with chemotherapy (gemcitabine) in vitro and in two xenografts in vivo. Importantly, perhexiline in combination with gemcitabine induces complete tumor regression in one PDAC xenograft. Mechanistically, this co-treatment causes energy and oxidative stress promoting apoptosis but does not exert inhibition of FAO. Yet, our molecular analysis indicates that the carnitine palmitoyltransferase 1C (CPT1C) isoform is a key player in the response to perhexiline and that patients with high CPT1C expression have better prognosis. Our study reveals that repurposing perhexiline in combination with chemotherapy is a promising approach to treat PDAC.

Metabolomic, DNA Methylomic, and Transcriptomic Profiling of Suberoylanilide Hydroxamic Acid Effects on LPS-Exposed Lung Epithelial Cells

Suberoylanilide hydroxamic acid (SAHA) is a histone deacetylase (HDAC) inhibitor with anticancer effects via epigenetic and non-epigenetic mechanisms. The role of SAHA in metabolic rewiring and epigenomic reprogramming to inhibit pro-tumorigenic cascades in lung cancer remains unknown. In this study, we aimed to investigate the regulation of mitochondrial metabolism, DNA methylome reprogramming, and transcriptomic gene expression by SAHA in lipopolysaccharide (LPS)-induced inflammatory model of lung epithelial BEAS-2B cells. LC/MS was used for metabolomic analysis, while next-generation sequencing was done to study epigenetic changes. The metabolomic study reveals that SAHA treatment significantly regulated methionine, glutathione, and nicotinamide metabolism with alteration of the metabolite levels of methionine, S-adenosylmethionine, S-adenosylhomocysteine, glutathione, nicotinamide, 1-methylnicotinamide, and nicotinamide adenine dinucleotide in BEAS-2B cells. Epigenomic CpG methyl-seq shows SAHA revoked a list of differentially methylated regions in the promoter region of the genes, such as HDAC11, miR4509-1, and miR3191. Transcriptomic RNA sequencing (RNA-seq) reveals SAHA abrogated LPS-induced differentially expressed genes encoding proinflammatory cytokines, including interleukin 1α (IL1α), IL1β, IL2, IL6, IL24, and IL32. Integrative analysis of DNA methylome-RNA transcriptome displays a list of genes, of which CpG methylation correlated with changes in gene expression. qPCR validation of transcriptomic RNA-seq data shows that SAHA treatment significantly reduced the LPS-induced mRNA levels of IL1β, IL6, DNA methyltransferase 1 (DNMT1), and DNMT3A in BEAS-2B cells. Altogether, SAHA treatment alters the mitochondrial metabolism, epigenetic CpG methylation, and transcriptomic gene expression to inhibit LPS-induced inflammatory responses in lung epithelial cells, which may provide novel molecular targets to inhibit the inflammation component of lung carcinogenesis.

PREVENTION RELEVANCE

Inflammation increases the risk of lung cancer and blocking inflammation could reduce the incidence of lung cancer. Herein, we demonstrate that histone deacetylase inhibitor suberoylanilide hydroxamic acid regulates metabolic rewiring and epigenetic reprogramming to attenuate lipopolysaccharide-driven inflammation in lung epithelial cells.

Tumor heterogeneity: An oncogenic driver of PDAC progression and therapy resistance under stress conditions

Pancreatic ductal adenocarcinoma (PDAC) is a clinically challenging disease usually diagnosed at advanced or metastasized stage. By this year end, there are an expected increase in 62,210 new cases and 49,830 deaths in the United States, with 90% corresponding to PDAC subtype alone. Despite advances in cancer therapy, one of the major challenges combating PDAC remains tumor heterogeneity between PDAC patients and within the primary and metastatic lesions of the same patient. This review describes the PDAC subtypes based on the genomic, transcriptional, epigenetic, and metabolic signatures observed among patients and within individual tumors. Recent studies in tumor biology suggest PDAC heterogeneity as a major driver of disease progression under conditions of stress including hypoxia and nutrient deprivation, leading to metabolic reprogramming. We therefore advance our understanding in identifying the underlying mechanisms that interfere with the crosstalk between the extracellular matrix components and tumor cells that define the mechanics of tumor growth and metastasis. The bilateral interaction between the heterogeneous tumor microenvironment and PDAC cells serves as another important contributor that characterizes the tumor-promoting or tumor-suppressing phenotypes providing an opportunity for an effective treatment regime. Furthermore, we highlight the dynamic reciprocating interplay between the stromal and immune cells that impact immune surveillance or immune evasion response and contribute towards a complex process of tumorigenesis. In summary, the review encapsulates the existing knowledge of the currently applied treatments for PDAC with emphasis on tumor heterogeneity, manifesting at multiple levels, impacting disease progression and therapy resistance under stress.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study

BACKGROUND

Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis.

METHODS

We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis.

RESULTS

Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34-3.53]). The three markers showed area under the receiver-operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19-9 (CA19-9) was added to the model.

CONCLUSION

The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19-9.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Integrative transcriptomic analysis identifies a novel gene signature to predict prognosis of pancreatic cancer in different subtypes

BACKGROUND

Recent advances on pancreatic cancer molecular classifications have identified several subtypes with distinct characteristics, treatment response, and prognosis. We aim to identify the consensus gene signature that could predict the prognosis of pancreatic cancer.

METHODS

Transcriptomic data was acquired from TCGA database. Differentially expressed genes (DEGs) were identified by comparing the Basal-like, Quasi-mesenchymal and Squamous subtype to other subtypes. A new model was constructed by the least absolute shrinkage and selection operator to stratify patients into high and low-risk groups. The prognosis, transcriptomic profiles, and immune infiltration were examined between these groups.

RESULTS

We constructed a signature consisting of nine genes, and the GSEA analysis showed that the genomic profile of high-risk tumors is associated with the basal-like and squamous gene set enrichment. Patients with high-risk tumors had worse overall survival (P < 0.001) and progression free survival (P = 0.033), and are associated with a higher expression of KRAS downstream targets such as SDC1, ITGB4 and SLC2A1, which are involved in KRAS mediated macropinocytosis and tumor invasion. Meanwhile, several recurrence-associated genes increased in the high-risk tumors, including ITGA3 and TP63, which have been shown to mediate enhancer-dependent genomic reprogramming towards the squamous phenotype. The tumor immune infiltration profile analysis showed that high-risk tumors are characterized with an immune suppressive microenvironment.

CONCLUSION

The integrative transcriptomic analysis identifies a consensus gene signature that can discriminate pancreatic cancer subtypes and determine patient prognosis by evaluating the genomic reprogramming and the level of immune infiltration profile in pancreatic cancer.

Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction

Pancreatic ductal adenocarcinoma (PDAC), has recently been found to be a heterogeneous disease, although the extension of its diversity remains to be fully understood. Here, we harmonize transcriptomic profiles derived from both PDAC epithelial and microenvironment cells to develop a Master Regulators (MR)-Gradient model that allows important inferences on transcriptional networks, epigenomic states, and metabolomics pathways that underlies this disease heterogeneity. This gradient model was generated by applying a blind source separation based on independent components analysis and robust principal component analyses (RPCA), following regulatory network inference. The result of these analyses reveals that PDAC prognosis strongly associates with the tumor epithelial cell phenotype and the immunological component. These studies were complemented by integration of methylome and metabolome datasets generated from patient-derived xenograft (PDX), together experimental measurements of metabolites, immunofluorescence microscopy, and western blot. At the metabolic level, PDAC favorable phenotype showed a positive correlation with enzymes implicated in complex lipid biosynthesis. In contrast, the unfavorable phenotype displayed an augmented OXPHOS independent metabolism centered on the Warburg effect and glutaminolysis. Epigenetically, we find that a global hypermethylation profile associates with the worst prognosis. Lastly, we report that, two antagonistic histone code writers, SUV39H1/SUV39H2 (H3K9Me3) and KAT2B (H3K9Ac) were identified key deregulated pathways in PDAC. Our analysis suggests that the PDAC phenotype, as it relates to prognosis, is determined by a complex interaction of transcriptomic, epigenomic, and metabolic features. Furthermore, we demonstrated that PDAC prognosis could be modulated through epigenetics.

Cellular metabolism in pancreatic cancer as a tool for prognosis and treatment (Review)

Pancreatic cancer (PC) has one of the highest fatality rates and the currently available therapeutic options are not sufficient to improve its overall poor prognosis. In addition to insufficient effectiveness of anticancer treatments, the lack of clear early symptoms and early metastatic spread maintain the PC survival rates at a low level. Metabolic reprogramming is among the hallmarks of cancer and could be exploited for the diagnosis and treatment of PC. PC is characterized by its heterogeneity and, apart from molecular subtypes, the identification of metabolic subtypes in PC could aid in the development of more individualized therapeutic approaches and may lead to improved clinical outcomes. In addition to the deregulated utilization of glucose in aerobic glycolysis, PC cells can use a wide range of substrates, including branched‑chain amino acids, glutamine and lipids to fulfil their energy requirements, as well as biosynthetic needs. The tumor microenvironment in PC supports tumor growth, metastatic spread, treatment resistance and the suppression of the host immune response. Moreover, reciprocal interactions between cancer and stromal cells enhance their metabolic reprogramming. PC stem cells (PCSCs) with an increased resistance and distinct metabolic properties are associated with disease relapses and cancer spread, and represent another significant candidate for therapeutic targeting. The present review discusses the metabolic signatures observed in PC, a disease with a multifaceted and often transient metabolic landscape. In addition, the metabolic pathways utilized by PC cells, as well as stromal cells are discussed, providing examples of how they could present novel targets for therapeutic interventions and elaborating on how interactions between the various cell types affect their metabolism. Furthermore, the importance of PCSCs is discussed, focusing specifically on their metabolic adaptations.

Impact of cancer metabolism on therapy resistance - Clinical implications

Despite an increasing arsenal of anticancer therapies, many patients continue to have poor outcomes due to the therapeutic failures and tumor relapses. Indeed, the clinical efficacy of anticancer therapies is markedly limited by intrinsic and/or acquired resistance mechanisms that can occur in any tumor type and with any treatment. Thus, there is an urgent clinical need to implement fundamental changes in the tumor treatment paradigm by the development of new experimental strategies that can help to predict the occurrence of clinical drug resistance and to identify alternative therapeutic options. Apart from mutation-driven resistance mechanisms, tumor microenvironment (TME) conditions generate an intratumoral phenotypic heterogeneity that supports disease progression and dismal outcomes. Tumor cell metabolism is a prototypical example of dynamic, heterogeneous, and adaptive phenotypic trait, resulting from the combination of intrinsic [(epi)genetic changes, tissue of origin and differentiation dependency] and extrinsic (oxygen and nutrient availability, metabolic interactions within the TME) factors, enabling cancer cells to survive, metastasize and develop resistance to anticancer therapies. In this review, we summarize the current knowledge regarding metabolism-based mechanisms conferring adaptive resistance to chemo-, radio-and immunotherapies as well as targeted therapies. Furthermore, we report the role of TME-mediated intratumoral metabolic heterogeneity in therapy resistance and how adaptations in amino acid, glucose, and lipid metabolism support the growth of therapy-resistant cancers and/or cellular subpopulations. We also report the intricate interplay between tumor signaling and metabolic pathways in cancer cells and discuss how manipulating key metabolic enzymes and/or providing dietary changes may help to eradicate relapse-sustaining cancer cells. Finally, in the current era of personalized medicine, we describe the strategies that may be applied to implement metabolic profiling for tumor imaging, biomarker identification, selection of tailored treatments and monitoring therapy response during the clinical management of cancer patients.

The BET Inhibitor JQ1 Augments the Antitumor Efficacy of Gemcitabine in Preclinical Models of Pancreatic Cancer

Gemcitabine is used to treat pancreatic cancer (PC), but is not curative. We sought to determine whether gemcitabine + a BET bromodomain inhibitor was superior to gemcitabine, and identify proteins that may contribute to the efficacy of this combination. This study was based on observations that cell cycle dysregulation and DNA damage augment the efficacy of gemcitabine. BET inhibitors arrest cells in G1 and allow increases in DNA damage, likely due to inhibition of expression of DNA repair proteins Ku80 and RAD51. BET inhibitors (JQ1 or I-BET762) + gemcitabine were synergistic in vitro, in Panc1, MiaPaCa2 and Su86 PC cell lines. JQ1 + gemcitabine was more effective in vivo than either drug alone in patient-derived xenograft models (P < 0.01). Increases in the apoptosis marker cleaved caspase 3 and DNA damage marker γH2AX paralleled antitumor efficacy. Notably, RNA-seq data showed that JQ1 + gemcitabine selectively inhibited HMGCS2 and APOC1 ~6-fold, compared to controls. These proteins contribute to cholesterol biosynthesis and lipid metabolism, and their overexpression supports tumor cell proliferation. IPA data indicated that JQ1 + gemcitabine selectively inhibited the LXR/RXR activation pathway, suggesting the hypothesis that this inhibition may contribute to the observed in vivo efficacy of JQ1 + gemcitabine.