MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis

Metabolic Reprogramming in Breast Cancer and Its Therapeutic Implications

Current standard-of-care (SOC) therapy for breast cancer includes targeted therapies such as endocrine therapy for estrogen receptor-alpha (ERα) positive; anti-HER2 monoclonal antibodies for human epidermal growth factor receptor-2 (HER2)-enriched; and general chemotherapy for triple negative breast cancer (TNBC) subtypes. These therapies frequently fail due to acquired or inherent resistance. Altered metabolism has been recognized as one of the major mechanisms underlying therapeutic resistance. There are several cues that dictate metabolic reprogramming that also account for the tumors’ metabolic plasticity. For metabolic therapy to be efficacious there is a need to understand the metabolic underpinnings of the different subtypes of breast cancer as well as the role the SOC treatments play in targeting the metabolic phenotype. Understanding the mechanism will allow us to identify potential therapeutic vulnerabilities. There are some very interesting questions being tackled by researchers today as they pertain to altered metabolism in breast cancer. What are the metabolic differences between the different subtypes of breast cancer? Do cancer cells have a metabolic pathway preference based on the site and stage of metastasis? How do the cell-intrinsic and -extrinsic cues dictate the metabolic phenotype? How do the nucleus and mitochondria coordinately regulate metabolism? How does sensitivity or resistance to SOC affect metabolic reprogramming and vice-versa? This review addresses these issues along with the latest updates in the field of breast cancer metabolism.

Large-scale profiling of serum metabolites in African American and European American patients with bladder cancer reveals metabolic pathways associated with patient survival

BACKGROUND

African Americans (AAs) experience a disproportionally high rate of bladder cancer (BLCA) deaths even though their incidence rates are lower than those of other patient groups. Using a metabolomics approach, this study investigated how AA BLCA may differ molecularly from European Americans (EAs) BLCA, and it examined serum samples from patients with BLCA with the aim of identifying druggable metabolic pathways in AA patients.

METHODS

Targeted metabolomics was applied to measure more than 300 metabolites in serum samples from 2 independent cohorts of EA and AA patients with BLCA and healthy EA and AA controls via liquid chromatography-mass spectrometry, and this was followed by the identification of altered metabolic pathways with a focus on AA BLCA. A subset of the differential metabolites was validated via absolute quantification with the Biocrates AbsoluteIDQ p180 kit. The clinical significance of the findings was further examined in The Cancer Genomic Atlas BLCA data set.

RESULTS

Fifty-three metabolites, mainly related to amino acid, lipid, and nucleotide metabolism, were identified that showed significant differences in abundance between AA and EA BLCA. For example, the levels of taurine, glutamine, glutamate, aspartate, and serine were elevated in serum samples from AA patients versus EA patients. By mapping these metabolites to genes, this study identified significant relations with regulators of metabolism such as malic enzyme 3, prolyl 3-hydroxylase 2, and lysine demethylase 2A that predicted patient survival exclusively in AA patients with BLCA.

CONCLUSIONS

This metabolic profile of serum samples might be used to assess risk progression in AA BLCA. These first-in-field findings describe metabolic alterations in AA BLCA and emphasize a potential biological basis for BLCA health disparities.

Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health

Recent remarkable advances in genome sequencing have enabled detailed maps of identified and interpreted genomic variation, dubbed "mutanomes." The availability of thousands of exome/genome sequencing data has prompted the emergence of new challenges in the identification of novel druggable targets and therapeutic strategies. Typically, mutanomes are viewed as one- or two-dimensional. The three-dimensional protein structural view of personal mutanomes sheds light on the functional consequences of clinically actionable mutations revealed in tumor diagnosis and followed up in personalized treatments, in a mutanome-informed manner. In this review, we describe the protein structural landscape of personal mutanomes and provide expert opinions on rational strategies for more streamlined oncological drug discovery and molecularly targeted therapies for each individual and each tumor. We provide the structural mechanism of orthosteric versus allosteric drugs at the atom-level via targeting specific somatic alterations for combating drug resistance and the "undruggable" challenges in solid and hematologic neoplasias. We discuss computational biophysics strategies for innovative mutanome-informed cancer immunotherapies and combination immunotherapies. Finally, we highlight a personal mutanome infrastructure for the emerging development of personalized cancer medicine using a breast cancer case study.

Integration of Metabolomics and Transcriptomics to Identify Gene-Metabolite Relationships Specific to Phenotype

Metabolomics plays an increasingly large role in translational research, with metabolomics data being generated in large cohorts, alongside other omics data such as gene expression. With this in mind, we provide a review of current approaches that integrate metabolomic and transcriptomic data. Furthermore, we provide a detailed framework for integrating metabolomic and transcriptomic data using a two-step approach: (1) numerical integration of gene and metabolite levels to identify phenotype (e.g., cancer)-specific gene-metabolite relationships using IntLIM and (2) knowledge-based integration, using pathway overrepresentation analysis through RaMP, a comprehensive database of biological pathways. Each step makes use of publicly available R packages ( https://github.com/mathelab/IntLIM and https://github.com/mathelab/RaMP-DB ), and provides a user-friendly web interface for analysis. These interfaces can be run locally through the package or can be accessed through our servers ( https://intlim.bmi.osumc.edu and https://ramp-db.bmi.osumc.edu ). The goal of this chapter is to provide step-by-step instructions on how to install the software and use the commands within the R framework, without the user interface (which is slower than running the commands through command line). Both packages are in continuous development so please refer to the GitHub sites to check for updates.

Crosstalk between Estrogen Signaling and Breast Cancer Metabolism

Estrogens and estrogen receptors (ERs) regulate metabolism in both normal physiology and in disease. The metabolic characteristics of intrinsic breast cancer subtypes change based on their ER expression. Crosstalk between estrogen signaling elements and several key metabolic regulators alters metabolism in breast cancer cells, and enables tumors to rewire their metabolism to adapt to poor perfusion, transient nutrient deprivation, and increased acidity. This leads to the selection of drug-resistant and metastatic clones. In this review we discuss studies revealing the role of estrogen signaling elements in drug resistance development and metabolic adaptation during breast cancer progression.

Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival

Background

Transcriptome analysis of breast cancer discovered distinct disease subtypes of clinical significance. However, it remains a challenge to define disease biology solely based on gene expression because tumor biology is often the result of protein function. Here, we measured global proteome and transcriptome expression in human breast tumors and adjacent non-cancerous tissue and performed an integrated proteotranscriptomic analysis.

Methods

We applied a quantitative liquid chromatography/mass spectrometry-based proteome analysis using an untargeted approach and analyzed protein extracts from 65 breast tumors and 53 adjacent non-cancerous tissues. Additional gene expression data from Affymetrix Gene Chip Human Gene ST Arrays were available for 59 tumors and 38 non-cancerous tissues in our study. We then applied an integrated analysis of the proteomic and transcriptomic data to examine relationships between them, disease characteristics, and patient survival. Findings were validated in a second dataset using proteome and transcriptome data from “The Cancer Genome Atlas” and the Clinical Proteomic Tumor Analysis Consortium.

Results

We found that the proteome describes differences between cancerous and non-cancerous tissues that are not revealed by the transcriptome. The proteome, but not the transcriptome, revealed an activation of infection-related signal pathways in basal-like and triple-negative tumors. We also observed that proteins rather than mRNAs are increased in tumors and show that this observation could be related to shortening of the 3′ untranslated region of mRNAs in tumors. The integrated analysis of the two technologies further revealed a global increase in protein-mRNA concordance in tumors. Highly correlated protein-gene pairs were enriched in protein processing and disease metabolic pathways. The increased concordance between transcript and protein levels was additionally associated with aggressive disease, including basal-like/triple-negative tumors, and decreased patient survival. We also uncovered a strong positive association between protein-mRNA concordance and proliferation of tumors. Finally, we observed that protein expression profiles co-segregate with a Myc activation signature and separate breast tumors into two subgroups with different survival outcomes.

Conclusions

Our study provides new insights into the relationship between protein and mRNA expression in breast cancer and shows that an integrated analysis of the proteome and transcriptome has the potential of uncovering novel disease characteristics.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0602-x) contains supplementary material, which is available to authorized users.

Suppression of Breast Cancer Stem Cells and Tumor Growth by the RUNX1 Transcription Factor

Breast cancer remains the most common malignant disease in women worldwide. Despite advances in detection and therapies, studies are still needed to understand the mechanisms underlying this cancer. Cancer stem cells (CSC) play an important role in tumor formation, growth, drug resistance, and recurrence. Here, it is demonstrated that the transcription factor RUNX1, well known as essential for hematopoietic differentiation, represses the breast cancer stem cell (BCSC) phenotype and suppresses tumor growth in vivo. The current studies show that BCSCs sorted from premalignant breast cancer cells exhibit decreased RUNX1 levels, whereas ectopic expression of RUNX1 suppresses tumorsphere formation and reduces the BCSC population. RUNX1 ectopic expression in breast cancer cells reduces migration, invasion, and in vivo tumor growth (57%) in mouse mammary fat pad. Mechanistically, RUNX1 functions to suppress breast cancer tumor growth through repression of CSC activity and direct inhibition of ZEB1 expression. Consistent with these cellular and biochemical results, clinical findings using patient specimens reveal that the highest RUNX1 levels occur in normal mammary epithelial cells and that low RUNX1 expression in tumors is associated with poor patient survival. IMPLICATIONS: The key finding that RUNX1 represses stemness in several breast cancer cell lines points to the importance of RUNX1 in other solid tumors where RUNX1 may regulate CSC properties.

Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers

Conventional cancer drug development has long been limited to organ- or tissue-specific cancer types. However, it has become increasingly known that specific genetic abnormalities are responsible for the carcinogenesis of multiple cancers. The recent US Food and Drug Administration (FDA) approval of the first multi-cancer drug, Keytruda, has demonstrated the feasibility of developing new drugs that target multiple cancers. Despite a promising future, methodological development for identifying multi-cancer molecular targets remains encumbered. This study developed a novel machine learning approach to identify such genes responsible for multiple cancers by synthesizing salient genomic information from cancer-specific classification models. This approach centered on the cross-cancer prediction method for identifying groups of cancers with high cross-cancer predictability. Furthermore, a robust hybrid classifier, comprising Prediction Analysis for Microarrays and Random Forest, was developed to integrate predictive models for gene inference. This approach has successfully identified key genes shared by endometrial cancer, mammary gland ductal carcinoma, and small cell lung cancer. The results are supported by published experimental evidence. This framework holds potential to transform the current methods of discovering multi-cancer molecular targets for clinical oncology.

Glutaminase inhibitors: a patent review

INTRODUCTION

The kidney-type glutaminase (GLS) controlling the first step of glutamine metabolism is overexpressed in many cancer cells. Targeting inhibition of GLS shows obvious inhibitory effects on cancer cell proliferation. Therefore, extensive research and development of GLS inhibitors have been carried out in industrial and academic institutions over the past decade to address this unmet medical need.

AREAS COVERED

This review covers researches and patent literatures in the field of discovery and development of small molecule inhibitors of GLS for cancer therapy over the past 16 years.

EXPERT OPINION

The detailed ligand-receptor interaction information from their complex structure not only guides the rational drug design, but also facilitates in silico structure-based virtual ligand screening of novel GLS inhibitors. Multi-drug combination administration is of great significance both in terms of safety and efficacy.

Metabolism and Epigenetic Interplay in Cancer: Regulation and Putative Therapeutic Targets

Alterations in the epigenome and metabolism affect molecular rewiring of cancer cells facilitating cancer development and progression. Modulation of histone and DNA modification enzymes occurs owing to metabolic reprogramming driven by oncogenes and expression of metabolism-associated genes is, in turn, epigenetically regulated, promoting the well-known metabolic reprogramming of cancer cells and, consequently, altering the metabolome. Thus, several malignant traits are supported by the interplay between metabolomics and epigenetics, promoting neoplastic transformation. In this review we emphasize the importance of tumour metabolites in the activity of most chromatin-modifying enzymes and implication in neoplastic transformation. Furthermore, candidate targets deriving from metabolism of cancer cells and altered epigenetic factors is emphasized, focusing on compounds that counteract the epigenomic-metabolic interplay in cancer.

Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from “The Cancer Genome Atlas (TCGA)” consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.

Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

Analysis of cerebrospinal fluid metabolites in patients with primary or metastatic central nervous system tumors

Cancer cells have altered cellular metabolism. Mutations in genes associated with key metabolic pathways (e.g., isocitrate dehydrogenase 1 and 2, IDH1/IDH2) are important drivers of cancer, including central nervous system (CNS) tumors. Therefore, we hypothesized that the abnormal metabolic state of CNS cancer cells leads to abnormal levels of metabolites in the CSF, and different CNS cancer types are associated with specific changes in the levels of CSF metabolites. To test this hypothesis, we used mass spectrometry to analyze 129 distinct metabolites in CSF samples from patients without a history of cancer (n = 8) and with a variety of CNS tumor types (n = 23) (i.e., glioma IDH-mutant, glioma-IDH wildtype, metastatic lung cancer and metastatic breast cancer). Unsupervised hierarchical clustering analysis shows tumor-specific metabolic signatures that facilitate differentiation of tumor type from CSF analysis. We identified differences in the abundance of 43 metabolites between CSF from control patients and the CSF of patients with primary or metastatic CNS tumors. Pathway analysis revealed alterations in various metabolic pathways (e.g., glycine, choline and methionine degradation, dipthamide biosynthesis and glycolysis pathways, among others) between IDH-mutant and IDH-wildtype gliomas. Moreover, patients with IDH-mutant gliomas demonstrated higher levels of D-2-hydroxyglutarate in the CSF, in comparison to patients with other tumor types, or controls. This study demonstrates that analysis of CSF metabolites can be a clinically useful tool for diagnosing and monitoring patients with primary or metastatic CNS tumors.

Sex-associated differences in baseline urinary metabolites of healthy adults

The biological basis for gender variability among disease states is not well established. There have been many prior efforts attempting to identify the unique urine metabolomic profiles associated with specific diseases. However, there has been little advancement in investigating the metabolomic differences associated with gender, which underlies the misconception that risk factors and treatment regimens should be the same for both male and female patients. This present study aimed to identify biologically-meaningful baseline sex-related differences using urine samples provided by healthy female and male participants. To elucidate whether urinary metabolic signatures are globally distinct between healthy males and females, we applied metabolomics profiling of primary metabolism with comprehensive bioinformatics analyses on urine samples from 60 healthy males and females. We found that levels of α-ketoglutarate and 4-hydroxybutyric acid increased 2.3-fold and 4.41-fold in males compared to females, respectively. Furthermore, chemical similarity enrichment analysis revealed that differentially expressed metabolites, such as saturated fatty acids, TCA, and butyrates, were significantly related to the gender effect. These findings indicate that there are baseline sex-related differences in urinary metabolism, which should be considered in biomarker discovery, diagnosis, and treatment of bladder diseases, such as interstitial cystitis.

Applications of metabolomics to study cancer metabolism

Reprogrammed metabolism supports tumor growth and provides a potential source of therapeutic targets and disease biomarkers. Mass spectrometry-based metabolomics has emerged as a broadly informative technique for profiling metabolic features associated with specific oncogenotypes, disease progression, therapeutic liabilities and other clinically relevant aspects of tumor biology. In this review, we introduce the applications of metabolomics to study deregulated metabolism and metabolic vulnerabilities in cancer. We provide examples of studies that used metabolomics to discover novel metabolic regulatory mechanisms, including processes that link metabolic alterations with gene expression, protein function, and other aspects of systems biology. Finally, we discuss emerging applications of metabolomics for in vivo isotope tracing and metabolite imaging, both of which hold promise to advance our understanding of the role of metabolic reprogramming in cancer.

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

OBJECTIVE

 The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

METHODS

 In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

RESULTS

 Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

CONCLUSIONS

 The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.

Comparative Transcriptome Profiling Reveals Coding and Noncoding RNA Differences in NSCLC from African Americans and European Americans

Purpose: To determine whether racial differences in gene and miRNA expression translates to differences in lung tumor biology with clinical relevance in African Americans (AAs) and European Americans (EAs).Experimental Design: The NCI-Maryland Case Control Study includes seven Baltimore City hospitals and is overrepresented with AA patients (∼40%). Patients that underwent curative NSCLC surgery between 1998 and 2014 were enrolled. Comparative molecular profiling used mRNA (n = 22 AAs and 19 EAs) and miRNA (n = 42 AAs and 55 EAs) expression arrays to track differences in paired fresh frozen normal tissues and lung tumor specimens from AAs and EAs. Pathway enrichment, predicted drug response, tumor microenvironment infiltration, cancer immunotherapy antigen profiling, and miRNA target enrichment were assessed.Results: AA-enriched differential gene expression was characterized by stem cell and invasion pathways. Differential gene expression in lung tumors from EAs was primarily characterized by cell proliferation pathways. Population-specific gene expression was partly driven by population-specific miRNA expression profiles. Drug susceptibility predictions revealed a strong inverse correlation between AA resistance and EA sensitivity to the same panel of drugs. Statistically significant differences in M1 and M2 macrophage infiltration were observed in AAs (P < 0.05); however, PD-L1, PD-L2 expression was similar between both.Conclusions: Comparative transcriptomic profiling revealed clear differences in lung tumor biology between AAs and EAs. Increased participation by AAs in lung cancer clinical trials are needed to integrate, and leverage, transcriptomic differences with other clinical information to maximize therapeutic benefit in both AAs and EAs. Clin Cancer Res; 23(23); 7412-25. ©2017 AACR.

Wild-type IDH2 promotes the Warburg effect and tumor growth through HIF1α in lung cancer

Hotspot mutations of isocitrate dehydrogenase 1 and 2 (IDH1/2) have been studied in several cancers. However, the function of wild-type IDH2 in lung cancer and the mechanism of its contribution to growth of cancer cells remain unknown. Here, we explored the role and mechanism of wild-type IDH2 in promoting growth of lung cancer.

Methods: Information regarding genomic and clinical application focusing on IDH2 in cancer was examined in several databases of more than 1,000 tumor samples. IDH2 expression was assessed by immunohistochemistry in tissues from lung cancer patients. The biological functions of IDH2 were evaluated by using cell-based assays and in vivo xenograft mouse models.

Results: Here we reported that wild-type IDH2 is up-regulated and is an indicator of poor survival in lung cancer and several other cancers. Targeting IDH2 with shRNA resulted in decreased HIF1α expression, leading to the attenuation of lung cancer cell proliferation and tumor growth. Treatment of lung cancer cells with AGI-6780 (a small molecule inhibitor of IDH2), PX-478 (an inhibitor of HIF1α) or incubation with octyl-α-KG inhibited lung cancer cell proliferation.

Conclusion: IDH2 promotes the Warburg effect and lung cancer cell growth, which is mediated through HIF1α activation followed by decreased α-KG. Therefore, IDH2 could possibly serve as a novel therapeutic target for lung cancer.

Benzylserine inhibits breast cancer cell growth by disrupting intracellular amino acid homeostasis and triggering amino acid response pathways

Background

Cancer cells require increased levels of nutrients such as amino acids to sustain their rapid growth. In particular, leucine and glutamine have been shown to be important for growth and proliferation of some breast cancers, and therefore targeting the primary cell-surface transporters that mediate their uptake, L-type amino acid transporter 1 (LAT1) and alanine, serine, cysteine-preferring transporter 2 (ASCT2), is a potential therapeutic strategy.

Methods

The ASCT2 inhibitor, benzylserine (BenSer), is also able to block LAT1 activity, thus inhibiting both leucine and glutamine uptake. We therefore aimed to investigate the effects of BenSer in breast cancer cell lines to determine whether combined LAT1 and ASCT2 inhibition could inhibit cell growth and proliferation.

Results

BenSer treatment significantly inhibited both leucine and glutamine uptake in MCF-7, HCC1806 and MDA-MB-231 breast cancer cells, causing decreased cell viability and cell cycle progression. These effects were not primarily leucine-mediated, as BenSer was more cytostatic than the LAT family inhibitor, BCH. Oocyte uptake assays with ectopically expressed amino acid transporters identified four additional targets of BenSer, and gas chromatography-mass spectrometry (GCMS) analysis of intracellular amino acid concentrations revealed that this BenSer-mediated inhibition of amino acid uptake was sufficient to disrupt multiple pathways of amino acid metabolism, causing reduced lactate production and activation of an amino acid response (AAR) through activating transcription factor 4 (ATF4).

Conclusions

Together these data showed that BenSer blockade inhibited breast cancer cell growth and viability through disruption of intracellular amino acid homeostasis and inhibition of downstream metabolic and growth pathways.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4599-8) contains supplementary material, which is available to authorized users.

AR negative triple negative or "quadruple negative" breast cancers in African American women have an enriched basal and immune signature

There is increasing evidence that Androgen Receptor (AR) expression has prognostic usefulness in Triple negative breast cancer (TNBC), where tumors that lack AR expression are considered “Quadruple negative” Breast Cancers (“QNBC”). However, a comprehensive analysis of AR expression within all breast cancer subtypes or stratified by race has not been reported. We assessed AR mRNA expression in 925 tumors from The Cancer Genome Atlas (TCGA), and 136 tumors in 2 confirmation sets. AR protein expression was determined by immunohistochemistry in 197 tumors from a multi-institutional cohort, for a total of 1258 patients analyzed. Cox hazard ratios were used to determine correlations to PAM50 breast cancer subtypes, and TNBC subtypes. Overall, AR-negative patients are diagnosed at a younger age compared to AR-positive patients, with the average age of AA AR-negative patients being, 49. AA breast tumors express AR at lower rates compared to Whites, independent of ER and PR expression (p<0.0001). AR-negative patients have a (66.60; 95% CI, 32–146) odds ratio of being basal-like compared to other PAM50 subtypes, and this is associated with an increased time to progression and decreased overall survival. AA “QNBC” patients predominately demonstrated BL1, BL2 and IM subtypes, with differential expression of E2F1, NFKBIL2, CCL2, TGFB3, CEBPB, PDK1, IL12RB2, IL2RA, and SOS1 genes compared to white patients. Immune checkpoint inhibitors PD-1, PD-L1, and CTLA-4 were significantly upregulated in both overall “QNBC” and AA “QNBC” patients as well. Thus, AR could be used as a prognostic marker for breast cancer, particularly in AA “QNBC” patients.

Expression of Fibroblast Growth Factor 5 (FGF5) and Its Influence on Survival of Breast Cancer Patients

BACKGROUND The clinical outcome of patients with breast cancer (BC) remains poor. MATERIAL AND METHODS We analyzed BC microarray studies GSE37751, GSE7390, and GSE21653 to investigate the expression of FGF5 gene between BC patients and their normal counterparts and the relationship between FGF5 expression and age, tumor size, histopathological grading, estrogen receptors, clinical risk group according to St Gallen criteria, clinical risk group according to NPI criteria, clinical risk group according to Veridex signature, distant metastasis-free survival (DMFS), time to distant metastasis (TDM), disease-free survival (DFS), and overall survival (OS) of BC patients. Gene set enrichment analysis (GSEA) was used to investigate the exact mechanisms. RESULTS FGF5 expression was significantly upregulated in BC patients relative to that in normal controls (P<0.0001). BC patients in the FGF5 low-expression group were correlated with better clinical characteristics, including tumor size, histopathological grading, estrogen receptors, clinical risk group according to St Gallen criteria, NPI criteria and Veridex signature, DMFS, TDM, and DFS compared with those in the FGF5 high-expression cohort. The result of GSEA indicated that FGF5 inhibits the proliferation of BC cells via ultraviolet response and TGF-b signaling. Quantitative PCR verified that FGF5 was overexpressed in patients with BC. CONCLUSIONS Our results suggest that FGF5 is an independent protective factor for BC patients.

Thermal proteome profiling of breast cancer cells reveals proteasomal activation by CDK4/6 inhibitor palbociclib

Palbociclib is a CDK4/6 inhibitor approved for metastatic estrogen receptor-positive breast cancer. In addition to G1 cell cycle arrest, palbociclib treatment results in cell senescence, a phenotype that is not readily explained by CDK4/6 inhibition. In order to identify a molecular mechanism responsible for palbociclib-induced senescence, we performed thermal proteome profiling of MCF7 breast cancer cells. In addition to affecting known CDK4/6 targets, palbociclib induces a thermal stabilization of the 20S proteasome, despite not directly binding to it. We further show that palbociclib treatment increases proteasome activity independently of the ubiquitin pathway. This leads to cellular senescence, which can be counteracted by proteasome inhibitors. Palbociclib-induced proteasome activation and senescence is mediated by reduced proteasomal association of ECM29. Loss of ECM29 activates the proteasome, blocks cell proliferation, and induces a senescence-like phenotype. Finally, we find that ECM29 mRNA levels are predictive of relapse-free survival in breast cancer patients treated with endocrine therapy. In conclusion, thermal proteome profiling identifies the proteasome and ECM29 protein as mediators of palbociclib activity in breast cancer cells.

A Metabolomics Study of BPTES Altered Metabolism in Human Breast Cancer Cell Lines

The Warburg effect is a well-known phenomenon in cancer, but the glutamine addiction in which cancer cells utilize glutamine as an alternative source of energy is less well known. Recent efforts have focused on preventing cancer cell proliferation associated with glutamine addiction by targeting glutaminase using the inhibitor BPTES (bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide). In the current study, an investigation of the BPTES induced changes in metabolism was made in two human breast cancer cell lines, MCF7 (an estrogen receptor dependent cell line) and MDA-MB231 (a triple negative cell line), relative to the non-cancerous cell line, MCF10A. NMR spectroscopy combined with a recently established smart-isotope tagging approach enabled quantitative analysis of 41 unique metabolites representing numerous metabolite classes including carbohydrates, amino acids, carboxylic acids and nucleotides. BPTES induced metabolism changes in the cancer cell lines were especially pronounced under hypoxic conditions with up to 1/3 of the metabolites altered significantly (p < 0.05) relative to untreated cells. The BPTES induced changes were more pronounced for MCF7 cells, with 14 metabolites altered significantly (p < 0.05) compared to seven for MDA-MB231. Analyses of the results indicate that BPTES affected numerous metabolic pathways including glycolysis, TCA cycle, nucleotide and amino acid metabolism in cancer. The distinct metabolic responses to BPTES treatment determined in the two breast cancer cell lines offer valuable metabolic information for the exploration of the therapeutic responses to breast cancer.

Signaling Pathways Regulating Redox Balance in Cancer Metabolism

The interplay between rewiring tumor metabolism and oncogenic driver mutations is only beginning to be appreciated. Metabolic deregulation has been described for decades as a bystander effect of genomic aberrations. However, for the biology of malignant cells, metabolic reprogramming is essential to tackle a harsh environment, including nutrient deprivation, reactive oxygen species production, and oxygen withdrawal. Besides the well-investigated glycolytic metabolism, it is emerging that several other metabolic fluxes are relevant for tumorigenesis in supporting redox balance, most notably pentose phosphate pathway, folate, and mitochondrial metabolism. The relationship between metabolic rewiring and mutant genes is still unclear and, therefore, we will discuss how metabolic needs and oncogene mutations influence each other to satisfy cancer cells’ demands. Mutations in oncogenes, i.e., PI3K/AKT/mTOR, RAS pathway, and MYC, and tumor suppressors, i.e., p53 and liver kinase B1, result in metabolic flexibility and may influence response to therapy. Since metabolic rewiring is shaped by oncogenic driver mutations, understanding how specific alterations in signaling pathways affect different metabolic fluxes will be instrumental for the development of novel targeted therapies. In the era of personalized medicine, the combination of driver mutations, metabolite levels, and tissue of origins will pave the way to innovative therapeutic interventions.

ADHFE1 is a MYC-linked oncogene that induces metabolic reprogramming and cellular de-differentiation in breast cancer

The oncometabolite, D-2-hydroxyglutarate, accumulates in various cancers because of acquired mutations in isocitrate dehydrogenase 1 & 2. Here, we describe a new mechanism for D-2-hydroxyglutarate accumulation in breast cancer. It involves c-Myc signaling and alcohol dehydrogenase, iron-containing protein 1 (ADHFE1) and leads to metabolic reprogramming, de-differentiation, and increased mammary tumorigenesis.

Correlated metabolomic, genomic, and histologic phenotypes in histologically normal breast tissue

Breast carcinogenesis is a multistep process accompanied by widespread molecular and genomic alterations, both in tumor and in surrounding microenvironment. It is known that tumors have altered metabolism, but the metabolic changes in normal or cancer-adjacent, nonmalignant normal tissues and how these changes relate to alterations in gene expression and histological composition are not well understood. Normal or cancer-adjacent normal breast tissues from 99 women of the Normal Breast Study (NBS) were evaluated. Data of metabolomics, gene expression and histological composition was collected by mass spectrometry, whole genome microarray, and digital image, respectively. Unsupervised clustering analysis determined metabolomics-derived subtypes. Their association with genomic and histological features, as well as other breast cancer risk factors, genomic and histological features were evaluated using logistic regression. Unsupervised clustering of metabolites resulted in two main clusters. The metabolite differences between the two clusters suggested enrichment of pathways involved in lipid metabolism, cell growth and proliferation, and migration. Compared with Cluster 1, subjects in Cluster 2 were more likely to be obese (body mass index ≥30 kg/m2, p<0.05), have increased adipose proportion (p<0.01) and associated with a previously defined Active genomic subtype (p<0.01). By the integrated analyses of histological, metabolomics and transcriptional data, we characterized two distinct subtypes of non-malignant breast tissue. Further research is needed to validate our findings, and understand the potential role of these alternations in breast cancer initiation, progression and recurrence.

Evidence-based approaches to reduce cancer health disparities: Discover, develop, deliver, and disseminate

The Texas Center for Health Disparities (TCHD) at the University of North Texas Health Science Center is a National Institute on Minority Health and Health Disparities-funded, specialized center of excellence for health disparities. TCHD organized its 12^th annual conference focusing on "Evidence-Based Approaches to Reduce Cancer Health Disparities: Discover, Develop, Deliver, and Disseminate." At this conference, experts in health care, biomedical sciences, and public health gathered to discuss the current status and strategies for reducing cancer health disparities. The meeting was conducted in three sessions on breast cancer, prostate cancer, and colorectal cancer disparities, in addition to roundtable discussions and a poster session. Each session highlighted differences in the effects of cancer, based on factors such as race/ethnicity, gender, socioeconomic status, and geographical location. In each session, expert speakers presented their findings, and this was followed by a discussion panel made up of experts in that field and cancer survivors, who responded to questions from the audience. This article summarizes the approaches to fundamental, translational, clinical, and public health issues in cancer health disparities discussed at the conference.

Metabolomics of oncogene-specific metabolic reprogramming during breast cancer

Background

The complex yet interrelated connections between cancer metabolism and oncogenic driver genes are relatively unexplored but have the potential to identify novel biomarkers and drug targets with prognostic and therapeutic value. The goal of this study was to identify global metabolic profiles of breast tumors isolated from multiple transgenic mouse models and to identify unique metabolic signatures driven by these oncogenes.

Methods

Using mass spectrometry (GC-MS, LC-MS/MS, and capillary zone electrophoresis (CZE)-MS platforms), we quantified and compared the levels of 374 metabolites in breast tissue from normal and transgenic mouse breast cancer models overexpressing a panel of oncogenes (PyMT, PyMT-DB, Wnt1, Neu, and C3-TAg). We also compared the mouse metabolomics data to published human metabolomics data already linked to clinical data.

Results

Through analysis of our metabolomics data, we identified metabolic differences between normal and tumor breast tissues as well as metabolic differences unique to each initiating oncogene. We also quantified the metabolic profiles of the mammary fat pad versus mammary epithelium by CZE-MS/MS. However, the differences between the tissues did not account for the majority of the metabolic differences between the normal mammary gland and breast tumor tissues. Therefore, the differences between the cohorts were unlikely due to cellular heterogeneity. Of the mouse models used in this study, C3-TAg was the only cohort with a tumor metabolic signature composed of ten metabolites that had significant prognostic value in breast cancer patients. Gene expression analysis identified candidate genes that may contribute to the metabolic reprogramming.

Conclusions

This study identifies oncogene-induced metabolic reprogramming within mouse breast tumors and compares the results to that of human breast tumors, providing a unique look at the relationship between and clinical value of oncogene initiation and metabolism during breast cancer.

Methylation-to-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, and Pathologic Complete Response in Multiple Cohorts

Prognostic biomarkers serve a variety of purposes in cancer treatment and research, such as prediction of cancer progression, and treatment eligibility. Despite growing interest in multi-omic data integration for defining prognostic biomarkers, validated methods have been slow to emerge. Given that breast cancer has been the focus of intense research, it is amenable to studying the benefits of multi-omic prognostic models due to the availability of datasets. Thus, we examined the efficacy of our methylation-to-expression feature model (M2EFM) approach to combining molecular and clinical predictors to create risk scores for overall survival, distant metastasis, and chemosensitivity in breast cancer. Gene expression, DNA methylation, and clinical variables were integrated via M2EFM to build models of overall survival using 1028 breast tumor samples and applied to validation cohorts of 61 and 327 samples. Models of distant recurrence-free survival and pathologic complete response were built using 306 samples and validated on 182 samples. Despite different populations and assays, M2EFM models validated with good accuracy (C-index or AUC ≥ 0.7) for all outcomes and had the most consistent performance compared to other methods. Finally, we demonstrated that M2EFM identifies functionally relevant genes, which could be useful in translating an M2EFM biomarker to the clinic.

D-2-hydroxyglutarate interferes with HIF-1α stability skewing T-cell metabolism towards oxidative phosphorylation and impairing Th17 polarization

D-2-hydroxyglutarate (D-2HG) is released by various types of malignant cells including acute myeloid leukemia (AML) blasts carrying isocitrate dehydrogenase (IDH) gain-of-function mutations. D-2HG acting as an oncometabolite promotes proliferation, anoikis, and differentiation block of hematopoietic cells in an autocrine fashion. However, prognostic impact of IDH mutations and high D-2HG levels remains controversial and might depend on the overall mutational context. An increasing number of studies focus on the permissive environment created by AML blasts to promote immune evasion. Impact of D-2HG on immune cells remains incompletely understood. Here, we sought out to investigate the effects of D-2HG on T-cells as key mediators of anti-AML immunity. D-2HG was efficiently taken up by T-cells in vitro, which is in line with high 2-HG levels measured in T-cells isolated from AML patients carrying IDH mutations. T-cell activation was slightly impacted by D-2HG. However, D-2HG triggered HIF-1a protein destabilization resulting in metabolic skewing towards oxidative phosphorylation, increased regulatory T-cell (Treg) frequency, and reduced T helper 17 (Th17) polarization. Our data suggest for the first time that D-2HG might contribute to fine tuning of immune responses.

Amino Acid Transporters and Glutamine Metabolism in Breast Cancer

Amino acid transporters are membrane transport proteins, most of which are members of the solute carrier families. Amino acids are essential for the survival of all types of cells, including tumor cells, which have an increased demand for nutrients to facilitate proliferation and cancer progression. Breast cancer is the most common malignancy in women worldwide and is still associated with high mortality rates, despite improved treatment strategies. Recent studies have demonstrated that the amino acid metabolic pathway is altered in breast cancer and that amino acid transporters affect tumor growth and progression. In breast cancer, glutamine is one of the key nutrients, and glutamine metabolism is closely related to the amino acid transporters. In this review, we focus on amino acid transporters and their roles in breast cancer. We also highlight the different subsets of upregulated amino acid transporters in breast cancer and discuss their potential applications as treatment targets, cancer imaging tracers, and drug delivery components. Glutamine metabolism as well as its regulation and therapeutic implication in breast cancer are also discussed.

IntLIM: integration using linear models of metabolomics and gene expression data

BACKGROUND

Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites.

RESULTS

The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally.

CONCLUSIONS

IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

Prediction of platinum-based chemotherapy efficacy in lung cancer based on LC-MS metabolomics approach

Lung cancer is the common cause of cancer-related death worldwide. Platinum-based chemotherapy is the cornerstone of treatment for lung cancer. Platinum sensitivity is a major possibility for effective cancer treatment. In this study, several potential biomarkers were identified for evaluating and predicting the response to platinum-based chemotherapy. LC-MS-based metabolomics was performed on plasma samples from 43 lung cancer patients with different chemotherapy efficacy. By combing multivariate statistical analysis, pathway analysis with correlation analysis, 8 potential biomarkers were significantly associated with platinum chemotherapy response. Moreover, a prediction model with these biomarkers involved in citric acid cycle, glutamate metabolism and amino acid metabolism, showed 100% sensitivity and 100% specificity for predicting chemotherapy response in a validation set. Interestingly, 2-hydroxyglutaric acid (2-HG) as an oncometabolite accumulated in lung cancer was remarkably elevated in the partial response (PR) patients. Collectively, our findings implicated that metabolomics can serve as a potential tool to select lung cancer patients that are more likely to benefit from the platinum-based treatment.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

On metabolic reprogramming and tumor biology: A comprehensive survey of metabolism in breast cancer

Altered metabolism in tumor cells has been a focus of cancer research for as long as a century but has remained controversial and vague due to an inhomogeneous overall picture. Accumulating genomic, metabolomic, and lastly panomic data as well as bioenergetics studies of the past few years enable a more comprehensive, systems-biologic approach promoting deeper insight into tumor biology and challenging hitherto existing models of cancer bioenergetics. Presenting a compendium on breast cancer-specific metabolome analyses performed thus far, we review and compile currently known aspects of breast cancer biology into a comprehensive network, elucidating previously dissonant issues of cancer metabolism. As such, some of the aspects critically discussed in this review include the dynamic interplay or metabolic coupling between cancer (stem) cells and cancer-associated fibroblasts, the intratumoral and intertumoral heterogeneity and plasticity of cancer cell metabolism, the existence of distinct metabolic tumor compartments in need of separate yet simultaneous therapeutic targeting, the reliance of cancer cells on oxidative metabolism and mitochondrial power, and the role of pro-inflammatory, pro-tumorigenic stromal conditioning. Comprising complex breast cancer signaling networks as well as combined metabolomic and genomic data, we address metabolic consequences of mutations in tumor suppressor genes and evaluate their contribution to breast cancer predisposition in a germline setting, reasoning for distinct personalized preventive and therapeutic measures. The review closes with a discussion on central root mechanisms of tumor cell metabolism and rate-limiting steps thereof, introducing essential strategies for therapeutic targeting.

Metabolism, Activity, and Targeting of D- and L-2-Hydroxyglutarates

Isocitrate dehydrogenases (IDH1/2) are frequently mutated in multiple types of human cancer, resulting in neomorphic enzymes that convert α-ketoglutarate (α-KG) to 2-hydroxyglutarate (2-HG). The current view on the mechanism of IDH mutation holds that 2-HG acts as an antagonist of α-KG to competitively inhibit the activity of α-KG-dependent dioxygenases, including those involved in histone and DNA demethylation. Recent studies have implicated 2-HG in activities beyond epigenetic modification. Multiple enzymes have been discovered that lack mutations but that can nevertheless produce 2-HG promiscuously under hypoxic or acidic conditions. Therapies are being developed to treat IDH-mutant cancers by targeting either the mutant IDH enzymes directly or the pathways sensitized by 2-HG.

A Landscape of Metabolic Variation across Tumor Types

Tumor metabolism is reorganized to support proliferation in the face of growth-related stress. Unlike the widespread profiling of changes to metabolic enzyme levels in cancer, comparatively less attention has been paid to the substrates/products of enzyme-catalyzed reactions, small-molecule metabolites. We developed an informatic pipeline to concurrently analyze metabolomics data from over 900 tissue samples spanning seven cancer types, revealing extensive heterogeneity in metabolic changes relative to normal tissue across cancers of different tissues of origin. Despite this heterogeneity, a number of metabolites were recurrently differentially abundant across many cancers, such as lactate and acyl-carnitine species. Through joint analysis of metabolomic data alongside clinical features of patient samples, we also identified a small number of metabolites, including several polyamines and kynurenine, which were associated with aggressive tumors across several tumor types. Our findings offer a glimpse onto common patterns of metabolic reprogramming across cancers, and the work serves as a large-scale resource accessible via a web application (http://www.sanderlab.org/pancanmet).

Downregulation of HSP60 disrupts mitochondrial proteostasis to promote tumorigenesis and progression in clear cell renal cell carcinoma

In the present study, we demonstrate that HSP60 is unequivocally downregulated in clear cell renal cell carcinoma (ccRCC) tissues compared to pericarcinous tissues. Overexpression of HSP60 in ccRCC cancer cells suppresses cell growth. HSP60 knockdown increases cell growth and proliferation in both cell culture and nude mice xenografts, and drives cells to undergo epithelial to mesenchymal transition (EMT). Our results propose that HSP60 silencing disrupts the integrity of the respiratory complex I and triggers the excessive ROS production, which promotes tumor progression in the following aspects: (1) ROS activates the AMPK pathway that promotes acquisition of the Warburg phenotype in HSP60-KN cells; (2) ROS generated by HSP60 knockdown or by rotenone inhibition drives cells to undergo EMT; and (3) the high level of ROS may also fragment the Fe-S clusters that up regulates ADHFe1 expression and the 2-hydroxygluterate (2-HG) production leading to changes in DNA methylation. These results suggest that the high level of ROS is needed for tumorigenesis and progression in tumors with the low HSP60 expression and HSP60 is a potential diagnostic biomarker as well as a therapeutic target in ccRCC.

Elevated d-2-hydroxyglutarate during colitis drives progression to colorectal cancer

d-2-hydroxyglutarate (D2HG) is produced in the tricarboxylic acid cycle and is quickly converted to α-ketoglutarate by d-2-hydroxyglutarate dehydrogenase (D2HGDH). In a mouse model of colitis-associated colon cancer (CAC), urine level of D2HG during colitis correlates positively with subsequent polyp counts and severity of dysplasia. The i.p. injection of D2HG results in delayed recovery from colitis and severe tumorigenesis. The colonic expression of D2HGDH is decreased in ulcerative colitis (UC) patients at baseline who progress to cancer. Hypoxia-inducible factor (Hif)-1α is a key regulator of D2HGDH transcription. Our study identifies urine D2HG and tissue D2HGDH expression as biomarkers to identify patients at risk for progressing from colitis to cancer. The D2HG/D2HGDH pathway provides potential therapeutic targets for the treatment of CAC.

2-Hydroxyglutarate produced by neomorphic IDH mutations suppresses homologous recombination and induces PARP inhibitor sensitivity

2-Hydroxyglutarate (2HG) exists as two enantiomers, (R)-2HG and (S)-2HG, and both are implicated in tumor progression via their inhibitory effects on α-ketoglutarate (αKG)-dependent dioxygenases. The former is an oncometabolite that is induced by the neomorphic activity conferred by isocitrate dehydrogenase 1 (IDH1) and IDH2 mutations, whereas the latter is produced under pathologic processes such as hypoxia. We report that IDH1/2 mutations induce a homologous recombination (HR) defect that renders tumor cells exquisitely sensitive to poly(adenosine 5'-diphosphate-ribose) polymerase (PARP) inhibitors. This "BRCAness" phenotype of IDH mutant cells can be completely reversed by treatment with small-molecule inhibitors of the mutant IDH1 enzyme, and conversely, it can be entirely recapitulated by treatment with either of the 2HG enantiomers in cells with intact IDH1/2 proteins. We demonstrate mutant IDH1-dependent PARP inhibitor sensitivity in a range of clinically relevant models, including primary patient-derived glioma cells in culture and genetically matched tumor xenografts in vivo. These findings provide the basis for a possible therapeutic strategy exploiting the biological consequences of mutant IDH, rather than attempting to block 2HG production, by targeting the 2HG-dependent HR deficiency with PARP inhibition. Furthermore, our results uncover an unexpected link between oncometabolites, altered DNA repair, and genetic instability.

Glutamine-derived 2-hydroxyglutarate is associated with disease progression in plasma cell malignancies

The production of the oncometabolite 2-hydroxyglutarate (2-HG) has been associated with c-MYC overexpression. c-MYC also regulates glutamine metabolism and drives progression of asymptomatic precursor plasma cell (PC) malignancies to symptomatic multiple myeloma (MM). However, the presence of 2-HG and its clinical significance in PC malignancies is unknown. By performing 13C stable isotope resolved metabolomics (SIRM) using U[13C6]Glucose and U[13C5]Glutamine in human myeloma cell lines (HMCLs), we show that 2-HG is produced in clonal PCs and is derived predominantly from glutamine anaplerosis into the TCA cycle. Furthermore, the 13C SIRM studies in HMCLs also demonstrate that glutamine is preferentially utilized by the TCA cycle compared with glucose. Finally, measuring the levels of 2-HG in the BM supernatant and peripheral blood plasma from patients with precursor PC malignancies such as smoldering MM (SMM) demonstrates that relatively elevated levels of 2-HG are associated with higher levels of c-MYC expression in the BM clonal PCs and with a subsequent shorter time to progression (TTP) to MM. Thus, measuring 2-HG levels in BM supernatant or peripheral blood plasma of SMM patients offers potential early identification of those patients at high risk of progression to MM, who could benefit from early therapeutic intervention.

Metabolism and Transcription in Cancer: Merging Two Classic Tales

Cellular plasticity, or the ability of a cancer cell to adapt to changes in the microenvironment, is a major determinant of cell survival and functionality that require the coordination of transcriptional programs with signaling and metabolic pathways. In this scenario, these pathways sense and integrate nutrient signals for the induction of coordinated gene expression programs in cancer. This minireview focuses on recent advances that shed light on the bidirectional relationship between metabolism and gene transcription, and their biological outcomes in cancer. Specifically, we will discuss how metabolic changes occurring in cancer cells impact on gene expression, both at the level of the epigenetic landscape and transcription factor regulation.

Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies

BACKGROUND

Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.

AIM OF REVIEW

This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.

Breast Cancer Metabolism

Despite advances in screening, therapy, and surveillance that have improved survival rates, breast cancer is still the most commonly diagnosed cancer and the second leading cause of cancer mortality among women [1]. Breast cancer is a highly heterogeneous disease rooted in a genetic basis and reflected in clinical behavior. The diversity of breast cancer hormone receptor status and the expression of surface molecules has guided therapy decisions for decades; however, subtype-specific treatment often yields diverse responses due to varying tumor evolution and malignant potential. Although understanding the mechanisms behind breast cancer heterogeneity is still a challenge, available evidence suggests that studying its metabolism has the potential to give valuable insight into the causes of these variations, as well as viable targets for intervention.

Comprehensive molecular biomarker identification in breast cancer brain metastases

Background

Breast cancer brain metastases (BCBM) develop in about 20–30% of breast cancer (BC) patients. BCBM are associated with dismal prognosis not at least due to lack of valuable molecular therapeutic targets. The aim of the study was to identify new molecular biomarkers and targets in BCBM by using complementary state-of-the-art techniques.

Methods

We compared array expression profiles of three BCBM with 16 non-brain metastatic BC and 16 primary brain tumors (prBT) using a false discovery rate (FDR) p < 0.05 and fold change (FC) > 2. Biofunctional analysis was conducted on the differentially expressed probe sets. High-density arrays were employed to detect copy number variations (CNVs) and whole exome sequencing (WES) with paired-end reads of 150 bp was utilized to detect gene mutations in the three BCBM.

Results

The top 370 probe sets that were differentially expressed between BCBM and both BC and prBT were in the majority comparably overexpressed in BCBM and included, e.g. the coding genes BCL3, BNIP3, BNIP3P1, BRIP1, CASP14, CDC25A, DMBT1, IDH2, E2F1, MYCN, RAD51, RAD54L, and VDR. A number of small nucleolar RNAs (snoRNAs) were comparably overexpressed in BCBM and included SNORA1, SNORA2A, SNORA9, SNORA10, SNORA22, SNORA24, SNORA30, SNORA37, SNORA38, SNORA52, SNORA71A, SNORA71B, SNORA71C, SNORD13P2, SNORD15A, SNORD34, SNORD35A, SNORD41, SNORD53, and SCARNA22. The top canonical pathway was entitled, role of BRCA1 in DNA damage response. Network analysis revealed key nodes as Akt, ERK1/2, NFkB, and Ras in a predicted activation stage. Downregulated genes in a data set that was shared between BCBM and prBT comprised, e.g. BC cell line invasion markers JUN, MMP3, TFF1, and HAS2. Important cancer genes affected by CNVs included TP53, BRCA1, BRCA2, ERBB2, IDH1, and IDH2. WES detected numerous mutations, some of which affecting BC associated genes as CDH1, HEPACAM, and LOXHD1.

Conclusions

Using complementary molecular genetic techniques, this study identified shared and unshared molecular events in three highly aberrant BCBM emphasizing the challenge to detect new molecular biomarkers and targets with translational implications. Among new findings with the capacity to gain clinical relevance is the detection of overexpressed snoRNAs known to regulate some critical cellular functions as ribosome biogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1370-x) contains supplementary material, which is available to authorized users.

Metabolic Footprints and Molecular Subtypes in Breast Cancer

Cancer treatment options are increasing. However, even among the same tumor histotype, interpatient tumor heterogeneity should be considered for best therapeutic result. Metabolomics represents the last addition to promising “omic” sciences such as genomics, transcriptomics, and proteomics. Biochemical transformation processes underlying energy production and biosynthetic processes have been recognized as a hallmark of the cancer cell and hold a promise to build a bridge between genotype and phenotype. Since breast tumors represent a collection of different diseases, understanding metabolic differences between molecular subtypes offers a way to identify new subtype-specific treatment strategies, especially if metabolite changes are evaluated in the broader context of the network of enzymatic reactions and pathways. Here, after a brief overview of the literature, original metabolomics data in a series of 92 primary breast cancer patients undergoing surgery at the Istituto Nazionale dei Tumori of Milano are reported highlighting a series of metabolic differences across various molecular subtypes. In particular, the difficult-to-treat luminal B subgroup represents a tumor type which preferentially relies on fatty acids for energy, whereas HER2 and basal-like ones show prevalently alterations in glucose/glutamine metabolism.

Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature

Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlas's identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.