Subtype-specific response to bevacizumab is reflected in the metabolome and transcriptome of breast cancer xenografts

The Importance of Hypoxia-Related to Hemoglobin Concentration in Breast Cancer

The importance of hemoglobin (Hgb) as a novel prognostic biomarker in predicting clinical features of cancers has been the subject of intense interest. Anemia is common in various types of cancer including breast cancer (BC) and is considered to be attributed to tumoral hypoxia. Cancer microenvironments are hypoxic compared with normal tissues, and this hypoxia is associated with Hgb concentration. Recent preclinical documents propose a direct or indirect correlation of intratumoral hypoxia, specifically along with acidity, with Hgb concentration and anemia. Analysis of the prognostic value of Hgb in BC patients has demonstrated increased hypoxia in the intratumoral environment. A great number of studies demonstrated that lower concentrations of Hgb before or during common cancer treatments, such as radiation and chemotherapy, is an essential risk factor for poor prognostic and survival, as well as low quality of life in BC patients. This data suggests a potential correlation between anemia and hypoxia in BC. While low Hgb levels are detrimental to BC invasion and survival, identification of a distinct and exact threshold for low Hgb concentration is challenging and inaccurate. The optimal thresholds for Hgb and partial pressure of oxygen (pO2) vary based on different factors including age, gender, therapeutic approaches, and tumor types. While necessitating further investigations, understanding the correlation of Hgb levels with tumoral hypoxia and oxygenation could improve exploring strategies to overcome radio-chemotherapy related anemia in BC patients. This review highlights the collective association of Hgb concentration and hypoxia condition in BC progression.

Novel Strategies Using Sagacious Targeting for Site-Specific Drug Delivery in Breast Cancer Treatment: Clinical Potential and Applications

For more than a decade, researchers have been working to achieve new strategies and smart targeting drug delivery techniques and technologies to treat breast cancer (BC). Nanotechnology presents a hopeful strategy for targeted drug delivery into the building of new therapeutics using the properties of nanomaterials. Nanoparticles are of high regard in the field of diagnosis and the treatment of cancer. The use of these nanoparticles as an encouraging approach in the treatment of various cancers has drawn the interest of researchers in recent years. In order to achieve the maximum therapeutic effectiveness in the treatment of BC, combination therapy has also been adopted, leading to minimal side effects and thus an enhancement in the quality of life for patients. This review article compares, discusses and criticizes the approaches to treat BC using novel design strategies and smart targeting of site-specific drug delivery systems.

Effective TME-related signature to predict prognosis of patients with head and neck squamous cell carcinoma

Introduction: The tumor microenvironment (TME) is crucial for the development of head and neck squamous cell carcinoma (HNSCC). However, the correlation of the characteristics of the TME and the prognosis of patients with HNSCC remains less known. Methods: In this study, we calculated the immune and stromal cell scores using the "estimate" R package. Kaplan-Meier survival and CIBERSORT algorithm analyses were applied in this study. Results: We identified seven new markers: FCGR3B, IGHV3-64, AC023449.2, IGKV1D-8, FCGR2A, WDFY4, and HBQ1. Subsequently, a risk model was constructed and all HNSCC samples were grouped into low- and high-risk groups. The results of both the Kaplan-Meier survival and receiver operating characteristic curve (ROC) analyses showed that the prognosis indicated by the model was accurate (0.758, 0.756, and 0.666 for 1-, 3- and 5-year survival rates). In addition, we applied the CIBERSORT algorithm to reveal the significant differences in the infiltration levels of immune cells between the two risk groups. Discussion: Our study elucidated the roles of the TME and identified new prognostic biomarkers for patients with HNSCC.

Early detection of treatment futility in patients with metastatic colorectal cancer

PURPOSE

Chemotherapy options for treating CRC have rapidly expanded in recent years, and few have predictive biomarkers. Oncologists are challenged with evidence-based selection of treatments, and response is evaluated retrospectively based on serial imaging beginning after 2-3 months. As a result, cumulative toxicities may appear in patients who will not benefit. Early recognition of non-benefit would reduce cumulative toxicities. Our objective was to determine treatment-related changes in the circulating metabolome corresponding to treatment futility.

METHODS

Metabolomic studies were performed on serial plasma samples from patients with CRC in a randomized controlled trial of cetuximab vs. cetuximab + brivanib (N = 188). GC-MS quantified named 94 metabolites and concentrations were evaluated at baseline, Weeks 1, 4 and 12 after treatment initiation. In a discovery cohort (N = 68), a model distinguishing changes in metabolites associated with radiographic disease progression and response was generated using OPLS-DA. A cohort of 120 patients was used for validation of the model.

RESULTS

By one week after treatment, a stable model of 21 metabolites could distinguish between progression and partial response (R2Y = 0.859; Q2Y = 0.605; P = 5e-4). In the validation cohort, patients with the biomarker had a significantly shorter OS (P < 0.0001). In a separate cohort of patients with HCC on axitinib, appearance of the biomarker also signified a shorter PFS (1.7 months vs. 9.2 months, P = 0.001).

CONCLUSION

We have identified changes in the metabolome that appear within 1 week of starting treatment associated with treatment futility. The novel approach described is applicable to future efforts in developing a biomarker for early assessment of treatment efficacy.

Glucose metabolic profiles evaluated by PET associated with molecular characteristic landscape of gastric cancer

BACKGROUND

Although FDG-PET is widely used in cancer, its role in gastric cancer (GC) is still controversial due to variable [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) uptake. Here, we sought to develop a genetic signature to predict high FDG-avid GC to plan individualized PET and investigate the molecular landscape of GC and its association with glucose metabolic profiles noninvasively evaluated by [¹⁸F]FDG-PET.

METHODS

Based on a genetic signature, PETscore, representing [¹⁸F]FDG avidity, was developed by imaging data acquired from thirty patient-derived xenografts (PDX). The PETscore was validated by [¹⁸F]FDG-PET data and gene expression data of human GC. The PETscore was associated with genomic and transcriptomic profiles of GC using The Cancer Genome Atlas.

RESULTS

Five genes, PLS1, PYY, HBQ1, SLC6A5, and NAT16, were identified for the predictive model for [¹⁸F]FDG uptake of GC. The PETscore was validated in independent PET data of human GC with qRT-PCR and RNA-sequencing. By applying PETscore on TCGA, a significant association between glucose uptake and tumor mutational burden as well as genomic alterations were identified.

CONCLUSION

Our findings suggest that molecular characteristics are underlying the diverse metabolic profiles of GC. Diverse glucose metabolic profiles may apply to precise diagnostic and therapeutic approaches for GC.

Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma

Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6-methylguanine-DNA methyl-transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research

Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular on the bioinformatics and state-of-the art techniques that accompany preclinical model development. We discuss the strength and limitations of currently used technologies, particularly 'omics profiling and bioinformatics analyses, in addressing the 'efficacy' of preclinical models, both for tumour characterization as well as their use in identifying potential therapeutics. We select pancreatic ductal adenocarcinoma (PDAC) as a case study to highlight the state of the art of the field, and address new avenues for improved bioinformatics characterization of preclinical models.

Metabolomic studies of breast cancer in murine models: A review

BACKGROUND

Metabolomic strategies have been extensively used to search for biomarkers of disease, including cancer, in biological complex mixtures such as cells, tissues and biofluids. In breast cancer research, murine models are of great value and metabolomics has been increasingly applied to characterize tumor or organ tissues, or biofluids, for instance to follow-up metabolism during cancer progression or response to specific therapies.

SCOPE OF REVIEW

This review briefly introduces the different murine models used in breast cancer research and proceeds to present the metabolomic studies reported so far to describe the deviant metabolic behavior associated to breast cancer, in each type of model: xenografts (cell- or patient-derived), spontaneous (naturally-occurring or genetically engineered) and carcinogen-induced. The type of sample and strategies followed are identified, as well as the main findings from of study.

MAJOR CONCLUSIONS

Metabolomics has gradually become relevant in characterizing murine models of breast cancer, using either Nuclear Magnetic Resonance (NMR) or Mass Spectromety (MS). Both tissue and biofluids are matrixes of interest in this context, although in some type of models, reports have focused primarily on the former. The aims of tissue studies have comprised the search for mechanistic knowledge of carcinogenesis, metastasis development and response/resistance to therapies. Biofluid metabolomics has mainly aimed at finding non-invasive biomarkers for early breast cancer detection or prognosis determination.

GENERAL SIGNIFICANCE

Metabolomics provides exquisite detail on murine tumor and systemic metabolism of breast cancer. This knowledge paves the way for the discovery of new biomarkers, potentially translatable to in vivo non-invasive patient follow-up.

Focus on the glycerophosphocholine pathway in choline phospholipid metabolism of cancer

Activated choline metabolism is a hallmark of carcinogenesis and tumor progression, which leads to elevated levels of phosphocholine and glycerophosphocholine in all types of cancer tested so far. Magnetic resonance spectroscopy applications have played a key role in detecting these elevated choline phospholipid metabolites. To date, the majority of cancer-related studies have focused on phosphocholine and the Kennedy pathway, which constitutes the biosynthesis pathway for membrane phosphatidylcholine. Fewer and more recent studies have reported on the importance of glycerophosphocholine in cancer. In this review article, we summarize the recent literature on glycerophosphocholine metabolism with respect to its cancer biology and its detection by magnetic resonance spectroscopy applications.

Glutamine to proline conversion is associated with response to glutaminase inhibition in breast cancer

INTRODUCTION

Glutaminase inhibitors target cancer cells by blocking the conversion of glutamine to glutamate, thereby potentially interfering with anaplerosis and synthesis of amino acids and glutathione. The drug CB-839 has shown promising effects in preclinical experiments and is currently undergoing clinical trials in several human malignancies, including triple-negative breast cancer (TNBC). However, response to glutaminase inhibitors is variable and there is a need for identification of predictive response biomarkers. The aim of this study was to determine how glutamine is utilized in two patient-derived xenograft (PDX) models of breast cancer representing luminal-like/ER+ (MAS98.06) and basal-like/triple-negative (MAS98.12) breast cancer and to explore the metabolic effects of CB-839 treatment.

EXPERIMENTAL

MAS98.06 and MAS98.12 PDX mice received CB-839 (200 mg/kg) or drug vehicle two times daily p.o. for up to 28 days (n = 5 per group), and the effect on tumor growth was evaluated. Expression of 60 genes and seven glutaminolysis key enzymes were determined using gene expression microarray analysis and immunohistochemistry (IHC), respectively, in untreated tumors. Uptake and conversion of glutamine were determined in the PDX models using HR MAS MRS after i.v. infusion of [5-¹³C] glutamine when the models had received CB-839 (200 mg/kg) or vehicle for 2 days (n = 5 per group).

RESULTS

Tumor growth measurements showed that CB-839 significantly inhibited tumor growth in MAS98.06 tumors, but not in MAS98.12 tumors. Gene expression and IHC analysis indicated a higher proline synthesis from glutamine in untreated MAS98.06 tumors. This was confirmed by HR MAS MRS of untreated tumors demonstrating that MAS98.06 used glutamine to produce proline, glutamate, and alanine, and MAS98.12 to produce glutamate and lactate. In both models, treatment with CB-839 resulted in accumulation of glutamine. In addition, CB-839 caused depletion of alanine, proline, and glutamate ([1-13C] glutamate) in the MAS98.06 model.

CONCLUSION

Our findings indicate that TNBCs may not be universally sensitive to glutaminase inhibitors. The major difference in the metabolic fate of glutamine between responding MAS98.06 xenografts and non-responding MAS98.12 xenografts is the utilization of glutamine for production of proline. We therefore suggest that addiction to proline synthesis from glutamine is associated with response to CB-839 in breast cancer. The effect of glutaminase inhibition in two breast cancer patient-derived xenograft (PDX) models. ¹³C HR MAS MRS analysis of tumor tissue from CB-839-treated and untreated models receiving ¹³C-labeled glutamine ([5-¹³C] Gln) shows that the glutaminase inhibitor CB-839 is causing an accumulation of glutamine (arrow up) in two PDX models representing luminal-like breast cancer (MAS98.06) and basal-like breast cancer (MAS98.12). In MAS98.06 tumors, CB-839 is in addition causing depletion of proline ([5-¹³C] Pro), alanine ([1-¹³C] Ala), and glutamate ([1-¹³C] Glu), which could explain why CB-839 causes tumor growth inhibition in MAS98.06 tumors, but not in MAS98.12 tumors.

A strategy for early detection of response to chemotherapy drugs based on treatment-related changes in the metabolome

We describe a biomarker-based approach to delivering chemotherapy that entails monitoring treatment changes in the circulating metabolome that reflect efficacy. In-vitro, multiple tumor cell lines were exposed to numerous chemotherapeutics. Supernatants were collected at baseline and 72 hours post treatment. MTT assays were used to quantify growth inhibition. Clinical samples were derived from a phase II clinical trial of second-line axitinib in patients with advanced hepatocellular carcinoma. Sera were collected at baseline and 2–4 weeks after treatment initiation. Response to therapy was estimated by CT scan at 8 weeks. Samples were analyzed by gas chromatography-mass spectrometry to identify metabolomic changes associated with response. In vitro, we found drug-specific and generalizable patterns of change in the extracellular metabolome accompany growth inhibition. A cell death signature was also identified. This approach was also applied to clinical samples. While the in vitro signatures were detectable in vivo, a more robust signal was identified clinically that appeared within 4 weeks of administering drug that distinguished individuals with a treatment response. These changes were extinguished as tumor growth resumed. Serial monitoring of the metabolome during chemotherapy is a means to follow treatment efficacy and emergence of resistance, informing the oncologist whether to modify treatment.

Development of a radiosensitivity gene signature for patients with soft tissue sarcoma

Adjuvant radiotherapy is an important clinical treatment option for the majority of sarcomas. The motivation of current study is to identify a gene signature and to predict radiosensitive patients who are most likely to benefit from radiotherapy. Using the public available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure for identifying a gene signature and predicting radiosensitive patients through. The result showed that the predicted radiosensitive patients who received radiotherapy had a significantly better survival with a reduced rate of new tumor event and disease progression. Strata analysis showed that the predicted radiosensitive patients had significantly better survival under radiotherapy independent of histologic types. A hierarchical cluster analysis was used to validate the gene signature, and the results showed the predicted sensitivity for each patient well matched the results from cluster analysis. Together, we demonstrate a radiosensitive molecular signature that can be potentially used for identifying radiosensitive patients with sarcoma.

Monitoring for Response to Antineoplastic Drugs: The Potential of a Metabolomic Approach

For most cancers, chemotherapeutic options are rapidly expanding, providing the oncologist with substantial choices. Therefore, there is a growing need to select the best systemic therapy, for any individual, that effectively halts tumor progression with minimal toxicity. Having the capability to predict benefit and to anticipate toxicity would be ideal, but remains elusive at this time. An alternative approach is an adaptive approach that involves close observation for treatment response and emergence of resistance. Currently, response to systemic therapy is estimated using radiographic tests. Unfortunately, radiographic estimates of response are imperfect and radiographic signs of response can be delayed. This is particularly problematic for targeted agents, as tumor shrinkage is often not apparent with these drugs. As a result, patients are exposed to prolonged courses of toxic drugs that may ultimately be found to be ineffective. A biomarker-based adaptive strategy that involves the serial analysis of the metabolome is attractive. The metabolome changes rapidly with changes in physiology. Changes in the circulating metabolome associated with various antineoplastic agents have been described, but further work will be required to understand what changes signify clinical benefit. We present an investigative approach for the discovery and validation of metabolomic response biomarkers, which consists of serial analysis of the metabolome and linkage of changes in the metabolome to measurable therapeutic benefit. Potential pitfalls in the development of metabolomic biomarkers of response and loss of response are reviewed.

Transcriptome analysis of the immune reaction of the pearl oyster Pinctada fucata to xenograft from Pinctada maxima

The pearl oyster Pinctada maxima exhibits great difficulty to culture pearls through nuclear insertion with an allograft, but it is easy for P. fucata to culture pearls after allografting. If P. fucata could be used as a surrogate mother to culture P. maxima pearls, it would benefit the pearl culture industry of P. maxima. However, this is blocked by the immune rejection of P. fucata against P. maxima mantle grafts. In this study, the immune responses of P. fucata hemocyte to allograft and xenograft were investigated after transplantation by transcriptome analysis. In total, 107.93 Gb clean reads were produced and assembled using the reference genome of P. fucata. Gene Ontology Term enrichment and KEGG enrichment analyses indicated that apoptosis, hippo signaling pathway, oxidation-reduction, MAPK signaling pathway, ribosome, protein processing in endoplasmic reticulum, purine metabolism, NF-kappa B signaling pathway, oxidative phosphorylation, Ras signaling pathway, and ubiquitin mediated proteolysis were involved in response to transplantation. Many genes related to oxidation-reduction reactions, the MAPK signaling pathway, and apoptosis were identified by comparison of the allograft group and the xenograft group at 0 h, 6 h, 12 h, 24 h, 48 h, 72 h, and 96 h post-transplantation. Among them, the expression levels of NADH dehydrogenase, succinate dehydrogenase and other dehydrogenases were increased significantly in the xenograft groups compared with allograft groups at 0 h post transplantation, indicating that a respiratory burst of neutrophils occurred immediately after xenograft transplantation. Additionally, HSP70 was highly expressed from 0 h to 96 h in the xenograft groups, indicating an oyster immune response to the xenograft. The genes enriched in the ribosome and hippo-signaling pathways were also identified, and expression patterns of these DEGs were different as compared between transplantation and control groups. Finally, altered expression levels of 10 randomly selected immune-related DEGs were confirmed by quantitative real-time PCR. These results indicated that oxidation-reduction is likely the key factor responsible for immune rejection to transplantation. The findings should provide some new insight into the molecular mechanism of immune rejection of the host against xenograft, and thus benefit to development of immunosuppressive reagents to facilitate effective xenograft pearling.

Re-expression of miR-200c suppresses proliferation, colony formation and in vivo tumor growth of murine claudin-low mammary tumor cells

Claudin-low breast cancer is a relatively rare breast cancer subtype. These cancers are typically ER-/PR-/HER2- and express high levels of mesenchymal genes as well as genes associated with inflammation, angiogenesis and stem cell function. In addition to alterations in gene expression, it was recently demonstrated that claudin-low breast cancers express very low levels of the miR-200 family of miRNAs. Given that each miRNA can regulate tens, hundreds or even thousands of genes, miRNAs are being evaluated as therapeutic targets. In this study we show that mammary tumors from MTB-IGFIR transgenic mice and cell lines derived from these tumors represent a model of human claudin-low breast cancer and murine claudin-low mammary tumors and cell lines express only very low levels of all five members of the miR-200 family. Reduced miR-200 family expression appears to be regulated via methylation as cells and tumors expressing low levels of miR-200 family members had higher levels of CpG methylation in a putative promoter region than tumors and cells expressing high levels of miR-200 family members. Re-expression of miR-200c in murine claudin-low mammary tumor cells inhibited tumor cell proliferation and colony formation in vitro and tumor growth in vivo. With respect to tumor growth in vivo, re-expression of miR-200c was associated with a reduction in tumor vasculature and expression of Flt1 and Vegfc. Therefore, miR-200c is an important regulator of mesenchymal tumor cell growth.

A serum metabolomic fingerprint of bevacizumab and temsirolimus combination as first-line treatment of metastatic renal cell carcinoma

BACKGROUND

Renal cell carcinoma is one of the most chemoresistant cancers, and its metastatic form requires administration of targeted therapies based on angiogenesis or mTOR inhibitors. Understanding how these treatments impact the human metabolism is essential to predict the host response and adjust personalised therapies. We present a metabolomic investigation of serum samples from patients with metastatic RCC (mRCC) to identify metabolic signatures associated with targeted therapies.

METHODS

Pre-treatment and serial on-treatment sera were available for 121 patients participating in the French clinical trial TORAVA, in which 171 randomised patients with mRCC received a bevacizumab and temsirolimus combination (experimental arm A) or a standard treatment: either sunitinib (B) or interferon-α+bevacizumab (C). Metabolic profiles were obtained using nuclear magnetic resonance spectroscopy and compared on-treatment or between treatments.

RESULTS

Multivariate statistical modelling discriminates serum profiles before and after several weeks of treatment for arms A and C. The combination A causes faster changes in patient metabolism than treatment C, detectable after only 2 weeks of treatment. Metabolites related to the discrimination include lipids and carbohydrates, consistently with the known RCC metabolism and side effects of the drugs involved. Comparison of the metabolic profiles for the three arms shows that temsirolimus, an mTOR inhibitor, is responsible for the faster host metabolism modification observed in the experimental arm.

CONCLUSIONS

In mRCC, metabolomics shows a faster host metabolism modification induced by a mTOR inhibitor as compared with standard treatments. These results should be confirmed in larger cohorts and other cancer types.

The promise of metabolic phenotyping in gastroenterology and hepatology

Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management.

Metabolic Signatures of Human Breast Cancer

Metabolomics has emerged as a new discovery tool with the promise of identifying therapeutic targets in cancer. Recent discoveries have described essential metabolomic pathways in breast cancer and characterized oncometabolites that drive tumor growth and progression. Oncogenes like MYC and tumor suppressor genes like TP53 prominently affect breast cancer biology through regulation of cell metabolism and mitochondrial biogenesis. These findings indicate that tumors with dominant mutations could be susceptible to inhibitors of disease metabolism. Moreover, various preclinical and clinical studies have linked tumor metabolism to therapeutic response and patient survival. Thus, recent advances suggest that metabolic profiling provides new opportunities to improve outcomes in breast cancer. In this review we summarize some of the identified roles of oncometabolites in breast cancer biology and highlight their clinical utility.

An 18-gene signature for vascular invasion is associated with aggressive features and reduced survival in breast cancer

Aims

Vascular invasion by tumor cells is known to be important for cancer progression. By microarray and qPCR analyses, we earlier identified an 18-gene signature associated with vascular involvement in endometrial cancer. Here, we explored the significance of this vascular invasion signature in multiple series of breast cancer patients.

Methods and Results

The study includes 11 open access gene expression data sets which collectively provide information on 2423 breast cancer patients. The 18-gene signature showed consistent associations with aggressive features of breast cancer, like high tumor grade, hormone receptor negativity, HER2 positivity, a basal-like phenotype, reduced patient survival, and response to neoadjuvant chemotherapy. Also, the vascular invasion signature was associated with several other gene expression profiles related to vascular biology and tumor progression, including the Oncotype DX breast cancer recurrence signature.

Conclusions

The 18-gene vascular invasion signature showed strong and consistent associations with aggressive features of breast cancer and reduced survival.

The future of NMR metabolomics in cancer therapy: towards personalizing treatment and developing targeted drugs?

There has been a recent shift in how cancers are defined, where tumors are no longer simply classified by their tissue origin, but also by their molecular characteristics. Furthermore, personalized medicine has become a popular term and it could start to play an important role in future medical care. However, today, a "one size fits all" approach is still the most common form of cancer treatment. In this mini-review paper, we report on the role of nuclear magnetic resonance (NMR) metabolomics in drug development and in personalized medicine. NMR spectroscopy has successfully been used to evaluate current and potential therapies, both single-agents and combination therapies, to analyze toxicology, optimal dose, resistance, sensitivity, and biological mechanisms. It can also provide biological insight on tumor subtypes and their different responses to drugs, and indicate which patients are most likely to experience off-target effects and predict characteristics for treatment efficacy. Identifying pre-treatment metabolic profiles that correlate to these events could significantly improve how we view and treat tumors. We also briefly discuss several targeted cancer drugs that have been studied by metabolomics. We conclude that NMR technology provides a key platform in metabolomics that is well-positioned to play a crucial role in realizing the ultimate goal of better tailored cancer medicine.

Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery

In this study, the feasibility of high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) of small tissue biopsies to distinguish between tumor and non-involved adjacent tissue was investigated. With the current methods, delineation of the tumor borders during breast cancer surgery is a challenging task for the surgeon, and a significant number of re-surgeries occur. We analyzed 328 tissue samples from 228 breast cancer patients using HR MAS MRS. Partial least squares discriminant analysis (PLS-DA) was applied to discriminate between tumor and non-involved adjacent tissue. Using proper double cross validation, high sensitivity and specificity of 91% and 93%, respectively was achieved. Analysis of the loading profiles from both principal component analysis (PCA) and PLS-DA showed the choline-containing metabolites as main biomarkers for tumor content, with phosphocholine being especially high in tumor tissue. Other indicative metabolites include glycine, taurine and glucose. We conclude that metabolic profiling by HR MAS MRS may be a potential method for on-line analysis of resection margins during breast cancer surgery to reduce the number of re-surgeries and risk of local recurrence.

Subtype-specific response to bevacizumab is reflected in the metabolome and transcriptome of breast cancer xenografts

Antiangiogenic therapy with bevacizumab has shown varying results in breast cancer clinical trials. Identifying robust biomarkers for selecting patients who may benefit from such treatment and for monitoring response is important for the future use of bevacizumab. Two established xenograft models representing basal-like and luminal-like breast cancer were used to study bevacizumab treatment response on the metabolic and gene expression levels. Tumor samples were obtained from mice treated with bevacizumab, doxorubicin or a combination of the two drugs, and high resolution magic angle spinning magnetic resonance spectroscopy and gene expression microarray analysis was performed. Combination treatment with bevacizumab showed the strongest growth inhibiting effect in basal-like tumors, and this was reflected by a significant change in the metabolomic and transcriptomic profiles. In the luminal-like xenografts, addition of bevacizumab did not improve the effect of doxorubicin. On the global transcriptomic level, the largest gene expression changes were observed for the most efficient treatment in both models. Glycerophosphocholine showed opposite response in the treated xenografts compared with untreated controls; lower in basal-like and higher in luminal-like tumors. Comparing combination therapy with doxorubicin monotherapy in basal-like xenografts, 14 genes showed significant differential expression, including very low density lipoprotein receptor (VLDLR) and hemoglobin, theta 1 (HBQ1). Bevacizumab-treated tumors were associated with a more hypoxic phenotype, while no evidence was found for associations between bevacizumab treatment and vascular invasion or tumor grade. This study underlines the importance of characterizing biological differences between subtypes of breast cancer to identify personalized biomarkers for improved patient stratification and evaluation of response to therapy.