Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas.

Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (1H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.

From adenoma to CRC stages: the oral-gut microbiome axis as a source of potential microbial and metabolic biomarkers of malignancy.

BACKGROUND: Approximately 95% of Colorectal cancers (CRC) consist of adenocarcinomas originating from colonic Adenomatous polyps (AP). Increasing importance in CRC occurrence and progression has been attributed to the gut microbiota; however, a huge proportion of microorganisms inhabit the human digestive system. So, to comprehensively study the microbial spatial variations and their role in CRC progression, from AP to the different CRC phases, a holistic vision is imperative, including the simultaneous evaluation of multiple niches from the gastrointestinal system. Through an integrated approach, we identified potential microbial and metabolic biomarkers, able to discriminate human CRC from AP and/or also the different Tumor node metastasis (TNM) staging. In addition, as the microbiota contributes to the production of essential metabolic products detectable in fecal samples, we analysed and compared metabolites obtained from CRC and AP patients by using a Nuclear magnetic resonance (NMR) approach. METHODS: In this observational study, saliva, tissue and stool samples from 61 patients, have been collected, including 46 CRC and 15 AP patients, age and sex-matched, undergoing surgery in 2018 at the Careggi University Hospital (Florence, Italy). First, the microbiota in the three-district between CRC and AP patients has been characterized, as well as in different CRC TNM stages. Subsequently, proton NMR spectroscopy has been used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profile of a restricted group of CRC and AP patients. RESULTS: CRC patients display a different profile of tissue and fecal microbiota with respect to AP patients. Significant differences have been observed in CRC tissue microbial clades, with a rise of the Fusobacterium genus. In addition, significant taxa increase at the genus level has been observed in stool samples of CRC patients. Furthermore, Fusobacterium found in intestinal tissue has been positively correlated with fecal Parvimonas, for the first time. Moreover, as predicted by metagenomics pathway analysis, a significant increase of lactate (p=0.037) has been observed in the CRC fecal metabolic profiles, and positively correlated with Bifidobacterium (p=0.036). Finally, minor bacterial differences in CRC patients at stage T2 (TNM classification) have been detected, with a raise of the Spirochaetota phylum in CRC samples, with a slight increase of the Alphaproteobacteria class in fecal samples. CONCLUSION: Our results suggest the importance of microbiota communities and oncometabolites in CRC development. Further studies on CRC/AP management with a focus on CRC assessment are needed to investigate novel microbial-related diagnostic tools aimed to improve therapeutic interventions.

Exploration of Blood Metabolite Signatures of Colorectal Cancer and Polyposis through Integrated Statistical and Network Analysis.

Colorectal cancer (CRC), one of the most prevalent and deadly cancers worldwide, generally evolves from adenomatous polyps. The understanding of the molecular mechanisms underlying this pathological evolution is crucial for diagnostic and prognostic purposes. Integrative systems biology approaches offer an optimal point of view to analyze CRC and patients with polyposis. The present study analyzed the association networks constructed from a publicly available array of 113 serum metabolites measured on a cohort of 234 subjects from three groups (66 CRC patients, 76 patients with polyposis, and 92 healthy controls), which concentrations were obtained via targeted liquid chromatography-tandem mass spectrometry. In terms of architecture, topology, and connectivity, the metabolite-metabolite association network of CRC patients appears to be completely different with respect to patients with polyposis and healthy controls. The most relevant nodes in the CRC network are those related to energy metabolism. Interestingly, phenylalanine, tyrosine, and tryptophan metabolism are found to be involved in both CRC and polyposis. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate molecular aspects of CRC.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments.

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using 1H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the 1H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients.

BACKGROUND: We previously showed that metabolomics predicts relapse in early breast cancer (eBC) patients, unselected by age. This study aims to identify a "metabolic signature" that differentiates eBC from advanced breast cancer (aBC) patients, and to investigate its potential prognostic role in an elderly population. METHODS: Serum samples from elderly breast cancer (BC) patients enrolled in 3 onco-geriatric trials, were retrospectively analyzed via proton nuclear magnetic resonance (1H NMR) spectroscopy. Three nuclear magnetic resonance (NMR) spectra were acquired for each serum sample: NOESY1D, CPMG, Diffusion-edited. Random Forest (RF) models to predict BC relapse were built on NMR spectra, and resulting RF risk scores were evaluated by Kaplan-Meier curves. RESULTS: Serum samples from 140 eBC patients and 27 aBC were retrieved. In the eBC cohort, median age was 76 years; 77% of patients had luminal, 10% HER2-positive and 13% triple negative (TN) BC. Forty-two percent of patients had tumors >2 cm, 43% had positive axillary nodes. Using NOESY1D spectra, the RF classifier discriminated free-from-recurrence eBC from aBC with sensitivity, specificity and accuracy of 81%, 67% and 70% respectively. We tested the NOESY1D spectra of each eBC patient on the RF models already calculated. We found that patients classified as "high risk" had higher risk of disease recurrence (hazard ratio (HR) 3.42, 95% confidence interval (CI) 1.58-7.37) than patients at low-risk. CONCLUSIONS: This analysis suggests that a "metabolic signature", identified employing NMR fingerprinting, is able to predict the risk of disease recurrence in elderly patients with eBC independently from standard clinicopathological features.

An omics approach to study trace metals in sera of hemodialysis patients treated with erythropoiesis stimulating agents.

Hemodialysis (HD) represents a life-sustaining treatment for patients with end-stage renal disease. However, it is associated with several complications, including anemia. Erythropoiesis-stimulating agents (ESAs) are often administered to HD patients with renal anemia, but a relevant proportion of them fail to respond to the therapy. Since trace metals are involved in several biological processes and their blood levels can be altered by HD, we study the possible association between serum trace metal concentrations and ratios with the administration and response to ESA. For this study, data and sample information of 110 HD patients were downloaded from the UC San Diego Metabolomics Workbench public repository (PR000565). The blood serum levels (and ratios) of antimony, cadmium, copper, manganese, molybdenum, nickel, selenium, tin, and zinc were studied applying an omics statistical approach. The Random Forest model was able to discriminate between HD-dependent patients treated and not treated with ESAs, with an accuracy of 71.7% (95% CI 71.5-71.9%). Logistic regression analysis identifies alterations of Mn, Mo, Cd, Sn, and several of their ratios as characteristic of patients treated with ESAs. Moreover, patients with scarce response to ESAs were shown to be characterized by reduced Mn to Ni and Mn to Sb ratios. In conclusion, our results show that trace metals, in particular manganese, play a role in the mechanisms underlying the human response to ESAs, and if further confirmed, the re-equilibration of their physiological levels could contribute to a better management of HD patients, hopefully reducing their morbidity and mortality.

The early reduction of left ventricular mass after sleeve gastrectomy depends on the fall of branched-chain amino acid circulating levels.

INTRODUCTION: Body-mass index is a major determinant of left-ventricular-mass (LVM). Bariatric-metabolic surgery (BMS) reduces cardiovascular mortality. Its mechanism of action, however, often encompasses a weight-dependent effect. In this translational study, we aimed at investigating the mechanisms by which BMS leads to LVM reduction and functional improvement. METHODS: Twenty patients (45.2 ± 8.5years) were studied with echocardiography at baseline and at 1,6,12 and 48 months after sleeve-gastrectomy (SG). Ten Wistar rats aged 10-weeks received high-fat diet ad libitum for 10 weeks before and 4 weeks after SG or sham-operation. An oral-glucose-tolerance-test was performed to measure whole-body insulin-sensitivity. Plasma metabolomics was analysed in both human and rodent samples. RNA quantitative Real-Time PCR and western blots were performed in rodent heart biopsies. The best-fitted partial-least-square discriminant-analysis model was used to explore the variable importance in the projection score of all metabolites. FINDINGS: Echocardiographic LVM (-12%,-23%,-28% and -43% at 1,6,12 and 48 months, respectively) and epicardial fat decreased overtime after SG in humans while insulin-sensitivity improved. In rats, SG significantly reduced LVM and epicardial fat, enhanced ejection-fraction and improved insulin-sensitivity compared to sham-operation. Metabolomics showed a progressive decline of plasma branched-chain amino-acids (BCAA), alanine, lactate, 3-OH-butyrate, acetoacetate, creatine and creatinine levels in both humans and rodents. Hearts of SG rats had a more efficient BCAA, glucose and fatty-acid metabolism and insulin signaling than sham-operation. BCAAs in cardiomyocyte culture-medium stimulated lipogenic gene transcription and reduced mRNA levels of key mitochondrial ß-oxidation enzymes promoting lipid droplet accumulation and glycolysis. INTERPRETATION: After SG a prompt and sustained decrease of the LVM, epicardial fat and insulin resistance was found. Animal and in vitro studies showed that SG improves cardiac BCAA metabolism with consequent amelioration of fat oxidation and insulin signaling translating into decreased intra-myocytic fat accumulation and reduced lipotoxicity. FUNDING: This work was supported by the University of Rome Sapienza.

Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps.

BACKGROUND: Colorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors. AIM: To characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP). METHODS: Proton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index. RESULTS: NMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified. CONCLUSION: In conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting.

Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan-Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer.

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient's unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

Metabolomics: The Stethoscope for the Twenty-First Century.

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

Metabolomics profile in gastrointestinal cancers: Update and future perspectives.

Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.

The inner temperature of the olives (cv. Leccino) before processing affects the volatile profile and the composition of the oil.

The effects of pre-processing decreasing temperature (19, 15 and 10 °C) of olive fruit (cv. Leccino) harvested at three developmental stages (semi-ripe, ripe, advanced ripening) have been evaluated on oil in terms of basic quality parameters, composition, organoleptic traits, and aroma profiles. A total of 40 metabolites (volatiles and non-volatiles) were identified by 1H NMR and GC/MS analyses. Multivariate statistical analysis showed that samples obtained from ripe and advanced ripe olives cooled at 10 and 15 °C better correlated with C6 aldehydes, mainly associated with herbal/green olfactory traits. Compounds responsible for sweet/fruity traits were more abundantly present in oil extracted from 19 °C olive samples. Decreasing pulp temperature before crushing also resulted in reduced presence of 1-penten-3-ol, 1-penten-3-one, acetic acid and ethyl alcohol, associated with specific defects of the oil. Results indicate that slightly lowering fruit temperature just before crushing modulates oil composition by reducing oil off flavours while enhancing green and fresh attributes in particular when ripe olives are processed.

Effect of Estrogen Receptor Status on Circulatory Immune and Metabolomics Profiles of HER2-Positive Breast Cancer Patients Enrolled for Neoadjuvant Targeted Chemotherapy.

HER2-positive breast cancer (BC) represents a heterogeneous cancer disease. In an attempt to identify new stratification models useful for prognosis and therapeutic strategy, we investigated the influence of estrogen receptor (ER) status on the host immune and metabolomics profile of HER2-positive BC patients enrolled for neoadjuvant targeted chemotherapy (NATC). The study enrolled 43 HER2-positive BC patients eligible for NATC based on the trastuzumab-paclitaxel combination. Baseline circulatory cytokines and 1H NMR plasma metabolomics profiles were investigated. Differences in the immune cytokines and metabolomics profile as a function of the ER status, and their association with clinical outcomes were studied by multivariate and univariate analysis. Baseline metabolomics profiles were found to discriminate HER2-positive ER(+) from ER(-) BC patients. Within the ER(+) group an immune-metabolomics model, based on TNF-α and valine, predicted pathological complete response to NATC with 90.9% accuracy (AUROC = 0.92, p = 0.004). Moreover, metabolomics information integrated with IL-2 and IL-10 cytokine levels were prognostic of relapse with an accuracy of 95.5%. The results indicate that in HER2-positive BC patients the ER status influences the host circulatory immune-metabolomics profile. The baseline immune-metabolomics assessment in combination with ER status could represent an independent stratification tool able to predict NATC response and disease relapse of HER2-positive patients.

Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification.

Despite recent refinements to the 21-gene g score, allowing a better identification of patients who may derive no benefit from the addition of adjuvant chemotherapy to that of endocrine therapy, patients with early breast cancer still stand to be over-treated in the setting of clinical and/or genomic uncertainty or discordance. Here we describe and demonstrate a potential approach of further refining the OncotypeDX risk score by metabolomic analysis of serum. In a clinical dataset (N = 87), the risk of recurrence was further sub-stratified by metabolomic signature, with an effective splitting of each Oncotype risk classification. A total of seven recurrences were recorded, with metabolomic analysis accurately predicting six of these. Contrastingly, the genomic risk score of the seven recurrences ranged across all three Oncotype classifications (one recurrence occurred in the "low"-risk group, three in the "intermediate" group and three in the "high"-risk group).

The metabolic fingerprints of HCV and HBV infections studied by Nuclear Magnetic Resonance Spectroscopy.

Few studies are available on metabolic changes in liver injuries and this is the first metabolomic study evaluating a group of HCV-positive patients, before and after viral eradication via DAA IFN-free regimens, using 1H-NMR to characterize and compare their serum fingerprints to naïve HBV-patients and healthy donors. The investigation clearly shows differences in the metabolomic profile of HCV patients before and after effective DAA treatment. Significant changes in metabolites levels in patients undergoing therapy suggest alterations in several metabolic pathways. It has been shown that 1H-NMR fingerprinting approach is an optimal technique in predicting the specific infection and the healthy status of studied subjects (Monte-Carlo cross validated accuracies: 86% in the HCV vs HBV model, 98.7% in the HCV vs HC model). Metabolite data collected support the hypothesis that the HCV virus induces glycolysis over oxidative phosphorylation in a similar manner to the Warburg effect in cancer, moreover our results have demonstrated a different action of the two viruses on cellular metabolism, corroborating the hypothesis that the metabolic perturbation on patients could be attributed to a direct role in viral infection. This metabolomic study has revealed some alteration in metabolites for the first time (2-oxoglutarate and 3-hydroxybutrate) concerning the HCV-infection model that could explain several extrahepatic manifestations associated with such an infection.

Metabolomics in breast cancer: A decade in review.

Breast cancer (BC) is a heterogeneous disease which has been characterised and stratified by many platforms such as clinicopathological risk factors, genomic assays, computer generated models, and various "-omic" technologies. Genomic, proteomic and transcriptomic analysis in breast cancer research is well established, and metabolomics, which can be considered a downstream manifestation of the former disciplines, is of growing interest. The past decade has seen significant progress made within the field of clinical metabolomic BC research, with several groups demonstrating results with significant promise in the setting of BC screening and biological characterisation, as well as future potential for prognostic metabolomic biomarkers.

De-escalating and escalating treatment beyond endocrine therapy in patients with luminal breast cancer.

Luminal breast cancers demonstrate significant molecular and clinical heterogeneity, despite the commonality of shared expression of the estrogen receptor (ER). To date, no clinical trial has prospectively investigated the optimal chemotherapy regime according to luminal type, highlighting a paucity of data furthermore required to guide treatment decisions. Current methods of predicting advantage from adjuvant chemotherapy lack refinement and can over-estimate the risk of relapse, inevitably leading to a proportion of patients being unnecessarily exposed to chemotherapy. This paper will explore the evidence behind modalities which may add further value to existing known clinicopathological and molecular profiling techniques in predicting clinical benefit from chemotherapy. Adjuvant chemotherapy regime choice in the context of early luminal breast cancer types will be discussed, and areas for further research and debate identified.

Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population.

Purpose: Detecting signals of micrometastatic disease in patients with early breast cancer (EBC) could improve risk stratification and allow better tailoring of adjuvant therapies. We previously showed that postoperative serum metabolomic profiles were predictive of relapse in a single-center cohort of estrogen receptor (ER)-negative EBC patients. Here, we investigated this further using preoperative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial.Experimental Design: Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or ≥6 years clinical follow-up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated on the basis of the likelihood of the sample being misclassified as metastatic.Results: In the training set, the RF model separated EBC from MBC with a discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an AUC of 0.747 in ROC analysis. Accuracy was maximized at 71.3% (sensitivity, 70.8%; specificity, 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of Adjuvant! Online risk of relapse score.Conclusions: In a multicenter group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathologic risk factors. Clin Cancer Res; 23(6); 1422-31. ©2017 AACR.