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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 strat...
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... Metabolomics analysis of breast cancer in the literature includes many quality articles combined into meaningful reviews 12,14 . None to our knowledge focuses strictly for breast cancer detection at the earliest stages through metabolomics and metallomics serum analysis. ...
... Some metaanalysis or recent studies highlight no significant differences in zinc serum concentration 57,58 . Nonetheless, zinc plays an important role in the cell cycle, and current trends in drug research focus on zinc transporters as possible molecular targets 59 Discrimination between breast cancer groups in relation to the control cohort is present in the literature [12][13][14] . Available research studies also focuse on discrimination regardless breast cancer progression 12,13,61,62 , considering only advanced stages, combining individual stages into broader subgroups (I + II and III + IV) 18 or focusing on amino acids composition 63,64 . ...
Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types—DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients’ metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.
... It is important to examine some literature data regarding the metabolites found to be associated with breast cancer survival in our analysis. Recently, a review on NMR and its relationship with breast cancer extensively surveyed the literature, exploring the connection between metabolites found in tissue, blood, and urine at distinct stages of breast cancer development [25]. Although the primary focus of that review was not patients undergoing NACT, some of the summarized data provide significant insights to contextualize our findings. ...
... Sci. 2024, 25, 8639 ...
Breast cancer (BC) remains a significant global health concern, with neoadjuvant chemotherapy (NACT) offering preoperative benefits like tumor downstaging and treatment response assessment. However, identifying factors influencing post-NACT treatment response and survival outcomes is challenging. Metabolomic approaches offer promising insights into understanding these outcomes. This study analyzed the serum of 80 BC patients before and after NACT, followed for up to five years, correlating with disease-free survival (DFS) and overall survival (OS). Using untargeted nuclear magnetic resonance (NMR) spectroscopy and a novel statistical model that avoids collinearity issues, we identified metabolic changes associated with survival outcomes. Four metabolites (histidine, lactate, serine, and taurine) were significantly associated with DFS. We developed a metabolite-related survival score (MRSS) from these metabolites, stratifying patients into low- and high-risk relapse groups, independent of classical prognostic factors. High-risk patients had a hazard ratio (HR) for DFS of 3.42 (95% CI 1.51–7.74; p = 0.003) after adjustment for disease stage and age. A similar trend was observed for OS (HR of 3.34, 95% CI 1.64–6.80; p < 0.001). Multivariate Cox proportional hazards analysis confirmed the independent prognostic value of the MRSS. Our findings suggest the potential of metabolomic data, alongside traditional markers, in guiding personalized treatment decisions and risk stratification in BC patients undergoing NACT. This study provides a methodological framework for leveraging metabolomics in survival analyses.
... A possible explanation could be urinary infections produced by microorganisms not detectable by traditional cultures, such as Mycobacterium tuberculosis, Chlamydia trachomatis or the genus Mycoplasma spp. In addition, urine metabolome is highly dependent on diet, environment and lifestyle, and consequently exhibits greater daily variability than serum or plasma [30]. It is also known that many types of pathologies are characterized by a certain odor and consequently, by a set of volatile substances. ...
Currently, urine samples for bacterial or fungal infections require a long diagnostic period (48 h). In the present work, a point-of-care device known as an electronic nose (eNose) has been designed based on the “smell print” of infections, since each one emits various volatile organic compounds (VOC) that can be registered by the electronic systems of the device and recognized in a very short time. Urine samples were analyzed in parallel using urine culture and eNose technology. A total of 203 urine samples were analyzed, of which 106 were infected and 97 were not infected. A principal component analysis (PCA) was performed using these data. The algorithm was initially capable of correctly classifying 49% of the total samples. By using SVM-based models, it is possible to improve the accuracy of the classification up to 74% when randomly using 85% of the data for training and 15% for validation. The model is evaluated as having a correct classification rate of 74%. In conclusion, the diagnostic accuracy of the eNose in urine samples is high, promising and amenable for further improvement, and the eNose has the potential to become a feasible, reproducible, low-cost and high-precision device to be applied in clinical practice for the diagnosis of urinary tract infections.
... Although NMR is a less sensitive technique, and requires more expensive instrumentation compared to MS, its major advantages lie in its high repeatability, noninvasiveness, and minimum sample preparation procedures [27]. Such advantages allow visualization of the actual metabolic state of the studied living organism at a particular point in time [28]. Hence, NMR metabolomics has been reported extensively in analyzing many biological specimens, including serum [29], plasma [30], tissues [31], cerebrospinal fluid [32], amniotic fluid [33], seminal fluid [34], and fecal extracts [35], as well as cell lysates and cell growth media [36]. ...
... Hence, NMR metabolomics has been reported extensively in analyzing many biological specimens, including serum [29], plasma [30], tissues [31], cerebrospinal fluid [32], amniotic fluid [33], seminal fluid [34], and fecal extracts [35], as well as cell lysates and cell growth media [36]. The most common pulse sequences used in 1 D NMR-based metabolomics studies include the 1 D NOESY, 1 H CPMG (Carr-Purcell-Meiboom-Gill), and 1 H diffusion-edited [28,37]. Employing these sequences allows the detection of low and high molecular weight metabolites, which are particularly useful in biofluids such as serum/plasma. ...
Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.
... Circulating blood metabolites and lipoproteins provide a picture of patients and of their biological characteristics within the dynamic context of cancer processes, considering both the effects the tumor exerts upon the host and the effects the host exerts upon the tumor. 4 Thus, metabolite/lipoprotein alterations could be used as potential biomarkers of cancer prognosis and patient survival. [5][6][7][8] In the current pilot, hypothesis-generating study we utilized blood plasma metabolomics and lipoproteomics, via proton nuclear magnetic resonance ( 1 H NMR) spectroscopy, coupled with standard blood analysis, to predict 2-year survival in a cohort of 45 metastatic STS (mSTS) patients. ...
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.
... This metabolic shift can already be detected in diagnosis through techniques such as the in vivo assessment of glucose metabolism using FDG-PET imaging. NMR-based metabolomics has shown promising results in breast cancer [85], in particular for prognostic and stratification purposes. NMR-based metabolomics has been also used to identify metabolic signatures in urine that can aid in the diagnosis of lung and colorectal cancer. ...
Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.
... Detecting cancer by metabolic profiling of biofluids facilitates easy and minimally invasive diagnostics, and allows for screening [21]. Metabolomics has been widely utilized in BC field [22][23][24][25][26][27][28][29] using different type of samples (i.e., blood plasma/serum, saliva, tissues, urine, and cells). ...
Breast cancer (BC) is the most common type of cancer among women in almost all countries worldwide and is one of the oncological pathologies for which is indicated fertility preservation, a type of procedure used to help keep a person’s ability to have children. Follicular fluid (FF) is a major component of oocyte microenvironment, which is involved in oocyte growth, follicular maturation, and in communication between germ and somatic cells; furthermore, it accumulates all metabolites during oocytes growth. To obtain information about changes on fertility due to cancer, we aimed at investigating potential biomarkers to discriminate between FF samples obtained from 16 BC patients and 10 healthy women undergoing in vitro fertilization treatments. An NMR-based metabolomics approach was performed to investigate the FF metabolic profiles; ELISA and western blotting assays were used to investigate protein markers of oxidative and inflammatory stress, which are processes closely related to cancer. Our results seem to suggest that FFs of BC women display some significant metabolic alterations in comparison to healthy controls, and these variations are also related with tumor staging.
... A metabolic shift from the production of phospholipids to the formation of glycine was observed in basal-like breast cancer xenografts in reference to the luminal-like ones [51]. The high glycine level in the clinical breast tumor samples was found to be a marker of a poor prognosis and a short survival [52]. Taking the other route of choline metabolism into account, the increased activity of choline kinase was observed in the highergrade tumors and was considered to be a marker of malignancy [53]. ...
Simple Summary
Breast cancer is the most prevalent malignancy all over the world. Intraoperative histological imaging of frozen tissue sections is frequently used in the evaluation of surgical margins for the presence of residual transformed cells. However, this method is time consuming, prone to sampling error and subjective interpretation. The purpose of this work was to investigate a sensitivity of the ¹H HR-MAS NMR (high-resolution magic angle spinning nuclear magnetic resonance) technique in the detection of cancer cells in the tissue material collected during breast-conserving surgery and to gain an insight into a metabolic reprogramming of intratumoral fibrosis. The multivariate classification models permitted discrimination of the cancerous from non-cancerous samples with an accuracy of 87%. The accumulation of several metabolites (inter alia lactate, glutamate, succinate) in the fibrotic tissue within the tumor in reference to the extratumoral fibrous connective tissue was detected. The results of our work contribute to the increased understanding of breast cancer heterogeneity.
Abstract
Breast tumors constitute the complex entities composed of cancer cells and stromal components. The compositional heterogeneity should be taken into account in bulk tissue metabolomics studies. The aim of this work was to find the relation between the histological content and ¹H HR-MAS (high-resolution magic angle spinning nuclear magnetic resonance) metabolic profiles of the tissue samples excised from the breast tumors and the peritumoral areas in 39 patients diagnosed with invasive breast carcinoma. The total number of the histologically verified specimens was 140. The classification accuracy of the OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) model differentiating the cancerous from non-involved samples was 87% (sensitivity of 72.2%, specificity of 92.3%). The metabolic contents of the epithelial and stromal compartments were determined from a linear regression analysis of the levels of the evaluated compounds against the cancer cell fraction in 39 samples composed mainly of cancer cells and intratumoral fibrosis. The correlation coefficients between the levels of several metabolites and a tumor purity were found to be dependent on the tumor grade (I vs II/III). The comparison of the levels of the metabolites in the intratumoral fibrosis (obtained from the extrapolation of the regression lines to 0% cancer content) to those levels in the fibrous connective tissue beyond the tumors revealed a profound metabolic reprogramming in the former tissue. The joint analysis of the metabolic profiles of the stromal and epithelial compartments in the breast tumors contributes to the increased understanding of breast cancer biology.
... Considering that all these entangled effects have an impact on the local and systemic patient metabolism, metabolomics represents a valid instrument to provide further insights into the CRC metabolic mechanisms. Metabolomics has already proved to be an excellent tool for biomedical investigations [11][12][13][14], and it has been opportunely applied to the study of CRC adopting different analytical strategies and kinds of samples [5,15]. To expand the vision from the particular to the general, it is necessary to perform a further step considering the shape of the metabolite-metabolite association networks [16]. ...
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.
... The application of metabolomic profiling to biological fluids has recently emerged as a powerful and reliable tool for identifying novel biomarkers to improve early diagnosis and prognostication and for predicting the response of cancer patients to treatment (13)(14)(15)(16). In this context, nuclear magnetic resonance (NMR) spectroscopy represents the only nondestructive technique able to rapidly identify and quantify complex mixtures of metabolites in small samples, and its use is increasing for successful patient stratification in various diseases, including cancer (17)(18)(19)(20). ...
Purpose
In metastatic colorectal cancer (mCRC) patients (pts), treatment strategies integrating liver resection with induction chemotherapy offer better 5-year survival rates than chemotherapy alone. However, liver resection is a complex and costly procedure, and recurrence occurs in almost 2/3rds of pts, suggesting the need to identify those at higher risk. The aim of this work was to evaluate whether the integration of plasma metabolomics and lipidomics combined with the multiplex analysis of a large panel of plasma cytokines can be used to predict the risk of relapse and other patient outcomes after liver surgery, beyond or in combination with clinical morphovolumetric criteria.
Experimental design
Peripheral blood metabolomics and lipidomics were performed by 600 MHz NMR spectroscopy on plasma from 30 unresectable mCRC pts treated with bevacizumab plus oxaliplatin-based regimens within the Obelics trial (NCT01718873) and subdivided into responder (R) and non-R (NR) according to 1-year disease-free survival (DFS): ≥ 1-year (R, n = 12) and < 1-year (NR, n = 18). A large panel of cytokines, chemokines, and growth factors was evaluated on the same plasma using Luminex xMAP-based multiplex bead-based immunoassay technology. A multiple biomarkers model was built using a support vector machine (SVM) classifier.
Results
Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing metabolomics profiles of samples collected at the time of response evaluation when resectability was established showed significantly different levels of metabolites between the two groups. Two metabolites, 3-hydroxybutyrate and histidine, significantly predicted DFS and overall survival. Lipidomics analysis confirmed clear differences between the R and NR pts, indicating a statistically significant increase in lipids (cholesterol, triglycerides and phospholipids) in NR pts, reflecting a nonspecific inflammatory response. Indeed, a significant increase in proinflammatory cytokines was demonstrated in NR pts plasma. Finally, a multiple biomarkers model based on the combination of presurgery plasma levels of 3-hydroxybutyrate, cholesterol, phospholipids, triglycerides and IL-6 was able to correctly classify patients by their DFS with good accuracy.
Conclusion
Overall, this exploratory study suggests the potential of these combined biomarker approaches to predict outcomes in mCRC patients who are candidates for liver metastasis resection after induction treatment for defining personalized management and treatment strategies.
