1H-NMR Plasma Lipoproteins Profile Analysis Reveals Lipid Metabolism Alterations in HER2-Positive Breast Cancer Patients

Quantitative Blood Serum IVDr NMR Spectroscopy in Clinical Metabolomics of Cancer, Neurodegeneration, and Internal Medicine

Despite more than two decades of metabolomics having joined the "omics" scenery, to date only a few novel blood metabolite biomarkers have found their way into the clinic. This is changing now by massive large-scale population metabolic phenotyping for both healthy and disease cohorts. Here, nuclear magnetic resonance (NMR) spectroscopy is a method of choice, as typical blood serum markers can be easily quantified and by knowledge of precise reference concentrations, more and more NMR-amenable biomarkers are established, moving NMR from research to clinical application. Besides customized approaches, to date two major commercial platforms have evolved based on either 600 MHz (14.1 Tesla) or 500 MHz (11.7 Tesla) high-field NMR systems. This chapter provides an introduction into the field of quantitative in vitro diagnostics research (IVDr) NMR at 600 MHz and its application within clinical research of cancer, neurodegeneration, and internal medicine.

Investigation of pH-dependent 1H NMR urine metabolite profiles for diagnosis of obesity-related disordering

BACKGROUND

Human urine is highly favorable for 1H NMR metabolomics analyses of obesity-related diseases, such as non-alcoholic fatty liver, type 2 diabetes, and hyperlipidemia (HL), due to its non-invasiveness and ease of large-scale collection. However, the wide range of intrinsic urine pH (5.5-8.5) results in inevitably chemical shift and signal intensity modulations in the 1H NMR spectra. For patients where acidic urine pH is closely linked to obesity-related disease phenotypes, the pH-dependent modulations complicate the spectral analysis and deteriorate quantifications of urine metabolites.

METHODS

We characterized human urine metabolites by NMR at intrinsic urine pH, across urine pH 4.5 to 9.5, to account for pH-dependent modulations. A pH-dependent chemical shift database for quantifiable urine metabolites was generated and integrated into a "pH intelligence" program developed for quantifications of pH-dependent modulations at various pH. The 1H NMR spectra of urines collected from patients with Ob-HL and healthy controls were compared to uncover potential metabolic biomarkers of Ob-HL disease.

RESULTS

Three urine metabolites were unveiled by pH-dependent NMR approach, i.e., TMAO, glycine, and pyruvic acid, with VIP score >1.0 and significant q-value < 0.05, that represent as potential biomarkers for discriminating Ob-HL from healthy controls. Further ROC-AUC analyses revealed that TMAO alone achieved the highest diagnostic accuracy (AUC 0.902), surpassed to that obtained by neutralizing pH approach (AUC 0.549) and enabled better recovering potential urine metabolites from the Ob-HL disease phenotypes.

CONCLUSIONS

We concluded that 1H NMR-derived urine metabolite profile represents a snapshot that can reveal the physiological condition of humans in either a healthy or diseased state under intrinsic urine pH. We demonstrated a systematic analysis of pH-dependent modulations on the human urine metabolite signals and further developed software for quantification of urine metabolite profiles with high accuracy, enabling the uncovering of potential metabolite biomarkers in clinical diagnosis applications.

Assessing Breast Cancer through Tumor Microenvironment Mapping of Collagen and Other Biomolecule Spectral Fingerprints─A Review

Breast cancer is a major challenge in the field of oncology, with around 2.3 million cases and around 670,000 deaths globally based on the GLOBOCAN 2022 data. Despite having advanced technologies, breast cancer remains the major type of cancer among women. This review highlights various collagen signatures and the role of different collagen types in breast tumor development, progression, and metastasis, along with the use of photoacoustic spectroscopy to offer insights into future cancer diagnostic applications without the need for surgery or other invasive techniques. Through mapping of the tumor microenvironment and spotlighting key components and their absorption wavelengths, we emphasize the need for extensive preclinical and clinical investigations.

Metabolomic Profiling of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy for Predicting Disease-Free and Overall Survival

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.

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.

Increased plasma lipids in triple-negative breast cancer and impairment in HDL functionality in advanced stages of tumors

The association between plasma lipids and breast cancer (BC) has been extensively explored although results are still conflicting especially regarding the relationship with high-density lipoprotein cholesterol (HDLc) levels. HDL mediates cholesterol and oxysterol removal from cells limiting sterols necessary for tumor growth, inflammation, and metastasis and this may not be reflected by measuring HDLc. We addressed recently diagnosed, treatment-naïve BC women (n = 163), classified according to molecular types of tumors and clinical stages of the disease, in comparison to control women (CTR; n = 150) regarding plasma lipids and lipoproteins, HDL functionality and composition in lipids, oxysterols, and apo A-I. HDL was isolated by plasma discontinuous density gradient ultracentrifugation. Lipids (total cholesterol, TC; triglycerides, TG; and phospholipids, PL) were determined by enzymatic assays, apo A-I by immunoturbidimetry, and oxysterols (27, 25, and 24-hydroxycholesterol), by gas chromatography coupled with mass spectrometry. HDL-mediated cell cholesterol removal was determined in macrophages previously overloaded with cholesterol and ¹⁴C-cholesterol. Lipid profile was similar between CTR and BC groups after adjustment per age. In the BC group, lower concentrations of TC (84%), TG (93%), PL (89%), and 27-hydroxicholesterol (61%) were observed in HDL, although the lipoprotein ability in removing cell cholesterol was similar to HDL from CRT. Triple-negative (TN) BC cases presented higher levels of TC, TG, apoB, and non-HDLc when compared to other molecular types. Impaired HDL functionality was observed in more advanced BC cases (stages III and IV), as cholesterol efflux was around 28% lower as compared to stages I and II. The altered lipid profile in TN cases may contribute to channeling lipids to tumor development in a hystotype with a more aggressive clinical history. Moreover, findings reinforce the dissociation between plasma levels of HDLc and HDL functionality in determining BC outcomes.

Association of plasma lipid metabolism profiles with overall survival for patients with gastric cancer undergoing gastrectomy based on 1H-NMR spectroscopy

BACKGROUND

Lipid metabolism dysregulation is a prominent metabolic alteration in various cancers. The study aimed to explore the association of plasma lipid metabolism profiles with overall survival (OS) for gastric cancer (GC) patients who received gastrectomy.

METHODS

GC patients who were treated with gastrectomy and measured with plasma lipid metabolism profiles using proton nuclear magnetic resonance (¹H-NMR) spectroscopy in Nanfang Hospital between January 1, 2017, and October 31, 2018, were recruited. The Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to analyze variables selected by univariate analysis for OS. An index of plasma lipid metabolism profiles, named plasma lipid metabolism index (PLMI), was constructed by variables' coefficients in LASSO regression to explore its association with OS and its role in the prediction model.

RESULTS

A total of 158 GC patients were included in this study. Four of the 110 lipid profiles, including LDL-5 Apo-B, LDL-4 Cholesterol, HDL-4 Apo-A2, and HDL-4 Free Cholesterol, were selected to construct the PLMI. The optimal cut-off value of PLMI for OS was used to classify the population into two subgroups, the high PLMI group (≥ - 0.163) and the low PLMI group (< - 0.163). The high PLMI group had a shorter OS (p = 0.0034) and was the independent risk factor for OS (Hazard Ratio = 2.13, 95% Confidence Interval (CI): 1.07-4.22, p = 0.031) after adjusting for perineural invasion and tumor stage. In subsets of the I-III stage and treating postoperative chemotherapy, high PLMI also had an unfavorable correlation with OS (p = 0.016 and p = 0.0086, respectively). The nomogram prediction models of both the training cohort and validation cohort showed good calibration and discrimination with the concordance indexes of 0.806 (95% CI, 0.732-0.880) in the training cohort and 0.794 (95% CI, 0.725-0.862) in the validation cohort.

CONCLUSIONS

This study found that the index derived from the LDL-5 Apo-B, LDL-4 Cholesterol, HDL-4 Apo-A2, and HDL-4 Free Cholesterol, was significantly associated with overall survival, suggesting that regulating lipid metabolisms might improve the prognosis for GC patients.

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.

Longitudinal Changes in Circulating Metabolites and Lipoproteins After Breast Cancer Treatment

The multimodal treatment of breast cancer may induce long term effects on the metabolic profile and increase the risk of future cardiovascular disease. In this study, we characterized longitudinal changes in serum lipoprotein subfractions and metabolites after breast cancer treatment, aiming to determine the long-term effect of different treatment modalities. Further, we investigated the prognostic value of treatment-induced changes in breast cancer-specific and overall 10-year survival. In this study, serum samples from breast cancer patients (n = 250) were collected repeatedly before and after radiotherapy, and serum metabolites and lipoprotein subfractions were quantified by NMR spectroscopy. Longitudinal changes were assessed by univariate and multivariate data analysis methods applicable for repeated measures. Distinct changes were detectable in levels of lipoprotein subfractions and circulating metabolites during the first year, with similar changes despite large differences in treatment regimens. We detect increased free cholesterol and decreased esterified cholesterol levels of HDL subfractions, a switch towards larger LDL particles and higher total LDL-cholesterol, in addition to a switch in the glutamine-glutamate ratio. Non-survivors had different lipid profiles from survivors already at baseline. To conclude, our results show development towards an atherogenic lipid profile in breast cancer patients with different treatment regimens.