Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer

Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers

Changes in plasma and fecal metabolomes in colorectal cancer (CRC) progression (normal-adenoma-CRC) remain unclear. Here, plasma and fecal samples were collected from four independent cohorts of 1,251 individuals (422 CRC, 399 colorectal adenoma [CRA], and 430 normal controls [NC]). By metabolomic profiling, signature plasma and fecal metabolites with consistent shift across NC, CRA, and CRC are identified, including CRC-enriched oleic acid and CRC-depleted allocholic acid. Oleic acid exhibits pro-tumorigenic effects in CRC cells, patient-derived organoids, and two murine CRC models, whereas allocholic acid has opposing effects. By integrative analysis, we found that oleic acid or allocholic acid directly binds to α-enolase or farnesoid X receptor-1 in CRC cells, respectively, to modulate cancer-associated pathways. Clinically, we establish a panel of 17 plasma metabolites that accurately diagnoses CRC in a discovery and three validation cohorts (AUC = 0.848-0.987). Overall, we characterize metabolite signatures, mechanistic significance, and diagnostic potential of plasma and fecal metabolomes in CRC.

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

BACKGROUND

Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

METHODS

Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

RESULTS

The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

CONCLUSION

We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer

BACKGROUND

Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear.

METHODS

Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients.

RESULTS

Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance.

CONCLUSIONS

This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy.

CLINICAL TRIAL REGISTRATION

The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

Metabolomic Insight into Implications of Induction Chemotherapy Followed by Concomitant Chemoradiotherapy in Locally Advanced Head and Neck Cancer

The present study compares two groups of locally advanced patients with head and neck squamous cell carcinoma (LA-HNSCC) undergoing concurrent chemoradiotherapy (cCHRT), specifically those for whom it is a first-line treatment and those who have previously received induction chemotherapy (iCHT). The crucial question is whether iCHT is a serious burden during subsequent treatment for LA-HNSCC and how iCHT affects the tolerance to cCHRT. Of the 107 LA-HNSCC patients, 54 received cisplatin-based iCHT prior to cCHRT. The patients were clinically monitored at weekly intervals from the day before until the completion of the cCHRT. The 843 blood samples were collected and divided into two aliquots: for laboratory blood tests and for nuclear magnetic resonance (NMR) spectroscopy (a Bruker 400 MHz spectrometer). The NMR metabolites and the clinical parameters from the laboratory blood tests were analyzed using orthogonal partial least squares analysis (OPLS) and the Mann-Whitney U test (MWU). After iCHT, the patients begin cCHRT with significantly (MWU p-value < 0.05) elevated blood serum lipids, betaine, glycine, phosphocholine, and reticulocyte count, as well as significantly lowered NMR inflammatory markers, serine, hematocrit, neutrophile, monocyte, red blood cells, hemoglobin, and CRP. During cCHRT, a significant increase in albumin and psychological distress was observed, as well as a significant decrease in platelet, N-acetyl-cysteine, tyrosine, and phenylalanine, in patients who received iCHT. Importantly, all clinical symptoms (except the decreased platelets) and most metabolic alterations (except for betaine, serine, tyrosine, glucose, and phosphocholine) resolve until the completion of cCHRT. In conclusion, iCHT results in hematological toxicity, altered lipids, and one-carbon metabolism, as well as downregulated inflammation, as observed at the beginning and during cCHRT. However, these complications are temporary, and most of them resolve at the end of the treatment. This suggests that iCHT prior to cCHRT does not pose a significant burden and should be considered as a safe treatment option for LA-HNSCC.

Amino Acid Profiles in the Biological Fluids and Tumor Tissue of CRC Patients

Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients.

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer

Simple Summary

Neoadjuvant chemotherapy (NACT) is offered to breast cancer (BC) patients to downstage the disease. However, some patients may not respond to NACT, being resistant. We used the serum metabolic profile by Nuclear Magnetic Resonance (NMR) combined with disease characteristics to differentiate between sensitive and resistant BC patients. We obtained accuracy above 80% for the response prediction and showcased how NMR can substantially enhance the prediction of response to NACT.

Abstract

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

Metabolic Profiling of Bladder Cancer Patients' Serum Reveals Their Sensitivity to Neoadjuvant Chemotherapy

Numerous patients with muscle-invasive bladder cancer develop low responsiveness to cisplatin. Our purpose was to explore differential metabolites derived from serum in bladder cancer patients treated with neoadjuvant chemotherapy (NAC). Data of patients diagnosed with cT2-4aNxM0 was collected. Blood samples were retained prospectively before the first chemotherapy for untargeted metabolomics by ¹H-NMR and UPLC-MS. To identify characterized metabolites, multivariate statistical analyses were applied, and the intersection of the differential metabolites discovered by the two approaches was used to identify viable biomarkers. A total of 18 patients (6 NAC-sensitive patients and 12 NAC-resistant patients) were enrolled. There were 29 metabolites detected by ¹H-NMR and 147 metabolites identified by UPLC-MS. Multivariate statistics demonstrated that in the sensitive group, glutamine and taurine were considerably increased compared to their levels in the resistant group, while glutamate and hypoxanthine were remarkably decreased. Pathway analysis and enrichment analysis showed significant alterations in amino acid pathways, suggesting that response to chemotherapy may be related to amino acid metabolism. In addition, hallmark analysis showed that DNA repair played a regulatory role. Overall, serum metabolic profiles of NAC sensitivity are significantly different in bladder cancer patients. Glycine, hypoxanthine, taurine and glutamine may be the potential biomarkers for clinical treatment. Amino acid metabolism has potential value in enhancing drug efficacy.

NMR-Based Metabolomics Analysis Predicts Response to Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most fatal type of breast cancer (BC). Due to the lack of relevant targeted drug therapy, in addition to surgery, chemotherapy is still the most common treatment option for TNBC. TNBC is heterogeneous, and different patients have an unusual sensitivity to chemotherapy. Only part of the patients will benefit from chemotherapy, so neoadjuvant chemotherapy (NAC) is controversial in the treatment of TNBC. Here, we performed an NMR spectroscopy–based metabolomics study to analyze the relationship between the patients’ metabolic phenotypes and chemotherapy sensitivity in the serum samples. Metabolic phenotypes from patients with pathological partial response, pathological complete response, and pathological stable disease (pPR, pCR, and pSD) could be distinguished. Furthermore, we conducted metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with three groups of TNBC patients. We evaluated the discriminative ability of metabolites related to significantly disturbed metabolic pathways by using the multi-receiver–operating characteristic (ROC) curve analysis. Three significantly disturbed metabolic pathways of glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism could be used as potential predictive models to distinguish three types of TNBC patients. These results indicate that a metabolic phenotype could be used to predict whether a patient is suitable for NAC. Metabolomics research could provide data in support of metabolic phenotypes for personalized treatment of TNBC.

Targeted metabolomic analysis of plasma fatty acids in acute myocardial infarction in young adults

BACKGROUND AND AIMS

Acute myocardial infarction (AMI) in young adults has distinct clinical features and risk profile as compared with that in elder adults. The pathophysiologic mechanism of AMI in young adults remains unclear. In this study, we used targeted metabolomics to measure metabolic profile and analyzed plasma fatty acids levels in young adults with AMI, seeking to determine whether circulating fatty acid metabolism was correlated with the occurrence of AMI in young adults.

METHODS AND RESULTS

Consecutive young and elder patients admitted to hospital for AMI were enrolled. Plasma samples of all participants were obtained after overnight fast and then measured using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) based targeted metabolomic platform. A total of 201 plasma metabolites were measured using UPLC-MS/MS. Several plasma fatty acids were significantly altered in young AMI patients compared with control or elder AMI patients, which also showed significant prediction value for AMI in young adults. Percentage of short chain fatty acids (SCFAs) was decreased and long chain increased in AMI as compared with control. Moreover, alpha-linolenic acid and linoleic acid metabolism (ALALAM) pathway metabolites were gradually increased in control, young, and elder AMI patients. Altered fatty acid correlation network further identified fatty acid metabolism disorder in AMI in young adults.

CONCLUSION

By utilizing targeted metabolomic technique, we have found several altered fatty acids and respective pathways that show diagnostic value for AMI in young adults. SCFA and long-chain fatty acid (LCFA) were differentially altered in AMI patients.

Baseline serum amino acid levels predict treatment response to augmentation with N-acetylcysteine (NAC) in a bipolar disorder randomised trial

N-acetylcysteine (NAC) acts on glutamatergic and redox systems, two systems implicated in the pathophysiology of bipolar disorder (BD). This has led to the investigation of NAC as a potential candidate for the treatment of BD. The aim of this study was to investigate metabolomic markers to identify predictors of NAC response in a cohort of BD participants. This study is a secondary analysis of a 16-week, multi-site, randomized, double-blinded, parallel-group, placebo-controlled trial in BD participants with a current acute depressive episode. This study included trial participants who received either NAC 2000 mg/day, or placebo. Participants (NAC: n = 31, placebo: n = 29) were assessed at baseline and week 16 using the Montgomery Åsberg Depression Rating Scale (MADRS) and were dichotomised into "responders" (MADRS at week 16 < 50% of MADRS at baseline) and "non-responders" (MADRS at week 16 > 50% at baseline). Untargeted gas chromatography-mass spectrometry analysis was performed to analyse baseline levels of 68 serum metabolites. Of the nine metabolites that differentiated placebo and NAC groups, five were amino acids with lower levels in the NAC responder group compared with the NAC non-responders. Further analysis generated a predictive model of MADRS improvement including glycine, norleucine, threonine, proline, phenylalanine, tyrosine, glutamic acid, lysine and leucine (R² = 0.853; adjusted R² = 0.733). This prediction model predicted 85% of the variance in MADRS outcome after adjunctive treatment with NAC. BD participants with lower serum levels of free amino acids at baseline may be more likely to respond to adjunctive treatment with NAC.

Molecular response to induction chemotherapy and its correlation with treatment outcome in head and neck cancer patients by means of NMR-based metabolomics

Background

The aim of this prospective study is to identify the biomarkers associated with the effects of induction chemotherapy (iCHT) in terms of the favorable/weaker response to the treatment in locally advanced head and neck squamous cells carcinomas (LA-HNSCC).

Methods

The studied group consisted of 53 LA-HNSCC patients treated with iCHT. The treatment tolerance was measured by the Common Terminology Criteria for Adverse Events (CTCAE). The response to the treatment was evaluated by the clinical, fiberoptic and radiological examinations made before and after iCHT (the TNM Classification of Malignant Tumors was used for classifying the extent of cancer spread). Proton nuclear magnetic resonance (¹H NMR) serum spectra of the samples collected before and after iCHT were acquired with a 400 MHz spectrometer and analyzed using the multivariate and univariate statistical methods.

Results

The molecular response to iCHT involves an increase of the serum lipids which is accompanied by the simultaneous decrease of alanine, glucose and N-acetyl-glycoprotein (NAG). Furthermore, in males, the iCHT induced changes in the lipid signals and NAG significantly correlate with the regression of the primary tumor. The OPLS-DA multivariate model identified two subgroups of the patients with a weaker metabolic and clinical response. The first one consisted of the patients with a significantly lower initial nodal stage, the second one showed no differences in the initial clinical and metabolic statuses.

Conclusions

The NMR-based metabolomic study of the serum spectra revealed that iCHT induces the marked changes in the LA-HNSCC patients’ metabolic profiles and makes it possible to stratify the patients according to their response to iCHT. These effects are sex dependent. Further studies on a larger scale accounting for sex and the clinical and metabolic factors are warranted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08137-4.

Signaling lipids as diagnostic biomarkers for ocular surface cicatrizing conjunctivitis

Abstract

Metabolomics has been applied to diagnose diseases, predict disease progression, and design therapeutic strategies in various areas of medicine. However, it remains to be applied to the ocular surface diseases, where biological samples are often of limited quantities. We successfully performed proof-of-concept metabolomics assessment of volume-limited cytology samples from a clinical form of chronic inflammatory cicatrizing conjunctivitis, i.e., ocular MMP and discovered metabolic changes of signaling lipid mediators upon disease onset and progression. The metabolomics assessment revealed active oxylipins, lysophospholipids, fatty acids, and endocannabinoids alterations, from which potential biomarkers linked to inflammatory processes were identified. Possible underlying mechanisms such as dysregulated enzyme activities (e.g., lipoxygenases, cytochrome P450, and phospholipases) were suggested which may be considered as potential therapeutic targets in future studies.

Key messages

Metabolic profile of the ocular surface can be measured using impression cytology samples.

Metabolomics analysis of ocular pemphigoid is presented for the first time.

The metabolomics assessment of OCP patients revealed active oxylipins, lysophospholipids, fatty acids, and endocannabinoids alterations.

Several oxylipins are identified as diagnostic biomarkers for OCP.

Qualitative and quantitative detection of liver injury with terahertz time-domain spectroscopy

Terahertz technology has been widely used as a nondestructive and effective detection method. Herein, terahertz time-domain spectroscopy was used to detect drug-induced liver injury in mice. Firstly, the boxplots were used to detect abnormal data. Then the maximal information coefficient method was used to search for the features strongly correlated with the degree of injury. After that, the liver injury model was built using the random forests method in machine learning. The results show that this method can effectively identify the degree of liver injury and thus provide an auxiliary diagnostic method for detecting minor liver injury.

In-hospital metabolite changes in infective endocarditis-a longitudinal 1H NMR-based study

Treatment of infective endocarditis (IE) is a 4-6-week provided course of intravenously administered antibiotics. The aim of this study was to investigate how serum metabolites as measured by proton nuclear magnetic resonance (¹H NMR) spectroscopy are changing over time during the active phase of IE, and to see whether these metabolite changes might be used to monitor recovery in these patients. Patients hospitalized with first-time IE at Herlev Hospital, Denmark, from September 2015 to June 2017 were included. Longitudinal blood sampling was performed and serum was analyzed using ¹H NMR. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was used to separate sample groups and analyze differences in metabolite profiles. Thirteen patients were included in the study (77% men, median age 62 years (IQR 53-77)). All patients were cured during the hospitalization without any relapse during 6 months of follow-up. We analyzed 61 serum samples (median 5 samples, range 2-8 per person) drawn in the treatment period after IE diagnosis. The main changes during the in-hospital period were decreased levels of glucose, mannose, leucine, isoleucine, phenylalanine, tyrosine, and signals from polyols and N-acetylated protein. The metabolomic changes could in contrast to the routinely used parameters CRP and leucocyte levels distinguish between the early and late stages of disease treatment. We present the first longitudinal study of ¹H NMR metabolomics in patients with infective endocarditis. The metabolomic changes show a promising strength compared to routinely used clinical parameters.