Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Exploration of plasma tryptophan levels along with Ki-67 expression binomial investigation for forecasting tumor aggressiveness within invasive ductal breast cancer

Ki-67 is a histological marker indicating cancer aggressiveness, while tryptophan (TRP) depletion modulates immune responses, including tumor aggressiveness. The study evaluates Ki-67's predictive value in relation to plasma TRP levels in invasive ductal carcinoma of breast cancer, aiming to improve understanding of tumor characteristics and clinical behavior. A study involving 165 women, measured plasma TRP levels and Ki-67 and analyzed their relationship with tumor aggressiveness markers using statistical analyses and predictive models. Our study highlighted a significant correlation between decreased plasma levels of TRP and a high mitotic index, measured by the Ki-67 marker (Pearson correlation coefficient r = - 0.402; p = 0.011). Tryptophan levels below 40 µmol/L were associated with a Ki-67 level above 15%, suggesting more active tumor growth in patients. Additionally, several risk factors for BC were identified within the studied population. The demographic and clinical characteristics of the participants include an average age of 63 years, plasma glucose levels above 1.2 g/L, and plasma TRP levels below 40 µmol/L, which are associated with an increased risk of BC. Furthermore, various polynomial logistic regression models indicate that TRP levels may be predicted based on Ki-67 expression, providing a promising approach to refine prognostic assessments. The study showed a correlation between low levels of tryptophan (TRP) and a high Ki-67 mitotic index in breast cancer patients, particularly in invasive ductal carcinoma, which is strongly linked to the aggressiveness of the disease. The integration of these markers into routine practice remains a technical and economic challenge.

Methods for multi-omic data integration in cancer research

Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy. However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature. Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.

Mechanisms of traditional Chinese medicine overcoming of radiotherapy resistance in breast cancer

Breast cancer stands as the most prevalent malignancy among women, with radiotherapy serving as a primary treatment modality. Despite radiotherapy, a subset of breast cancer patients experiences local recurrence, attributed to the intrinsic resistance of tumors to radiation. Therefore, there is a compelling need to explore novel approaches that can enhance cytotoxic effects through alternative mechanisms. Traditional Chinese Medicine (TCM) and its active constituents exhibit diverse pharmacological actions, including anti-tumor effects, offering extensive possibilities to identify effective components capable of overcoming radiotherapy resistance. This review delineates the mechanisms underlying radiotherapy resistance in breast cancer, along with potential candidate Chinese herbal medicines that may sensitize breast cancer cells to radiotherapy. The exploration of such herbal interventions holds promise for improving therapeutic outcomes in the context of breast cancer radiotherapy resistance.

Multi-omic analysis of dysregulated pathways in triple negative breast cancer

The aggressive characteristics of triple-negative breast cancer (TNBC) and the absence of targeted medicines make TNBC a challenging clinical case. The molecular landscape of TNBC has been well-understood thanks to recent developments in multi-omic analysis, which have also revealed dysregulated pathways and possible treatment targets. This review summarizes the utilization of multi-omic approaches in elucidating TNBC's complex biology and therapeutic avenues. Dysregulated pathways including cell cycle progression, immunological modulation, and DNA damage response have been uncovered in TNBC by multi-omic investigations that integrate genomes, transcriptomics, proteomics, and metabolomics data. Methods like this pave the door for the discovery of new therapeutic targets, such as the EGFR, PARP, and mTOR pathways, which in turn direct the creation of more precise treatments. Recent developments in TNBC treatment strategies, including immunotherapy, PARP inhibitors, and antibody-drug conjugates, show promise in clinical trials. Emerging biomarkers like MUC1, YB-1, and immune-related markers offer insights into personalized treatment approaches and prognosis prediction. Despite the strengths of multi-omic analysis in offering a more comprehensive view and personalized treatment strategies, challenges exist. Large sample sizes and ensuring high-quality data remain crucial for reliable findings. Multi-omic analysis has revolutionized TNBC research, shedding light on dysregulated pathways, potential targets, and emerging biomarkers. Continued research efforts are imperative to translate these insights into improved outcomes for TNBC patients.

Are We Ready to Reclassify Crohn's Disease Using Molecular Classification?

Crohn's disease (CD) is a type of inflammatory bowel disease. The number of IBD cases worldwide was estimated to be 4.9 million in 2019. CD exhibits heterogeneity in clinical presentation, anatomical involvement, disease behaviour, clinical course and response to treatment. The classical description of CD involves transmural inflammation with skip lesions anywhere along the entire gastrointestinal tract. The complexity and heterogeneity of Crohn's disease is not currently reflected in the conventional classification system. Though the knowledge of Crohn's pathophysiology remains far from understood, the established complex interplay of the omics-genomics, transcriptomics, proteomics, epigenomics, metagenomics, metabolomics, lipidomics and immunophenomics-provides numerous targets for potential molecular markers of disease. Advancing technology has enabled identification of small molecules within these omics, which can be extrapolated to differentiate types of Crohn's disease. The multi-omic future of Crohn's disease is promising, with potential for advancements in understanding of its pathogenesis and implementation of personalised medicine.

Molecular mechanisms of multi-omic regulation in breast cancer

Breast cancer is a complex disease that is influenced by the concurrent influence of multiple genetic and environmental factors. Recent advances in genomics and other high throughput biomolecular techniques (-omics) have provided numerous insights into the molecular mechanisms underlying breast cancer development and progression. A number of these mechanisms involve multiple layers of regulation. In this review, we summarize the current knowledge on the role of multiple omics in the regulation of breast cancer, including the effects of DNA methylation, non-coding RNA, and other epigenomic changes. We comment on how integrating such diverse mechanisms is envisioned as key to a more comprehensive understanding of breast carcinogenesis and cancer biology with relevance to prognostics, diagnostics and therapeutics. We also discuss the potential clinical implications of these findings and highlight areas for future research. Overall, our understanding of the molecular mechanisms of multi-omic regulation in breast cancer is rapidly increasing and has the potential to inform the development of novel therapeutic approaches for this disease.

DDIT4 Downregulation by siRNA Approach Increases the Activity of Proteins Regulating Fatty Acid Metabolism upon Aspirin Treatment in Human Breast Cancer Cells

Repositioning of aspirin for a more effective breast cancer (BC) treatment requires identification of predictive biomarkers. However, the molecular mechanism underlying the anticancer activity of aspirin remains fully undefined. Cancer cells enhance de novo fatty acid (FA) synthesis and FA oxidation to maintain a malignant phenotype, and the mechanistic target of rapamycin (mTORC1) is required for lipogenesis. We, therefore, aimed to test if the expression of mTORC1 suppressor DNA damage-inducible transcript (DDIT4) affects the activity of main enzymes in FA metabolism after aspirin treatment. MCF-7 and MDA-MB-468 human BC cell lines were transfected with siRNA to downregulate DDIT4. The expression of carnitine palmitoyltransferase 1 A (CPT1A) and serine 79-phosphorylated acetyl-CoA carboxylase 1 (ACC1) were analyzed by Western Blotting. Aspirin enhanced ACC1 phosphorylation by two-fold in MCF-7 cells and had no effect in MDA-MB-468 cells. Aspirin did not change the expression of CPT1A in either cell line. We have recently reported DDIT4 itself to be upregulated by aspirin. DDIT4 knockdown resulted in 1.5-fold decreased ACC1 phosphorylation (dephosphorylation activates the enzyme), 2-fold increased CPT1A expression in MCF-7 cells, and 2.8-fold reduced phosphorylation of ACC1 following aspirin exposure in MDA-MB-468 cells. Thus, DDIT4 downregulation raised the activity of main lipid metabolism enzymes upon aspirin exposure which is an undesired effect as FA synthesis and oxidation are linked to malignant phenotype. This finding may be clinically relevant as DDIT4 expression has been shown to vary in breast tumors. Our findings justify further, more extensive investigation of the role of DDIT4 in aspirin's effect on fatty acid metabolism in BC cells.

Molecular, Metabolic, and Nutritional Changes after Metabolic Surgery in Obese Diabetic Patients (MoMen): A Protocol for a Multicenter Prospective Cohort Study

Metabolic surgery is an essential option in the treatment of obese patients with type 2 diabetes (T2D). Despite its known advantages, this surgery still needs to be introduced in Malaysia. In this prospective study, the pathophysiological mechanisms at the molecular level will be studied and the metabolomics pathways of diabetes remission will be explored. The present study aims to evaluate the changes in the anthropometric measurements, body composition, phase angle, diet intake, biochemistry parameters, adipokines, microRNA, and metabolomics, both pre- and post-surgery, among obese diabetic patients in Malaysia. This is a multicenter prospective cohort study that will involve obese patients (n = 102) with a body mass index (BMI) of ≥25 kg/m2 (Asian BMI categories: WHO/IASO/IOTF, 2000) who will undergo metabolic surgery. They will be categorized into three groups: non-diabetes, prediabetes, and diabetes. Their body composition will be measured using a bioimpedance analyzer (BIA). The phase angle (PhA) data will be analyzed. Venous blood will be collected from each patient for glycated hemoglobin (HbA1c), lipids, liver, renal profile, hormones, adipokines, and molecular and metabolomics analyses. The serum microRNA will be measured. A gene expression study of the adipose tissue of different groups will be conducted to compare the groups. The relationship between the 1HNMR-metabolic fingerprint and the patients’ lifestyles and dietary practices will be determined. The factors responsible for the excellent remission of T2D will be explored in this study.

Monoclonal antibodies in breast cancer: A critical appraisal

In breast cancer, mAbs can play multifunctional roles like targeting cancer cells, sometimes directly attacking them, helping in locating and delivering therapeutic drugs to targets, inhibiting cell growth and blocking immune system inhibitors, etc. Monoclonal antibodies are also one of the important successful treatment strategies especially against HER2 but they have not been explored much for other types of breast cancers especially in triple negative breast cancers. Monoclonal antibodies impact the feasibility of antigen specificity, bispecific and trispecific mAbs have opened new doors for more targeted specific efficacy. Monoclonal antibodies can be used diversly and with efficacy as compared to other methods of treatment thus maining it a suitable candidate for breast cancer treatment. However, mAbs treatment also causes various side effects such as fever, trembling, fatigue, headache and muscle pain, nausea/vomiting, difficulty in breathing, rashes and bleeding. Understanding the pros and cons of this strategy, we have explored in this review, the current and future potential capabilities of monoclonal antibodies with respect to diagnosis and treatment of breast cancer. DATA AVAILABILITY: Not applicable.

Investigating the Function of Human Jumping Translocation Breakpoint Protein (hJTB) and Its Interacting Partners through In-Solution Proteomics of MCF7 Cells

Human jumping translocation breakpoint (hJTB) gene is located on chromosome 1q21 and is involved in unbalanced translocation in many types of cancer. JTB protein is ubiquitously present in normal cells but it is found to be overexpressed or downregulated in various types of cancer cells, where this protein and its isoforms promote mitochondrial dysfunction, resistance to apoptosis, genomic instability, proliferation, invasion and metastasis. Hence, JTB could be a tumor biomarker for different types of cancer, such as breast cancer (BC), and could be used as a drug target for therapy. However, the functions of the protein or the pathways through which it increases cell proliferation and invasiveness of cancer cells are not well-known. Therefore, we aim to investigate the functions of JTB by using in-solution digestion-based cellular proteomics of control and upregulated and downregulated JTB protein in MCF7 breast cancer cell line, taking account that in-solution digestion-based proteomics experiments are complementary to the initial in-gel based ones. Proteomics analysis allows investigation of protein dysregulation patterns that indicate the function of the protein and its interacting partners, as well as the pathways and biological processes through which it functions. We concluded that JTB dysregulation increases the epithelial-mesenchymal transition (EMT) potential and cell proliferation, harnessing cytoskeleton organization, apical junctional complex, metabolic reprogramming, and cellular proteostasis. Deregulated JTB expression was found to be associated with several proteins involved in mitochondrial organization and function, oxidative stress (OS), apoptosis, and interferon alpha and gamma signaling. Consistent and complementary to our previous results emerged by using in-gel based proteomics of transfected MCF7 cells, JTB-related proteins that are overexpressed in this experiment suggest the development of a more aggressive phenotype and behavior for this luminal type A non-invasive/poor-invasive human BC cell line that does not usually migrate or invade compared with the highly metastatic MDA-MB-231 cells. This more aggressive phenotype of MCF7 cells related to JTB dysregulation and detected by both in-gel and in-solution proteomics could be promoted by synergistic upregulation of EMT, Mitotic spindle and Fatty acid metabolism pathways. However, in both JTB dysregulated conditions, several downregulated JTB-interacting proteins predominantly sustain antitumor activities, attenuating some of the aggressive phenotypical and behavioral traits promoted by the overexpressed JTB-related partners.

Integrated Metabolomics and Lipidomics Approach for the Study of Metabolic Network and Early Diagnosis in Cerebral Infarction

Cerebral infarction (CI) remains a major cause of high mortality and long-term disability worldwide. The exploration of biomarkers and pathogenesis is crucial for the early diagnosis of CI. Although the understanding of metabolic perturbations underlying CI has increased in recent years, the relationship between altered metabolites and disease pathogenesis has only been partially elucidated and requires further investigation. In this study, we performed an integrated metabolomics and lipidomics analysis on 59 healthy subjects and 47 CI patients. Ultimately, 49 metabolite and 68 lipid biomarkers were identified and enriched in 24 disturbed pathways. The metabolic network revealed a significant interaction between altered lipids and other metabolites. Using receiver operating characteristic curve (ROC) analysis, a panel of three polar metabolites and seven lipids was optimized in the training set, which included taurine, oleoylcarnitine, creatinine, PE(22:6/P-18:0), Cer 34:2, GlcCer(d18:0/18:0), DG 44:0, LysoPC(16:0), 22:6-OH/LysoPC, and TAG58:7-FA22:4. Subsequently, a support vector machine (SVM) model was constructed and validated, which showed excellent predictive ability in the validation set. Thereby, the integrated metabolomics and lipidomics approach could contribute to a comprehensive understanding of the metabolic dyshomeostasis associated with the pathogenesis of underlying CI. The present research may promote a deeper understanding and early diagnosis of CI in the clinic. All raw data were deposited in PRIDE (PXD036199).

NMR-based metabolomic analysis identifies RON-DEK-β-catenin dependent metabolic pathways and a gene signature that stratifies breast cancer patient survival

BACKGROUND

Advances in detection techniques and treatment have increased the diagnosis of breast cancer at early stages; however, recurrence occurs in all breast cancer subtypes, and both recurrent and de novo metastasis are typically treatment resistant. A growing body of evidence supports the notion that metabolic plasticity drives cancer recurrence. RON and DEK are proteins that promote cancer metastasis and synergize mechanistically to activate β-catenin, but the metabolic consequences are unknown.

METHODS

To ascertain RON-DEK-β-catenin dependent metabolic pathways, we utilized an NMR-based metabolomics approach to determine steady state levels of metabolites. We also interrogated altered metabolic pathway gene expression for prognostic capacity in breast cancer patient relapse-free and distant metastasis-free survival and discover a metabolic signature that is likely associated with recurrence.

RESULTS

RON-DEK-β-catenin loss showed a consistent metabolite regulation of succinate and phosphocreatine. Consistent metabolite alterations between RON and DEK loss (but not β-catenin) were found in media glucose consumption, lactate secretion, acetate secretion, and intracellular glutamine and glutathione levels. Consistent metabolite alterations between RON and β-catenin loss (and not DEK) were found only in intracellular lactate levels. Further pathway hits include β-catenin include glycolysis, glycosylation, TCA cycle/anaplerosis, NAD+ production, and creatine dynamics. Genes in these pathways epistatic to RON-DEK-β-catenin were used to define a gene signature that prognosticates breast cancer patient survival and response to chemotherapy.

CONCLUSIONS

The RON-DEK-β-catenin axis regulates the numerous metabolic pathways with significant associations to breast cancer patient outcomes.

An Optimized Workflow for the Analysis of Metabolic Fluxes in Cancer Spheroids Using Seahorse Technology

Three-dimensional cancer models, such as spheroids, are increasingly being used to study cancer metabolism because they can better recapitulate the molecular and physiological aspects of the tumor architecture than conventional monolayer cultures. Although Agilent Seahorse XFe96 (Agilent Technologies, Santa Clara, CA, United States) is a valuable technology for studying metabolic alterations occurring in cancer cells, its application to three-dimensional cultures is still poorly optimized. We present a reliable and reproducible workflow for the Seahorse metabolic analysis of three-dimensional cultures. An optimized protocol enables the formation of spheroids highly regular in shape and homogenous in size, reducing variability in metabolic parameters among the experimental replicates, both under basal and drug treatment conditions. High-resolution imaging allows the calculation of the number of viable cells in each spheroid, the normalization of metabolic parameters on a per-cell basis, and grouping of the spheroids as a function of their size. Multivariate statistical tests on metabolic parameters determined by the Mito Stress test on two breast cancer cell lines show that metabolic differences among the studied spheroids are mostly related to the cell line rather than to the size of the spheroid. The optimized workflow allows high-resolution metabolic characterization of three-dimensional cultures, their comparison with monolayer cultures, and may aid in the design and interpretation of (multi)drug protocols.