Serum lipidomics for exploring biomarkers of bortezomib therapy in patients with multiple myeloma

Comprehensive analysis of serum cytokines in patients with multiple myeloma before and after lenalidomide and dexamethasone

Multiple myeloma (MM) is an incurable B-cell malignancy often accompanied by profound immunodeficiency. Lenalidomide (Len) is an immunomodulatory drug that exerts promising therapeutic effects on MM through the immune system. However, predictive markers related to the effects of Len treatment are not fully understood. This study aimed to identify candidate biomarkers for predicting the clinical efficacy of Len and dexamethasone (Ld) therapy through a comprehensive analysis of serum cytokines. The levels of 48 cytokines in the serum of patients with MM just before Ld therapy (n = 77), at the time of best response (n = 56), and at disease progression (n = 49) were measured and evaluated. Patients with high IL-18 and M-CSF levels showed significantly shorter progression-free survival and overall survival (OS). In contrast, patients with high PDGF-BB levels had longer survival. Moreover, low levels of G-CSF, IL-7, IL-8, and SDF-1α were associated with shorter OS after Ld therapy. During Ld therapy, pro-inflammatory cytokines such as IL-2Rα, IL-18, and TNF-α were decreased, while IFN-γ was increased. IL-4 and IL-6 levels increased during disease progression. In conclusion, this study provides a better understanding of the association between cytokines and the efficacy of Ld therapy as well as the unique changes in cytokines related to inflammatory and immune responses during Ld therapy.

Bortezomib-induced peripheral neuropathy: Clinical features, molecular basis, and therapeutic approach

Bortezomib is the first-line standard and most effective chemotherapeutic for multiple myeloma; however, bortezomib-induced peripheral neuropathy (BIPN) severely affects the chemotherapy regimen and has long-term impact on patients under maintenance therapy. The pathogenesis of BIPN is poorly understood, and basic research and development of BIPN management drugs are in early stages. Besides chemotherapy dose reduction and regimen modification, no recommended prevention and treatment approaches are available for BIPN apart from the International Myeloma Working Group guidelines for peripheral neuropathy in myeloma. An in-depth exploration of the pathogenesis of BIPN, development of additional therapeutic approaches, and identification of risk factors are needed. Optimizing effective and standardized BIPN treatment plans and providing more decision-making evidence for clinical diagnosis and treatment of BIPN are necessary. This article reviews the recent advances in BIPN research; provides an overview of clinical features, underlying molecular mechanisms, and therapeutic approaches; and highlights areas for future studies.

Plasma Lipidomic Profiling Identifies Elevated Triglycerides as Potential Risk Factor in Chemotherapy-Induced Peripheral Neuropathy

PURPOSE

Chemotherapy-induced peripheral neuropathy (CIPN) is a dose-limiting side effect of cytotoxic cancer treatment, often necessitating dose reduction (DR) or chemotherapy discontinuation (CD). Studies on peripheral neuropathy related to chemotherapy, obesity, and diabetes have implicated lipid metabolism. This study examined the association between circulating lipids and CIPN.

METHODS

Lipidomic analysis was performed on plasma samples from 137 patients receiving taxane-based treatment. CIPN was graded using Total Neuropathy Score-clinical version (TNSc) and patient-reported outcome measure European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-CIPN (EORTC-QLQ-CIPN20).

RESULTS

A significant proportion of elevated baseline lipids were associated with high-grade CIPN defined by TNSc and EORTC-QLQ-CIPN20 including triacylglycerols (TGs). Multivariable Cox regression on lipid species, adjusting for BMI, age, and diabetes, showed several elevated baseline TG associated with shorter time to DR/CD. Latent class analysis identified two baseline lipid profiles with differences in risk of CIPN (hazard ratio, 2.80 [95% CI, 1.50 to 5.23]; P = .0013). The higher risk lipid profile had several elevated TG species and was independently associated with DR/CD when modeled with other clinical factors (diabetes, age, BMI, or prior numbness/tingling).

CONCLUSION

Elevated baseline plasma TG is associated with an increased risk of CIPN development and warrants further validation in other cohorts. Ultimately, this may enable therapeutic intervention.

Identification of a cholesterol metabolism-related prognostic signature for multiple myeloma

Multiple myeloma (MM) is a prevalent hematological malignancy that poses significant challenges for treatment. Dysregulated cholesterol metabolism has been linked to tumorigenesis, disease progression, and therapy resistance. However, the correlation between cholesterol metabolism-related genes (CMGs) and the prognosis of MM remains unclear. Univariate Cox regression analysis and LASSO Cox regression analysis were applied to construct an overall survival-related signature based on the Gene Expression Omnibus database. The signature was validated using three external datasets. Enrichment analysis and immune analysis were performed between two risk groups. Furthermore, an optimal nomogram was established for clinical application, and its performance was assessed by the calibration curve and C-index. A total of 6 CMGs were selected to establish the prognostic signature, including ANXA2, CHKA, NSDHL, PMVK, SCAP and SQLE. The prognostic signature demonstrated good prognostic performance and correlated with several important clinical parameters, including number of transplants, International Staging System, albumin, beta2-Microglobulin and lactate dehydrogenase levels. The function analysis and immune analysis revealed that the metabolic pathways and immunologic status were associated with risk score. The nomogram incorporating the signature along with other clinical characteristics was constructed and the discrimination was verified by the calibration curve and C-index. Our findings indicated the potential prognostic connotation of cholesterol metabolism in MM. The development and validation of the prognostic signature is expected to aid in predicting prognosis and guiding precision treatment for MM.

Lipid metabolic vulnerabilities of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy worldwide, characterized by abnormal proliferation of malignant plasma cells within a tumor-permissive bone marrow microenvironment. Metabolic dysfunctions are emerging as key determinants in the pathobiology of MM. In this review, we highlight the metabolic features of MM, showing how alterations in various lipid pathways, mainly involving fatty acids, cholesterol and sphingolipids, affect the growth, survival and drug responsiveness of MM cells, as well as their cross-talk with other cellular components of the tumor microenvironment. These findings will provide a new path to understanding the mechanisms underlying how lipid vulnerabilities may arise and affect the phenotype of malignant plasma cells, highlighting novel druggable pathways with a significant impact on the management of MM.

Evolution of predictive risk factor analysis for chemotherapy-related toxicity

The causes of variation in toxicity to the same treatment regimen among seemingly similar patients remain largely unknown. There was tremendous optimism that the patient's germline genome would be strongly predictive of treatment-related toxicity and could be used to personalize treatment and improve therapeutic outcomes. However, there has been limited success in discovering robust pharmacogenetic predictors of treatment-related toxicity and even less progress in translating the few validated predictors into clinical practice. It is apparent that identification of toxicity predictors that can be used to predict and prevent treatment-related toxicity will require thinking beyond germline genomics. To that end, we propose an integrated biomarker discovery approach that recognizes that a patient's toxicity risk is determined by the cumulative effects of a broad range of "omic" and non-omic factors. This commentary describes the limited success in discovering and translating clinical and pharmacogenetic toxicity predictors into clinical practice. We illustrate the evolution of cancer toxicity biomarker discovery and translation through studies of taxane-induced peripheral neuropathy, which is one of the most common and debilitating side effects of cancer treatment. We then discuss the opportunities for discovering non-genomic (e.g., metabolomic, lipidomic, transcriptomic, proteomic, microbiomic, medical, behavioral, environmental) and integrated biomarkers that may be more strongly predictive of toxicity risk and the potential challenges with translating integrated biomarkers into clinical practice. This integrated biomarker discovery approach may circumvent some of the major limitations in toxicity biomarker science and move precision oncology treatment forward so that patients receive maximum treatment benefit with minimal toxicity.

Chemotherapy-Induced Peripheral Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating side effect of many common anti-cancer agents that can lead to dose reduction or treatment discontinuation, which decrease chemotherapy efficacy. Long-term CIPN can interfere with activities of daily living and diminish the quality of life. The mechanism of CIPN is not yet fully understood, and biomarkers are needed to identify patients at high risk and potential treatment targets. Metabolomics can capture the complex behavioral and pathophysiological processes involved in CIPN. This chapter is to review the CIPN metabolomics studies to find metabolic pathways potentially involved in CIPN. These potential CIPN metabolites are then investigated to determine whether there is evidence from studies of other neuropathy etiologies such as diabetic neuropathy and Leber hereditary optic neuropathy to support the importance of these pathways in peripheral neuropathy. Six potential biomarkers and their putative mechanisms in peripheral neuropathy were reviewed. Among these biomarkers, histidine and phenylalanine have clear roles in neurotransmission or neuroinflammation in peripheral neuropathy. Further research is needed to discover and validate CIPN metabolomics biomarkers in large clinical studies.

A new prediction model integrated serum lipid profile for patients with multiple myeloma

Purpose: This study aimed to explore a predictive risk-stratification model combing clinical characteristics and lipid profiles in multiple myeloma (MM) patients. Methods: The data of 275 patients in Sun Yat-Sen University Cancer Center were retrospectively analyzed and randomly divided into the training (n = 138) and validation (n=137) cohorts. Triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), lactate dehydrogenase (LDH), Apolipoprotein B (Apo B) and Apo B/Apolipoprotein A1 (Apo A1) ratio were the prognostic factors identified through univariate and multivariate Cox analysis. Results: A 6-prognostic factor model was constructed based on Lasso regression. Patients were divided into low- and high-risk groups and the former group showed longer overall survival (OS) time (p<0.05). The area under the curve (AUC) of the risk score model for 5-and 10-year OS were 0.756 [95% CI: 0.661-0.850] and 0.940 [95% CI: 0.883-0.997], which exhibited better accuracy than International Staging System (ISS) and Durie and Salmon (DS) stage. Conclusion: This study aims to combine the lipid metabolism profile with the clinical characteristics of MM patients to generate a prognostic model. The nomogram integrating ISS stage and risk score increased the prediction accuracy. This model can monitor lipid profile as a simple and effective method, which has certain clinical significance for improving the accuracy of the prognosis and exploring potential therapeutic targets.

Fluid Biomarkers for Monitoring Structural Changes in Polyneuropathies: Their Use in Clinical Practice and Trials

Reliable and responsive tools for monitoring disease activity and treatment outcomes in patients with neuropathies are lacking. With the emergence of ultrasensitive blood bioassays, proteins released with nerve damage are potentially useful response biomarkers for many neurological disorders, including polyneuropathies. In this review, we provide an overview of the existing literature focusing on potential applications in polyneuropathy clinical care and trials. Whilst several promising candidates have been identified, no studies have investigated if any of these proteins can serve as response biomarkers of longitudinal disease activity, except for neurofilament light (NfL). For NfL, limited evidence exists supporting a role as a response biomarker in Guillain-Barré syndrome, vasculitic neuropathy, and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). Most evidence exists for NfL as a response biomarker in hereditary transthyretin-related amyloidosis (hATTR). At the present time, the role of NfL is therefore limited to a supporting clinical tool or exploratory endpoint in trials. Future developments will need to focus on the discovery of additional biomarkers for anatomically specific and other forms of nerve damage using high-throughput technologies and highly sensitive analytical platforms in adequality powered studies of appropriate design. For NfL, a better understanding of cut-off values, the relation to clinical symptoms and long-term disability as well as dynamics in serum on and off treatment is needed to further expand and proceed towards implementation.

Neuromuscular complications of cancer therapy

PURPOSE OF THE REVIEW

The neuromuscular complications of cancer therapy include chemotherapy-induced peripheral neurotoxicity (CIPN), immune-related neuromuscular complications to immune checkpoint inhibitors and radiation-induced neuropathy/plexopathy. With a wider focus on CIPN, we will discuss new pathogenetic insights, recent predictive biomarkers and emerging therapies for neuromuscular complications of cancer therapy.

RECENT FINDINGS

Findings from recent preclinical studies have improved our knowledge on new CIPN pathogenetic pathways, including the activation of senescence-like processes in neurons, axonal degeneration and neuroinflammation. Metabolomics and serum neurofilament light chain levels appear the most promising biomarkers to predict CIPN development and severity. There is some recent evidence of promising pharmacological compounds to prevent or treat CIPN, and new drugs are in early development and testing.

SUMMARY

A multimodal assessment, with neurophysiological, imaging and patient-reported outcome measures, coupled with the use of reliable blood or genetic biomarkers, may offer pathogenetic grounds for future preventive and symptomatic strategies for the multidisciplinary treatment of neuromuscular complications of cancer therapy.

Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Blood molecular biomarkers for chemotherapy-induced peripheral neuropathy: From preclinical models to clinical practice

Chemotherapy-induced peripheral neuropathy (CIPN) has long been recognized as a clinically significant issue in patients treated with antineoplastic drugs. This common long-term toxic side-effect which negatively impacts the outcome of the disease can lead to disability and have detrimental effects on patients' quality of life. Since axonal injury is a prominent feature of CIPN, responsible for several sensory symptoms, including pain, sensory loss and hypersensitivity to mechanical and/or cold stimuli in the hands and feet, neurophysiological assessments remain the gold standard for clinical diagnosis of CIPN. Given the large impact of CIPN on cancer patients, there is increasing emphasis on biomarkers of adverse outcomes in safety assessment and translational research, to prevent permanent neuroaxonal damage. Since the results on reliable blood molecular markers for axonal degeneration are still controversial, here we provide a brief overview of blood molecular biomarkers used for assessing and/or predicting CIPN in preclinical and clinical settings.

Pathological Mechanisms of Bortezomib-Induced Peripheral Neuropathy

Bortezomib, a first-generation proteasome inhibitor widely used in chemotherapy for hematologic malignancy, has effective anti-cancer activity but often causes severe peripheral neuropathy. Although bortezomib-induced peripheral neuropathy (BIPN) is a dose-limiting toxicity, there are no recommended therapeutics for its prevention or treatment. One of the most critical problems is a lack of knowledge about pathological mechanisms of BIPN. Here, we summarize the known mechanisms of BIPN based on preclinical evidence, including morphological abnormalities, involvement of non-neuronal cells, oxidative stress, and alterations of transcriptional programs in both the peripheral and central nervous systems. Moreover, we describe the necessity of advancing studies that identify the potential efficacy of approved drugs on the basis of pathological mechanisms, as this is a convincing strategy for rapid translation to patients with cancer and BIPN.

Application of comprehensive lipidomics to biomarker research on adverse drug reactions

Lipidomics is a relatively new field of omics that focuses on lipids, one of the major categories of metabolites. Owing to their various functions, lipids are considered suitable targets for biomarker development; in addition, lipidomics analysis of adverse drug reactions (ADRs) has been conducted recently. In this review, I have summarized information on comprehensive lipidomics, which involves the analysis of global lipids in a non-targeted manner. Mass spectrometry-based platforms are currently the dominant lipidomics platform owing to their versatile features. I have also summarized the application of lipidomics in biomarker research on ADRs caused by therapeutic drugs in humans and rodents. Additionally, general concerns in and emerging approaches of lipidomics research on ADR have been highlighted. Although biomarkers identified using the lipidomics analysis of ADRs have not been qualified, reported candidates will be evaluated for clinical application. In addition, novel biomarker candidates will be developed via classical and new approaches exemplified in this review.

Applications of Lipidomics in Tumor Diagnosis and Therapy

Lipids have many critical biological functions in cancer. There are characteristic changes of lipid metabolism and metabolites in different physiological and pathological processes. Lipidomics is an emerging discipline of metabolomics for systematic analysis of lipids in organisms, tissues, or cells and the molecules that interact with them. With the development of new analytical techniques, especially the application and development of mass spectrometry techniques, the determination of lipids can be carried out quickly and accurately and has a high throughput. A large number of studies have shown that abnormal lipid metabolism is closely related to the occurrence and development of tumors. The application of lipidomics technology can reveal changes in lipids and relative abnormal metabolic pathways associated with tumors. Moreover, it shows a wide range of application prospects in the identification of tumor lipid biomarkers, early tumor diagnosis, and the discovery of antitumor drug targets. This chapter mainly introduces the application and development direction of lipidomics in the diagnosis and therapy of different tumors.

Serum lipidomics for exploring biomarkers of bortezomib therapy in patients with multiple myeloma

Although the proteasome inhibitor bortezomib (BTZ) shows excellent efficacy in multiple myeloma (MM), a fraction of patients has a suboptimal or no response to this agent. In addition, BTZ-induced peripheral neuropathy (BiPN), a frequent side-effect of this therapy, limits its use in some patients. This study aimed to explore serum lipid biomarker candidates to predict the response to BTZ and the severity of BiPN. Fifty-nine serum samples were collected from patients with MM prior to receiving BTZ plus low-dose dexamethasone therapy. Serum levels of phospholipids, sphingolipids, neutral lipids, and polyunsaturated fatty acids and their oxidation products were measured by a comprehensive lipidomic study. Overall, 385 lipid metabolites were identified in patients' sera; lower levels of several glycerophospholipids, sphingolipids, and cholesteryl esters were associated with a poor treatment response. Metabolites related to platelet-activating factor biosynthesis and cholesterol metabolism appeared particularly relevant. Furthermore, several lysophosphatidylcholines, phosphatidylcholines, ceramides, neutral lipids, and oxidative fatty acids were significantly increased or decreased in patients with BiPN grades ranging from G0 to G3. Among these compounds, mediators reportedly inducing myelin breakdown and stimulating inflammatory responses were prominent. Although further study is necessary to validate these biomarker candidates, our results contribute to the development of predictive biomarkers for response to BTZ treatment, or ensuing severe BiPN, in patients with MM.