Variability of serum novel serum peptide biomarkers correlates with the disease states of multiple myeloma

Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach

This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.

Research advances in endometriosis-related signaling pathways: A review

Endometriosis (EM) is characterized by the existence of endometrial mucosa outside the uterine cavity, which causesinfertility, persistent aches, and a decline in women's quality of life. Both hormone therapies and nonhormone therapies, such as NSAIDs, are ineffective, generic categories of EM drugs. Endometriosis is a benign gynecological condition, yet it shares a number of features with cancer cells, including immune evasion, survival, adhesion, invasion, and angiogenesis. Several endometriosis-related signaling pathways are comprehensively reviewed in this article, including E2, NF-κB, MAPK, ERK, PI3K/Akt/mTOR, YAP, Wnt/β-catenin, Rho/ROCK, TGF-β, VEGF, NO, iron, cytokines and chemokines. To find and develop novel medications for the treatment of EM, it is essential to implicitly determine the molecular pathways that are disordered during EM development. Additionally, research on the shared pathways between EM and tumors can provide hypotheses or suggestions for endometriosis therapeutic targets.

Circulating Proteins Associated with Response and Resistance to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer

Simple Summary

The goal of this study was to find circulating proteins that can be easily sampled and incorporated into a clinical setting to improve predictive treatment response in HER2-positive breast cancer patients receiving neoadjuvant chemotherapy. We looked for potential biomarkers in serum, which we identified using two proteomics techniques: qualitative LC-MS/MS and a quantitative assay that assessed protein expression between responders and non-responders HER2-positive breast cancer patients to neoadjuvant chemotherapy.

Abstract

Despite the increasing use of neoadjuvant chemotherapy (NAC) in HER2-positive breast cancer (BC) patients, the clinical problem of predicting individual treatment response remains unanswered. Furthermore, the use of ineffective chemotherapeutic regimens should be avoided. Serum biomarker levels are being studied more and more for their ability to predict therapy response and aid in the development of personalized treatment regimens. This study aims to identify effective protein networks and biomarkers to predict response to NAC in HER2-positive BC patients through an exhaustive large-scale LC-MS/MS-based qualitative and quantitative proteomic profiling of serum samples from responders and non-responders. Serum samples from HER2-positive BC patients were collected before NAC and were processed by three methods (with and without nanoparticles). The qualitative analysis revealed differences in the proteomic profiles between responders and non-responders, mainly in proteins implicated in the complement and coagulation cascades and apolipoproteins. Qualitative analysis confirmed that three proteins (AFM, SERPINA1, APOD) were correlated with NAC resistance. In this study, we show that serum biomarker profiles can predict treatment response and outcome in the neoadjuvant setting. If these findings are further developed, they will be of significant clinical utility in the design of treatment regimens for individual BC patients.

FGA Controls VEGFA Secretion to Promote Angiogenesis by Activating the VEGFR2-FAK Signalling Pathway

BACKGROUND

Our previous work revealed the high expression of fibrinogen alpha chain (FGA) in patients with endometriosis (EM) and that it could promote the migration and invasion of endometrial stromal cells. Angiogenesis is the key condition for the development of EM. This study was aimed to elucidate the role of FGA in endometrial stromal cells involved in angiogenesis in EM.

METHODS

Immunohistochemistry was used to detect the microvessel density (MVD) and VEGF expression in the eutopic endometrium samples from EM and non-EM. The conditioned medium (CM) of human primary eutopic endometrial stromal cells (EuESC) and immortalized endometrial stromal cell line hEM15A with FGA knockdown were collected and used to treat human umbilical vein endothelial cells (HUVECs). Then, tube formation assay, EdU assay, wound assay, transwell assay and flow cytometry assays were performed to assess the function of HUEVCs in vitro. The angiogenic capability of HUVECs was further measured using a matrigel plug assay with BALB/c nude mice in vivo. Immunofluorescence was used to detect the expression of F-actin and VE-cadherin. RT-PCR and western blotting were used to detect the expression of angiogenesis-related factors in endometrial stromal cells and downstream signalling pathways in HUVECs.

RESULTS

MVD and VEGF expression in the eutopic endometrium of EM patients were significantly higher than those in the normal endometrium of non-EM patients, and the increased MVD in EM indicates an increased risk of recurrence. Functionally, we found that CM of endometrial stromal cells with FGA knockdown could inhibit HUEVCs migration and tube formation in vitro and in vivo, while having no significant effect on HUVECs proliferation, apoptosis and cell cycle. Mechanically, the expression of VEGFA, PDGF, FGF-B, VEGF, MMP-2 and MMP-9 was reduced in hEM15A cells with FGA knockdown. CM of hEM15A cells with FGA knockdown reduced the number of microfilaments and pseudopodia, as well as the expression of VE-cadherin, and inhibited the activity of VEGFR2 and the FAK signalling pathway in HUVECs.

CONCLUSION

Our study demonstrated FGA could enhance the interaction between endometrial stromal cells and HUVECs via the potential VEGA-VEGFR-FAK signalling axis and promote EM angiogenesis, revealing a promising therapeutic approach for EM.

Prognostic Value of Resistance Proteins in Plasma Cells from Multiple Myeloma Patients Treated with Bortezomib-Based Regimens

While multiple myeloma (MM) treatment with proteasome inhibitors and other agents yields encouraging results, primary and secondary resistance remains an emerging problem. An important factor in such treatment resistance is the overexpression of several proteins. The present study comprehensively evaluates the expression of POMP, PSMB5, NRF2, XBP1, cMAF and MAFb proteins in plasma cells isolated from the bone marrow of 39 MM patients treated with bortezomib-based regimens using an enzyme-linked immunosorbent assay (ELISA). The proteins were selected on the basis of previous laboratory and clinical studies in bortezomib-treated MM patients. It was found that the expression of the investigated proteins did not significantly differ between bortezomib-sensitive and bortezomib-refractory patients. However, the expression of some proteins correlated with overall survival (OS); this was significantly shorter in patients with higher POMP expression (HR 2.8, 95% CI: 1.1–7.0, p = 0.0277) and longer in those with higher MAFB expression (HR 0.32, 95% CI: 0.13–0.80, p = 0.0147). Our results indicate that a high expression of POMP and MAFB in MM plasma cells may serve as predictors of OS in MM patients treated with bortezomib-based regimens. However, further studies are needed to determine the role of these factors in effective strategies for improving anti-myeloma therapy.

Liquid biopsy: an evolving paradigm for the biological characterisation of plasma cell disorders

Liquid biopsies-a source of circulating cell-free nucleic acids, proteins and extracellular vesicles-are currently being explored for the quantitative and qualitative characterisation of the tumour genome and as a mode of non-invasive therapeutic monitoring in cancer. Emerging data suggest that liquid biopsies might offer a potentially simple, non-invasive, repeatable strategy for diagnosis, prognostication and therapeutic decision making in a genetically heterogeneous disease like multiple myeloma (MM), with particular applicability in subsets of patients where conventional markers of disease burden may be less informative. In this review, we describe the emerging utility of the evaluation of circulating tumour DNA, extracellular RNA, cell-free proteins and metabolites and extracellular vesicles in MM.

Clinical Proteomics of Biofluids in Haematological Malignancies

Since the emergence of high-throughput proteomic techniques and advances in clinical technologies, there has been a steady rise in the number of cancer-associated diagnostic, prognostic, and predictive biomarkers being identified and translated into clinical use. The characterisation of biofluids has become a core objective for many proteomic researchers in order to detect disease-associated protein biomarkers in a minimally invasive manner. The proteomes of biofluids, including serum, saliva, cerebrospinal fluid, and urine, are highly dynamic with protein abundance fluctuating depending on the physiological and/or pathophysiological context. Improvements in mass-spectrometric technologies have facilitated the in-depth characterisation of biofluid proteomes which are now considered hosts of a wide array of clinically relevant biomarkers. Promising efforts are being made in the field of biomarker diagnostics for haematologic malignancies. Several serum and urine-based biomarkers such as free light chains, β-microglobulin, and lactate dehydrogenase are quantified as part of the clinical assessment of haematological malignancies. However, novel, minimally invasive proteomic markers are required to aid diagnosis and prognosis and to monitor therapeutic response and minimal residual disease. This review focuses on biofluids as a promising source of proteomic biomarkers in haematologic malignancies and a key component of future diagnostic, prognostic, and disease-monitoring applications.

A Fibrinogen Alpha Fragment Mitigates Chemotherapy-Induced MLL Rearrangements

Rearrangements in the Mixed Lineage Leukemia breakpoint cluster region (MLLbcr) are frequently involved in therapy-induced leukemia, a severe side effect of anti-cancer therapies. Previous work unraveled Endonuclease G as the critical nuclease causing initial breakage in the MLLbcr in response to different types of chemotherapeutic treatment. To identify peptides protecting against therapy-induced leukemia, we screened a hemofiltrate-derived peptide library by use of an enhanced green fluorescent protein (EGFP)-based chromosomal reporter of MLLbcr rearrangements. Chromatographic purification of one active fraction and subsequent mass spectrometry allowed to isolate a C-terminal 27-mer of fibrinogen α encompassing amino acids 603 to 629. The chemically synthesized peptide, termed Fα27, inhibited MLLbcr rearrangements in immortalized hematopoietic cells following treatment with the cytostatics etoposide or doxorubicin. We also provide evidence for protection of primary human hematopoietic stem and progenitor cells from therapy-induced MLLbcr breakage. Of note, fibrinogen has been described to activate toll-like receptor 4 (TLR4). Dissecting the Fα27 mode-of action revealed association of the peptide with TLR4 in an antagonistic fashion affecting downstream NFκB signaling and pro-inflammatory cytokine production. In conclusion, we identified a hemofiltrate-derived peptide inhibitor of the genome destabilizing events causing secondary leukemia in patients undergoing chemotherapy.

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.

Proteomic Alterations in Multiple Myeloma: A Comprehensive Study Using Bone Marrow Interstitial Fluid and Serum Samples

Multiple myeloma (MM) is a plasma cell-associated cancer and exists as the second most common hematological malignancy worldwide. Although researchers have been working on MM, a comprehensive quantitative Bone Marrow Interstitial Fluid (BMIF) and serum proteomic analysis from the same patients’ samples is not yet reported. The present study involves the investigation of alterations in the BMIF and serum proteome of MM patients compared to controls using multipronged quantitative proteomic approaches viz., 2D-DIGE, iTRAQ, and SWATH-MS. A total of 279 non-redundant statistically significant differentially abundant proteins were identified by the combination of three proteomic approaches in MM BMIF, while in the case of serum 116 such differentially abundant proteins were identified. The biological context of these dysregulated proteins was deciphered using various bioinformatic tools. Verification experiments were performed in a fresh independent cohort of samples using immunoblotting and mass spectrometry based SRM assays. Thorough data evaluation led to the identification of a panel of five proteins viz., haptoglobin, kininogen 1, transferrin, and apolipoprotein A1 along with albumin that was validated using ELISA in a larger cohort of serum samples. This panel of proteins could serve as a useful tool in the diagnosis and understanding of the pathophysiology of MM in the future.

Distinguishing and Biochemical Phenotype Analysis of Epilepsy Patients Using a Novel Serum Profiling Platform

Diagnosis of non-symptomatic epilepsy includes a history of two or more seizures and brain imaging to rule out structural changes like trauma, tumor, infection. Such analysis can be problematic. It is important to develop capabilities to help identify non-symptomatic epilepsy in order to better monitor and understand the condition. This understanding could lead to improved diagnostics and therapeutics. Serum mass peak profiling was performed using electrospray ionization mass spectrometry (ESI-MS). A comparison of sera mass peaks between epilepsy and control groups was performed via leave one [serum sample] out cross-validation (LOOCV). MS/MS peptide analysis was performed on serum mass peaks to compare epilepsy patient and control groups. LOOCV identified significant differences between the epilepsy patient group and control group (p = 10⁻²²). This value became non-significant (p = 0.10) when the samples were randomly allocated between the groups and reanalyzed by LOOCV. LOOCV was thus able to distinguish a non-symptomatic epilepsy patient group from a control group based on physiological differences and underlying phenotype. MS/MS was able to identify potential peptide/protein changes involved in this epilepsy versus control comparison, with 70% of the top 100 proteins indicating overall neurologic function. Specifically, peptide/protein sera changes suggested neuro-inflammatory, seizure, ion-channel, synapse, and autoimmune pathways changing between epilepsy patients and controls.

Serum proteomics analysis of candidate predictive biomarker panel for the diagnosis of trastuzumab-based therapy resistant breast cancer

Human epidermal growth factor receptor 2 (HER2)-positive is a particularly aggressive type of the breast cancer. Trastuzumab-based therapy is a standard treatment for HER2-positive breast cancer, but some patients are resistant to the therapy. Serum proteins have been used to predict therapeutic benefit for various cancers, but whether serum proteins can serve as biomarkers for HER2-positive breast cancer remains unclear. Using an isobaric Tandem Mass Tag (TMT) label-based quantitative proteomic, we discovered 18 differentially expressed proteins in the serum of trastuzumab-based therapy resistant patients before therapy. Then, four proteins were selected and validated using an LC-MS/MS-based multiple reaction monitoring quantification method, and it was confirmed that three proteins (SRGN, LDHA and CST3) were correlated with trastuzumab-based therapy resistance. Finally, the trastuzumab-based therapy resistance diagnostic score was calculated and acquired by means of a logistic regression pattern based on the level of these three proteins. In summary, we develop a serum-based protein signature that potentially predicts the therapeutic effects of trastuzumab-based therapy for HER2-positive breast cancer patients.

New Markers of Renal Failure in Multiple Myeloma and Monoclonal Gammopathies

* Correspondence: kasiajanda@op [...].

Proteomics and functional study reveal marginal zone B and B1 cell specific protein as a candidate marker of multiple myeloma

Multiple myeloma (MM) is a plasma cell‑associated cancer and accounts for 13% of all hematological malignancies, worldwide. MM still remains an incurable plasma cell malignancy with a poor prognosis due to a lack of suitable markers. Therefore, discovering novel markers and targets for diagnosis and therapeutics of MM is essential. The present study aims to identify markers associated with MM malignancy using patient‑derived MM mononuclear cells (MNCs). Label‑free quantitative proteomics analysis revealed a total of 192 differentially regulated proteins, in which 79 proteins were upregulated and 113 proteins were found to be downregulated in MM MNCs as compared to non‑hematological malignant samples. The identified differentially expressed candidate proteins were analyzed using various bioinformatics tools, including Ingenuity Pathway Analysis (IPA), Protein Analysis THrough Evolutionary Relationships (PANTHER), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Database for Annotation, Visualization and Integrated Discovery (DAVID) to determine their biological context. Among the 192 candidate proteins, marginal zone B and B1 cell specific protein (MZB1) was investigated in detail using the RPMI-8226 cell line model of MM. The functional studies revealed that higher expression of MZB1 is associated with promoting the progression of MM pathogenesis and could be established as a potential target for MM in the future.

Proteomics-inspired precision medicine for treating and understanding multiple myeloma

Introduction

Remarkable progress in molecular characterization methods has led to significant improvements in how we manage multiple myeloma (MM). The introduction of novel therapies has led to significant improvements in overall survival over the past 10 years. However, MM remains incurable and treatment choice is largely based on outdated risk-adaptive strategies that do not factor in improved treatment outcomes in the context of modern therapies.

Areas covered

This review discusses current risk-adaptive strategies in MM and the clinical application of proteomics in the monitoring of treatment response, disease progression, and minimal residual disease (MRD). We also discuss promising biomarkers of disease progression, treatment response, and chemoresistance. Finally, we will discuss an immunomics-based approach to monoclonal antibody (mAb), vaccine, and CAR-T cell development.

Expert opinion

It is an exciting era in oncology with basic scientific knowledge translating in novel therapeutic approaches to improve patient outcomes. With the advent of effective immunotherapies and targeted therapies, it has become crucial to identify biomarkers to aid in the stratification of patients based on anticipated sensitivity to chemotherapy. As a paradigm of diseases highly dependent on protein homeostasis, multiple myeloma provides the perfect opportunity to investigate the use of proteomics to aid in precision medicine.