Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer

Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting

BACKGROUND AND OBJECTIVE

Neoadjuvant chemotherapy results in various responses when used to treat locally advanced gastric cancer, we aimed to develop and validate a predictive model of the response to neoadjuvant chemotherapy in patients with gastric cancer.

METHODS

A total of 128 patients with locally advanced gastric cancer who underwent pre-treatment computed tomography (CT) scanning followed by neoadjuvant chemoradiotherapy were included (training cohort: n = 64; validation cohort: n = 64). We built a radiomics score combined with laboratory parameters to create a nomogram for predicting the efficacy of neoadjuvant chemotherapy and calculating scores for risk factors.

RESULTS

The radiomics score system demonstrated good stability and prediction performance for the response to neoadjuvant chemotherapy, with the area under the curve of the training and validation cohorts being 0.8 and 0.64, respectively. The radiomics score proved to be an independent risk factor affecting the efficacy of neoadjuvant chemotherapy. In addition to the radiomics score, four other risk factors were included in the nomogram, namely the platelet-to-lymphocyte ratio, total bilirubin, ALT/AST, and CA199. The model had a C-index of 0.8.

CONCLUSIONS

Our results indicated that radiomics features could be potential biomarkers for the early prediction of the response to neoadjuvant treatment.

Exploring Canine Mammary Cancer through Liquid Biopsy: Proteomic Profiling of Small Extracellular Vesicles

(Background). Canine mammary tumors (CMTs) have emerged as an important model for understanding pathophysiological aspects of human disease. Liquid biopsy (LB), which relies on blood-borne biomarkers and offers minimal invasiveness, holds promise for reflecting the disease status of patients. Small extracellular vesicles (SEVs) and their protein cargo have recently gained attention as potential tools for disease screening and monitoring. (Objectives). This study aimed to isolate SEVs from canine patients and analyze their proteomic profile to assess their diagnostic and prognostic potential. (Methods). Plasma samples were collected from female dogs grouped into CMT (malignant and benign), healthy controls, relapse, and remission groups. SEVs were isolated and characterized using ultracentrifugation (UC), nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM). Proteomic analysis of circulating SEVs was conducted using liquid chromatography-mass spectrometry (LC-MS). (Results). While no significant differences were observed in the concentration and size of exosomes among the studied groups, proteomic profiling revealed important variations. Mass spectrometry identified exclusive proteins that could serve as potential biomarkers for mammary cancer. These included Inter-alpha-trypsin inhibitor heavy chain (ITIH2 and ITI4), phosphopyruvate hydratase or alpha enolase (ENO1), eukaryotic translation elongation factor 2 (eEF2), actin (ACTB), transthyretin (TTR), beta-2-glycoprotein 1 (APOH) and gelsolin (GSN) found in female dogs with malignant tumors. Additionally, vitamin D-binding protein (VDBP), also known as group-specific component (GC), was identified as a protein present during remission. (Conclusions). The results underscore the potential of proteins found in SEVs as valuable biomarkers in CMTs. Despite the lack of differences in vesicle concentration and size between the groups, the analysis of protein content revealed promising markers with potential applications in CMT diagnosis and monitoring. These findings suggest a novel approach in the development of more precise and effective diagnostic tools for this challenging clinical condition.

Integrative proteome analysis of bone marrow interstitial fluid and serum reveals candidate signature for acute myeloid leukemia

Acute myeloid leukemia (AML) is an aggressive form of blood cancer and clinically highly heterogeneous characterized by the accumulation of clonally proliferative immature precursors of myeloid lineage leading to bone marrow failure. Although, the current diagnostic methods for AML consist of cytogenetic and molecular assessment, there is a need for new markers that can serve as useful candidates in diagnosis, prognosis and understanding the pathophysiology of the disease. This study involves the investigation of alterations in the bone marrow interstitial fluid and serum proteome of AML patients compared to controls using label-free quantitative proteomic approach. A total of 201 differentially abundant proteins were identified in AML BMIF, while in the case of serum 123 differentially abundant proteins were identified. The bioinformatics analysis performed using IPA revealed several altered pathways including FAK signalling, IL-12 signalling and production of macrophages etc. Verification experiments were performed in a fresh independent cohort of samples using MRM assays led to the identification of a panel of three proteins viz., PPBP, APOH, ENOA which were further validated in a new cohort of serum samples by ELISA. The three-protein panel could be helpful in the diagnosis, prognosis and understanding of the pathophysiology of AML in the future. BIOLOGICAL SIGNIFICANCE: Acute Myeloid Leukemia (AML) is a type haematological malignancy which constitute one third of total leukemias and it is the most common acute leukemia in adults. In the current clinical practice, the evaluation of diagnosis and progression of AML is largely based on morphologic, immunophenotypic, cytogenetic and molecular assessment. There is a need for new markers/signatures which can serve as useful candidates in diagnosis and prognosis. The present study aims to identify and validate candidate biosignature for AML which can be useful in diagnosis, prognosis and understand the pathophysiology of the disease. Here, we identified 201 altered proteins in AML BMIF and 123 in serum. Among these altered proteins, a set of three proteins viz., pro-platelet basic protein (CXCL7), enolase 1 (ENO1) and beta-2-glycoprotein 1 (APOH) were significantly increased in AML BMIF and serum suggest that this panel of proteins could help in future AML disease management and thereby improving the survival expectancy of AML patients.

Designing a predictive Framework: Immune-Related Gene-Based nomogram and prognostic model for kidney renal papillary cell carcinoma

BACKGROUND

Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients.

AIM

The aim of this study is to employ a comprehensive analysis of data for the identification of prognosis genes, leading to the development of a nomogram with strong predictive capabilities. The objective is to provide a valuable statistical tool that, when implemented in a clinical setting, can offer patients an early opportunity for treatment and enhance their chances of ultimate recovery from this life-threatening disease.

METHODS

Different packages in R were used to analyze RNA-seq data from the TCGA data portal. Multivariate Cox regression analysis and Kaplan-Meier analysis were also used to investigate the prognostic values of immune-related genes and construct the predictive model and nomogram. A p-value < 0.05 was considered to be significant.

RESULTS

A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway. 9 genes were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Immunohistochemistry results show that APOH, BIRC5, CCL19, and GRN were significantly increased in kidney cancer. B cells and CD4 + T cells were positively correlated with risk score model.

CONCLUSION

A prognostic model was successfully created based on 9 immune-related genes correlated with overall survival in KIRP. This work aims to provide some insight into therapeutic approaches and prognostic predictors of KIRP.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Nitrogen-enhanced carbon quantum dots mediated immunosensor for electrochemical detection of HER2 breast cancer biomarker

Using nitrogen-enhanced carbon quantum dots (N-CQDs) on a coated graphite sheet (GS) substrate (N-CQDs/GS), a simple strategy for the electrochemical detection of human epidermal growth factor receptor 2 (HER2), a breast cancer biomarker, was investigated. The bovine serum albumin (BSA)-modified HER2 Antibody/N-CQDs/GS immunoelectrode enabled excellent activity preservation for the biosensor, while the GS electrode provided a highly stable and conducting substrate. With a linear response range of 0.1 ng/mL-1 ng/mL and a low detection limit of 4.8 pg/mL. Meanwhile, the methodology demonstrated optimal specificity, stability, and reproducibility for detecting HER-2 protein in breast cancer patients untreated blood samples.

Deciphering a proteomic signature for the early detection of breast cancer from breast milk: the role of quantitative proteomics

INTRODUCTION

Breast cancer is one of the most prevalent cancers among women in the United States. Current research regarding breast milk has been focused on the composition and its role in infant growth and development. There is little information about the proteins, immune cells, and epithelial cells present in breast milk which can be indicative of the emergence of BC cells and tumors.

AREAS COVERED

We summarize all breast milk studies previously done in our group using proteomics. These studies include 1D-PAGE and 2D-PAGE analysis of breast milk samples, which include within woman and across woman comparisons to identify dysregulated proteins in breast milk and the roles of these proteins in both the development of BC and its diagnosis. Our projected outlook for the use of milk for cancer detection is also discussed.

EXPERT OPINION

Analyzing the samples by multiple methods allows one to interrogate a set of samples with various biochemical methods that complement each other, thus providing a more comprehensive proteome. Complementing methods like 1D-PAGE, 2D-PAGE, in-solution digestion and proteomics analysis with PTM-omics, peptidomics, degradomics, or interactomics will provide a better understanding of the dysregulated proteins, but also the modifications or interactions between these proteins.

Expression of Soluble Form of Aurora A as a Predictive Factor for Neoadjuvant Therapy in Breast Cancer Patients: A Single-Center Pilot Study

PURPOSE

To search for new predictive breast cancer biomarkers. We analyzed the serum concentrations of biomarkers involved in carcinogenesis, which can also be targeted by therapy.

METHODS

In a single-center prospective study, the serum levels of Aurora A, thymidine kinase 1, and human epidermal growth factor receptor type 3 (HER3) were determined in 119 women with BC before neoadjuvant treatment using ELISA kits.

RESULTS

The following clinical data were analyzed: age; TNM; the expression of ER, PGR, HER2, and Ki67; histological grade (G); and the response to neoadjuvant treatment (NAT) in the residual tumor burden classification (RCB). A complete pathological response (pCR) was achieved after NAT in 41 patients (34%). The highest proportion of the patients with a confirmed pCR was found for triple negative breast cancer (TNBC) (62.5%); non-luminal HER2-positive (52.6%) cancer subtypes (p = 0.0003); and in the G3 group (50%; p = 0.0078). The patients with higher levels of Aurora A were more likely to achieve pCR (p = 0.039). In the multivariate analysis, the serum Aurora A levels ≥ 4.75 ng/mL correlated with a higher rate of pCR (OR: 3.5; 95% CI: 1.2-10.1; p = 0.023).

CONCLUSIONS

We showed that in a biologically heterogeneous group of BC patients, the pretreatment serum Aurora A levels were of significant value in predicting the response to NAT.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Inside-Out of Complement in Cancer

The role of complement in cancer has received increasing attention over the last decade. Recent studies provide compelling evidence that complement accelerates cancer progression. Despite the pivotal role of complement in fighting microbes, complement seems to suppress antitumor immunity via regulation of host cell in the tumor microenvironment. Although most studies link complement in cancer to complement activation in the extracellular space, the discovery of intracellular activation of complement, raises the question: what is the relevance of this process for malignancy? Intracellular activation is pivotal for the survival of immune cells. Therefore, complement can be important for tumor cell survival and growth regardless of the role in immunosuppression. On the other hand, because intracellular complement (the complosome) is indispensable for activation of T cells, these functions will be essential for priming antitumor T cell responses. Here, we review functions of complement in cancer with the consideration of extra and intracellular pathways of complement activation and spatial distribution of complement proteins in tumors and periphery and provide our take on potential significance of complement as biomarker and target for cancer therapy.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Breast cancer in the era of integrating “Omics” approaches

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

C3aR Signaling Inhibits NK-cell Infiltration into the Tumor Microenvironment in Mouse Models

Many solid tumors have low levels of cytotoxic CD56^dim natural killer (NK) cells, suggesting that CD56^dim NK-cell exclusion from the tumor microenvironment (TME) contributes to the decreased response rate of immunotherapy. Complement component 3a (C3a) is known for its tumor-promoting and immunosuppressive roles in solid tumors. Previous reports have implicated the involvement of the C3a receptor (C3aR) in immune cell trafficking into the TME. C3aR is predominantly expressed on the surface of activated cytotoxic NK cells, but a specific role for C3aR in NK-cell biology has not been investigated. Because solid tumors generate elevated C3a and have decreased NK-cell infiltration, we hypothesized that C3aR might play a role in cytotoxic NK-cell recruitment into the TME. Our results indicate that blocking C3aR signaling in NK cells increased NK-cell infiltration into the TME in mouse models and led to tumor regression. Because the critical lymphocyte trafficking integrin LFA-1 orchestrates the migration of activated NK cells, we wanted to gain insight into the interaction between C3aR signaling and LFA-1. Our results demonstrated that direct interaction between C3aR and LFA-1, which led to a high-affinity LFA-1 conformation, decreased NK-cell infiltration into the TME. We propose that approaches to enhance cytotoxic NK-cell infiltration into the TME, through either disrupting C3a and C3aR interaction or inhibiting the formation of high-affinity LFA-1, represent a new strategy to improve the efficiency of immunotherapy for cancer treatment.

Survival analysis in breast cancer using proteomic data from four independent datasets

Breast cancer clinical treatment selection is based on the immunohistochemical determination of four protein biomarkers: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates. We searched studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysis were calculated to assess prognostic power. False Discovery Rate was computed to correct for multiple hypothesis testing. We established a database integrating protein expression data and survival information from four independent cohorts for 1229 breast cancer patients. In all four studies combined, a total of 7342 unique proteins were identified, and 1417 of these were identified in at least three datasets. ESR1, PGR, and HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p < 0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient = 0.17, p = 0.0399). An additional panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. Finally, we expanded our previously established web tool designed to validate survival-associated biomarkers by including the proteomic datasets analyzed in this study ( https://kmplot.com/ ). In summary, large proteomic studies now provide sufficient data enabling the validation and ranking of potential protein biomarkers.

Sequential Protein Capture in Multiplex Single Molecule Arrays: A Strategy for Eliminating Assay Cross-Reactivity

Measurements of multiple biomolecules within the same biological sample are important for many clinical applications to enable accurate disease diagnosis or classification. These disease-related biomarkers often exist at very low levels in biological fluids, necessitating ultrasensitive measurement methods. Single-molecule arrays (Simoa), a bead-based digital enzyme-linked immunosorbent assay, is the current state of the art for ultrasensitive protein detection and can detect sub-femtomolar protein concentrations, but its ability to achieve high-order multiplexing without cross-reactivity remains a challenge. Here, a sequential protein capture approach for multiplex Simoa assays is implemented to eliminate cross-reactivity between binding reagents by sequentially capturing each protein analyte and then incubating each capture bead with only its corresponding detection antibody. This strategy not only reduces cross-reactivity to background levels and significantly improves measurement accuracies, but also enables higher-order multiplexing. As a proof of concept, the sequential multiplex Simoa assay is used to measure five different cytokines in plasma samples from Coronavirus Disease 2019 (COVID-19) patients. The ultrasensitive sequential multiplex Simoa assays will enable the simultaneous measurements of multiple low-abundance analytes in a time- and cost-effective manner and will prove especially critical in many cases where sample volumes are limited.

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.

An Immunoregulatory Role for Complement Receptors in Murine Models of Breast Cancer

The complement system has demonstrated roles in regulating tumor growth, although these may differ between tumor types. The current study used two murine breast cancer models (EMT6 and 4T1) to investigate whether pharmacological targeting of receptors for complement proteins C3a (C3aR) and C5a (C5aR1) is protective in murine breast cancer models. In contrast to prior studies in other tumor models, treatment with the selective C5aR1 antagonist PMX53 had no effect on tumor growth. However, treatment of mice with a dual C3aR/C5aR1 agonist (YSFKPMPLaR) significantly slowed mammary tumor development and progression. Examination of receptor expression by quantitative polymerase chain reaction (qPCR) analysis showed very low levels of mRNA expression for either C3aR or C5aR1 by EMT6 or 4T1 mammary carcinoma cell lines compared with the J774 macrophage line or bone marrow-derived macrophages. Moreover, flow cytometric analysis found no evidence of C3aR or C5aR1 protein expression by either EMT6 or 4T1 cells, leading us to hypothesize that the tumor inhibitory effects of the dual agonist are indirect, possibly via regulation of the anti-tumor immune response. This hypothesis was supported by flow cytometric analysis of tumor infiltrating leukocyte populations, which demonstrated a significant increase in T lymphocytes in mice treated with the C3aR/C5aR1 agonist. These results support an immunoregulatory role for complement receptors in primary murine mammary carcinoma models. They also suggest that complement activation peptides can influence the anti-tumor response in different ways depending on the cancer type, the host immune response to the tumor and levels of endogenous complement activation within the tumor microenvironment.

Immune-related protein signature in serum stratify relapsed mantle cell lymphoma patients based on risk

Background

Response to modern treatment strategies, which combine cytotoxic compounds with immune stimulatory agents and targeted treatment is highly variable among MCL patients. Thus, providing prognostic and predictive markers for risk adapted therapy is warranted and molecular information that can help in patient stratification is a necessity. In relapsed MCL, biopsies are rarely available and molecular information from tumor tissue is often lacking. Today, the main tool to access risk is the MCL international prognostic index (MIPI), which does not include detailed biological information of relevance for different treatment options. To enable continuous monitoring of patients, non-invasive companion diagnostic tools are needed which can further reduce cost and patient distress and enable efficient measurements of biological markers.

Methods

We have assessed if serum-based protein profiling can identify immune related proteins that stratify relapsed MCL patients based on risk. Overall, 371 scFv targeting 158 proteins were assessed using an antibody microarray platform. We profiled patients (n = 44) who had been treated within the MCL6-Philemon trial combining targeted and immune-modulatory treatment.

Results

The downstream processing led to the identification of the relapsed immune signature (RIS) consisting of 11 proteins with potential to stratify patients with long and short overall survival (OS). Moreover, in this population, MIPI alone failed to separate high, intermediate and low risk patients, but a combined index based on MIPI together with RIS, MIPI_ris, showed improved performance and significantly stratified all three risk groups based on OS.

Conclusions

Our results show that addition of biological parameters to previous prognostic indices improves patient stratification among patients treated with BTK inhibitor triplet combination, particularly, in the identification of an extreme high risk group.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07678-4.

Apolipoprotein mimetics in cancer

Peptides have many advantages over traditional therapeutics, including small molecules and other biologics, because of their low toxicity and immunogenicity, while still exhibiting efficacy. This review discusses the benefits and mechanism of action of apolipoprotein mimetic peptides in tumor biology and their potential utility in treating various cancers. Among lipoproteins in the circulation, high-density lipoprotein (HDL) and its constituents including apolipoprotein A-I (apoA-I; the predominant protein in HDL), apoJ, and apoE, harbor anti-tumorigenic activities. Peptides that mimic apoA-I function have been developed through molecular mimicry of the amphipathic α-helices of apoA-I. Oral apoA-I mimetic peptides remodel HDL, promote cholesterol efflux, sequester oxidized lipids, and activate anti-inflammatory processes. ApoA-I and apoJ mimetic peptides ameliorate various metrics of cancer progression and have demonstrated efficacy in preclinical models in the inhibition of ovarian, colon, breast, and metastatic lung cancers. Apolipoprotein mimetic peptides are poorly absorbed when administered orally and rapidly degraded when injected into the circulation. The small intestine is the major site of action for apoA-I mimetic peptides and recent studies suggest that modulation of immune cells in the lamina propria of the small intestine is, in part, a potential mechanism of action. Finally, several recent studies underscore the use of reconstituted HDL as target-specific nanoparticles carrying poorly soluble or unstable therapeutics to tumors even across the blood-brain barrier. Preclinical studies suggest that these versatile recombinant lipoprotein based nanoparticles and apolipoprotein mimetics can serve as safe, novel drug delivery, and therapeutic agents for the treatment of a number of cancers.

Complement System: Promoter or Suppressor of Cancer Progression?

Constituent of innate immunity, complement is present in the tumor microenvironment. The functions of complement include clearance of pathogens and maintenance of homeostasis, and as such could contribute to an anti-tumoral role in the context of certain cancers. However, multiple lines of evidence show that in many cancers, complement has pro-tumoral actions. The large number of complement molecules (over 30), the diversity of their functions (related or not to the complement cascade), and the variety of cancer types make the complement-cancer topic a very complex matter that has just started to be unraveled. With this review we highlight the context-dependent role of complement in cancer. Recent studies revealed that depending of the cancer type, complement can be pro or anti-tumoral and, even for the same type of cancer, different models presented opposite effects. We aim to clarify the current knowledge of the role of complement in human cancers and the insights from mouse models. Using our classification of human cancers based on the prognostic impact of the overexpression of complement genes, we emphasize the strong potential for therapeutic targeting the complement system in selected subgroups of cancer patients.

Serum proteomics analysis of feline mammary carcinoma based on label-free and PRM techniques

Background

Feline mammary carcinoma is the third most common cancer that affects female cats.

Objectives

The purpose of this study was to screen differential serum proteins in feline and clarify the relationship between them and the occurrence of feline mammary carcinoma.

Methods

Chinese pastoral cats were used as experimental animals. Six serum samples from cats with mammary carcinoma (group T) and six serum samples from healthy cats (group C) were selected. Differential protein analysis was performed using a Label-free technique, while parallel reaction monitoring (PRM) was performed to verify the screened differential proteins.

Results

A total of 82 differential proteins were detected between group T and group C, of which 55 proteins were down regulated and 27 proteins were up regulated. Apolipoprotein A-I, Apolipoprotein A-II (ApoA-II), Apolipoprotein B (ApoB), Apolipoprotein C-III (ApoC-III), coagulation factor V, coagulation factor X, C1q, albumen (ALB) were all associated with the occurrence of feline mammary carcinoma. Differential proteins were involved in a total of 40 signaling pathways, among which the metabolic pathways associated with feline mammary carcinoma were the complement and coagulation cascade and cholesterol metabolism. According to the Label-free results, ApoB, ApoC-III, ApoA-II, FN1, an uncharacterized protein, and ALB were selected for PRM target verification. The results were consistent with the trend of the label-free.

Conclusions

This experimen is the first to confirm ApoA-II and ApoB maybe new feline mammary carcinoma biomarkers and to analyze their mechanisms in the development of such carcinoma in feline.

Novel serum peptide model revealed by MALDI-TOF-MS and its diagnostic value in early bladder cancer

BACKGROUND

Bladder cancer is the ninth most common cancer worldwide and has high morbidity and mortality. We aimed to search for potential serum peptide biomarkers and establish a diagnostic model for early bladder cancer.

METHODS

A total of 67 bladder cancer patients and 64 healthy volunteers were randomly divided into a training set and testing set 1. There were 30 hematuria patients used as testing set 2. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry based on weak cation exchange magnetic beads was used to obtain and analyze the serum peptide profiles between bladder cancer patients and healthy volunteers in the training set. Serum peptide diagnostic model through a k-nearest neighbor algorithm, was established and validated, and significantly differentially expressed protein biomarkers were ultimately identified.

RESULTS

We constructed a diagnostic model containing five peptides (m/z 1954.9, m/z 2081.0, m/z 3938.3, m/z 3946.5, and m/z 4268.8). In the training set, the area under the curve (AUC) value of the five-peptide model was 0.923, and the sensitivity and specificity was 93.75% and 96.77%, respectively. In testing set 1, the sensitivity and specificity was 91.43% and 90.91%, respectively, and the specificity of testing set 2 was 73.33%. For early-stage bladder cancer, the sensitivity and specificity was 92.31% and 93.75%, respectively; the sensitivity of early low-grade bladder cancer was 90.00%; and the AUC value was 0.944.

CONCLUSION

The five-peptide diagnostic model established in this study had high sensitivity and specificity, especially in the diagnosis of early bladder cancer, and could differentiate between healthy volunteers and hematuria patients.

Apolipoprotein C1: Its Pleiotropic Effects in Lipid Metabolism and Beyond

Apolipoprotein C1 (apoC1), the smallest of all apolipoproteins, participates in lipid transport and metabolism. In humans, APOC1 gene is in linkage disequilibrium with APOE gene on chromosome 19, a proximity that spurred its investigation. Apolipoprotein C1 associates with triglyceride-rich lipoproteins and HDL and exchanges between lipoprotein classes. These interactions occur via amphipathic helix motifs, as demonstrated by biophysical studies on the wild-type polypeptide and representative mutants. Apolipoprotein C1 acts on lipoprotein receptors by inhibiting binding mediated by apolipoprotein E, and modulating the activities of several enzymes. Thus, apoC1 downregulates lipoprotein lipase, hepatic lipase, phospholipase A2, cholesterylester transfer protein, and activates lecithin-cholesterol acyl transferase. By controlling the plasma levels of lipids, apoC1 relates directly to cardiovascular physiology, but its activity extends beyond, to inflammation and immunity, sepsis, diabetes, cancer, viral infectivity, and-not last-to cognition. Such correlations were established based on studies using transgenic mice, associated in the recent years with GWAS, transcriptomic and proteomic analyses. The presence of a duplicate gene, pseudogene APOC1P, stimulated evolutionary studies and more recently, the regulatory properties of the corresponding non-coding RNA are steadily emerging. Nonetheless, this prototypical apolipoprotein is still underexplored and deserves further research for understanding its physiology and exploiting its therapeutic potential.

Apolipoprotein A-I (ApoA-I), Immunity, Inflammation and Cancer

Apolipoprotein A-I (ApoA-I), the major protein component of high-density lipoproteins (HDL) is a multifunctional protein, involved in cholesterol traffic and inflammatory and immune response regulation. Many studies revealing alterations of ApoA-I during the development and progression of various types of cancer suggest that serum ApoA-I levels may represent a useful biomarker contributing to better estimation of cancer risk, early cancer diagnosis, follow up, and prognosis stratification of cancer patients. In addition, recent in vitro and animal studies disclose a more direct, tumor suppressive role of ApoA-I in cancer pathogenesis, which involves anti-inflammatory and immune-modulatory mechanisms. Herein, we review recent epidemiologic, clinicopathologic, and mechanistic studies investigating the role of ApoA-I in cancer biology, which suggest that enhancing the tumor suppressive activity of ApoA-I may contribute to better cancer prevention and treatment.

Serum Proteomic Signatures of Male Breast Cancer

BACKGROUND

To date, the elucidation of serum protein alterations in male breast cancer (MBC) has not been extensively studied, due to the rarity of the disease.

MATERIALS AND METHODS

In the present work, two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) were employed to detect differences in serum protein expression between patients with MBC and healthy controls.

RESULTS

A panel of differentially expressed serum proteins was identified, including proteins involved in the regulation of the cell cycle [e.g. cell division cycle 7-related protein kinase (CDC7)], in mitochondrial function [e.g. mitochondrial aldehyde dehydrogenase (ALDH2) and dimethyladenosine transferase 1 (TFB1M)], in lipid metabolism and transport [e.g. apolipoprotein A-I (APOA1) and E (APOE)], in apoptosis and immune response [e.g. CD5 antigen-like (CD5L), clusterin (CLUS) and C-C motif chemokine 14 (CCL14)], in transcription (e.g. protein SSX3 and signal transducer and activator of transcription 3 (STAT3)], in invasion and metastasis (e.g. alpha-2-HS-glycoprotein (FETUA)], in estrogen synthesis [aromatase (CYP19A1)] and other diverse biological roles [e.g. actin-related protein 2/3 complex subunit 4 (ARPC4), dual specificity mitogen-activated protein kinase kinase 4 (MP2K4), ectoderm-neural cortex protein 1 (ENC1), and matrix metalloproteinase-27 (MMP27)].

CONCLUSION

These findings provide valuable insight into the distinct clinicopathological features of MBC and indicate that select serum proteomic markers may help improve MBC management.

Ibudilast sensitizes glioblastoma to temozolomide by targeting Macrophage Migration Inhibitory Factor (MIF)

Recurrence in patients with glioblastoma (GBM) is inevitable resulting in short survival times, even in patients with O-6-Methylguanine-DNA Methyltransferase (MGMT) methylation. Other pathways must be activated to escape from temozolomide (TMZ) treatment, however acquired resistance mechanisms to TMZ are not well understood. Herein, frozen tumors from 36 MGMT methylated patients grouped according to overall survival were extracted and proteins were profiled using surface-enhanced laser desorption/ionization (SELDI) with time-of flight (TOF) proteomics to identify low molecular weight proteins that associated with poor survival outcomes. Overexpression of macrophage migration inhibitory factor (MIF) was identified in human GBM specimens that were MGMT methylated but showed poor survival. This correlation was confirmed in an independent cohort of human GBM. MIF overexpression has been reported in several cancer types, including GBM. We repurposed ibudilast, a specific MIF inhibitor, and treated patient derived cell lines. Ibudilast showed modest anti-proliferative activity however, when combined with TMZ, significant synergism was observed, resulting in cell cycle arrest and apoptosis. In vivo, combined ibudilast and TMZ treatment of a patient derived xenograft (PDX) model resulted in significantly longer overall survival. Our findings have significant clinical implications for people with GBM. Since clinical trials involving ibudilast have shown no adverse side effects and the drug readily penetrates the blood brain barrier, treatment of GBM with this combination is clinically achievable.

MALDI-TOF-MS Analysis in the Identification of Urine Proteomic Patterns of Gestational Trophoblastic Disease

Gestational trophoblastic disease (GTD) is a group of highly aggressive, rare tumors. Human chorionic gonadotropin is a common biomarker used in the diagnosis and monitoring of GTD. To improve our knowledge of the pathology of GTD, we performed protein-peptide profiling on the urine of patients affected with gestational trophoblastic neoplasm (GTN). We analyzed urine samples from patients diagnosed with GTN (n = 26) and from healthy pregnant and non-pregnant controls (n = 17) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Ions were examined in a linear mode over a m/z range of 1000–10,000. All GTN urine samples were analyzed before and after treatment and compared with those of the controls. The statistical analyses included multivariate classification algorithms as well as ROC curves. Urine sample analyses revealed there were significant differences in the composition of the ions between the evaluated groups. Comparing the pre-treatment and group with the pregnant controls, we identified two discriminatory proteins: hemoglobin subunit α (m/z = 1951.81) and complement C4A (m/z = 1895.43). Then, comparing urine samples from the post-treatment cases with those from the non-pregnant controls, we identified the peptides uromodulin fragments (m/z = 1682.34 and 1913.54) and complement C4A (m/z = 1895.43).

High-Density Lipoprotein Components and Functionality in Cancer: State-of-the-Art

Cancer is the second leading cause of death in western countries, and thus represents a major global public health issue. Whilst it is well-recognized that diet, obesity, and smoking are risk factors for cancer, the role of low levels of high-density lipoprotein cholesterol (HDL-C) in cancer is less well appreciated. Conflicting evidence suggests that serum HDL-C levels may be either positively or negatively associated with cancer incidence and mortality. Such disparate associations are supported in part by the multitude of high-density lipoprotein (HDL) functions that can all have an impact on cancer cell biology. The aim of this review is to provide a comprehensive overview of the crosstalk between HDLs and cancer, focusing on the molecular mechanisms underlying this association.

Systematic Glycolytic Enzyme Activity Analysis from Human Serum with PEP Technology

A functional proteomics technology was used to systematically monitor metabolic enzyme activities from resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP. Both qualitative and quantitative differences in the metabolic enzyme activity could be detected between cancer patient and control group, providing excellent biomarker candidates for cancer diagnosis and drug development. This technology has a wide range of applications; it can be used for rapid functional protein purification and characterization as well as drug target identification and validation. The ability for the PEP technology to efficiently separate and recover functional proteins makes it useful for the analysis of any proteins and its variants; this is especially advantageous for enzyme families with large number of enzymes such as protein kinases, phosphatases, proteases, and metabolic enzymes.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Blood-based protein biomarkers in breast cancer

Breast cancer (BCa) is a significant healthcare problem on women worldwide. Thus, early detection is very important to reduce mortality. Furthermore, better BCa prognosis could improve selection of patients eligible for adjuvant therapy. New markers for early diagnosis, accurate prognosis and prediction of response to treatment are necessary to improve BCa care. The present review summarizes important aspects of the potential usefulness of modern technologies, strategies, and scientific findings in proteomic research for discovery of breast cancer-associated blood-based protein biomarkers in the clinic.

Proteomics analysis of human breast milk to assess breast cancer risk

Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy-associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non-invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)-based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from eight women provided five paired-groups (cancer versus control) for analysis of dysregulatedproteins: two within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and three across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the five comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research. Data are available via ProteomeXchange with identifier PXD007066.

Overexpression of the MRE11-RAD50-NBS1 (MRN) complex in rectal cancer correlates with poor response to neoadjuvant radiotherapy and prognosis

BACKGROUND

The MRE11/RAD50/NBS1 (MRN) complex plays an essential role in detecting and repairing double-stranded breaks, and thus the potential roles of MRE11, RAD50 and NBS1 proteins in the pathogenesis of various cancers is the subject of investigation. This study was aimed at assessing the three-protein panel of MRN complex subunits as a potential radiosensitivity marker and evaluating the prognostic and clinicopathological implications of MRN expression in rectal cancer.

METHODS

Samples from 265 rectal cancer patients treated with surgery and adjuvant chemoradiotherapy, including samples from 55 patients who were treated with neoadjuvant radiotherapy between 2000 and 2011, were analyzed. Expression of MRN complex proteins in tissue samples was determined by immunohistochemistry. Univariate and multivariate analyses were carried out to identify clinicopathological characteristics that are associated with the MRN three-protein panel expression in rectal cancer samples.

RESULTS

In Kaplan-Meier survival analyses, we found that high level expression of MRN complex proteins in postoperative samples was associated with poor disease-free (p = 0.021) and overall (P = 0.002) survival. Interestingly, high MRN expression also correlated with poor disease-free (P = 0.047) and overall (P = 0.024) survival in the neoadjuvant radiotherapy subgroup. In multivariate analysis, combined MRN expression (hazard ratio = 2.114, 95% confidence interval 1.096-4.078, P = 0.026) and perineural invasion (hazard ratio = 2.160, 95% confidence interval 1.209-3.859, P = 0.009) were significantly associated with a worse disease-free survival.

CONCLUSIONS

Expression levels of MRN complex proteins significantly predict disease-free survival in rectal cancer patients, including those treated with neoadjuvant radiotherapy, and may have value in the management of these patients.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation

Blood proteome analysis for biomarker discovery represents one of the most challenging tasks to be achieved through clinical proteomics due to the sample complexity, such as the extreme heterogeneity of proteins in very dynamic concentrations, and to the observation of proper sampling and storage conditions. Quantitative and qualitative proteomics profiling of plasma and serum could be useful both for the early detection of diseases and for the evaluation of pathological status. Two main sources of variability can affect the precision and accuracy of the quantitative experiments designed for biomarker discovery and validation. These sources are divided into two categories, pre-analytical and analytical, and are often ignored; however, they can contribute to consistent errors and misunderstanding in biomarker research. In this chapter, we review critical pre-analytical and analytical variables that can influence quantitative proteomics. According to guidelines accepted by proteomics community, we propose some recommendations and strategies for a proper proteomics analysis addressed to biomarker studies.

PDLIM5 identified by label-free quantitative proteomics as a potential novel biomarker of papillary thyroid carcinoma

In order to better understand the mechanisms underlying the development of papillary thyroid carcinoma (PTC), and to identify new potential biomarkers, high-resolution label-free mass spectrometry was performed on PTC tissues and adjacent normal thyroid tissues from six patients. In this process, 2788 proteins were identified, out of which 49 proteins presented significant differences between PTC tissues and adjacent normal thyroid tissues. Gene ontology revealed that the majority of these proteins are involved in the catalytic activity and binding. We selected three proteins with differential expressions: PDZ and LIM domain 5 (PDLIM5), PDLIM1 and ALDH1A1; Protein expressions were further verified by RT-PCR and western blot. Among these, expression of PDLIM5 and PDLIM1 was up-regulated, while that of ALDH1A1 was down-regulated in PTC tissues. Next, we confirmed their expression through quantitative dot blot (QDB) technique. We found that knockdown of PDLIM5 expression in the B-CPAP cell line could inhibit the migration, invasion and proliferation of PTC cells. In addition, PDLIM5 knockdown reduced Ras and Phospho-ERK1/2 expression. Thus, we suggested that PDLIM5 promotes PTC via activation of the Ras-ERK pathway. Our research provides new molecular insight into the function of PDLIM5, which may assist in studying the mechanism of PTC. In addition, PDLIM5 could be further explored as a potential candidate for PTC treatment.

Identification of Prognostic and Susceptibility Markers in Chronic Myeloid Leukemia Using Next Generation Sequencing

BACKGROUND

Incidence of Chronic Myeloid Leukemia (CML) is continuously increasing and expected to reach 100,000 patients every year by 2030. Though the discovery of Imatinib Mesylate (IM) has brought a paradigm shift in CML treatment, 20% patients show resistance to this tyrosine kinase inhibiter (TKI). Therefore, it is important to identify markers, which can predict the occurrence and prognosis of CML. Clinical Exome Sequencing, panel of more than 4800 genes, was performed in CML patients to identify prognostic and susceptibility markers in CML.

METHODS

Enrolled CML patients (n=18) were segregated as IM responders (n=10) and IM failures (n=8) as per European Leukemia Net (ELN), 2013 guidelines. Healthy controls (n=5) were also enrolled. DNA from blood of subjects was subjected to Next Generation Sequencing. Rare mutations present in one patient group and absent in another group were considered as prognostic markers, whereas mutations present in more than 50% patients were considered as susceptibility markers.

RESULT

Mutations in genes associated with cancer related functions were found in different patient groups. Four variants: rs116201358, rs4014596, rs52897880 and rs2274329 in C8A, UNC93B1, APOH and CA6 genes, respectively, were present in IM responders; whereas rs4945 in MFGE8 was present in IM failures. Mutations in HLA-DRB1 (rs17878951), HLA-DRB5 (rs137863146), RPHN2 (rs193179333), CYP2F1 (rs116958555), KCNJ12 (rs76684759) and FUT3 (rs151218854) were present as susceptibility markers.

CONCLUSION

The potential genetic markers discovered in this study can help in predicting response to IM as frontline therapy. Susceptibility markers may also be used as panel for individuals prone to have CML.

Salivary Glycopatterns as Potential Biomarkers for Screening of Early-Stage Breast Cancer

OBJECTIVE

We systematically investigated and assessed the alterations of salivary glycopatterns and possibility as biomarkers for diagnosis of early-stage breast cancer.

DESIGN

Alterations of salivary glycopatterns were probed using lectin microarrays and blotting analysis from 337 patients with breast benign cyst or tumor (BB) or breast cancer (I/II stage) and 110 healthy humans. Their diagnostic models were constructed by a logistic stepwise regression in the retrospective cohort. Then, the performance of the diagnostic models were assessed by ROC analysis in the validation cohort. Finally, a double-blind cohort was tested to confirm the application potential of the diagnostic models.

RESULTS

The diagnostic models were constructed based on 9 candidate lectins (e.g., PHA-E+L, BS-I, and NPA) that exhibited significant alterations of salivary glycopatterns, which achieved better diagnostic powers with an AUC value >0.750 (p<0.001) for the diagnosis of BB (AUC: 0.752, sensitivity: 0.600, and specificity: 0.835) and I stage breast cancer (AUC: 0.755, sensitivity: 0.733, and specificity: 0.742) in the validation cohort. The diagnostic model of I stage breast cancer exhibited a high accuracy of 0.902 in double-blind cohort.

CONCLUSIONS

This study could contribute to the screening for patients with early-stage breast cancer based on precise alterations of salivary glycopatterns.

An efficient biomarker panel for diagnosis of breast cancer using surface-enhanced laser desorption ionization time-of-flight mass spectrometry

Breast cancer (BC) is the most frequently diagnosed cancer that affects women worldwide. Early detection of BC is important to improve survival rates and decrease mortality. The aim of the present study was to investigate serum biomarkers using surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to distinguish patients with BC from the healthy population and patients with benign breast diseases (BBDs). A total of 62 patients with invasive ductal carcinoma, as confirmed by histopathology, and 47 non-cancerous individuals (NCIs) [16 healthy controls (HCs) and 31 patients with BBD] were enrolled in the present study. Serum protein profiles were determined by SELDI-TOF-MS using an immobilized metal affinity capture array. Serum from patients with BC were compared with that from the HC group using univariate and multivariate statistical analyses. A total of 118 clusters were generated from the individual serum. Univariate analysis revealed that 5 peaks were significantly downregulated (m/z 1,452, 2,670, 3,972, 5,354 and 5,523; P<0.001) and 4 were upregulated (m/z 6,850, 7,926, 8,115 and 8,143; P<0.001) in patients with BC compared with the HC group. A comparison of patients with BC and patients with BBD revealed an additional 9 protein peaks. Among these, 3 peaks (m/z 3,972, 5,336 and 11,185) were significantly downregulated and 6 peaks (m/z 4,062, 4,071, 4,609, 6,850, 8,115 and 8,133) were significantly upregulated. A total of 3 peaks [mass-to-change ratio (m/z) 3,972, 6,850 and 8,115 (BC2)] were common in both sets. The results of the present study suggest that a 4 protein peak set [m/z 3,972, 6,850 and 8,115 (BC2) and 8,949 (BC3)] could be used to distinguish patients with BC from NCI.

An ultrasensitive detection of miRNA-155 in breast cancer via direct hybridization assay using two-dimensional molybdenum disulfide field-effect transistor biosensor

MicroRNAs (miRNAs), critical biomarkers of acute and chronic diseases, play key regulatory roles in many biological processes. As a result, robust assay platforms to enable an accurate and efficient detection of low-level miRNAs in complex biological samples are of great significance. In this work, a label-free and direct hybridization assay using molybdenum disulfide (MoS₂) field-effect transistor (FET) biosensor has been developed for ultrasensitive detection of miRNA-155 as a breast cancer biomarker in human serum and cell-line samples. MoS₂, the novel 2D layered material with excellent physical and chemical properties, was prepared through sequential solvent exchange method and was used as an active channel material. MoS₂ was comprehensively characterized by spectroscopic and microscopic methods and it was applied for fabrication of FET device by drop-casting MoS₂ flacks suspension onto the FET surface. MoS₂ FET device showed a relatively low subthreshold swing of 48.10mV/decade and a high mobility of 1.98 × 10³cm²V^-1s^-1. Subsequently, probe miRNA-155 strands were immobilized on the surface of the MoS₂ FET device. Under optimized conditions detection limit of 0.03fM and concentration range 0.1fM to 10nM were achieved. The developed biosensor not only was capable to identification of fully matched versus one-base mismatch miRNA-155 sequence, but also it could detect target miRNA-155 in spiked real human serum and extracts from human breast cancer cell-line samples. This approach paves a way for label-free, early detection of miRNA as a biomarker in cancer diagnostics with very high sensitivity and good specificity, thus offering a significant potential for clinical application.

Research progresses on the functional polypeptides in the detection and imaging of breast cancer

Polypeptides as functional groups continue to garner significant interest in the detection and imaging of breast cancer, working as recognition elements, signal sources, building blocks and therapeutic reagents, etc.

Complement in cancer: untangling an intricate relationship

In tumour immunology, complement has traditionally been considered as an adjunctive component that enhances the cytolytic effects of antibody-based immunotherapies, such as rituximab. Remarkably, research in the past decade has uncovered novel molecular mechanisms linking imbalanced complement activation in the tumour microenvironment with inflammation and suppression of antitumour immune responses. These findings have prompted new interest in manipulating the complement system for cancer therapy. This Review summarizes our current understanding of complement-mediated effector functions in the tumour microenvironment, focusing on how complement activation can act as a negative or positive regulator of tumorigenesis. It also offers insight into clinical aspects, including the feasibility of using complement biomarkers for cancer diagnosis and the use of complement inhibitors during cancer treatment.

Complement anaphylatoxins C3a and C5a: Emerging roles in cancer progression and treatment

Recent insights into the role of complement anaphylatoxins C3a and C5a in cancer provide new opportunities for the development of innovative biomarkers and therapeutic strategies. These two complement activation products can maintain chronic inflammation, promote an immunosuppressive microenvironment, induce angiogenesis, and increase the motility and metastatic potential of cancer cells. Still, the diverse heterogeneity of responses mediated by these peptides poses a challenge both to our understanding of the role played by these molecules in cancer progression and to the development of effective treatments. This review attempts to summarize the evidence surrounding the involvement of anaphylatoxins in the biological contexts associated with tumor progression. We also describe the recent developments that support the inhibition of anaphylatoxins, or their cognate receptors C3aR and C5aR1, as a treatment option for maximizing the clinical efficacy of current immunotherapies that target the PD-1/PD-L1 immune checkpoint.

Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using SILAC-SPROX

Proteomic methods for disease state characterization and biomarker discovery have traditionally utilized quantitative mass spectrometry methods to identify proteins with altered expression levels in disease states. Here we report on the large-scale use of protein folding stability measurements to characterize different subtypes of breast cancer using the stable isotope labeling with amino acids in cell culture and stability of proteins from rates of oxidation (SILAC-SPROX) technique. Protein folding stability differences were studied in a comparison of two luminal breast cancer subtypes, luminal-A and -B (i.e., MCF-7 and BT-474 cells, respectively), and in a comparison of a luminal-A and basal subtype of the disease (i.e., MCF-7 and MDA-MB-468 cells, respectively). The 242 and 445 protein hits identified with altered stabilities in these comparative analyses included a large fraction with no significant expression level changes. This suggests thermodynamic stability measurements create a new avenue for protein biomarker discovery. A number of the identified protein hits are known from other biochemical studies to play a role in tumorigenesis and cancer progression. This not only substantiates the biological significance of the protein hits identified using the SILAC-SPROX approach, but it also helps elucidate the molecular basis for their disregulation and/or disfunction in cancer.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery

BACKGROUND

Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.

METHODS

Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.

RESULTS

A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).

CONCLUSIONS

The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Identification of potential serum biomarkers for breast cancer using a functional proteomics technology

BACKGROUND

Cancer is a genetic disease; its development and metastasis depend on the function of many proteins. Human serum contains thousands of proteins; it is a window for the homeostasis of individual's health. Many of the proteins found in the human serum could be potential biomarkers for cancer early detection and drug efficacy evaluation.

METHODS

In this study, a functional proteomics technology was used to systematically monitor metabolic enzyme and protease activities from resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP. All the experiments were repeated at least twice to ensure the validity of the findings.

RESULTS

For the first time, significant differences were found between breast cancer patient serum and normal serum in two families of enzymes known to be involved in cancer development and metastasis: metabolic enzymes and proteases. Multiple enzyme species were identified in the serum assayed directly or after enrichment. Both qualitative and quantitative differences in the metabolic enzyme and protease activity were detected between breast cancer patient and control group, providing excellent biomarker candidates for breast cancer diagnosis and drug development.

CONCLUSIONS

This study identified several potential functional protein biomarkers from breast cancer patient serum. It also demonstrated that the functional proteomics technology, PEP, can be applied to the analysis of any functional proteins in human serum which contains thousands of proteins. The study indicated that the functional domain of the human serum could be unlocked with the PEP technology, pointing to a novel alternative for the development of diagnosis biomarkers for breast cancer and other diseases.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.