Oncoproteomics: Current status and future opportunities

Extracellular vesicles associated microRNAs: Their biology and clinical significance as biomarkers in gastrointestinal cancers

Gastrointestinal (GI) cancers, including colorectal, gastric, esophageal, pancreatic, and liver, are associated with high mortality and morbidity rates worldwide. One of the underlying reasons for the poor survival outcomes in patients with these malignancies is late disease detection, typically when the tumor has already advanced and potentially spread to distant organs. Increasing evidence indicates that earlier detection of these cancers is associated with improved survival outcomes and, in some cases, allows curative treatments. Consequently, there is a growing interest in the development of molecular biomarkers that offer promise for screening, diagnosis, treatment selection, response assessment, and predicting the prognosis of these cancers. Extracellular vesicles (EVs) are membranous vesicles released from cells containing a repertoire of biological molecules, including nucleic acids, proteins, lipids, and carbohydrates. MicroRNAs (miRNAs) are the most extensively studied non-coding RNAs, and the deregulation of miRNA levels is a feature of cancer cells. EVs miRNAs can serve as messengers for facilitating interactions between tumor cells and the cellular milieu, including immune cells, endothelial cells, and other tumor cells. Furthermore, recent years have witnessed considerable technological advances that have permitted in-depth sequence profiling of these small non-coding RNAs within EVs for their development as promising cancer biomarkers -particularly non-invasive, liquid biopsy markers in various cancers, including GI cancers. Herein, we summarize and discuss the roles of EV-associated miRNAs as they play a seminal role in GI cancer progression, as well as their promising translational and clinical potential as cancer biomarkers as we usher into the area of precision oncology.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.

Integrating analysis of proteome profile and drug screening identifies therapeutic potential of MET pathway for the treatment of malignant peripheral nerve sheath tumor

BACKGROUND

Malignant peripheral nerve sheath tumor (MPNST) is an aggressive sarcoma with a poor prognosis that requires novel therapeutic agents. Proteome information is useful for identifying new therapeutic candidates because it directly reflects the biological phenotype. Additionally, in vitro drug screening is an effective tool to identify candidate drugs for common cancers. Hence, we attempted to identify novel therapeutic candidates for MPNST by integrating proteomic analysis and drug screening.

METHODS

We performed comprehensive proteomic analysis on 23 MPNST tumor samples using liquid chromatography-tandem mass spectrometry to identify therapeutic targets. We also conducted drug screening of six MPNST cell lines using 214 drugs.

RESULTS

Proteomic analysis revealed that the MET and IGF pathways were significantly enriched in the local recurrence/distant metastasis group of MPNST, whereas drug screening revealed that 24 drugs showed remarkable antitumor effects on the MPNST cell lines. By integrating the results of these two approaches, MET inhibitors, crizotinib and foretinib, were identified as novel therapeutic candidates for the treatment of MPNST.

CONCLUSIONS

We successfully identified novel therapeutic candidates for the treatment of MPNST, namely crizotinib and foretinib, which target the MET pathway. We hope that these candidate drugs will contribute to the treatment of MPNST.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Potential Biomarkers for Liver Cancer Diagnosis Based on Multi-Omics Strategy

Liver cancer is the fourth leading cause of cancer-related death worldwide. Hepatocellular carcinoma (HCC) accounts for about 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients are eligible for curative therapy mainly due to the lack of early-detection strategies, highlighting the significance of reliable and accurate biomarkers. The integration of multi-omics became an important tool for biomarker screening and unique alterations in tumor-associated genes, transcripts, proteins, post-translational modifications and metabolites have been observed. We here summarized the novel biomarkers for HCC diagnosis based on multi-omics technology as well as the clinical significance of these potential biomarkers in the early detection of HCC.

Small cell lung cancer; recent advances of its biology and therapeutic perspective

Lung cancer is historically divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). While the therapeutic efficacy of NSCLC has improved due to the development of molecular targeted therapy and immune checkpoint inhibitors (ICIs) treatment, there has been very slow progress in the therapeutic advances of SCLC. Since SCLC is a deadly disease with rapid progression and early metastasis and comprises approximately 10% of lung cancer cases, more attention should be given to the therapeutic strategy for SCLC. Most SCLC cases respond to cytotoxic drugs, cisplatin, and etoposide. The objective response rate to the standard regimen is reported to be approximately 70% that is sufficient as standard therapy. However, almost all tumors recur and become refractory to chemotherapy which is the most important problem of this deadly disease. Recently, for the first time in several decades, ICIs have changed the standard therapy for SCLC. It must be emphasized that although ICIs paved the new way for SCLC therapy, more precise and effective therapy for SCLC is desired. Unfortunately, precise molecular mechanisms of SCLC are yet to be understood. Recent elaborate studies on the cell biology of SCLC uncovered several important aspects of molecular mechanisms. Gene profiling of cancer cells can be done using modern technology like next-generation sequencing (NGS). In this minireview, we describe the advances of modern technology in SCLC research and consider future therapeutic strategies based on the molecular mechanisms of SCLC.

Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery

Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen.

Proteomics of Multiple Sclerosis: Inherent Issues in Defining the Pathoetiology and Identifying (Early) Biomarkers

Multiple Sclerosis (MS) is a demyelinating disease of the human central nervous system having an unconfirmed pathoetiology. Although animal models are used to mimic the pathology and clinical symptoms, no single model successfully replicates the full complexity of MS from its initial clinical identification through disease progression. Most importantly, a lack of preclinical biomarkers is hampering the earliest possible diagnosis and treatment. Notably, the development of rationally targeted therapeutics enabling pre-emptive treatment to halt the disease is also delayed without such biomarkers. Using literature mining and bioinformatic analyses, this review assessed the available proteomic studies of MS patients and animal models to discern (1) whether the models effectively mimic MS; and (2) whether reasonable biomarker candidates have been identified. The implication and necessity of assessing proteoforms and the critical importance of this to identifying rational biomarkers are discussed. Moreover, the challenges of using different proteomic analytical approaches and biological samples are also addressed.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

HUMOS: How to Understand My Orbitrap Spectrum?-An Interactive Web-Based Tool to Teach the Basics of Mass-Spectrometry-Based Proteomics

The Orbitrap mass analyzer can provide high mass accuracy and throughput, which has significantly improved proteome research and made this type of instrumentation one of the most frequently applied in proteomics. The efficient use of Orbitrap mass spectrometers requires training. Students in the field of proteomics can benefit from a deeper understanding of the Orbitrap technology to comprehend mass spectral interpretation, troubleshooting, and judgment of experimental settings. Unfortunately, the cost of high-end mass spectrometers limits the implementation of this type of equipment in educational laboratories. Guided by these concerns, we developed an eLearning web application called HUMOS aimed to help teach Orbitrap mass spectrometry. Although a typical proteomics experiment includes the use of several different technologies, such as liquid chromatography, mass spectrometry, and bioinformatics, the learning objectives of HUMOS are focused on mass spectrometry. HUMOS models a mass spectrum of a peptide mixture, allowing us to investigate the influence of mass spectral acquisition parameters. By changing the parameters and observing the differences, students can learn more about the mass spectral resolution, duty cycle, throughput of the analysis, ion accumulation, and spectral dynamic range and get familiar with advanced spectral acquisition methods, such as BoxCar. HUMOS is an open-source software published under the Apache license; the live installation is available at http://humos.bmb.sdu.dk.

Unique repertoire of anti-carbohydrate antibodies in individual human serum

Humoral immunity to pathogens and other environmental challenges is paramount to maintain normal health, and individuals lacking or unable to make antibodies are at risk. Recent studies indicate that many human protective antibodies are against carbohydrate antigens; however, little is known about repertoires and individual variation of anti-carbohydrate antibodies in healthy individuals. Here we analyzed anti-carbohydrate antibody repertoires (ACARs) of 105 healthy individual adult donors, aged 20–60⁺ from different ethnic backgrounds to explore variations in antibodies, as defined by binding to glycan microarrays and by affinity purification. Using microarrays that contained > 1,000 glycans, including antigens from animal cells and microbes, we profiled the IgG and IgM ACARs from all donors. Each donor expressed many ACAs, but had a relatively unique ACAR, which included unanticipated antibodies to carbohydrate antigens not well studied, such as chitin oligosaccharides, Forssman-related antigens, globo-type antigens, and bacterial glycans. We also saw some expected antibodies to ABO(H) blood group and α-Gal-type antigens, although these also varied among individuals. Analysis suggests differences in ACARs are associated with ethnicity and age. Thus, each individual ACAR is relatively unique, suggesting that individualized information could be useful in precision medicine for predicting and monitoring immune health and resistance to disease.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

Proteomic investigation on bio-corona of Au, Ag and Fe nanoparticles for the discovery of triple negative breast cancer serum protein biomarkers

Nowadays, there are no targeted therapeutic modalities for triple negative breast cancer (TNBC). This disease is associated with poor prognosis and worst clinical outcome because of the aggressive nature of the tumor, delayed diagnosis, and non-specific symptoms in the early stages. Therefore, identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes remains an urgent clinical requirement. New user-friendly and cheap methods for biomarker identification are needed, and nanotechnology offers new opportunities. When dispersed in blood, nanoparticles (NPs) are covered by a protein shell termed "protein corona" (PC). While alterations in protein patterns are challeging to detect by conventional blood analyses, PC acts as a "nano-concentrator" of serum proteins with affinity for NPs' surface. So, the characterization of PC could allow the detection of otherwise undetectable changes in protein concentration at an early stage of the disease or after chemotherapy or surgery. To explore this research idea, serum samples from 8 triple negative breast cancer (TNBC) patients and 8 patients without malignancy were allowed to interact with gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm). Here, in order to identify biomarker candidates in serum of TNBC patients, these nanomaterials were combined with electrophoretic separation (SDS-PAGE) to performed qualitative and quantitative comparisons of the serum proteomes of TNBC patients (n = 8) and healthy controls (n = 8) by liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis. The results were validated through a sequential window acquisition of all theoretical mass spectra (SWATH) analysis, performed in total serum samples (patients and controls) using this approach as a multiple reaction monitoring (MRM) analysis. SIGNIFICANCE: It is well known that several proteins presented in human serum are important biomarkers for the diagnosis or prognosis of different diseases, as triple negative breast cancer (TNBC). Determining how nanomaterials as gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm) interact with human serum will assist not only in understanding their effects on the biological system (biocompability and toxicity), but also to obtain information for developing novel nanomaterials with high specificity and selectivity towards proteins with an important biological function (prognostic and diagnostic protein biomarkers).