Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

CDK Inhibition Reverses Acquired 5-Fluorouracil Resistance in Hepatocellular Carcinoma Cells

Background. 5-Fluorouracil (5-FU) has been widely applied in treating cancers. However, its usage is largely limited in hepatocellular carcinoma (HCC), due to acquired resistance. Here, we aim to identify target proteins and investigate their roles in 5-FU sensitivity of HCC cells. Methods. Mass spectrometry (MS) proteomics was performed on 5-FU-resistant cell line (BEL7402/5-FU) and its parental cell line (BEL7402) with 5-FU treatment. In order to identify potential targets, we compared the proteomics between two cell line groups and used bioinformatics tools to select hub proteins from all differentially expressed proteins. Results. We finally focused on a group of cell cycle-related kinases (CDKs). By CCK8 assay, we confirmed that the CDK inhibitor significantly decreased the IC50 of 5-FU-resistant cells. Conclusions. Our study verified that CDK inhibition can reverse 5-FU resistance of HCC cells.

Deep learning approaches for data-independent acquisition proteomics

INTRODUCTION

Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

AREAS COVERED

This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring, and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus, and Web of Science databases.

EXPERT OPINION

While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods.

Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma

The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.

Mapping chemotherapeutic drug distribution in cancer cell spheroids using 2D-TOF-SIMS and LESA-TIMS-MS

Three-dimensional (3D) cancer cell cultures grown in the form of spheroids are effective models for the study of in vivo-like processes simulating cancer tumor pharmacological dynamics and morphology. In this study, we show the advantages of Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS) combined with in situ Liquid Extraction Surface Analysis coupled to trapped Ion Mobility Spectrometry Mass Spectrometry (LESA-TIMS-TOF MS) for high spatial resolution mapping and quantitation of ABT-737, a chemotherapeutic drug, at the level of single human colon carcinoma cell spheroids (HCT 116 MCS). 2D-TOF-SIMS studies of consecutive sections (∼16 μm thick slices) showed that ABT-737 is homogenously distributed in the outer layers of the HCT 116 MCS. Complementary in situ LESA-TIMS-TOF MS/MS measurements confirmed the presence of the ABT-737 drug in the MCS slides by the observation of the molecular ion [M + H]+m/z and mobility, and the charateristic fragmentation pattern. LESA-TIMS-TOF MS allowed a quantitative assessment of the ABT-737 drug of the control MCS slice spiked with ABT-737 standard over the 0.4-4.1 ng range and MCS treated starting at 10 μM for 24 h. These experiments showcase an effective protocol for unambigous characterization and 3D mapping of chemotherapeutic drug distribution at the single MCS level.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers' proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders.

Challenging the heterogeneity of disease presentation in malignant melanoma-impact on patient treatment

There is an increasing global interest to support research areas that can assist in understanding disease and improving patient care. The National Cancer Institute (NIH) has identified precision medicine-based approaches as key research strategies to expedite advances in cancer research. The Cancer Moonshot program ( https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative ) is the largest cancer program of all time, and has been launched to accelerate cancer research that aims to increase the availability of therapies to more patients and, ultimately, to eradicate cancer. Mass spectrometry-based proteomics has been extensively used to study the molecular mechanisms of cancer, to define molecular subtypes of tumors, to map cancer-associated protein interaction networks and post-translational modifications, and to aid in the development of new therapeutics and new diagnostic and prognostic tests. To establish the basis for our melanoma studies, we have established the Southern Sweden Malignant Melanoma Biobank. Tissues collected over many years have been accurately characterized with respect to the tumor and patient information. The extreme variability displayed in the protein profiles and the detection of missense mutations has confirmed the complexity and heterogeneity of the disease. It is envisaged that the combined analysis of clinical, histological, and proteomic data will provide patients with a more personalized medical treatment. With respect to disease presentation, targeted treatment and medical mass spectrometry analysis and imaging, this overview report will outline and summarize the current achievements and status within malignant melanoma. We present data generated by our cancer research center in Lund, Sweden, where we have built extensive capabilities in biobanking, proteogenomics, and patient treatments over an extensive time period.

Direct molecular dissection of tumor parenchyma from tumor stroma in tumor xenograft using mass spectrometry-based glycoproteomics

The most widely used cancer animal model is the human-murine tumor xenograft. Unbiased molecular dissection of tumor parenchyma versus stroma in human-murine xenografts is critical for elucidating dysregulated protein networks/pathways and developing therapeutics that may target these two functionally codependent compartments. Although antibody-reliant technologies (e.g., immunohistochemistry, imaging mass cytometry) are capable of distinguishing tumor-proper versus stromal proteins, the breadth or extent of targets is limited. Here, we report an antibody-free targeted cross-species glycoproteomic (TCSG) approach that enables direct dissection of human tumor parenchyma from murine tumor stroma at the molecular/protein level in tumor xenografts at a selectivity rate presently unattainable by other means. This approach was used to segment/dissect and obtain the protein complement phenotype of the tumor stroma and parenchyma of the metastatic human lung adenocarcinoma A549 xenograft, with no need for tissue microdissection prior to mass-spectrometry analysis. An extensive molecular map of the tumor proper and the associated microenvironment was generated along with the top functional N-glycosylated protein networks enriched in each compartment. Importantly, immunohistochemistry-based cross-validation of selected parenchymal and stromal targets applied on human tissue samples of lung adenocarcinoma and normal adjacent tissue is indicative of a noteworthy translational capacity for this unique approach that may facilitate identifications of novel targets for next generation antibody therapies and development of real time preclinical tumor models.

Proteomics for early detection of colorectal cancer: recent updates

INTRODUCTION

Colorectal cancer (CRC) is a common type of cancer with a relatively poor survival rate. The survival rate of patients could be improved if CRC is detected early. Biomarkers associated with early stages of tumor development might provide useful tools for the early diagnosis of colorectal cancer. Areas covered: Online searches using PubMed and Google Scholar were performed using keywords and with a focus on recent proteomic studies. The aim of this review is to highlight the need for biomarkers to improve the detection rate of early CRC and provide an overview of proteomic technologies used for biomarker discovery and validation. This review will also discuss recent proteomic studies which focus on identifying biomarkers associated with the early stages of CRC development. Expert commentary: A large number of CRC biomarkers are increasingly being identified by proteomics using diverse approaches. However, the clinical relevance and introduction of these markers into clinical practice cannot be determined without a robust validation process. The size of validation cohorts remains a major limitation in many biomarker studies.

Current status of proteomics of soft tissue sarcomas

INTRODUCTION

Proteomics has been used in soft tissue sarcoma (STS) research in the attempts to improve the understanding of the disease background and develop novel clinical applications. Using various proteomics modalities, aberrant regulations of numerous intriguing proteins were identified in STSs, and the possible utilities of identified proteins as biomarkers or therapeutic targets have been explored. STS is an exceptionally diverse group of malignant diseases with highly complex molecular backgrounds and, therefore, an overview of the achievements and prospects of STS proteomics could enhance our knowledge of the possibilities and limitations of cancer proteomics. Areas covered: This review examines all STSs that have been examined using proteomics modalities, discussing unique aspects, limitations, and possible improvements of individual reports. To contribute to the current progress in cancer treatment development using novel anti-cancer drugs, proteomics plays a central role in linking cutting-edge technologies, application of proteogenomics, patient-derived cancer models, and biobanking system. Expert commentary: Therefore, proteomic-based STS research will be developed as an interdisciplinary science. STS proteomics will be further developed based on the interaction of oncologists with basic researchers in various fields, aimed at obtaining an enhanced understanding of the biology of the disease and achieving superior clinical outcomes for patients.

Proteomic Analysis of Serum Amyloid A as a Potential Marker in Intestinal Behçet's Disease

BACKGROUND/AIMS

Data regarding biomarkers to understand disease pathogenesis and to assess disease activity of intestinal Behçet's disease (BD) are limited. Therefore, we aimed to investigate the differentially expressed proteins in sera from patients with intestinal BD and to search for biomarkers using mass spectrometry-based proteomic analysis.

METHODS

Serum samples were pooled for the screening study, and two-dimensional electrophoresis (2-DE) was performed to characterize the proteins present in intestinal BD patients. Candidate protein spots were identified using matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry (MALDI-TOF/TOF MS) and bioinformatic analysis. To validate the proteomic results, serum samples from an independent cohort were assessed by enzyme-linked immunosorbent assay.

RESULTS

Pooled serum samples were used for 2-DE, and approximately 400 protein spots were detected in the sera of intestinal BD patients. Of the 22 differentially expressed proteins, 3 were successfully identified using MALDI-TOF/TOF MS. The three up-regulated proteins identified in the intestinal BD group included fibrin, apolipoprotein A-IV, and serum amyloid A (SAA). Serum SAA in intestinal BD patients (2.76 ± 2.50 ng/ml) was significantly higher than that in controls (1.68 ± 0.90 ng/ml, p = 0.007), which is consistent with the proteomic results. In addition, the level of IL-1β in patients with intestinal BD (8.96 ± 1.23 pg/ml) was higher than that in controls (5.40 ± 0.15 pg/ml, p = 0.009). SAA released by HT-29 cells was markedly increased by tumor necrosis factor-α (TNF-α) and lipopolysaccharides stimulation.

CONCLUSIONS

Our proteomic analysis revealed that SAA was up-regulated in intestinal BD patients.

Affinity Proteomics Exploration of Melanoma Identifies Proteins in Serum with Associations to T-Stage and Recurrence

BACKGROUND

Blood-based proteomic profiling may aid and expand our understanding of diseases and their different phenotypes. The aim of the presented study was to profile serum samples from patients with malignant melanoma using affinity proteomic assays to describe proteins in the blood stream that are associated to stage or recurrence of melanoma.

MATERIAL AND METHODS

Multiplexed protein analysis was conducted using antibody suspension bead arrays. A total of 232 antibodies against 132 proteins were selected from (i) a screening with 4595 antibodies and 32 serum samples from melanoma patients and controls, (ii) antibodies used for immunohistochemistry, (iii) protein targets previously related with melanoma. The analysis was performed with 149 serum samples from patients with malignant melanoma. Antibody selectivity was then assessed by Western blot, immunocapture mass spectrometry, and epitope mapping. Lastly, indicative antibodies were applied for IHC analysis of melanoma tissues.

RESULTS

Serum levels of regucalcin (RGN) and syntaxin 7 (STX7) were found to be lower in patients with both recurring tumors and a high Breslow's thickness (T-stage 3/4) compared to low thickness (T-stage 1/2) without disease recurrence. Serum levels of methylenetetrahydrofolate dehydrogenase 1-like (MTHFD1L) were instead elevated in sera of T3/4 patients with recurrence. The analysis of tissue sections with S100A6 and MTHFD1L showed positive staining in a majority of patients with melanoma, and S100A6 was significantly associated to T-stage.

CONCLUSIONS

Our findings provide a starting point to further study RGN, STX7, MTHFD1L and S100A6 in serum to elucidate their involvement in melanoma progression and to assess a possible contribution to support clinical indications.