Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer

Immunoarray Measurements of Parathyroid Hormone-Related Peptides Combined with Other Biomarkers to Diagnose Aggressive Prostate Cancer

Parathyroid hormone-related peptide (PTHrP) is related to bone metastasis and hypercalcemia in prostate and breast cancers and should be an excellent biomarker for aggressive forms of these cancers. Current clinical detection protocols for PTHrP are immunoradiometric assay and radioimmunoassay but are not sensitive enough to detect PTHrPs at early stages. We recently evaluated a prostate cancer biomarker panel, including serum monocyte differentiation antigen (CD-14), ETS-related gene protein, pigment epithelial-derived factor, and insulin-like growth factor-1, with promise for identifying aggressive prostate cancers. This panel predicted the need for patient biopsy better than PSA alone. In the present paper, we report an ultrasensitive microfluidic assay for PTHrPs and evaluate their diagnostic value and the value of including them with our prior biomarker panel to diagnose aggressive forms of prostate cancer. The immunoarray features screen-printed carbon sensor electrodes coated with 5 nm glutathione gold nanoparticles with capture antibodies attached. PTHrPs are bound to a secondary antibody attached to a polyhorseradish peroxidase label and delivered to the sensors to provide high sensitivity when activated by H₂O₂ and a mediator. We obtained an unprecedented 0.3 fg mL^-1 limit of detection for PTHrP bioactive moieties PTHrP 1-173 and PTHrP 1-86. We also report the first study of PTHrPs in a large serum pool to identify aggressive malignancies. In assays of 130 human patient serum samples, PTHrP levels distinguished between aggressive and indolent prostate cancers with 83-91% clinical sensitivity and 78-96% specificity. Logistic regression identified the best predictive model as a combination of PTHrP 1-86 and vascular endothelial growth factor-D. PTHrP 1-173 alone also showed a high ability to differentiate aggressive and indolent cancers.

Nanotechnology: A Promising Approach for Cancer Diagnosis, Therapeutics and Theragnosis

Cancer remains the most devastating disease and the major cause of mortality worldwide. Although early diagnosis and treatment are the key approach in fighting against cancer, the available conventional diagnostic and therapeutic methods are not efficient. Besides, ineffective cancer cell selectivity and toxicity of traditional chemotherapy remain the most significant challenge. These limitations entail the need for the development of both safe and effective cancer diagnosis and treatment options. Due to its robust application, nanotechnology could be a promising method for in-vivo imaging and detection of cancer cells and cancer biomarkers. Nanotechnology could provide a quick, safe, cost-effective, and efficient method for cancer management. It also provides simultaneous diagnosis and treatment of cancer using nano-theragnostic particles that facilitate early detection and selective destruction of cancer cells. Updated and recent discussions are important for selecting the best cancer diagnosis, treatment, and management options, and new insights on designing effective protocols are utmost important. This review discusses the application of nanotechnology in cancer diagnosis, therapeutics, and theragnosis and provides future perspectives in the field.

Pan-cancer analysis of the prognosis and immunological role of AKAP12: A potential biomarker for resistance to anti-VEGF inhibitors

The primary or acquired resistance to anti-VEGF inhibitors remains a common problem in cancer treatment. Therefore, identifying potential biomarkers enables a better understanding of the precise mechanism. Through the GEO database, three profiles associated with bevacizumab (BV) resistance to ovarian cancer, glioma, and non-small-cell lung carcinoma, respectively, were collected for the screening process, and two genes were found. A-kinase anchor protein 12 (AKAP12), one of these two genes, correlates with tumorigenesis of some cancers. However, the role of AKAP12 in pan-cancer remains poorly defined. The present study first systematically analyzed the association of AKAP12 with anti-VEGF inhibitors’ sensitivity, clinical prognosis, DNA methylation, protein phosphorylation, and immune cell infiltration across various cancers via bioinformatic tools. We found that AKAP12 was upregulated in anti-VEGF therapy-resistant cancers, including ovarian cancer (OV), glioblastoma (GBM), lung cancer, and colorectal cancer (CRC). A high AKAP12 expression revealed dismal prognoses in OV, GBM, and CRC patients receiving anti-VEGF inhibitors. Moreover, AKAP12 expression was negatively correlated with cancer sensitivity towards anti-VEGF therapy. Clinical prognosis analysis showed that AKAP12 expression predicted worse prognoses of various cancer types encompassing colon adenocarcinoma (COAD), OV, GBM, and lung squamous cell carcinoma (LUSC). Gene mutation status may be a critical cause for the involvement of AKAP12 in resistance. Furthermore, lower expression of AKAP12 was detected in nearly all cancer types, and hypermethylation may explain its decreased expression. A decreased phosphorylation of T1760 was observed in breast cancer, clear-cell renal cell carcinoma, and lung adenocarcinoma. For the immunologic significance, AKAP12 was positively related to the abundance of pro-tumor cancer-associated fibroblasts (CAFs) in various types of cancer. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested that “cell junction organization” and “MAPK pathway” participated in the effect of AKAP12. Importantly, we discovered that AKAP12 expression was greatly associated with metastasis of lung adenocarcinoma as well as differential and angiogenesis of retinoblastoma through investigating the single-cell sequencing data. Our study showed that the dual role of AKAP12 in various cancers and AKAP12 could serve as a biomarker of anti-VEGF resistance in OV, GBM, LUSC, and COAD.

Periodic Surface-Enhanced Raman Scattering-Encoded Magnetic Beads for Reliable Quantitative Surface-Enhanced Raman Scattering-Based Multiplex Bioassay

Surface-enhanced Raman scattering (SERS)-based immunoassay on encoded beads is highly attractive with the advantages of ultrasensitivity, multiplex and high throughput. However, it was a great challenge to screen out in-focus signals of the immunoconjugated SERS nanoprobes on spherical bead conveniently. Here, periodic SERS-encoded magnetic beads (PSE-MBs) were developed through droplet optofluidic technique by using monodisperse SERS-encoded magnetic nanospheres as building blocks. The designed PSE-MBs not only exhibit huge coding capacity, but also provide the strongest and reproducible SERS coding signals as "in-focus beacons". When PSE-MBs are used as capture carriers in SERS-based immunoassay, both multiple target analytes and in-focus signals of SERS nanoprobes could be easily identified according to the collected SERS coding signals. Thus, reliable quantitative analysis of multiple target analytes could be conveniently achieved by such detection protocol. Additionally, the magnetic ingredient in PSE-MBs made the operation easily during the bioassay. The multiple advantages of PSE-MBs including large coding capacity, in-focus beacons and magnetic operation endorse them to be robust capture carriers in reliable quantitative SERS-based multiplex immunoassay.

Recent advances in nanomaterials-based optical sensors for detection of various biomarkers (inorganic species, organic and biomolecules)

This review briefly emphasizes the different detection approaches (electrochemical sensors, chemiluminescence, surface-enhanced Raman scattering), functional nanostructure materials (quantum dots, metal nanoparticles, metal nanoclusters, magnetic nanomaterials, metal oxide nanoparticles, polymer-based nanomaterials, and carbonaceous nanomaterials) and detection mechanisms. Further, this review emphasis on the integration of functional nanomaterials with optical spectroscopic techniques for the identification of various biomarkers (nucleic acids, glucose, uric acid, oxytocin, dopamine, ascorbic acid, bilirubin, spermine, serotonin, thiocyanate, Pb²⁺ , Cu²⁺ , Hg²⁺ , F^- , peptides, and cancer biomarkers (mucin 1, prostate specific antigen, carcinoembryonic antigen, CA15-3, human epidermal growth factor receptor 2, C-reactive protein, and interleukin-6). Analytical characteristics of nanomaterials-based optical sensors are summarized in Tables, providing the insights of nanomaterials-based optical sensors for biomarkers detection. Finally, the opportunities and challenges of nanomaterials-based optical analytical approaches for the detection of various biomarkers (inorganic, organic, biomolecules, peptides and proteins) are discussed.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hampers their use in research and clinical application. Mass spectrometry measures of fifty N-glycans, on seven serum proteins in liver disease, were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized FDA-supported BioCompute Object. Using the biomarker data model allows capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers, can integrate N-glycan biomarker data with multi-source biomedical data, and can foster discovery and insight within a unified data framework for glycan biomarker representation thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

Prognostic factors for survival and ambulatory status at 8 weeks with metastatic spinal cord compression in the SCORAD randomised trial

BACKGROUND

Metastatic spinal cord compression (MSCC) carries a poor prognosis and management is based on the likelihood of maintaining mobility and predicted survival.

PATIENTS AND METHOD

SCORAD is a randomised trial of 686 patients comparing a single dose of 8 Gy radiotherapy with 20 Gy in 5 fractions. Data was split into a training set (412, 60%) and a validation set (274, 40%). A multivariable Cox regression for overall survival (OS) and a logistic regression for ambulatory status at 8 weeks were performed in the training set using baseline factors and a backward selection regression to identify a parsimonious model with p ≤ 0.10. Receiver Operating Characteristic (ROC) analysis evaluated model prognostic performance in the validation set. Validation of the final survival model was performed in a separate registry dataset (n = 348).

RESULTS

The survival Cox model identified male gender, lung, gastrointestinal, and other types of cancer, compression at C1-T12, presence of non-skeletal metastases and poor ambulatory status all significantly associated with worse OS (all p < 0.05). The ROC AUC for the selected model was 75% (95%CI: 69-81) in the SCORAD validation set and 68% (95%CI: 62-74) in the external validation registry data. The logistic model for ambulatory outcome identified primary tumour breast or prostate, ambulatory status grade 1 or 2, bladder function normal and prior chemotherapy all significantly associated with increased odds of ambulation at 8 weeks (all p < 0.05). The ROC AUC for the selected model was 72.3% (95% CI 62.6-82.0) in the validation set.

CONCLUSIONS

Primary breast or prostate cancer, and good ambulatory status at presentation, are favourable prognostic factors for both survival and ambulation after treatment.

miRNAs as molecular biomarkers for prostate cancer

MicroRNAs (miRNAs) are short noncoding RNA able to regulate specific mRNA stability, thus influencing target gene expression. Disrupted levels of several miRNA have been associated with prostate cancer, the leading cause of cancer death among men and the fifth leading cause of death worldwide. Here, we investigated whether miR-145, miR-148, and miR-185 circulating levels in plasma could be used as molecular biomarkers, to allow distinguishing between individuals with benign prostatic hyperplasia, precancerous lesion, and prostate cancer. In this study, we recruited 170 urological clinic patients with suspected prostate cancer who underwent prostate biopsy. Total RNA was isolated from plasma, and TaqMan MicroRNA assays were used to analyze miR-145, miR-185, and miR-148 expression. First, differential miRNA expression among patient groups was evaluated. Then, miRNA levels were combined with clinical assessment outcomes, including results from invasive tests, using multivariate analysis to examine their ability in discriminating among the three patient groups. Our results suggest that miRNA is a promising molecular tool for clinical management of at-risk patients.

A Dual Ranking Algorithm Based on the Multiplex Network for Heterogeneous Complex Disease Analysis

Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompleteness of the network and the ignorance of a large amount of information about physical and functional interactions between biological components. Other methods that directly integrate multiple types of interactions into an aggregate network have the risks that different types of data may conflict with each other and the characteristics and topologies of each individual network are lost. Meanwhile, biomarkers used in clinical trials should have the characteristics of small quantity and strong discriminate ability. In this study, we developed a multiplex network-based dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for diagnosis, prognosis, and classification. The results showed that DualRank outperformed competing methods and could identify biomarkers with the small quantity, great prediction performance (average AUC = 0.818) and biological interpretability.

Technologies for Frugal and Sensitive Point-of-Care Immunoassays

Immunoassays are a powerful tool for sensitive and quantitative analysis of a wide range of biomolecular analytes in the clinic and in research laboratories. However, enzyme-linked immunosorbent assay (ELISA)-the gold-standard assay-requires significant user intervention, time, and clinical resources, making its deployment at the point-of-care (POC) impractical. Researchers have made great strides toward democratizing access to clinical quality immunoassays at the POC and at an affordable price. In this review, we first summarize the commercially available options that offer high performance, albeit at high cost. Next, we describe strategies for the development of frugal POC assays that repurpose consumer electronics and smartphones for the quantitative detection of analytes. Finally, we discuss innovative assay formats that enable highly sensitive analysis in the field with simple instrumentation.

Single-Color Barcoding for Multiplexed Hydrogel Bead-Based Immunoassays

Current developments in precision medicine require the simultaneous detection of an increasing number of biomarkers in heterogeneous, complex solutions, such as blood samples. To meet this need, immunoassays on barcoded hydrogel beads have been proposed, although the encoding and decoding of these barcodes is usually complex and/or resource-intensive. Herein, an efficient method for the fabrication of barcoded, functionalized hydrogel beads is presented. The hydrogel beads are generated using droplet-based microfluidics in combination with photochemically induced C-H insertion reactions, allowing photo-crosslinking, (bio-) functionalization, and barcode integration to be performed in a single step. The generated functionalized beads carry single-color barcodes consisting of green-fluorescent particles of different sizes and concentrations, allowing simple and simultaneous readout with a standard plate reader. As a test example, the performance of barcoded hydrogel beads (3 × 3 matrix) functionalized with capture molecules of interest (e.g., antigens) is investigated for the detection of Lyme-disease-specific antibodies in patient sera. The described barcoding strategy for hydrogel beads does not interfere with the bioanalytical process and captivates by its simplicity and versatility, making it an attractive candidate for multiplex bioanalytical processes.

Electrochemical DNA Scaffold-Based Sensing Platform for Multiple Modes of Protein Assay and a Keypad Lock System

Development of a flexible, easy-to-use, and well-controllable DNA-based sensing platform would provide enormous opportunities to boost molecular diagnosis and signal transduction or information processing. Herein, a duplex DNA scaffold containing a bulge was deployed for the fabrication of a simple and general DNA-based electrochemical sensing platform. It could be harnessed for different signal output behaviors (one signal-off and two signal-on modes) toward a single-step analysis of the target protein. The detection limit toward the target protein could reach about 0.1 nM. Also, it could be used as a streamlined electrochemical workflow for the successive monitoring of protein binding. Furthermore, such an electrochemical sensing platform could be explored for the operation of the concatenated AND logic gates as a molecular keypad lock system. The current sensing platform based on only one duplex DNA scaffold presented features such as simple biosensor design and fabrication, flexible operation for different signal outputs, sensitive and selective protein detection, and expandable logic operation. It thus would pave a broad road toward the development of high-performance biosensors or logic devices to be applied for molecular diagnosis or computing.

Utility of Serum Ki-67 as a Marker for Malignancy in Dogs

Simple Summary

Although serum tumour markers offer an uncomplicated, non-invasive examination method and possible therapeutic options, they are still rarely used in veterinary medicine. Our marker of interest, the Ki-67 protein, can only be detected in the active phases of the cell cycle. Therefore, it is a suitable marker for assessing the proliferating cell fraction of an organism and can thus provide information about potentially present, rapid-growing tumour tissue. The purpose of our study was to determine whether Ki-67 could be considered as a possible tumour marker in canine serum for veterinary medicine. We measured serum concentrations of Ki-67 in dogs with various malignant tumours, such as carcinomas, sarcomas, and lymphomas. In the dogs with malignant tumours we determined significantly higher serum Ki-67 concentrations compared with healthy dogs and dogs with non-malignant diseases. No significant difference in serum Ki-67 concentration was observed between the different types of cancer or between benign and malignant mammary tumours. Our investigations also included some inflammatory parameters measured in blood, such as neutrophils, lymphocytes, and monocytes, with mixed results. The results of our study suggest that Ki-67 may be useful as a potential serum tumour marker, providing information about the presence of malignant diseases in a dog.

Abstract

Tumour markers are scarcely used in veterinary medicine, although they are non-invasive, contribute to a faster diagnosis and new therapeutic options. The nuclear protein Ki-67 is absent in G₀-phase but is detectable throughout all active phases of the cell cycle. Consequently, it is used as a marker for the proliferating cell fraction of a cell population and thus could indicate neoplastic tissue present. Our study is designed to show whether Ki-67 can be considered as a potential canine serum tumour marker for veterinary medicine. We measured serum concentrations of Ki-67 in dogs with various malignant tumours (carcinomas (n = 35); sarcomas (n = 26); lymphomas (n = 21)) using a commercially available quantitative sandwich ELISA from mybiosource. Dogs with malignant tumours showed significantly higher serum Ki-67 concentrations compared to healthy dogs (n = 19) and non-neoplastic diseased dogs (n = 26). No significant difference in serum Ki-67 concentration was detected between carcinoma, sarcoma, and lymphoma, nor between mammary adenocarcinoma and adenoma. In our investigations we also included some inflammatory parameters measured in blood, such as neutrophils, lymphocytes, and monocytes, and gained mixed results. The results of our study suggest that Ki-67 may be useful as a potential serum tumour marker, providing information about the presence of malignancies in a dog.

SOCS3 Acts as an Onco-immunological Biomarker With Value in Assessing the Tumor Microenvironment, Pathological Staging, Histological Subtypes, Therapeutic Effect, and Prognoses of Several Types of Cancer

The suppressor of cytokine signaling (SOCS) family contains eight members, including SOCS1–7 and CIS, and SOCS3 has been shown to inhibit cytokine signal transduction in various signaling pathways. Although several studies have currently shown the correlations between SOCS3 and several types of cancer, no pan-cancer analysis is available to date. We used various computational tools to explore the expression and pathogenic roles of SOCS3 in several types of cancer, assessing its potential role in the pathogenesis of cancer, in tumor immune infiltration, tumor progression, immune evasion, therapeutic response, and prognostic. The results showed that SOCS3 was downregulated in most The Cancer Genome Atlas (TCGA) cancer datasets but was highly expressed in brain tumors, breast cancer, esophageal cancer, colorectal cancer, and lymphoma. High SOCS3 expression in glioblastoma multiforme (GBM) and brain lower-grade glioma (LGG) were verified through immunohistochemical experiments. GEPIA and Kaplan–Meier Plotter were used, and this bioinformatics analysis showed that high SOCS3 expression was associated with a poor prognosis in the majority of cancers, including LGG and GBM. Our analysis also indicated that SOCS3 may be involved in tumor immune evasion via immune cell infiltration or T-cell exclusion across different types of cancer. In addition, SOCS3 methylation was negatively correlated with mRNA expression levels, worse prognoses, and dysfunctional T-cell phenotypes in various types of cancer. Next, different analytical methods were used to select genes related to SOCS3 gene alterations and carcinogenic characteristics, such as STAT3, SNAI1, NFKBIA, BCL10, TK1, PGS1, BIRC5, TMC8, and AFMID, and several biological functions were identified between them. We found that SOCS3 was involved in cancer development primarily through the JAK/STAT signaling pathway and cytokine receptor activity. Furthermore, SOCS3 expression levels were associated with immunotherapy or chemotherapy for numerous types of cancer. In conclusion, this study showed that SOCS3 is an immune-oncogenic molecule that may possess value as a biomarker for diagnosis, treatment, and prognosis of several types of cancer in the future.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer

Background

Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.

Methods

Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.

Results

Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.

Conclusion

Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09349-x.

Bio-Receptors Functionalized Nanoparticles: A Resourceful Sensing and Colorimetric Detection Tool for Pathogenic Bacteria and Microbial Biomolecules

Pathogenic bacteria and several biomolecules produced by cells and living organisms are common biological components posing a harmful threat to global health. Several studies have devised methods for the detection of varying pathogenic bacteria and biomolecules in different settings such as food, water, soil, among others. Some of the detection studies highlighting target pathogenic bacteria and biomolecules, mechanisms of detection, colorimetric outputs, and detection limits have been summarized in this review. In the last 2 decades, studies have harnessed various nanotechnology-based methods for the detection of pathogenic bacteria and biomolecules with much attention on functionalization techniques. This review considers the detection mechanisms, colorimetric prowess of bio-receptors and compares the reported detection efficiency for some bio-receptor functionalized nanoparticles. Some studies reported visual, rapid, and high-intensity colorimetric detection of pathogenic bacteria and biomolecules at a very low concentration of the analyte. Other studies reported slight colorimetric detection only with a large concentration of an analyte. The effectiveness of bio-receptor functionalized nanoparticles as detection component varies depending on their selectivity, specificity, and the binding interaction exhibited by nanoparticles, bio-receptor, and analytes to form a bio-sensing complex. It is however important to note that the colorimetric properties of some bio-receptor functionalized nanoparticles have shown strong and brilliant potential for real-time and visual-aided diagnostic results, not only to assess food and water quality but also for environmental monitoring of pathogenic bacteria and a wide array of biomolecules.

Novel Field-Based Protein Detection Method Using Light Transmission Spectroscopy and Antibody Functionalized Gold Nanoparticles

Protein detection is a universal tool critical to many applications in medicine, agriculture, and biotechnology. We developed a novel protein detection method combining light transmission spectroscopy and particle-size analysis of gold nanospheres monovalently functionalized with polyclonal antibodies and applied it to an emerging challenge for such technologies─the monitoring of environmental proteins (eProteins) present in natural aquatic systems. These are an underreported source of pollution and include the pseudopersistent Cry toxins that enter aquatic ecosystems from surrounding genetically engineered crops. The assay is capable of detecting proteins in complex matrices, such as water samples collected in the field, making it a competitive assay for eProtein detection. It is sensitive, reaching 1.25 ng mL^-1, and we demonstrate its application to the detection of Cry1Ab from subsurface tile-drain and streamwater samples from agricultural waterways. The assay can also be quickly adapted for other protein detection applications in the future.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

Correlation of Repeat Measurements of 27 Candidate Protein Markers for Colorectal Cancer Screening Taken Three Years and Multiple Freeze-Thaw Cycles Apart

In recent years the blood proteome has been increasingly researched for biomarkers for early detection of colorectal cancer (CRC). Blood samples from screening studies are often subject to preanalytical variability and repeated freeze−thaw cycles. We aimed to assess the correlation of repeat measurements of 27 candidate protein markers for CRC screening taken three years and multiple freeze−thaw cycles apart. The concentrations of 27 protein markers were measured in plasma samples of 39 newly detected CRC cases from a cohort of 9245 participants of screening colonoscopies. The proteins were measured using proximity extension assays (PEA) carried out on the same set of samples twice, three years apart, with an average of three freeze−thaw cycles in between the two measurements. Pearson’s product moment correlation coefficients were calculated. Correlation coefficients ranged from +0.43 to +0.97, with a median of 0.67 and an interquartile range of +0.58 to +0.84, with all p-values of correlation being <0.01 (<0.0005 for 22 proteins, <0.001 for 4 proteins). Repeat measurements of the 27 protein biomarkers for CRC screening performed three years later, and on average three freeze−thaw cycles apart, showed moderate to high levels of correlation. Apart from the effects of freeze−thaw cycles, slightly different preprocessing performed on the data may have contributed to recorded differences between measurements.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

The era of Cas12 and Cas13 CRISPR-based disease diagnosis

Clustered regularly interspaced short palindromic repeats (CRISPR) and associated protein (Cas) systems, since their discovery, have found growing applications in cell imaging, transcription modulation, therapeutics and diagnostics. Discovery of Cas12 and Cas13 have brought a new dimension to the field of disease diagnosis. These endonucleases have been extensively used for diagnosis of viral diseases in humans and animals and to a lesser extent in plants. The exigency of SARS-CoV-2 pandemic has highlighted the potential of CRISPR-Cas systems and sparked the development of innovative point-of-care diagnostic technologies. Rapid adaptation of CRISPR-chemistry combined with sensitive read-outs for emerging pathogens make them ideal candidates for detection and management of diseases in future. CRISPR-based approaches have been recruited for the challenging task of cancer detection and prognosis. It stands to reason that the field of CRISPR-Cas-based diagnosis is likely to expand with Cas12 and Cas13 playing a pivotal role. Here we focus exclusively on Cas12- and Cas13-based molecular diagnosis in humans, animals and plants including the detection of SARS-coronavirus. The CRISPR-based diagnosis of plant and animal diseases have not found adequate mention in previous reviews. We discuss various advancements, the potential shortfalls and challenges in the widespread adaptation of this technology for disease diagnosis.

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.

Detection of Early-Stage Pancreatic Ductal Adenocarcinoma From Blood Samples: Results of a Multiplex Biomarker Signature Validation Study

INTRODUCTION

The IMMray PanCan-d test combines an 8-plex biomarker signature with CA19-9 in a proprietary algorithm to detect pancreatic ductal adenocarcinoma (PDAC) in serum samples. This study aimed to validate the clinical performance of the IMMray PanCan-d test and to better understand test performance in Lewis-null (le/le) individuals who cannot express CA19-9.

METHODS

Serum samples from 586 individuals were analyzed with the IMMray PanCan-d biomarker signature and CA19-9 assay, including 167 PDAC samples, 203 individuals at high risk of familial/hereditary PDAC, and 216 healthy controls. Samples were collected at 11 sites in the United States and Europe. The study was performed by Immunovia, Inc (Marlborough, MA), and sample identity was blinded throughout the study. Test results were automatically generated using validated custom software with a locked algorithm and predefined decision value cutoffs for sample classification.

RESULTS

The IMMray PanCan-d test distinguished PDAC stages I and II (n = 56) vs high-risk individuals with 98% specificity and 85% sensitivity and distinguished PDAC stages I-IV vs high-risk individuals with 98% specificity and 87% sensitivity. We identified samples with a CA19-9 value of 2.5 U/mL or less as probable Lewis-null (le/le) individuals. Excluding these 55 samples from the analysis increased the IMMray PanCan-d test sensitivity to 92% for PDAC stages I-IV (n = 157) vs controls (n = 379) while maintaining specificity at 99%; test sensitivity for PDAC stages I and II increased from 85% to 89%.

DISCUSSION

These results demonstrate the IMMray PanCan-d blood test can detect PDAC with high specificity (99%) and sensitivity (92%).

Effective Identification of Maternal Malignancies in Pregnancies Undergoing Noninvasive Prenatal Testing

Background: The existence of maternal malignancy may cause false-positive results or failed tests of NIPT. Though recent studies have shown multiple chromosomal aneuploidies (MCA) are associated with malignancy, there is still no effective solution to identify maternal cancer patients from pregnant women with MCA results using NIPT. We aimed to develop a new method to effectively detect maternal cancer in pregnant women with MCA results using NIPT and a random forest classifier to identify the tissue origin of common maternal cancer types.

Methods: For examination, 496 participants with MCA results via NIPT were enrolled from January 2016 to June 2019 at BGI. Cancer and non-cancer participants were confirmed through the clinical follow-up. The cohort comprising 42 maternal cancer cases and 294 non-cancer cases enrolled from January 2016 to December 2017 was utilized to develop a method named mean of the top five chromosome z scores (MTOP5Zscores). The remaining 160 participants enrolled from January 2018 to June 2019 were used to validate the performance of MTOP5Zscores. We established a random forest model to classify three common cancer types using normalized Pearson correlation coefficient (NPCC) values, z scores of 22 chromosomes, and seven plasma tumor markers (PTMs) as predictor variables.

Results: 62 maternal cancer cases were confirmed with breast cancer, liver cancer, and lymphoma, the most common cancer types. MTOP5Zscores showed a sensitivity of 85% (95% confidence interval (CI), 62.11–96.79%) and specificity of 80% (95% CI, 72.41–88.28%) in the detection of maternal cancer among pregnant women with MCA results. The sensitivity of the classifier was 93.33, 66.67, and 50%, while specificity was 66.67, 90, and 97.06%, and positive predictive value (PPV) was 60.87, 72.73, and 80% for the prediction of breast cancer, liver cancer, and lymphoma, respectively.

Conclusion: This study presents a solution to identify maternal cancer patients from pregnant women with MCA results using NIPT, indicating it as a value-added application of NIPT in the detection of maternal malignancies in addition to screening for fetal aneuploidies with no extra cost.

Ultrasensitive DNA-Biomacromolecule Sensor for the Detection Application of Clinical Cancer Samples

Diagnostic testing of biological macromolecules is of great significance for early warning of disease and cancer. Nevertheless, restricted by limited surface area and large steric hindrance, sensitive detection of macromolecules with interface-based sensing method remains challenging. Here, a "biphasic replacement" electrochemical aptamer-based (BRE-AB) sensing strategy which placed capture reaction of the biomacromolecule in a homogeneous solution phase and replaced with a small diameter of single-stranded DNA to attach to the interface is introduced. Using the BRE-AB sensor, the ultrasensitive detection of luteinizing hormone (LH) with the detection limit of 10 × 10^-12 m is demonstrated. Molecular Dynamics simulations are utilized to explore the binding mechanism of aptamer and target LH. Moreover, it is confirmed that the BRE-AB sensor has excellent sensing performance in whole blood and undiluted plasma. Using the BRE-AB sensor, the LH concentrations in 40 clinical samples are successfully quantified and it is found that LH is higher expressed in breast cancer patients. Furthermore, the sensor enables simple, low-cost, and easy to regenerate and reuse, indicating potentially applicable for point-of-care biological macromolecules diagnostics.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

Molecularly Imprinted Polymers Outperform Lectin Counterparts and Enable More Precise Cancer Diagnosis

Accurately analysing the particular glycosylation status of protein biomarkers is of significant importance in the precise, early diagnosis of cancer. Existing methods mainly rely on the use of antibodies and lectins. However, due to the macroscopic and microscopic heterogeneity of glycans, precise analysis of glycosylation status still remains a challenge. Molecularly imprinted polymers (MIPs), as a synthetic alternative to antibodies or lectins, may provide new solutions but have not yet been explored. Herein, we report an appealing strategy called triple MIP-based plasmonic immunosandwich assay (triMIP-PISA) for precise cancer diagnosis in terms of the relative glycosylation expression of glycoprotein biomarkers. As proof of the principle, alpha fetoprotein (AFP), which has been used as a clinical biomarker for early detection of hepatocellular carcinoma (HCC), as well as its Lens culinaris agglutinin (LCA)-reactive fraction (AFP-L3), which is mainly composed of core-fucosylated glycans, were used as two target proteoforms to test in this study. Using two MIPs that can specifically recognize the peptide sequence of AFP as well as a fucose-imprinted MIP that can specifically recognize the AFP-L3 fraction, facile simultaneous plasmon-enhanced Raman detection of AFP and AFP-L3 in serum was achieved, which allowed HCC patients to be distinguished from healthy individuals. Due to the excellent recognition properties of the MIPs that are comparable to those of antibodies and superior to those of lectins, our triMIP-PISA method exhibited improved precision as compared with an antibody plus lectin-based immunofluorescence assay. Thus, this strategy opened a new avenue towards the precise diagnosis of cancer.

A triple molecularly imprinted polymer (MIP)-based plasmonic assay was developed for precise cancer diagnosis in terms of the relative glycosylation expression of glycoprotein biomarkers.

Rapid Preparation of Fluorescent Carbon Dots from Pine Needles for Chemical Analysis

Fluorescent carbon dots with blue, green, and red emissions were rapidly prepared from modified pine needles through microwave irradiation in a one-pot reaction. The fluorescence intensity and emission versatility for a carbon source were experimentally optimized. The reaction times were under 10 min and the reaction temperatures were lower than 220 °C. Potential applications of magnetic fluorescence-linked immunoassays of carcinoembryonic antigen (CEA) and tumor necrosis factor-alpha (TNF-α) were presented. The detection limits for CEA and TNF-α (3.1 and 2.8 pg mL^-1, respectively) are lower than those presented in other reports, whereas the linear ranges for CEA and TNF-α (9 pg mL^-1 to 18 ng mL^-1 and 8.5 pg mL^-1 to 17 ng mL^-1, respectively) are wider than those presented in other reports. Magnetic immunoassays with fluorescent CD_s prepared from pine needles can enable rapid, sensitive, and selective detections for biochemical analysis.

Multi-omics reveals novel prognostic implication of SRC protein expression in bladder cancer and its correlation with immunotherapy response

PURPOSE

This study aims to identify potential prognostic biomarkers of bladder cancer (BCa) based on large-scale multi-omics data and investigate the role of SRC in improving predictive outcomes for BCa patients and those receiving immune checkpoint therapies (ICTs).

METHODS

Large-scale multi-comic data were enrolled from the Cancer Proteome Atlas, the Cancer Genome Atlas and gene expression omnibus based on machining-learning methods. Immune infiltration, survival and other statistical analyses were implemented using R software in cancers (n = 12,452). The predictive value of SRC was performed in 81 BCa patients receiving ICT from aa validation cohort (n = 81).

RESULTS

Landscape of novel candidate prognostic protein signatures of BCa patients was identified. Differential BECLIN, EGFR, PKCALPHA, ANNEXIN1, AXL and SRC expression significantly correlated with the outcomes for BCa patients from multiply cohorts (n = 906). Notably, risk score of the integrated prognosis-related proteins (IPRPs) model exhibited high diagnostic accuracy and consistent predictive ability (AUC = 0.714). Besides, we tested the clinical relevance of baseline SRC protein and mRNA expression in two independent confirmatory cohorts (n = 566) and the prognostic value in pan-cancers. Then, we found that elevated SRC expression contributed to immunosuppressive microenvironment mediated by immune checkpoint molecules of BCa and other cancers. Next, we validated SRC expression as a potential biomarker in predicting response to ICT in 81 BCa patient from FUSCC cohort, and found that expression of SRC in the baseline tumour tissues correlated with improved survival benefits, but predicts worse ICT response.

CONCLUSION

This study first performed the large-scale multi-omics analysis, distinguished the IPRPs (BECLIN, EGFR, PKCALPHA, SRC, ANNEXIN1 and AXL) and revealed novel prediction model, outperforming the currently traditional prognostic indicators for anticipating BCa progression and better clinical strategies. Additionally, this study provided insight into the importance of biomarker SRC for better prognosis, which may inversely improve predictive outcomes for patients receiving ICT and enable patient selection for future clinical treatment.

Crosstalk between circRNAs and the PI3K/AKT signaling pathway in cancer progression

Circular RNAs (circRNAs), covalently closed noncoding RNAs, are widely expressed in eukaryotes and viruses. They can function by regulating target gene expression, linear RNA transcription and protein generation. The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway plays key roles in many biological and cellular processes, such as cell proliferation, growth, invasion, migration, and angiogenesis. It also plays a pivotal role in cancer progression. Emerging data suggest that the circRNA/PI3K/AKT axis modulates the expression of cancer-associated genes and thus regulates tumor progression. Aberrant regulation of the expression of circRNAs in the circRNA/PI3K/AKT axis is significantly associated with clinicopathological characteristics and plays an important role in the regulation of biological functions. In this review, we summarized the expression and biological functions of PI3K-AKT-related circRNAs in vitro and in vivo and assessed their associations with clinicopathological characteristics. We also further discussed the important role of circRNAs in the diagnosis, prognostication, and treatment of cancers.

Liquid Biopsy-Based Colorectal Cancer Screening via Surface Markers of Circulating Tumor Cells

Colorectal cancer (CRC) is ranked second for cancer-related deaths worldwide with approximately half of the patients being diagnosed at the late stages. The untimely detection of CRC results in advancement to the metastatic stage and nearly 90% of cancer-related deaths. The early detection of CRC is crucial to decrease its overall incidence and mortality rates. The recent introduction of circulating tumor cells (CTCs) has enabled a less invasive sampling method from liquid biopsies, besides revealing key information toward CRC metastasis. The current gold standard for CTC identification is the CellSearch® system (Veridex). This first-generation instrumentation relies on a single cell surface marker (CSM) to capture and count CTCs. Detection of CTCs allows the identification of patients at risk for metastasis, whereas CTC enumeration could improve risk assessment, monitoring of systemic therapy, and detection of therapy resistance in advanced metastatic CRC. In this review, we compared the pros and cons between single CSM-based CTC enrichment techniques and multi-marker-based systems. We also highlighted the challenges faced in the routine implementation of CSM-dependent CTC detection methods in CRC screening, prediction, prognosis, disease monitoring, and therapy selection toward precision medicine, as well as the dwelling on post-CTC analysis and characterization methods.

Porous Silicon-Based Aptasensors: Toward Cancer Protein Biomarker Detection

The anterior gradient homologue-2 (AGR2) protein is an attractive biomarker for various types of cancer. In pancreatic cancer, it is secreted to the pancreatic juice by premalignant lesions, which would be an ideal stage for diagnosis. Thus, designing assays for the sensitive detection of AGR2 would be highly valuable for the potential early diagnosis of pancreatic and other types of cancer. Herein, we present a biosensor for label-free AGR2 detection and investigate approaches for enhancing the aptasensor sensitivity by accelerating the target mass transfer rate and reducing the system noise. The biosensor is based on a nanostructured porous silicon thin film that is decorated with anti-AGR2 aptamers, where real-time monitoring of the reflectance changes enables the detection and quantification of AGR2, as well as the study of the diffusion and target-aptamer binding kinetics. The aptasensor is highly selective for AGR2 and can detect the protein in simulated pancreatic juice, where its concentration is outnumbered by orders of magnitude by numerous proteins. The aptasensor's analytical performance is characterized with a linear detection range of 0.05-2 mg mL^-1, an apparent dissociation constant of 21 ± 1 μM, and a limit of detection of 9.2 μg mL^-1 (0.2 μM), which is attributed to mass transfer limitations. To improve the latter, we applied different strategies to increase the diffusion flux to and within the nanostructure, such as the application of isotachophoresis for the preconcentration of AGR2 on the aptasensor, mixing, or integration with microchannels. By combining these approaches with a new signal processing technique that employs Morlet wavelet filtering and phase analysis, we achieve a limit of detection of 15 nM without compromising the biosensor's selectivity and specificity.

The application of CA72-4 in the diagnosis, prognosis, and treatment of gastric cancer

The role of conventional serum tumor marker, carbohydrate antigen 72-4 (CA72-4), in assisting diagnosis, monitoring dynamic progression, and evaluating the prognosis of gastric cancer (GC) should not be ignored, especially in the Chinese population. Even though CA72-4 has been used in clinical practice for decades, its modest positivity rate, sensitivity, and specificity did not meet the high demand of the clinical application. However, over the years, some progress in the functions of CA72-4 has been achieved, suggesting that CA72-4 can still be considered a promising marker in oncology. As a biomarker, CA72-4 can achieve improved sensitivity (SEN) and specificity (SPE) when combined with other biomarkers, selecting suitable reference values, improving detection techniques, and identifying the risk threshold. As a predictor, elevated serum CA72-4 levels were found to be significantly associated with prognostic risk factors, further assessing therapeutic validity and resectability. Recently, an effective method to reduce the toxicity of CA72-4 targeted therapy has been developed. Moreover, CA72-4 could induce novel aptamers to react with tumor cells and enhance the efficacy of trastuzumab in HER2-positive GC. Therefore, in this review, we discuss the most recent application of CA72-4 in the diagnosis, prognosis, and treatment of GC.

Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures

Simple Summary

Patients with Triple Negative Breast Cancer (TNBC) have a poor prognosis due to high inter-tumor heterogeneity and absence of effective targeted treatments. Through quantification of ongoing processes in each individual with TNBC, we propose an explanation on why certain previously suggested monotherapies, such as anti-EGFR, are not effective. We experimentally demonstrate that monotherapies or drug combinations that are not adjusted accurately to the patient-specific ongoing processes may create an evolutionary pressure on a tumor leading to the emergence of previously undetected or untargeted cellular subpopulations. We show for example that certain TNBC tumors may benefit from therapies targeting estrogen receptors (ER), similarly to ER positive cancers. When untargeted, those tumors may develop large ER positive subpopulations. We propose that anti-TNBC therapy should be accurately tailored to the personalized molecular processes and that incomplete or “wrong” treatments may generate diverse evolutionary routes of TNBC tumors leading to drug resistance.

Abstract

Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1–4 altered protein–protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development.

MXD3 as an onco-immunological biomarker encompassing the tumor microenvironment, disease staging, prognoses, and therapeutic responses in multiple cancer types

MAX dimerization (MXD) protein 3 (MXD3) is a member of the MXD family of basic-helix-loop-helix-leucine-zipper (bHLHZ) transcription factors that plays pivotal roles in cell cycle progression and cell proliferation. However, there is insufficient scientific evidence on the pathogenic roles of MXD3 in various cancers and whether MXD3 plays a role in the immuno-oncology context of the tumor microenvironment, pathogenesis, prognosis, and therapeutic response of different tumors through certain common molecular mechanisms; thus, we saw a need to conduct the present in silico pan-cancer study. Using various computational tools, we interrogated the role of MXD3 in tumor immune infiltration, immune evasion, tumor progression, therapy response, and prognosis of cohorts from various cancer types. Our results indicated that MXD3 was aberrantly expressed in almost all The Cancer Genome Atlas (TCGA) cancer types and subtypes and was associated with the tumor stage, metastasis, and worse prognoses of various cohorts. Our results also suggested that MXD3 is associated with tumor immune evasion via different mechanisms involving T-cell exclusion in different cancer types and by tumor infiltration of immune cells in thymoma (THYM), liver hepatocellular carcinoma (LIHC), and head and neck squamous cell carcinoma (HNSC). Methylation of MXD3 was inversely associated with messenger (m)RNA expression levels and mediated dysfunctional T-cell phenotypes and worse prognoses of cohorts from different cancer types. Finally, we found that genetic alterations and oncogenic features of MXD3 were concomitantly associated with deregulation of the DBN1, RAB24, SLC34A1, PRELID1, LMAN2, F12, GRK6, RGS14, PRR7, and PFN3 genes and were connected to phospholipid transport and ion homeostasis. Our results also suggested that MXD3 expression is associated with immune or chemotherapeutic outcomes in various cancers. In addition, higher MXD3 expression levels were associated with decreased sensitivity of cancer cell lines to several mitogen-activated protein kinase kinase (MEK) inhibitors but led to increased activities of other kinase inhibitors, including Akt inhibitors. Interestingly, MXD3 exhibited higher predictive power for response outcomes and overall survival of immune checkpoint blockade sub-cohorts than three of seven standardized biomarkers. Altogether, our study strongly suggests that MXD3 is an immune-oncogenic molecule and could serve as a biomarker for cancer detection, prognosis, therapeutic design, and follow-up.

CRISPR/Cas-Based In Vitro Diagnostic Platforms for Cancer Biomarker Detection

Timely diagnosis is of great benefit to improve the survival rate of cancer patients. Body fluid cancer biomarker detection is a critical kind of noninvasive method for cancer diagnosis. Nevertheless, traditional methods for cancer biomarker detection always rely on a large-scale instrument and involve sophisticated operation. Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas)-based in vitro diagnosis can simplify the detection procedures and improve sensitivity and specificity, holding great promise as the next-generation molecular diagnostic technology. In this Feature, we introduce the working mechanisms of different kinds of CRISPR/Cas systems for biosensing and CRISPR/Cas-mediated detection strategies for different kinds of cancer biomarkers including nucleic acids, proteins, and extracellular vesicles. In addition, the perspective and challenges of CRISPR/Cas-based strategies for cancer biomarkers are discussed.

Assessment of Proteolysis by Pyrylium and Other Fluorogenic Reagents

Aims

We aim to evaluate the potential application of amine reactive fluorogenic reagents for estimating enzymatic proteolysis.

Background

Proteolytic enzymes play important roles in regulating many physiological processes in living organisms.

Objectives

Assessment of protein degradation by using reagents for protein assay techniques.

Methods

We have assayed samples at the start and after 30-60 minutes incubation with trypsin by Chromeo P503 (Py 1 pyrylium compound) and CBQCA (3-(4-carboxybenzoyl) quinoline-2-carboxaldehyde) as amine reactive reagents and NanoOrange as non-amine reactive dye.

Results

All BSA prepared samples with trypsin have shown significantly higher fluorescence intensity (FI) versus controls (which reflects proteolysis) when assayed by Chromeo P503 (Py 1 pyrylium compound) and CBQCA (3-(4-carboxybenzoyl) quinoline-2-carboxaldehyde) as amine reactive reagents. However, same samples assayed with NanoOrange as non-amine reactive reagent did not show any significant variation between samples containing trypsin and controls.

Conclusion

These results are confirming reliability of highly sensitive protein assays utilizing amine reactive fluorogenic reagents for general estimation of proteolysis.

Glycan-specific antibodies as potential cancer biomarkers: a focus on microarray applications

Glycosylation is one of the most common posttranslational modifications of proteins and lipids. In the case of tumors, cell transformation accompanied by aberrant glycosylation results in the expression of tumor-associated glycans that promote tumor invasion. As part of the innate immunity, anti-glycan antibodies recognize tumor-associated glycans, and these antibodies can be present in the bloodstream in the early stages of cancer. Recently, anti-glycan antibody profiles have been of interest in various cancer studies. Novel advantages in the field of analytical techniques have simplified the analysis of anti-glycan antibodies and made it easier to have more comprehensive knowledge about their functions. One of the robust approaches for studying anti-glycan antibodies engages in microarray technology. The analysis of glycan microarrays can provide more expanded information to simultaneously specify or suggest the role of antibodies to a wide variety of glycans in the progression of different diseases, therefore making it possible to identify new biomarkers for diagnosing cancer and/or the state of the disease. Thus, in this review, we discuss antibodies to various glycans, their application for diagnosing cancer and one of the most promising tools for the investigation of these molecules, microarrays.

NetAUC: A network-based multi-biomarker identification method by AUC optimization

Complex diseases are caused by a variety of factors, and their diagnosis, treatment and prognosis are usually difficult. Proteins play an indispensable role in living organisms and perform specific biological functions by interacting with other proteins or biomolecules, their dysfunction may lead to diseases, it is a natural way to mine disease-related biomarkers from protein-protein interaction network. AUC, the area under the receiver operating characteristics (ROC) curve, is regarded as a gold standard to evaluate the effectiveness of a binary classifier, which measures the classification ability of an algorithm under arbitrary distribution or any misclassification cost. In this study, we have proposed a network-based multi-biomarker identification method by AUC optimization (NetAUC), which integrates gene expression and the network information to identify biomarkers for the complex disease analysis. The main purpose is to optimize two objectives simultaneously: maximizing AUC and minimizing the number of selected features. We have applied NetAUC to two types of disease analysis: 1) prognosis of breast cancer, 2) classification of similar diseases. The results show that NetAUC can identify a small panel of disease-related biomarkers which have the powerful classification ability and the functional interpretability.

Advances in Multiplexed Paper-Based Analytical Devices for Cancer Diagnosis: A Review of Technological Developments

Cancer is one of the leading causes of death worldwide producing estimated cost of $161.2 billion in the US in 2017 only. Early detection of cancer would not only reduce cancer mortality rates but also dramatically reduce healthcare costs given that the 17 million new cancer cases in 2018 are estimated to grow 27.5 million new cases by 2040. Analytical devices based upon paper substrates could provide effective, rapid, and extremely low cost alternatives for early cancer detection compared to existing testing methods. However, low concentrations of biomarkers in body fluids as well as the possible association of any given biomarker with multiple diseases remain as one of the greatest challenges to widespread adoption of these paper-based devices. However, recent advances have opened the possibility of detecting multiple biomarkers within the same device, which could be predictive of a patient's condition with unprecedented cost-effectiveness. Accordingly, this review highlights the recent advancements in paper-based analytical devices with a multiplexing focus. The primary areas of interest include lateral flow assay and microfluidic paper-based assay formats, signal amplification approaches to enhance the sensitivity for a specific cancer type, along with current challenges and future outlook for the detection of multiple cancer biomarkers.

Nanomaterial-Based Fluorescence Resonance Energy Transfer (FRET) and Metal-Enhanced Fluorescence (MEF) to Detect Nucleic Acid in Cancer Diagnosis

Nucleic acids, including DNA and RNA, have received prodigious attention as potential biomarkers for precise and early diagnosis of cancers. However, due to their small quantity and instability in body fluids, precise and sensitive detection is highly important. Taking advantage of the ease-to-functionality and plasmonic effect of nanomaterials, fluorescence resonance energy transfer (FRET) and metal-enhanced fluorescence (MEF)-based biosensors have been developed for accurate and sensitive quantitation of cancer-related nucleic acids. This review summarizes the recent strategies and advances in recently developed nanomaterial-based FRET and MEF for biosensors for the detection of nucleic acids in cancer diagnosis. Challenges and opportunities in this field are also discussed. We anticipate that the FRET and MEF-based biosensors discussed in this review will provide valuable information for the sensitive detection of nucleic acids and early diagnosis of cancers.

The geometry of clinical labs and wellness states from deeply phenotyped humans

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.

Novel therapeutic approaches targeting endotypes of severe airway disease

Introduction: Severe asthma and chronic rhinosinusitis (CRS), with nasal polyps (CRSwNP) and without nasal polyps (CRSsNP), are heterogeneous diseases characterized by different mechanistic pathways (endotypes) and variable clinical presentations (phenotypes).Areas covered: This review provides the clinician with an overview of the prevalence and clinical impact of severe chronic upper and lower airways disease and suggests a novel therapeutic approach with biological agents with possible biomarkers. To select relevant literature for inclusion in this review, we conducted a literature search using the PubMed database, using terms 'severe airways disease' AND 'endotype' AND 'treatment.' The literature review was performed for publication years 2010-2020, restricting the articles to humans and English language publications.Expert opinion: The coronavirus disease (COVID-19) pandemic has brought forth many challenges for patients with severe airway disease and healthcare practitioners involved in care. These patients could have an increased risk of developing severe SARS-CoV-2 disease, although treatment with biologics is not associated with a worse prognosis. Eosinopenia on hospital admission plays a key role as a diagnostic and prognostic biomarker.

Extracellular vesicle drug occupancy enables real-time monitoring of targeted cancer therapy

Current technologies to measure drug-target interactions require complex processing and invasive tissue biopsies, limiting their clinical utility for cancer treatment monitoring. Here we develop an analytical platform that leverages circulating extracellular vesicles (EVs) for activity-based assessment of tumour-specific drug-target interactions in patient blood samples. The technology, termed extracellular vesicle monitoring of small-molecule chemical occupancy and protein expression (ExoSCOPE), utilizes bio-orthogonal probe amplification and spatial patterning of molecular reactions within matched plasmonic nanoring resonators to achieve in situ analysis of EV drug dynamics. It measures changes in drug occupancy and protein composition in molecular subpopulations of EVs. When used to monitor various targeted therapies, the ExoSCOPE revealed EV signatures that closely reflected cellular treatment efficacy. We further applied the technology for clinical cancer diagnostics and treatment monitoring. Using a small volume of blood, the ExoSCOPE accurately classified disease status and rapidly distinguished between targeted treatment outcomes, within 24 h after treatment initiation.

Imaging-based spectrometer-less optofluidic biosensors based on dielectric metasurfaces for detecting extracellular vesicles

Biosensors are indispensable tools for public, global, and personalized healthcare as they provide tests that can be used from early disease detection and treatment monitoring to preventing pandemics. We introduce single-wavelength imaging biosensors capable of reconstructing spectral shift information induced by biomarkers dynamically using an advanced data processing technique based on an optimal linear estimator. Our method achieves superior sensitivity without wavelength scanning or spectroscopy instruments. We engineered diatomic dielectric metasurfaces supporting bound states in the continuum that allows high-quality resonances with accessible near-fields by in-plane symmetry breaking. The large-area metasurface chips are configured as microarrays and integrated with microfluidics on an imaging platform for real-time detection of breast cancer extracellular vesicles encompassing exosomes. The optofluidic system has high sensing performance with nearly 70 1/RIU figure-of-merit enabling detection of on average 0.41 nanoparticle/µm2 and real-time measurements of extracellular vesicles binding from down to 204 femtomolar solutions. Our biosensors provide the robustness of spectrometric approaches while substituting complex instrumentation with a single-wavelength light source and a complementary-metal-oxide-semiconductor camera, paving the way toward miniaturized devices for point-of-care diagnostics.