Biomarker Discovery Applications Research Articles

Osteosarcoma, chondrosarcoma and Ewing sarcoma: Clinical aspects, biomarker discovery and liquid biopsy.

Bone sarcomas, although rare, are associated with significant morbidity and mortality. The most frequent primary bone cancers include osteosarcoma, chondrosarcoma and Ewing sarcoma. The treatment approaches are heterogeneous and mainly chosen based on precise tumour staging. Unfortunately, clinical outcome has not changed significantly in over 30 years and tumour grade is still the best prognosticator of metastatic disease and survival. An option to improve this scenario is to identify molecular biomarkers in the early stage of the disease, or even before the disease onset. Blood-based liquid biopsies are a promising, non-invasive way to achieve this goal and there are an increasing number of studies which investigate their potential application in bone cancer diagnosis, prognosis and personalised therapy. This review summarises the interplay between clinical and molecular aspects of the three main bone sarcomas, alongside biomarker discovery and promising applications of liquid biopsy in each tumour context.

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO2 max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.

Analysis of soluble immune checkpoint proteins using quantitative multiplex microbead-based immunoassays

Abstract Immune-checkpoint receptors play critical roles in immune regulations and maintenance of immune homeostasis. Recently, a series of soluble immune checkpoint proteins have been found in circulation, and are putative biomarkers in infection, cancer, and autoimmune diseases. We have developed two multiplex immuno-oncology (I/O) panels. Human I/O Checkpoint Protein Panel 1 (17-plex) consists of 17 targets: BTLA, CD27, CD28, CD40, CD80, CD86, CTLA-4, GITR, GITRL, HVEM, ICOS, LAG-3, PD-1, PD-L1, PD-L2, TIM-3, TLR-2. Human I/O Checkpoint Protein Panel 2 (31-plex) consists of 31 targets: 4-1BBL, CD73, APRIL, Arginase-1, B7-H2, B7-H3, B7-H4, B7-H5, B7-H6, BAFF, CD25, CD30, CD40L, 4-1BB, CD226, E-Cadherin, FGL1, Gal-1, Gal-3, Granulysin, Granzyme B, IDO1, MICA, MICB, Nectin-2, Nectin-4, OX40, Perforin, CD155, Siglec-7, and Siglec-9. Using these multiplex arrays, we have simultaneously quantitated the expression levels of the above 48 key immune regulators in cancer serum samples and in cell culture supernatants. Soluble checkpoint protein signatures generated from this multiplex approach revealed differential expression of sBTLA, sCD27, sCD40, sCD86, sHVEM, sPD-1, sTIM-3, sTLR-2, sCD40L, Gal-3, Gal-1, FGL1, BAFF in the colorectal or breast cancer serum samples, compared to healthy serum controls. Heatmaps demonstrate the detection of checkpoint proteins in the conditioned media collected from human tumor cell lines and stimulated PBMCs. In summary, our results demonstrate that these two multiplex immunoassays we developed are useful research tools for the simultaneous quantitation of circulating immune checkpoint proteins, as well as for its potential application in biomarker discovery and translational research.

AI in Measurement Science.

Measurement of biological systems containing biomolecules and bioparticles is a key task in the fields of analytical chemistry, biology, and medicine. Driven by the complex nature of biological systems and unprecedented amounts of measurement data, artificial intelligence (AI) in measurement science has rapidly advanced from the use of silicon-based machine learning (ML) for data mining to the development of molecular computing with improved sensitivity and accuracy. This review presents an overview of fundamental ML methodologies and discusses their applications in disease diagnostics, biomarker discovery, and imaging analysis. We next provide the working principles of molecular computing using logic gates and arithmetical devices, which can be employed for in situ detection, computation, and signal transduction for biological systems. This review concludes by summarizing the strengths and limitations of AI-involved biological measurement in fundamental and applied research.

Biomarkers of Response to Biologic Therapy in Juvenile Idiopathic Arthritis.

Background: Juvenile idiopathic arthritis (JIA) is the most common chronic inflammatory arthritis of childhood, characterized by various clinical phenotypes associated with variable prognosis. Significant progress has been achieved with the use of biologic treatments, which specifically block pro-inflammatory molecules involved in the disease pathogenesis. The most commonly used biologics in JIA are monoclonal antibodies and recombinant proteins targeting interleukins 1 (IL-1) and 6 (IL-6), and tumor necrosis factor α (TNF-α). Several biomarkers have been investigated in JIA. Aims: To assess the level of evidence available regarding the role of biomarkers in JIA related to guiding clinical and therapeutic decisions, providing disease prognostic information, facilitating disease activity monitoring and assessing biologic treatment response in JIA, as well as propose new strategies for biologic therapy-related biomarker use in JIA. Methods: We searched PubMed for relevant literature using predefined key words corresponding to several categories of biomarkers to assess their role in predicting and assessing biologic treatment response and clinical remission in JIA. Results: We reviewed serological, cellular, genetic, transcriptomic and imaging biomarkers, to identify candidates that are both well-established and widely used, as well as newly investigated in JIA on biologic therapy. We evaluated their role in management of JIA as well as identified the unmet needs for new biomarker discovery and better clinical applications. Conclusion: Although there are no ideal biomarkers in JIA, we identified serological biomarkers with potential clinical utility. We propose strategies of combining biomarkers of response to biologics in JIA, as well as routine implementation of clinically acceptable imaging biomarkers for improved disease assessment performance.

Novel high-throughput applications for NAFLD diagnostics and biomarker discovery

Novel high-throughput applications for NAFLD diagnostics and biomarker discovery

High resolution estimates of relative gene abundance with quantitative ratiometric regression PCR (qRR-PCR).

Quantification of the relative abundance of genetic traits has broad applications for biomarker discovery, diagnostics, and assessing gene expression in biological research. Relative quantification of genes is traditionally done with the 2-ΔΔCT method using quantitative real-time polymerase chain reaction (qPCR) data, which is often limited in resolution beyond orders of magnitude difference. The latest techniques for quantification of nucleic acids employ digital PCR or microarrays which involve lengthy sample preparation and complex instrumentation. In this work, we describe a quantitative ratiometric regression PCR (qRR-PCR) method for computing relative abundance of genetic traits in a sample with high resolution from a single duplexed real-time quantitative PCR assay. Instead of comparing the individual cycle threshold (Ct) values as is done for the 2-ΔΔCT method, our qRR-PCR algorithm leverages the innate relationship of co-amplified PCR targets to measure their relative quantities using characteristic curves derived from the normalized ratios of qPCR fluorescence curves. We demonstrate the utility of this technique for discriminating the fractional abundance of mixed alleles with resolution below 5%.

Clinical Proteomics for the Analysis of Circulating Extracellular Vesicles.

In recent years, technical improvements in proteomics have allowed its rapid application for biomarker discovery, new drug target identification, and the study of disease progression and drug resistance. The clinical potential of circulating extracellular vesicles (EVs) as a source of biomarkers is one of the reasons why several research groups have recently applied proteomics to their study. A large variety of proteomic approaches such as gel-based proteomics and bottom-up and top-down mass spectrometry have been applied to the study of EVs. In this chapter, we will present basic protocols for gel-based and quantitative MS-based approaches applied to the study of EVs.

Recent Advances in Microfluidics-Based Chromatography—A Mini Review

Microfluidics-based liquid chromatography is based on the miniaturization of the different types of liquid chromatography (LC) systems (e.g., affinity, adsorption, size exclusion, ion exchange) on a microchip to perform on-chip separation of different types of analytes. On-chip chromatography finds applications in genomics, proteomics, biomarker discovery, and environmental analysis. Microfluidics-based chromatography has good reproducibility and small sample consumption. However, the on-chip chromatography fabrication techniques are often more challenging to perform than conventional LC column preparation. Different research groups have attempted to develop different techniques to fabricate microfluidics-based LC systems. In this review, we will summarize the recent advances in microfluidics-based chromatography.

Glass Fiber-Supported Hybrid Monolithic Spin Tip for Enrichment of Phosphopeptides from Urinary Extracellular Vesicles.

Extracellular vesicles (EVs) are attracting increasing interest with their intriguing role in intercellular communications. Protein phosphorylation in EVs is of great importance for understanding intercellular signaling processes. However, the study of EV phosphoproteomics is impeded by their relatively low amount in limited clinical sample volumes, and it is necessary to have a sensitive and efficient enrichment method for EV phosphopeptides. Herein, a novel Ti(IV)-functionalized and glass fiber-supported hybrid monolithic spin tip, termed PhosTip, was prepared for enriching phosphopeptides from urinary EVs. Glass fiber as the stationary phase positions the hybrid monolith in a standard pipet tip and prevents the monolith from distortion during experiments. The preparation procedure for the new PhosTip is simple and time-saving. The hybrid monolithic PhosTip provides excellent enrichment efficiency of low-abundance phosphopeptides from cell digests and urinary EVs with minimum contamination and sample loss. Using the PhosTip, we demonstrate that 5373 and 336 unique phosphopeptides were identified from 100 and 1 μg of cell lysates, while 3919 and 217 unique phosphopeptides were successfully identified from 10 and 1 mL of urinary samples, respectively. The PhosTip was finally applied to enrich phosphopeptides in urine EVs from prostate cancer patients and healthy controls and quantify 118 up-regulated proteins with phosphosites in prostate cancer samples. These results demonstrated that the PhosTip could be a simple and convenient tool for enriching phosphopeptides from clinical samples and for broader applications in biomarker discovery.

Abstract 854: An artificial intelligence pipeline for imaging mass cytometry data analysis and its application in ovarian cancer prognostic biomarker discovery

Abstract Imaging mass cytometry (IMC) is a powerful platform which enables high-dimensional, single-cell analysis of cell type and state. However, reliable methods used to analyze the IMC data remain to be developed. We seek to build an artificial intelligence (AI)-based analytics pipeline for imaging mass cytometry (IMC) to increase the accuracy of cell segmentation and spatial information extraction and apply the AI pipeline to analyze the IMC data derived from high-grade serous ovarian cancer samples for patient overall survival prediction. Multiplexed spatial analysis of the tumor microenvironment by IMC was performed on 41 formalin fixed paraffin embedded (FFPE) tissue samples obtained from treatment naïve high- grade serous ovarian cancer patients using a panel of 24 metal-tagged antibodies that are specific to tumor, immune and stromal cell markers. IMC data was collected using a a Helios CyTOF instrument equipped with Hyperion Imaging System (Fluidigm). Mask Region-Convolution Neural Network (Mask R-CNN) model was used for cell segmentation. Cell subtypes were derived by iterative phenograph clustering with different marker combinations and different subsets of cells in each iteration. Tumor area was calculated as a thresholded Gaussian blurred image of the density map of the center-of-mass of Keratin positive tumor cells. All cells with center-of-mass located within the tumor region were classified as intratumoral cells. The average cell composition in the nearest neighborhood of each cell type (distance between the center of mass of two cells < 20 μm) in the tumor cell compartment was computed. The results showed that cell segmentation by Mask R-CNN has a higher accuracy than traditional watershed segmentation. Significantly more granzymeB+ CD8+ T cells and CD11b+ Vista+ cells were found in long-term survivors comparing to short-term survivors in both tumor and stromal cell compartments of the tumor microenvironment. Additionally, CD196+, CD45RO+ and CD73+ cell densities in the tumor cell compartment were significantly lower in long-term than short-term survivors. Our results also showed that the mean numbers of CD73+ cells adjacent to Vista- CD4+ T cells, macrophages and B cells were significantly lower in long-term than short-term survivors. The mean number of granzymeB+ CD8+ T cells adjacent to Vista- CD4+ T cells was significantly higher in long-term than short-term survivors. These data demonstrated that the deep learning-based cell segmentation method achieved higher accuracy than the conventional watershed segmentation method. Furthermore, our AI pipeline can automatically extract cell count and cell neighborhood information in tumor. The cell count and neighborhood information could be further employed as features for machine learning to generate predictive biomarkers for ovarian cancer patient survival. Citation Format: Ying Zhu, Jianting Sheng, Sammy Ferri-Borgogno, Tsz-Lun Yeung, Jared K. Burks, Samuel C. Mok, Stephen T. Wong. An artificial intelligence pipeline for imaging mass cytometry data analysis and its application in ovarian cancer prognostic biomarker discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 854.

5th Festival of genomics, 29th - 30th January 2020, London UK.

5th Festival of genomics, 29th - 30th January 2020, London UK.

Validation of an exome and transcriptome based diagnostic platform enabling clinical cancer therapy selection and emerging composite biomarkers for immunotherapy.

e15583 Background: While immunotherapy has become a pillar of cancer treatment, diagnostic biomarkers that consistently predict patient response to these therapies have remained elusive. There is an increasing need for the development of integrative, composite biomarkers that can model the complex biology driving response and/or resistance to immunotherapy more effectively than existing single-analyte approaches. However, the majority of current cancer diagnostic panels, with their focus on a small set of genes, provide limited ability to support these emerging advanced biomarkers. Methods: To address these limitations, we developed and validated NeXT Dx, a comprehensive enhanced exome and transcriptome based diagnostic platform designed to simultaneously characterize tumor and immune genomics from a single limited FFPE sample. To achieve higher accuracy and sensitivity for an exome scale diagnostic platform, we developed an augmented exome assay that improves uniformity of coverage across all ~20,000 genes, including boosted coverage of 248 clinically-relevant cancer genes. We validated this assay using genomic DNA and RNA extracted from tumor-derived cell-lines, constructs, clinical FFPE samples, and proficiency testing samples. The assay utilizes > = 25ng of co-extracted DNA and RNA which were sequenced using Illumina NovaSeq instruments at our CAP-accredited, CLIA-certified laboratory. Additional assay enhancements for HLA, immune repertoire, and oncoviruses were designed to further optimize the platform for immunotherapy biomarker discovery applications. Results: Validation of NeXT Dx demonstrated a performance of 99.5% sensitivity and 99.8% positive predictive value (PPV) for SNVs with > = 5% AF; 98.7% sensitivity and 97.4% PPV for indels with > = 10% AF; 97.2% sensitivity and 94.6% PPV for CNAs in samples with > = 30% tumor content; 94.9% sensitivity and 94.9% PPV for fusions; and a 2.1% error rate for MSI classification. TMB was calculated using gold-standard whole exome data from SNVs and indels. Typical median coverage depth was > 1,000X for 248 clinically-relevant genes, ~300X for the remaining (whole exome) footprint. Conclusions: With NeXT Dx, we demonstrate a exome/transcriptome scale diagnostic platform that can detect current clinical biomarkers with high sensitivity as well as support emerging, advanced biomarkers that integrate across both tumor and immune features.

DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty cycle. It was always tempting to implement approaches that do not require MS/MS, yet they were constantly failing to achieve a meaningful depth of quantitative proteome coverage within short experimental times, which is particularly important for clinical or biomarker-discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free method, DirectMS1, for bottom-up proteomics. The method is compared with the standard MS/MS-based data-dependent acquisition approach for proteome-wide analysis using 5 min LC gradients. Specifically, we demonstrate identification of 1 000 protein groups for a standard HeLa cell line digest. The amount of loaded sample was varied in a range from 1 to 500 ng, and the method demonstrated 10-fold higher sensitivity. Combined with the recently introduced Diffacto approach for relative protein quantification, DirectMS1 outperforms most popular MS/MS-based label-free quantitation approaches because of significantly higher protein sequence coverage.

Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance

Genetic research of elite athletic performance has been hindered by the complex phenotype and the relatively small effect size of the identified genetic variants. The aims of this study were to identify genetic predisposition to elite athletic performance by investigating genetically-influenced metabolites that discriminate elite athletes from non-elite athletes and to identify those associated with endurance sports. By conducting a genome wide association study with high-resolution metabolomics profiling in 490 elite athletes, common variant metabolic quantitative trait loci (mQTLs) were identified and compared with previously identified mQTLs in non-elite athletes. Among the identified mQTLs, those associated with endurance metabolites were determined. Two novel genetic loci in FOLH1 and VNN1 are reported in association with N-acetyl-aspartyl-glutamate and Linoleoyl ethanolamide, respectively. When focusing on endurance metabolites, one novel mQTL linking androstenediol (3alpha, 17alpha) monosulfate and SULT2A1 was identified. Potential interactions between the novel identified mQTLs and exercise are highlighted. This is the first report of common variant mQTLs linked to elite athletic performance and endurance sports with potential applications in biomarker discovery in elite athletic candidates, non-conventional anti-doping analytical approaches and therapeutic strategies.

Protein and Proteome Measurements with Microfluidic Chips.

Major scientific discoveries in the last two decades have been enabled by the development of high-throughput sequencing and mass spectrometry (MS) technologies. Omics analysis, at the genome, transcriptome, proteome, or metabolome levels has paved the way for the elucidation of many molecular mechanisms that underlie disease, biomarker and drug-target discovery, and diagnostics and precision medicine applications. With the realization that the biological interpretation of results generated from biological tissues or cell-derived samples is highly impacted by cell heterogeneity in an apparently homogeneous cell population, many efforts have been redirected toward the omics characterization of single cells. As miniaturized devices represent ideal platforms for the analysis of small sample amounts, omics analysis has found a flourishing ground in the microfluidics instrumentation development arena. This review aims at capturing the latest protein and proteome analysis technologies that have been developed and implemented on microfluidic platforms, with focus on developments that have been documented in the past two years, and emphasis placed on challenging aspects that relate to sensitivity and handling sample complexity, as well as on applications that target the solving of demanding biological and biomedical problems.

Abstract 446: Plasma genome sequencing identifies prostate cancer patients that are sensitive to platinum-based therapy

Abstract Castration-resistant prostate cancer (CRPC) is a lethal disease. A subset of these patients present with atypical clinical characteristics and aggressive disease behavior. These patients, classified as Aggressive Variant Prostate Cancer (AVPC), may derive benefit from platinum-based chemotherapy. However, the identification of AVPC patients is often challenging due to heterogeneity in clinical presentations and potentially in biological drivers, which may not be fully reflected in a biopsy obtained from a single tumor site. To address these challenges, we developed an unbiased genome sequencing method called PEGASUS (Plasma Exome and Genome Analysis by Size-Selection and Unbiased Sequencing) that enables the simultaneous detection of copy number aberrations and exome mutations from circulating-tumor DNA (ctDNA). We applied this method to plasma specimens obtained from 160 CRPC patients with and without AVPC participating in a randomized trial of cabazitaxel alone or in combination with carboplatin. We were able to successfully isolate ctDNA (>2 ng) for genomic profiling using PEGASUS in 91/160 (57%) CRPC patients. Among these 91 CRPC patients, we detected common prostate cancer mutations as well as genomic copy number changes in genes such as RB1, PTEN and TP53 and several other genes associated with DNA repair. One of the most salient features that distinguished the patients’ outcomes was whether they had diploid or aneuploid genomes detected by whole genome profiling of the ctDNA. Patients with aneuploid ctDNA had worse progression-free and overall survival. Overall, our data shows the feasibility of performing whole-genome and exome profiling of ctDNA in CRPC patients to characterize the molecular features associated with distinct clinical presentations. Candidate markers associated to benefit from platinum-based chemotherapy are being evaluated. These results pave the way for future clinical applications in biomarker discovery to assist treatment decisions in prostate cancer patients. Citation Format: Naveen Ramesh, Emi Sei, Pei Ching Tsai, Christopher Logothetis, Paul Corn, Ana Aparicio, Amado J. Zurita, Nicholas E. Navin. Plasma genome sequencing identifies prostate cancer patients that are sensitive to platinum-based therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 446.

Abstract 2460: A comprehensive computational framework for interpretation of high-throughput cancer data using annotated pathways

Abstract Introduction: Identification of pathways that are associated with experimental gene expression profiles is critical for understanding the biological mechanisms in cancer studies. Current standard pathway analysis methods such as Over-representation Analysis and Gene Set Enrichment Analysis often only use the gene symbols of the annotated pathways and typically disregard the biological interactions. This approach is not sensitive to the dysregulation of central genes in the pathway networks whose perturbations may critically affect the biological mechanisms. Methods: In this study, we have developed a framework that leverages the networks of interactions from the KEGG database to infer the underlying pathways from high-throughput gene expression profiles of cancer data. This methodology uses novel network analysis models with optimized sensitivity towards genes with key positions in pathways, particularly cancer-genes. We have previously shown that our novel network models create a distinction between the topological position of cancer genes and non-cancer related genes in pathways. In this study, we have devised a statistical pipeline to incorporate the network evidences in refining standard pathway analysis models. We have also tested our model on a battery of multiple cancer datasets and incorporated synthetic data evaluation approaches to verify the performance of our model. Results: The results show that our network-based model is capable of detecting known and well-studied pathway associations when other methods fail to capture them. For example, our model identifies the PI3K-Akt signaling pathway from 274 samples (benign and malignant) from GSE9899 ovarian cancer dataset, when only a few number genes (19) are perturbed (adjusted p-value = 0.003). In comparison, over-representation analysis only produces an adjusted p-value of 0.15 using the same data for the same pathway. This observation outlines an instance where our model makes informative interpretations, given that the dysfunction of PI3K-Akt pathway is studied in ovarian cancer. On the same dataset, our methodology identifies the association of Ras signaling pathway with the ovarian cancer from only 7 perturbations (adjusted p-value = 0.034). In comparison, over-representation analysis produces an adjusted p-value of 1.00. Discussion: The network-based model of this study provides a new perspective for interpretation of high-throughput cancer data by accounting for the topological position of the genes with respect to their associated pathways. This methodology allows researchers to identify pathways that are associated with the perturbations. It also enables to evaluate the importance of the perturbation with regards to the organization of the pathways. This methodology is potentially beneficial to applications in biomarker discovery and drug target development. Citation Format: Pourya Naderi Yeganeh, M. Taghi Mostafavi. A comprehensive computational framework for interpretation of high-throughput cancer data using annotated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2460.

Abstract 1991: Exploring the potential of exosomes for cancer diagnosis and management: Comparative analysis of isolation methods for optimum yield purity and downstream applications

Abstract Exosomes have received significant attention due to their involvement in several diseases, including cancer, and are being explored as a tool for disease diagnosis and management. As a result, several kits based on different principles are now available in the market for exosome isolation. In this study, we have compared the relative efficacy of four commercial kits from Invitrogen, 101Bio, Wako and iZON along with conventional ultracentrifugation-based method for exosome yield, purity and quality for downstream applications. Cancer cell culture supernatant was used as an abundant and uniform source of exosomes for all methods and exosome quantity, size distribution, zeta potential, marker expression, RNA (qRT-PCR) and protein (mass spectrometry) quality was determined. The Invitrogen method showed the highest yield but the preparation showed broader size distribution likely due to co-precipitation of microvesicles and had the least magnitude of zeta potential (dispersion stability). Exosome preparations from other methods showed accepted size range (<150 nm) with a variable zeta potential (-12mV to -29mV) with good purity. The gel filtration method; however, showed a broader size distribution in the lower size range, which could be due contamination of aggregates of proteins and vitamins molecules. Quality of RNA isolated from all preparations was comparable; however, proteins isolated from Invitrogen method-based exosomal preparation showed contamination of polyethylene glycol (PEG) in mass spectrometry analyses. Similarly, other chemical impurities coming from the precipitant in Invitrogen kit could also be the reason for toxicity of resultant exosomal preparation in biological growth measurement assay. Together, these findings from comparative analyses should serve as a guide for choice and further optimization of exosome isolation methods for their desired downstream applications in cancer biology, therapy and/or biomarker discovery. Citation Format: Girijesh K. Patel, Mohammad Aslam Khan, Haseeb Zubair, Sanjeev Kumar Srivastava, Moh’d Khushman, Seema Singh, Ajay Pratap Singh. Exploring the potential of exosomes for cancer diagnosis and management: Comparative analysis of isolation methods for optimum yield purity and downstream applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1991.

Combination Strategy of Reactive and Catalytic Matrices for Qualitative and Quantitative Profiling of N-Glycans in MALDI-MS.

Qualitative and quantitative analysis of N-glycans by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is hampered mainly by the low ionization efficiency of analytes and their poor cocrystallization with traditional organic acid matrices. In the present study, a combination strategy of reactive and catalytic matrices (CSRCM) was proposed for the on-target derivatization and detection of reducing N-glycans: a novel reactive matrix, i.e., 2,5-dihydroxybenzohydrazide (DHBH), having a skeleton structure similar to that of DHB, was designed and synthesized, and this reactive matrix was mixed with catalytic matrix DHB to form a rationally combinatorial matrix (DHB/DHBH). Qualitatively, DHB/DHBH could improve the ionization efficiency of reducing carbohydrates significantly. Quantitatively, the acid-base chemistry of DHB/DHBH leads to a uniform cocrystallization of analytes-matrix mixtures. Consequently, CSRCM provides accurate quantitation for N-glycans with high derivatization efficiency and good linearity (R2 > 0.99) within 2 orders of magnitude on the basis of an internal standard method. Furthermore, the CSRCM was successfully applied to evaluating N-glycan in serum samples of colorectal cancer patients, thus showing potential in clinical applications for biomarker discovery.