High-throughput Biomarker Discovery Research Articles

Abstract LBA029: Development of computational tools for evaluating differential protein expression relative to spatial oxygen gradients using imaging mass cytometry

Abstract The solid tumor microenvironment is highly heterogeneous, conferring numerous differences in tumor cell gene expression and phenotype. One predominant driver of this heterogeneity is variable oxygenation. Low oxygen, termed hypoxia, has been shown to increase metastasis, radiation resistance, and lead to an overall poor patient prognosis. Characterizing hypoxia in patients can be used to personalize an adjuvant therapy targeting resistant hypoxic cell populations, which may otherwise survive conventional therapies. Because of the spatial heterogeneity intrinsic to hypoxia, current approaches to measure hypoxia, such as using polarographic oxygen electrodes, may underestimate the severity of hypoxia. Endogenous markers such as CA9 and VEGFA provide some semblance of evaluating hypoxia, however expression of such markers is not always solely driven by oxygenation. Multiple markers in tandem may prove to be a more reliable metric for evaluating hypoxia than individual markers alone. Here, we present an open-source collection of QuPath and Python scripts for analyzing differential protein expression relative to hypoxia gradients for imaging mass cytometry (IMC) data. IMC enables highly multiplexed imaging of tissue sections at the single cell resolution, with no spectral overlap of immunohistochemical markers. This is achieved through substitution of fluorophores with unique isotopes of heavy metals, and a mass spectrometer in place of a conventional fluorescent microscope. Previous work in our lab has identified 17 markers with extensive heterogeneity in clinical glioblastoma multiforme (GBM) patient samples. Pimonidazole, an exogenous hypoxia probe, has been administered to these patients, and is detectable through immunohistochemical techniques such as IMC. Serving as our ground truth for hypoxia, we conduct a differential gene expression analysis across regions of relative hypoxia for all markers in our panel, using a variety of supervised and unsupervised tools available in the open-source digital pathology platform QuPath, and through custom data visualization and statistical analysis tools built in Python. As such, we present an end-to-end image analysis workflow for evaluating hypoxia-regulated differential protein expression of clinical GBM cases imaged through IMC. This method is applicable for evaluating differential gene expression across multiple disease sites, multiplexed imaging modalities, and histomorphological regions of interest. Such computational methods enable high throughput biomarker discovery, while also being used to elucidate novel oxygen-dependent biological pathways. Citation Format: Mark Zaidi, Phoebe Lombard, Sheila Mansouri, Gelareh Zadeh, Trevor D McKee, Bradly G Wouters. Development of computational tools for evaluating differential protein expression relative to spatial oxygen gradients using imaging mass cytometry [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr LBA029.

Integrated proteomic sample preparation with combination of on-line high-abundance protein depletion, denaturation, reduction, desalting and digestion to achieve high throughput plasma proteome quantification

In this study, we developed an integrated plasma proteome sample preparation system, by which high-abundance proteins from human plasma were first depleted by immunoaffinity column, followed by on-line middle and low-abundance proteins denaturation, reduction, desalting and tryptic digestion. To evaluate the performance of such a system, 20 μL plasma was processed automatically, followed by 1-h gradient liquid chromatography-mass spectrometry analysis (LC-MS). Compared to conventional in-solution protocols, not only the sample preparation time could be shortened from 20 h to 20 min, but also the number of identified proteins were greatly increased by 1.4–2.0 times. Such an integrated system allows us to process 36 human plasma samples per day, with more than 300 proteins and 52 FDA approved disease markers per sample being identified. With combination of such an integrated sample preparation system with label-free single-shot LC-MS/MS, the human plasma proteins could be quantified across more than 6 orders of magnitude of abundance range with high reproducibility (Pearson R = 0.99, n = 9). In addition, the relative quantification of human plasma samples from diabetic retinopathy patients and diabetic patients demonstrated the feasibility of our developed workflow for clinic plasma proteome profiling. All these results demonstrated that our developed integrated plasma proteome sample preparation system would provide a new tool for high throughput biomarker discovery.

Towards High-throughput Immunomics for Infectious Diseases: Use of Next-generation Peptide Microarrays for Rapid Discovery and Mapping of Antigenic Determinants

Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens.

Automation Highlights from the Literature

Automation Highlights from the Literature

Next Generation MUT-MAP, a High-Sensitivity High-Throughput Microfluidics Chip-Based Mutation Analysis Panel

Molecular profiling of tumor tissue to detect alterations, such as oncogenic mutations, plays a vital role in determining treatment options in oncology. Hence, there is an increasing need for a robust and high-throughput technology to detect oncogenic hotspot mutations. Although commercial assays are available to detect genetic alterations in single genes, only a limited amount of tissue is often available from patients, requiring multiplexing to allow for simultaneous detection of mutations in many genes using low DNA input. Even though next-generation sequencing (NGS) platforms provide powerful tools for this purpose, they face challenges such as high cost, large DNA input requirement, complex data analysis, and long turnaround times, limiting their use in clinical settings. We report the development of the next generation mutation multi-analyte panel (MUT-MAP), a high-throughput microfluidic, panel for detecting 120 somatic mutations across eleven genes of therapeutic interest (AKT1, BRAF, EGFR, FGFR3, FLT3, HRAS, KIT, KRAS, MET, NRAS, and PIK3CA) using allele-specific PCR (AS-PCR) and Taqman technology. This mutation panel requires as little as 2 ng of high quality DNA from fresh frozen or 100 ng of DNA from formalin-fixed paraffin-embedded (FFPE) tissues. Mutation calls, including an automated data analysis process, have been implemented to run 88 samples per day. Validation of this platform using plasmids showed robust signal and low cross-reactivity in all of the newly added assays and mutation calls in cell line samples were found to be consistent with the Catalogue of Somatic Mutations in Cancer (COSMIC) database allowing for direct comparison of our platform to Sanger sequencing. High correlation with NGS when compared to the SuraSeq500 panel run on the Ion Torrent platform in a FFPE dilution experiment showed assay sensitivity down to 0.45%. This multiplexed mutation panel is a valuable tool for high-throughput biomarker discovery in personalized medicine and cancer drug development.

Ultradepletion of Human Plasma using Chicken Antibodies: A Proof of Concept Study

Human plasma arguably represents the most comprehensive version of the human proteome. Despite its immense theoretical discovery potential, plasma has many high and medium abundance proteins that mask low abundance protein disease biomarkers of relevance, making the discovery of novel diagnostic markers particularly difficult. Some form of protein depletion and/or fractionation is essential in order to detect markers of low abundance. Here, we describe a "proof of concept" two-pronged approach to immunodeplete abundant proteins from human plasma. The method, called API (Abundant Protein Immunodepletion), involves the fractionation of plasma using dual ion exchange columns (protein repetitive orthogonal offline fractionation (PROOF)) to simplify the proteome, the production of polyclonal IgY against each fraction and finally using the purified antibodies in a immunodepletion column. We explored the use of this product for immunodepletion of human plasma and identified a total of 165 nonredundant proteins after depletion. Of these, 38 proteins that were not previously identified in nondepleted plasma were now detected. It is envisaged that further optimization of the method as well as its cyclic implementation (by reinjecting depleted plasma into chickens for second round of antibody production) can make this technology highly robust, extremely cost-effective, and ideal for high throughput biomarker discovery.

Molecular Biology of Lung Cancer: Diagnosis and Management of Lung Cancer, 3rd ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines

Based on recent bench and clinical research, the treatment of lung cancer has been refined, with treatments allocated according to histology and specific molecular features. For example, targeting mutations such as epidermal growth factor receptor (EGFR) with tyrosine kinase inhibitors has been particularly successful as a treatment modality, demonstrating response rates in selected patients with adenocarcinoma tumors harboring EGFR mutations that are significantly higher than those for conventional chemotherapy. However, the development of new targeted therapies is, in part, highly dependent on an improved understanding of the molecular underpinnings of tumor initiation and progression, knowledge of the role of molecular aberrations in disease progression, and the development of highly reproducible platforms for high-throughput biomarker discovery and testing. In this article, we review clinically relevant research directed toward understanding the biology of lung cancer. The clinical purposes of this research are (1) to identify susceptibility variants and field molecular alterations that will promote the early detection of tumors and (2) to identify tumor molecular alterations that serve as therapeutic targets, prognostic biomarkers, or predictors of tumor response. We focus on research developments in the understanding of lung cancer somatic DNA mutations, chromosomal aberrations, epigenetics, and the tumor microenvironment, and how they can advance diagnostics and therapeutics.

Abstract 2499: Diagnostic serum biomarkers for canine hemangiosarcoma: a potential model of human angiosarcoma.

Abstract People suffering from uncommon diseases often do not benefit from the advances in medical research due to lack of research. One such disease is angiosarcoma, a highly aggressive cancer originating from blood vessels with risk factors including toxic exposure, radiotherapy and lymphedema. Angiosarcoma in dogs (commonly called hemangiosarcoma) is a very similar disease with much higher prevalence, comprising up to 7% of all canine malignancies. For both species, an accurate diagnostic method is needed for more favorable outcome. We developed a biomarker discovery pipeline consisting of a high-throughput biomarker discovery platform and a database for data mining and statistical analysis for candidate identification. The platform screens for differential glycosylation of serum glycoproteins as potential biomarkers utilizing 20 individual lectins followed by direct coupling to tandem mass spectrometry. The database facilitates the identification of differential glycosylation and produces a ranked list of potential biomarker candidates. Using this pipeline, sera from 10 dogs with hemangiosarcoma with 10 age and sex-matched control sera were processed and analyzed. Candidates from the database were ranked based on its ability to classify normal and cancer. The ranked list of candidates for this screen composed of glycoproteins involved in cell migration, tumor immune response, and glycoproteins reported to be altered in human and canine angiosarcoma and other cancer types. Some examples will be shown to illustrate the change in specific glycosylation structures associated with cancer as measured by binding to specific lectin(s) while binding to other lectins are not changed. These glycosylation-specific changes are potentially more specific diagnostic biomarkers than circulating proteins alone. We are currently validating the identified candidates and pursuing the development of clinical tests to improve diagnosis and clinical outcomes of angiosarcoma for both dogs and humans. Citation Format: Eunju Choi, David Chen, Kim-Anh Lê Cao, Helle Bielefeldt-Ohmann, Caroline A. O'Leary, Michelle M. Hill. Diagnostic serum biomarkers for canine hemangiosarcoma: a potential model of human angiosarcoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2499. doi:10.1158/1538-7445.AM2013-2499

Mutation Scanning Using MUT-MAP, a High-Throughput, Microfluidic Chip-Based, Multi-Analyte Panel

Targeted anticancer therapies rely on the identification of patient subgroups most likely to respond to treatment. Predictive biomarkers play a key role in patient selection, while diagnostic and prognostic biomarkers expand our understanding of tumor biology, suggest treatment combinations, and facilitate discovery of novel drug targets. We have developed a high-throughput microfluidics method for mutation detection (MUT-MAP, mutation multi-analyte panel) based on TaqMan or allele-specific PCR (AS-PCR) assays. We analyzed a set of 71 mutations across six genes of therapeutic interest. The six-gene mutation panel was designed to detect the most common mutations in the EGFR, KRAS, PIK3CA, NRAS, BRAF, and AKT1 oncogenes. The DNA was preamplified using custom-designed primer sets before the TaqMan/AS-PCR assays were carried out using the Biomark microfluidics system (Fluidigm; South San Francisco, CA). A cross-reactivity analysis enabled the generation of a robust automated mutation-calling algorithm which was then validated in a series of 51 cell lines and 33 FFPE clinical samples. All detected mutations were confirmed by other means. Sample input titrations confirmed the assay sensitivity with as little as 2 ng gDNA, and demonstrated excellent inter- and intra-chip reproducibility. Parallel analysis of 92 clinical trial samples was carried out using 2–100 ng genomic DNA (gDNA), allowing the simultaneous detection of multiple mutations. DNA prepared from both fresh frozen and formalin-fixed, paraffin-embedded (FFPE) samples were used, and the analysis was routinely completed in 2–3 days: traditional assays require 0.5–1 µg high-quality DNA, and take significantly longer to analyze. This assay can detect a wide range of mutations in therapeutically relevant genes from very small amounts of sample DNA. As such, the mutation assay developed is a valuable tool for high-throughput biomarker discovery and validation in personalized medicine and cancer drug development.

Liquid Chromatography-Mass Spectrometric Multiple Reaction Monitoring-based Strategies for Expanding Targeted Profiling towards Quantitative Metabolomics

Recent advances in analytical methodologies have made it possible to bring metabolomic profiling into quantitative metabolomics that permits precise measurements of comprehensive small-molecule profiles. Modern liquid chromatography-tandem mass spectrometry (LC-MS/MS) with multiple reaction monitoring (MRM) mode serves as the foundation for accurate simultaneous multi-analyte quantitation across large sample sets to provide high-quality information on target molecular profiles in complex systems. Despite the intrinsic multiplexing potential of the LC-MRM-MS technique, the key bottleneck in current LC-MRM-based assays is generally the limited analyte coverage and throughput capacity. Nowadays, the MRM-based approach has emerged as an attractive strategy for quantitative proteomic analysis and high-throughput biomarker discovery. So far, the full potential of the contemporary LCMRM methodology unleashed for quantitative metabolite profiling and metabolomic measurements of non-peptidic small molecules is rarely discussed. In this review we attempt to provide an overview on the major recent developments in LC-MRM-based strategies for quantitative profiling of multi- and non-target small molecules in biological samples. This article highlights the utility and power of the LC-MRM-based targeted approaches as valuable bioanalytical tools for low-cost, multiplexed quantitation on a large scale, with special emphasis on the promise of combining various strategies for expanding coverage and throughput of the LC-MRM-based assays to cover the gap between a widely targeted profiling and large-scale unknown screening towards comparative or quantitative metabolomics. General issues raised in metabolite profiling, such as basic aspects of bioanalysis, methodological dilemmas and challenges in quantitative metabolomics are addressed, and different strategies to circumvent the existing bottleneck and potential pitfalls of the current LC-MRM-MS techniques are outlined. In addition, the rudiments of LC-MRM-MS and its recent applications in combination with such strategies for biomarker quantitation and verification is also described.

A TMA De-Arraying Method for High Throughput Biomarker Discovery in Tissue Research

BackgroundTissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide.MethodologyThis study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap.ConclusionThis study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.

Towards proteome standards: The use of absolute quantitation in high-throughput biomarker discovery

The use of proteomics to profile biological fluids and identify therein biomarkers for cancer and other diseases was initially received with considerable excitement. However, results have fallen short of the expectations. Traditionally, protein biomarkers have been identified by measurement of relative expression changes between case and control samples from which differentially expressed proteins are then considered to represent biomarker candidates. We argue that current individual proteomics-based biomarker discovery studies lack the statistical strength for the identification of high-confidence biomarkers. Instead, multi-group efforts are necessary to facilitate the generation of sufficient sample sizes. This is contingent on the ability to collate and cross-compare data from different studies, which will require the use of a common metric or standards. Though profound, the technical challenges for absolute protein quantification can be overcome. The use of matrix specific, shared standards for absolute quantitation presents an opportunity to facilitate the much needed, but currently impossible, comparisons of different studies. In addition to community-wide approaches to standardize pre-analytical biomarker research studies, it is also important to establish means to integrate experimental data from different studies in order to assess the usefulness of proposed biomarkers with sufficient statistical certainty.

Identification of Biomarkers for Type 2 Diabetes and Its Complications: A Bioinformatic Approach

The long asymptomatic period before the onset of chronic diseases presents opportunities for disease prevention. Many chronic diseases like type 2 diabetes and its complications may be preventable by avoiding factors that trigger the disease process (primary prevention) or by use of therapies that modulate the disease process before the onset of clinical symptoms (secondary prevention). Accurate prediction and identification using biomarkers will be useful for disease prevention and initiation of proactive therapies to those individuals who are most likely to develop the disease. Recent technological advances in genetics, genomics, proteomics, and bioinformatics offer great opportunities for biomarker discovery. In this review, type 2 diabetes and its complications are used as examples to discuss pertinent issues related to high throughput biomarker discovery using bioinformatic pathways.

The application of genomic and proteomic technologies in predictive, preventive and personalized medicine

The long asymptomatic period before the onset of chronic diseases offers good opportunities for disease prevention. Indeed, many chronic diseases may be preventable by avoiding those factors that trigger the disease process (primary prevention) or by use of therapy that modulates the disease process before the onset of clinical symptoms (secondary prevention). Accurate prediction is vital for disease prevention so that therapy can be given to those individuals who are most likely to develop the disease. The utility of predictive markers is dependent on three parameters, which must be carefully assessed: sensitivity, specificity and positive predictive value. Specificity is important if a biomarker is to be used to identify individuals either for counseling or for preventive therapy. However, a reciprocal relationship exists between sensitivity and specificity. Thus, successful biomarkers will be highly specific without sacrificing sensitivity. Unfortunately, biomarkers with ideal specificity and sensitivity are difficult to find for many diseases. One potential solution is to use the combinatorial power of a large number of biomarkers, each of which alone may not offer satisfactory specificity and sensitivity. Recent technological advances in genetics, genomics, proteomics, and bioinformatics offer a great opportunity for biomarker discovery. The newly identified biomarkers have the potential to bring increased accuracy in disease diagnosis and classification, as well as therapeutic monitoring. In this review, we will use type 1 diabetes (T1D) as an example, when appropriate, to discuss pertinent issues related to high throughput biomarker discovery.

The use of surface‐enhanced laser desorption/ionization time‐of‐flight mass spectrometry to detect putative breast cancer markers in saliva: a feasibility study

Technologies are now available enabling saliva to be used to diagnose disease, predict disease progression, and monitor therapeutic efficacy. This pilot study describes the use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI) to detect putative breast cancer markers in saliva. Salivary specimens were analyzed as either pooled cancer saliva specimens, or individual specimens from healthy women and women diagnosed with carcinoma of the breast. The specimens were applied to a variety of protein chip arrays, washed extensively to remove unbound analytes and analyzed on a SELDI mass spectrometer. The results of this initial study suggest that the WCX protein chip array prepared and washed at pH 3.5 yielded the most promising results. Additionally, the analyses revealed a number of proteins that were higher in intensity among the cancer subjects when compared with controls. These salivary proteins were present at the 18, 113, 170, 228 and 287 km/z ranges using SELDI analyses. The study suggests that saliva may be useful for high-throughput biomarker discovery.

High-Throughput Biomarker Discovery and Identification by Mass Spectrometry

Native peptides and proteins are of increasing interest in biomedical research because they hold promise to represent a large number of useful diagnostic and therapeutic biomarkers. Discovery attempts from patient samples have to deal with the complexity of biology from a disease perspective as well as with a high individual variability. High throughput screening of samples is therefore the strategy of choice to detect relevant peptidic biomarkers, and requires a high order of automation particularly in the detection process. In this contribution, a novel technical approach employing a fully automated MALDI-TOF/TOF mass spectrometer is described. This approach combines high throughput biomarker discovery with the identification of corresponding endogenous peptides in one instrument and from the same set of samples. The degree of automation allows the analysis of thousands of chromatographic fractions corresponding to up to one hundred patient samples per day. The applied relative quantification via Differential Peptide Display((R)) is performed in a label-free way and shows a dynamic range of up to four orders of magnitude in the accessible peptide concentrations. The typical limit of detection is in the mid- to low-picomolar range for body fluids such as blood plasma, urine and cerebrospinal fluid. Sequence assignment via MALDI-TOF/TOF mass spectrometry is carried out either in an overview approach, characterizing rapidly the peptide composition e.g. of a novel sample, or in a directed approach, analyzing a list of biomarker candidates deduced from statistically significant abundance differences from the biomarker discovery process.

Macro/Nano-Structured Silicon as Solid Support for Antibody Arrays: Surface Design, Reproducibility, and Assay Characteristics Enabling Discovery of Kallikrein Gene Products for Prostate Disease Diagnostics

To facilitate high-throughput biomarker discovery and high-density protein-chip array analyses of complex biological samples, a novel macro-and nanoporous silicon surface for protein microarrays was developed. The surface offers three-dimensional surface enlarging properties and spot confinement, enabling both high sensitivity bioassays and design of high density arrays. Reproducible manufacturing of the protein chip surface was accomplished as demonstrated by the low imprecision when standard IgG bioassays were performed at 100 pM antigen level on a series of protein chips scanned at widely different locations within a silicon wafer, as well as between different wafers from two different manufacturers. The relative standard deviation (RSD) of fluorescence spot intensity within an array on a chip was less than 20%. Mean spot intensity RSD was 19% for all 25 microarray chips in the study. Within-manufacturer-lot RSDs in chips from either manufacturer were <15% of mean spot intensity. The detection limit and dynamic range of the novel protein chip surface were examined to evaluate whether they match criteria required in a search for novel biomarkers such as for prostate cancer. Monoclonal IgG against prostate-specific antigen (PSA) was arrayed on the porous silicon chips. These were subsequently incubated in serum samples containing widely different levels of fluorescence-labeled PSA. Detection of PSA in serum at concentrations from 0.7 ng/mL (26 pM) up to 104-fold higher levels verified assay characteristics required in the search for prostate biomarkers (e.g., kallikrein gene products) at clinically relevant levels.