Quantify Biomarker Expression Research Articles

Interlaboratory comparison of high-throughput protein biomarker assay quantifications for radiation exposure classification.

In the event of a widespread radiological incident, thousands of individuals will require rapid assessment of exposure using validated biodosimetry assays to inform clinical triage. In this scenario, multiple biodosimetry laboratories may be necessary for large-volume sample processing. To meet this need, we have developed a high-throughput assay for the rapid measurement of intracellular protein biomarkers in human peripheral blood samples using an Imaging Flow Cytometry (IFC) platform. The objective of this work was to harmonize and validate the reproducibility of our blood biomarker assay for radiation exposure across three IFC instruments, two located at Columbia University (CU) and the third at Health Canada. The Center for Radiological Research (CRR) at CU served as the central laboratory and reference instrument, where samples were prepared in triplicate, labeled with two radiation responsive leukocyte biomarkers (BAX and phosphor-p53 (Ser37)), and distributed for simultaneous interrogation by each IFC. Initial tests showed that significantly different baseline biomarker measurements were generated on each instrument when using the same acquisition settings, suggesting that harmonization of signal intensities is necessary. Subsequent tests harmonized biomarker measurements after irradiation by modulating laser intensity using two reference materials: unstained samples and standardized rainbow beads. Both methods generated measurements on each instrument without significant differences between the new and references instruments, allowing for the use of one master template to quantify biomarker expression across multiple instruments. Deming regression analyses of 0-5 Gy dose-response curves showed overall good correlation of BAX and p53 values across new and reference instruments. While Bland-Altman analyses indicated low to moderate instrument biases, ROC Curve analyses ultimately show successful discrimination between exposed and unexposed samples on each instrument (AUC values > 0.85).

Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology.

IntroductionWe sought to develop a novel method for a fully automated, robust quantification of protein biomarker expression within the epithelial component of high-grade serous ovarian tumors (HGSOC). Rather than defining thresholds for a given biomarker, the objective of this study in a small cohort of patients was to develop a method applicable to the many clinical situations in which immunomarkers need to be quantified. We aimed to quantify biomarker expression by correlating it with the heterogeneity of staining, using a non-subjective choice of scoring thresholds based on classical mathematical approaches. This could lead to a universal method for quantifying other immunohistochemical markers to guide pathologists in therapeutic decision-making.MethodsWe studied a cohort of 25 cases of HGSOC for which three biomarkers predictive of the response observed ex vivo to the BH3 mimetic molecule ABT-737 had been previously validated by a pathologist. We calibrated our algorithms using Stereology analyses performed by two experts to detect immunohistochemical staining and epithelial/stromal compartments. Immunostaining quantification within Stereology grids of hexagons was then performed for each histological slice. To define thresholds from the staining distribution histograms and to classify staining within each hexagon as low, medium, or high, we used the Gaussian Mixture Model (GMM).ResultsStereology analysis of this calibration process produced a good correlation between the experts for both epithelium and immunostaining detection. There was also a good correlation between the experts and image processing. Image processing clearly revealed the respective proportions of low, medium, and high areas in a single tumor and showed that this parameter of heterogeneity could be included in a composite score, thus decreasing the level of discrepancy. Therefore, agreement with the pathologist was increased by taking heterogeneity into account.Conclusion and discussionThis simple, robust, calibrated method using basic tools and known parameters can be used to quantify and characterize the expression of protein biomarkers within the different tumor compartments. It is based on known mathematical thresholds and takes the intratumoral heterogeneity of staining into account. Although some discrepancies need to be diminished, correlation with the pathologist’s classification was satisfactory. The method is replicable and can be used to analyze other biological and medical issues. This non-subjective technique for assessing protein biomarker expression uses a fully automated choice of thresholds (GMM) and defined composite scores that take the intra-tumor heterogeneity of immunostaining into account. It could help to avoid the misclassification of patients and its subsequent negative impact on therapeutic care.

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images.

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

Reliable quantification of protein expression and cellular localization in histological sections.

In targeted therapy, patient tumors are analyzed for aberrant activations of core cancer pathways, monitored based on biomarker expression, to ensure efficient treatment. Thus, diagnosis and therapeutic decisions are often based on the status of biomarkers determined by immunohistochemistry in combination with other clinical parameters. Standard evaluation of cancer specimen by immunohistochemistry is frequently impeded by its dependence on subjective interpretation, showing considerable intra- and inter-observer variability. To make treatment decisions more reliable, automated image analysis is an attractive possibility to reproducibly quantify biomarker expression in patient tissue samples. We tested whether image analysis could detect subtle differences in protein expression levels. Gene dosage effects generate well-graded expression patterns for most gene-products, which vary by a factor of two between wildtype and haploinsufficient cells lacking one allele. We used conditional mouse models with deletion of the transcription factors Stat5ab in the liver as well Junb deletion in a T-cell lymphoma model. We quantified the expression of total or activated STAT5AB or JUNB protein in normal (Stat5ab+/+ or JunB+/+), hemizygous (Stat5ab+/Δ or JunB+/Δ) or knockout (Stat5abΔ/Δ or JunBΔ/Δ) settings. Image analysis was able to accurately detect hemizygosity at the protein level. Moreover, nuclear signals were distinguished from cytoplasmic expression and translocation of the transcription factors from the cytoplasm to the nucleus was reliably detected and quantified using image analysis. We demonstrate that image analysis supported pathologists to score nuclear STAT5AB expression levels in immunohistologically stained human hepatocellular patient samples and decreased inter-observer variability.

Abstract B182: Utilization of quantitative multiplex immunofluorescence to assess biomarkers in ovarian cancer; ability to create an objective phenotype with respect to outcome.

Abstract Introduction:Ovarian cancer is the fifth most common cause of cancer deaths among women with the majority of cases diagnosed at an advanced stage where therapeutic options are limited to surgical resection and adjuvant platinum-based chemotherapy regimens. Given the paucity of reliable tissue biomarkers to predict response to therapy, we used a validated quantitative multiplex immunofluorescent (QMIF)platform on formalin-fixed, paraffin-embedded (FFPE) ovarian tissues to quantify biomarker expression patterns and assessed association with outcome. Methods: 89 patients were evaluated using tissue microarrays consisting of three 0.6mm tumor cores per diagnostic specimen; fallopian tubes and four different ovarian cell lines were included as pos/neg controls. CK7, VEGF, PERK1/2 and TP53 were evaluated with QMIF, with individual and combined antibody features constructed utilizing intensity and area values. Kaplan-Meier with log-rank test along with multivariate Cox proportional hazards model were employed to determine biomarker assocation with respect to overall (OS) and progression free survival (PFS). Results: Mean age 59yrs, 80% serous, 88% Grade 3 and 85% >/= Stage IIIC. Median follow-up 32m; 5-year OS 51% and platinum resistance 54%. In multivariate analysis CK7+TP53+ was an independent prognostic marker for both PFS and OS (p-value <0.001 and 0.04), i.e greater amounts associated with decreased survival. By contrast, increased levels of PERK1/2 were associated with a decreased tumor recurrence and increased platinum sensitivity (p=0.020 and 0.006) while increasing amounts of VEGF were associated with greater tumor recurrence (p=0.004) as well as platinum sensitvity (p=0.03).Conclusion: Quantitative assessment of TP53, PERK1/2 and VEGF provides valuable (biological) information with respect to chemoresponse and tumor recurrence. The application of QMIF to biomarker investigation will provide another level of complexity to tumor phenotypic characterization which is currently not available using standard subjective, chromogenic IHC assays. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B182. Citation Format: Joel Cardenas-Goicoechea, Nguyen Long, Tamara Kalir, Michael J. Donovan. Utilization of quantitative multiplex immunofluorescence to assess biomarkers in ovarian cancer; ability to create an objective phenotype with respect to outcome. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B182.

Improved tumor contrast achieved by single time point dual-reporter fluorescence imaging

In this study, we demonstrate a method to quantify biomarker expression that uses an exogenous dual-reporter imaging approach to improve tumor signal detection. The uptake of two fluorophores, one nonspecific and one targeted to the epidermal growth factor receptor (EGFR), were imaged at 1 h in three types of xenograft tumors spanning a range of EGFR expression levels (n=6 in each group). Using this dual-reporter imaging methodology, tumor contrast-to-noise ratio was amplified by >6 times at 1 h postinjection and >2 times at 24 h. Furthermore, by as early as 20 min postinjection, the dual-reporter imaging signal in the tumor correlated significantly with a validated marker of receptor density (P<0.05, r=0.93). Dual-reporter imaging can improve sensitivity and specificity over conventional fluorescence imaging in applications such as fluorescence-guided surgery and directly approximates the receptor status of the tumor, a measure that could be used to inform choices of biological therapies.

Validation of housekeeping genes as internal controls for studying biomarkers of endocrine-disrupting chemicals in disk abalone by real-time PCR

Our experiments were designed to identify suitable housekeeping genes (HKGs) in disk abalone as internal controls to quantify biomarker expression following endocrine disrupting chemicals (EDCs). Relative expression levels of twelve candidate HKGs were examined by real-time reverse transcription PCR (qRT-PCR) in gill and hepatopancreas of abalone following a 7-day challenge with either tributyltin chloride (TBT) or 17β-estradiol (E2). The expression levels of several conventional HKGs, such as 18s rRNA, glyceraldehyde-3-phosphate dehydrogenase and β-actin, were significantly altered by the challenges, indicating that they might not be suitable internal controls. Instead, the geNorm analysis pinpointed ribosomal protein L-5/ elongation factor 1 and ribosomal protein L-5/ succinate dehydrogenase as the most stable HKGs under TBT and E2 challenges, respectively. Moreover, these three HKGs also showed the highest stabilities overall amongst different tissues, genders and EDC challenges. The expression of a biomarker gene, cytochrome P450 4B (CYP4), was also investigated and exhibited a significant increase after the challenges. Importantly, when unsuitable HKGs were used for normalization, the influence of two EDCs on CYP4 expression was imprecisely overestimated or underestimated, which strongly emphasized the importance of selecting appropriately validated HKGs as internal controls in biomarker studies.

Laser cytometry of human tissues and tumors: Proliferation and therapeutic applications

Tumors in suspension such as leukemias are relatively easy to study using cytometric instruments. Fresh and archival samples of solid, nonhematological tumors pose considerable problems for cytometric assays. These problems include tissue disaggregation and cell extraction, tumor heterogeneity, and biomarker preservation. Solid tumors must be disaggregated by mechanical or enzymatic techniques. These can introduce cell and epitope damage, and they may yield an unrepresentative sample. All these factors have a significant bearing on the interpretation of cytometric data. The labeling index (LI) is a generic way to quantify biomarker expression in cell populations. It is the proportion of cells expressing the biomarker above a given threshold in the total population under study. The LI describes a snapshot of the number of labeled cells in a tissue or tumor sample at one time point. It gives no indication of the rate of turnover or transit of the labeled cells in the population. Cytometry allows quantitation of many cell surfaces, cytoplasmic, and nuclear proteins and epitopes, for which monoclonal antibodies and fluorescent tags offer accurate identification.