Diagnostic Microarray Research Articles

The Use of CGH Arrays for Identifying Copy Number Variations in Children with Autism Spectrum Disorder.

Autism spectrum disorders (ASDs) encompass a broad group of neurodevelopmental disorders with varied clinical symptoms, all being characterized by deficits in social communication and repetitive behavior. Although the etiology of ASD is heterogeneous, with many genes involved, a crucial role is believed to be played by copy number variants (CNVs). The present study examines the role of copy number variation in the development of isolated ASD, or ASD with additional clinical features, among a group of 180 patients ranging in age from two years and four months to 17 years and nine months. Samples were taken and subjected to array-based comparative genomic hybridization (aCGH), the gold standard in detecting gains or losses in the genome, using a 4 × 180 CytoSure Autism Research Array, with a resolution of around 75 kb. The results indicated the presence of nine pathogenic and six likely pathogenic imbalances, and 20 variants of uncertain significance (VUSs) among the group. Relevant variants were more prevalent in patients with ASD and additional clinical features. Twelve of the detected variants, four of which were probably pathogenic, would not have been identified using the routine 8 × 60 k microarray. These results confirm the value of microarrays in ASD diagnostics and highlight the need for dedicated tools.

Tumor-Stroma Proportion to Predict Chemoresistance in Patients With Ovarian Cancer

Platinum-based chemotherapy is the backbone of standard-of-care treatment for patients with advanced-stage, high-grade serous carcinoma (HGSC), the most common form of ovarian cancer; however, one-third of patients have or acquire chemoresistance toward platinum-based therapies. To demonstrate the utility of tumor-stroma proportion (TSP) as a predictive biomarker of chemoresistance of HGSC, progression-free survival (PFS), and overall survival (OS). This prognostic study leveraged tumors from patients with HGSC in The Cancer Genome Atlas (TCGA) cohort (1993-2013) and an independent cohort of resected clinical specimens from patients with HGSC (2004-2014) available in diagnostic and tissue microarray formats from the University of Tübingen in Germany. Data analysis was conducted from January 2021 to January 2024. Diagnosis of HGSC. Principal outcome measures were the ability of TSP to predict platinum chemoresistance, PFS, and OS. Using hematoxylin and eosin-stained slides from the Tübingen cohort (used for routine diagnostic assessment from surgical specimens) as well as tissue microarrays, representative sections of tumors for scoring of TSP were identified using previously evaluated cutoffs of 50% stroma or greater (high TSP) and less than 50% stroma (low TSP). Digitized slides from the TCGA Cohort were analyzed and scored in a similar fashion. Kaplan-Meier time-to-event functions were fit to estimate PFS and OS. The study included 103 patients (mean [SD] age, 61.6 [11.1] years) from the TCGA cohort and 192 patients (mean [SD] age at diagnosis, 63.7 [11.1] years) from the Tübingen cohort. In the TCGA cohort, there was no significant association of TSP levels with chemoresistance, PFS, or OS. However, in the Tübingen cohort, high TSP was associated with significantly shorter PFS (HR, 1.586; 95% CI, 1.093-2.302; P = .02) and OS (hazard ratio [HR], 1.867; 1.249-2.789; P = .002). Patients with chemoresistant tumors were twice as likely to have high TSP as compared to patients with chemosensitive tumors (HR, 2.861; 95% CI, 1.256-6.515; P = .01). In tissue microarrays from 185 patients from the Tübingen cohort, high TSP was again associated with significantly shorter PFS (HR, 1.675; 95% CI, 1.012-2.772 P = .04) and OS (HR, 2.491; 95% CI, 1.585-3.912; P < .001). In this prognostic study, TSP was a consistent and reproducible marker of clinical outcome measures of HGSC, including PFS, OS, and platinum chemoresistance. Accurate and cost-effective predictive biomarkers of platinum chemotherapy resistance are needed to identify patients most likely to benefit from standard treatments, and TSP can easily be implemented and integrated into prospective clinical trial design and adapted to identify patients who are least likely to benefit long-term from conventional platinum-based cytotoxic chemotherapy treatment at the time of initial diagnosis.

En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis

Machine learning now is used across many sectors and provides consistently precise predictions. The machine learning system is able to learn effectively because the training dataset contains examples of previously completed tasks. After learning how to process the necessary data, researchers have proven that machine learning algorithms can carry out the whole work autonomously. In recent years, cancer has become a major cause of the worldwide increase in mortality. Therefore, early detection of cancer improves the chance of a complete recovery, and Machine Learning (ML) plays a significant role in this perspective. Cancer diagnostic and prognosis microarray dataset is available with the biopsy dataset. Because of its importance in making diagnoses and classifying cancer diseases, the microarray data represents a massive amount. It may be challenging to do an analysis on a large number of datasets, though. As a result, feature selection is crucial, and machine learning provides classification techniques. These algorithms choose the relevant features that help build a more precise categorization model. Accurately classifying diseases is facilitated as a result, which aids in disease prevention. This work aims to synthesize existing knowledge on cancer diagnosis using machine learning techniques into a compact report. Current research work aims to propose an ensemble-based machine learning model En-PaFlower using Particle Swarm Optimization (PSO) as the feature selection algorithm, Flower Pollination algorithm (FPA) as the optimization algorithm with the majority voting algorithm. Finally, the performance of the proposed algorithm is evaluated over three different types of cancer disease datasets with accuracy, precision, recall, specificity, and F-1 Score etc as the evaluation parameters. The empirical analysis shows that the proposed methodology shows highest accuracy as 95.65%.

Tumor Heterogeneity Confounds Lymphocyte Metrics in Diagnostic Lung Cancer Biopsies.

The immune microenvironment is involved in fundamental aspects of tumorigenesis, and immune scores are now being developed for clinical diagnostics. To evaluate how well small diagnostic biopsies and tissue microarrays (TMAs) reflect immune cell infiltration compared to the whole tumor slide, in tissue from patients with non-small cell lung cancer. A TMA was constructed comprising tissue from surgical resection specimens of 58 patients with non-small cell lung cancer, with available preoperative biopsy material. Whole sections, biopsies, and TMA were stained for the pan-T lymphocyte marker CD3 to determine densities of tumor-infiltrating lymphocytes. Immune cell infiltration was assessed semiquantitatively as well as objectively with a microscopic grid count. For 19 of the cases, RNA sequencing data were available. The semiquantitative comparison of immune cell infiltration between the whole section and the biopsy displayed fair agreement (intraclass correlation coefficient [ICC], 0.29; P = .01; CI, 0.03-0.51). In contrast, the TMA showed substantial agreement compared with the whole slide (ICC, 0.64; P < .001; CI, 0.39-0.79). The grid-based method did not enhance the agreement between the different tissue materials. The comparison of CD3 RNA sequencing data with CD3 cell annotations confirmed the poor representativity of biopsies as well as the stronger correlation for the TMA cores. Although overall lymphocyte infiltration is relatively well represented on TMAs, the representativity in diagnostic lung cancer biopsies is poor. This finding challenges the concept of using biopsies to establish immune scores as prognostic or predictive biomarkers for diagnostic applications.

Fixed but Feasible: New Opportunities for Molecular Diagnostics in Transplantation.

Fixed but Feasible: New Opportunities for Molecular Diagnostics in Transplantation.

O’Donnell-Luria-Rodan syndrome: description of a second multinational cohort and refinement of the phenotypic spectrum

BackgroundO’Donnell-Luria-Rodan syndrome (ODLURO) is an autosomal-dominant neurodevelopmental disorder caused by pathogenic, mostly truncating variants in KMT2E. It was first described by O’Donnell-Luria et al in 2019 in a cohort of...

A Decentralized Kidney Transplant Biopsy Classifier for Transplant Rejection Developed Using Genes of the Banff-Human Organ Transplant Panel.

IntroductionA decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario.Materials and MethodsGene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version.ResultsThe best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively.DiscussionThis kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel.

The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview.

The importance of microarrays in diagnostics and medicine has drastically increased in the last few years. Nevertheless, the efficiency of a microarray-based assay intrinsically depends on the density and functionality of the biorecognition elements immobilized onto each sensor spot. Recently, researchers have put effort into developing new functionalization strategies and technologies which provide efficient immobilization and stability of any sort of molecule. Here, we present an overview of the most widely used methods of surface functionalization of microarray substrates, as well as the most recent advances in the field, and compare their performance in terms of optimal immobilization of the bioreceptor molecules. We focus on label-free microarrays and, in particular, we aim to describe the impact of surface chemistry on two types of microarray-based sensors: microarrays for single particle imaging and for label-free measurements of binding kinetics. Both protein and DNA microarrays are taken into consideration, and the effect of different polymeric coatings on the molecules’ functionalities is critically analyzed.

Predicting fetoplacental mosaicism during cfDNA-based NIPT

Cell-free DNA-based noninvasive prenatal testing (cfDNA-based NIPT) using maternal blood is highly sensitive for detecting fetal trisomies. However, false-positive and false-negative results can occur, which prevents NIPT from being a diagnostic test. Fetoplacental mosaicism is one of the main reasons for discordant test results. It is therefore important to understand this phenomenon to enable more comprehensive and appropriate genetic counselling. The present review aims to summarize the current knowledge of fetoplacental mosaicism ascertained during cfDNA-based NIPT and refers to the development of recent analytical pipelines for its detection during pregnancy. Publications are emerging demonstrating that genome-wide approaches to analyzing cfDNA can detect chromosomal aneuploidy other than the common trisomies. Despite the high accuracy of current cfDNA-based NIPT, a substantial number of false-positive and false-negative test results remain. Biological causes, such as fetal or (confined) placental mosaicism have been identified using advanced bioinformatics algorithms. Fetoplacental mosaicism can occur as part of normal pregnancy development, hence clinical practice standards recommend confirmation of positive NIPT results with a diagnostic karyotype or microarray study. cfDNA-based NIPT for fetal chromosomal aneuploidies is not diagnostic because of false-positive and false-negative test results. Recently, novel algorithms have been described that identify pregnancies with an increased risk of fetoplacental mosaicism. Reporting the presence of fetoplacental mosaicism during pregnancy can influence risk estimation and improve genetic counseling.

Молекулярное кариотипирование в практике пренатальной диагностики: опыт, проблемы, перспективы

В последнее время происходит активное внедрение высокоразрешающих методов диагностики для выявления хромосомных аномалий у человека. Так, использование микрочиповой диагностики (aCGH) позволяет выявлять хромосомные аномалии у 6 % плодов с нормальным кариотипом, определённым стандартными цитогенетическими методами. При этом для пренатальной диагностики aCGH используется в значительно меньших объемах. В первую очередь это обусловлено получением большого объема информации и сложностями её интерпретации. В настоящей работе приводятся результаты использования aCGH для пренатальной диагностики в НИИ медицинской генетики Томского НИМЦ. Исследованы 44 образца плодного материала методом aCGH. В 20 случаях (45 %) идентифицирован геномный дисбаланс, сбалансированный кариотип был выявлен в 21 образце (48 %). В трех случаях (7 %) результат не был получен вследствие недостаточного количества или низкого качества биологического материала. Среди 20 случаев с хромосомными аномалиями было 4 образца с анеуплоидиями и 16 образцов с вариациями числа копий ДНК (CNV), из них 13 делеций и 10 дупликаций. Все CNV, выявленные в результате исследования, не представлены или представлены единичными случаями в базе данных DGV. По клинической значимости выявленные CNV распределились следующим образом: 5 аномалий с неясной клинической значимостью, 5 вероятно патогенных и 11 патогенных. Патогенные CNV были представлены 7 делециями и 4 дупликациями, идентифицированными в 10 образцах. Кроме «целевых» патогенных CNV, выявляются и так называемые случайные патогенетически значимые CNV. Случайные находки характерны для всех широкогогеномных и полногеномных исследований и вызывают сложности при медико-генетическом консультировании, связанные с информированием пациентов и возможным прерыванием беременности. В настоящем исследовании было выявлено по пять CNV (24 %), относящихся к группам случайных патогенных и с неясной клинической значимостью. Результаты настоящей работы свидетельствуют о необходимости использования современных молекулярно-цитогенетических методов для пренатальной диагностики хромосомных аномалий. При этом необходимо учитывать возникновение новых методических и этических вопросов, которые наиболее остро стоят при использовании этих методов в пренатальной диагностике. Recently, there has been an active introduction of high-resolution diagnostic methods for detecting chromosomal abnormalities in humans. Thus, the use of microarray diagnostics allows detecting 6% more chromosomal abnormalities in fetuses with a normal karyotype based on the results of standard cytogenetic methods. At the same time, as part of the prenatal diagnosis, aCGH is used in much smaller amounts. This is primarily due to the receipt of a large amount of information and difficulties with its interpretation. This paper presents the results of using the aCGH method in prenatal diagnosis at the Research Institute of Medical Genetics of the Tomsk National Research Medical Center. In the framework of prenatal diagnosis, 44 samples of fetal material were examined using the aCGH method. Prenatal diagnosis using microarray in 20 cases (45%) allowed to identify chromosomal abnormalities, a balanced karyotype was detected in 21 samples (48%). In three cases (7%) the result was not obtained due to insufficient quantity or low quality of the biological material. Among the 20 cases with chromosomal abnormalities, 4 samples with aneuploidy and 16 samples with CNV. In 16 samples of fetal material without aneuploidy, 13 deletions and 10 duplications were identified. All the adjustments included in this list are not represented or represented by isolated cases in the Database of Genomic Variants. When analyzing the clinical significance of the CNV, were identified 11 pathogenic, 5 probably pathogenic and 5 CNV with uncertain significance. Pathogenic rearrangements were represented by 7 deletions and 4 duplications identified in 10 samples. In addition to the “Primary Finding” of pathogenic CNVs, so-called “Incidental Finding” of pathogenetically significant rearrangements are also detected. The identification of “Incidental Finding” is characteristic of all genome-wide studies and leads to the emergence of ethical issues related to informing patients and the possible termination of pregnancy. In the present study, five “Incidental Finding” pathogenic CNVs and five rearrangements with uncertain significance were identified. The results of this work indicate the need to use modern molecular cytogenetic methods for the prenatal diagnosis of chromosomal abnormalities. It is necessary to take into account the emergence of new methodological and ethical issues that are most acute when using these methods in prenatal diagnosis.

Peptides for Infectious Diseases: From Probe Design to Diagnostic Microarrays.

Peptides and peptidomimetics have attracted revived interest regarding their applications in chemical biology over the last few years. Their chemical versatility, synthetic accessibility and the ease of storage and management compared to full proteins have made peptides particularly interesting in diagnostic applications, where they proved to efficiently recapitulate the molecular recognition properties of larger protein antigens, and were proven to be able to capture antibodies circulating in the plasma and serum of patients previously exposed to bacterial or viral infections. Here, we describe the development, integration and application of strategies for computational prediction and design, advanced chemical synthesis, and diagnostic deployment in multiplexed assays of peptide-based materials which are able to bind antibodies of diagnostic as well as therapeutic interest. By presenting successful applications of such an integrated strategy, we argue that they will have an ever-increasing role in both basic and clinical realms of research, where important advances can be expected in the next few years.

Sequence-Specific End Labeling of Oligonucleotides (SSELO)-Based Microbial Detection.

The sequence-specific end labeling of oligonucleotides (SSELO) is an alternative labelling approach for the short-oligonucleotide diagnostic microarrays that was firstly described by Rudi and coworkers (ScientificWorldJournal 3:578-584, 2003). SSELO approach is unique in a way that it shifts the specificity determining step from hybridization to labeling, ensuring both high specificity (with careful probe design even single nucleotide polymorphisms (SNPs) can be detected) and sensitivity (detection sensitivity in the range of 0.1% relative abundance has been demonstrated) of the diagnostic system. These features make SSELO approach a perfect choice for the development of microbial diagnostic microarrays, in particular in the frame of foodborne bacterial pathogen detection.

Development and Assessment of a Diagnostic DNA Oligonucleotide Microarray for Detection and Typing of Meningitis-Associated Bacterial Species

Meningitis is commonly caused by infection with a variety of bacterial or viral pathogens. Acute bacterial meningitis (ABM) can cause severe disease, which can progress rapidly to a critical life-threatening condition. Rapid diagnosis of ABM is critical, as this is most commonly associated with severe sequelae with associated high mortality and morbidity rates compared to viral meningitis, which is less severe and self-limiting. We have designed a microarray for detection and diagnosis of ABM. This has been validated using randomly amplified DNA targets (RADT), comparing buffers with or without formamide, in glass slide format or on the Alere ArrayTubeTM (Alere Technologies GmbH) microarray platform. Pathogen-specific signals were observed using purified bacterial nucleic acids and to a lesser extent using patient cerebral spinal fluid (CSF) samples, with some technical issues observed using RADT and glass slides. Repurposing the array onto the Alere ArrayTubeTM platform and using a targeted amplification system increased specific and reduced nonspecific hybridization signals using both pathogen nucleic and patient CSF DNA targets, better revealing pathogen-specific signals although sensitivity was still reduced in the latter. This diagnostic microarray is useful as a laboratory diagnostic tool for species and strain designation for ABM, rather than for primary diagnosis.

The preparation and clinical application of diagnostic DNA microarray for the detection of pathogens in intracranial bacterial and fungal infections

The present study prepared 2 types of DNA diagnostic chips based on 16S ribosomal DNA (rDNA) and 18S-28S rDNA, and evaluated their values in the detection of pathogens in intracranial bacterial/fungal infections. A total of 14 probes of bacteria (Klebsiella pneumonia, Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli, Haemophilus influenza, Stenotrophomonas maltophilia, Neisseria meningitidis, Enterobacter spp., Enterococcus faecalis, Enterococcus faecium, Listeria monocytogenes, Staphylococcus aureus, Streptococcus pneumonia and coagulase negative staphylococcus) and 4 probes of fungi (Candida albicans, Candida tropicalis, Candida glabrata and Cryptococcus neoformans), determined frequently in cerebrospinal fluid (CSF), were designed and used for preparation of microarrays. CSF samples from 88 patients with clinically suspected intracranial infection and standard strains were used to evaluate the chips. The same samples were also analyzed by culture and sequencing. The results demonstrated that the sensitivity, specificity and false-positive rate of the microarray assay compared with culture method were 100 vs. 68.3% (P<0.05), 97.1 vs. 100%, and 2.9 vs. 0%, respectively. The minimum concentration of detection with the chips was 10 cfu ml−1 for bacteria and 100 cfu ml−1 for fungi. The specificity of the probes was confirmed, and no cross-reaction was detected in the present study. Furthermore, 13 cases were positive in the microarray and negative in culture. However, 4 cases were not identified as clear pathogens and only positive in the 16S probe sites. The diagnostic DNA microarray for intracranial infections has proven to be more rapid and sensitive, and it may be a better option for clinical application than culture methods.

Clinical experience of laboratory follow-up with noninvasive prenatal testing using cell-free DNA and positive microdeletion results in 349 cases.

Screening via noninvasive prenatal testing (NIPT) involving the analysis of cell-free DNA (cfDNA) from plasma has become readily available to screen for chromosomal and DNA aberrations through maternal blood. This report reviews a laboratory's experience with follow-up of positive NIPT screens for microdeletions. Patients that were screened positive by NIPT for a microdeletion involving 1p, 4p, 5p, 15q, or 22q who underwent diagnostic studies by either chorionic villus sampling or amniocentesis were evaluated. The overall positive predictive value for 349 patients was 9.2%. When a microdeletion was confirmed, 39.3% of the cases had additional abnormal microarray findings. Unrelated abnormal microarray findings were detected in 11.8% of the patients in whom the screen positive microdeletion was not confirmed. Stretches of homozygosity in the microdeletion were frequently associated with a false positive cfDNA microdeletion result. Overall, this report reveals that while cfDNA analysis will screen for microdeletions, the positive predictive value is low; in our series it is 9.2%. Therefore, the patient should be counseled accordingly. Confirmatory diagnostic microarray studies are imperative because of the high percentage of false positives and the frequent additional abnormalities not delineated by cfDNA analysis.

CNVs affecting cancer predisposing genes (CPGs) detected as incidental findings in routine germline diagnostic chromosomal microarray (CMA) testing

BackgroundIdentification of CNVs through chromosomal microarray (CMA) testing is the first-line investigation in individuals with learning difficulties/congenital abnormalities. Although recognised that CMA testing may identify CNVs encompassing a cancer predisposition...

Abstract 5409: Assessment of the MammaPrint 70-gene profile using RNA sequencing technology

Abstract Introduction: Improvements in RNA processing have enabled microarray diagnostics for formalin-fixed, paraffin-embedded (FFPE) tissue. Recently, MammaPrint, a prognostic 70-gene profile for early-stage breast cancer, was successfully translated to FFPE tissue showing to be substantially equivalent to fresh tissue. In recent years, RNA-sequencing (RNA-Seq) became the standard method for transcriptome analysis, because of its low background signal and its ability of quantifying a large dynamic range of expression levels. Here we report a preliminary analysis of the FFPE MammaPrint 70-gene profile using RNA-Seq technology and the comparison with the MammaPrint® microarray diagnostic test in a series of FFPE samples. Methods: RNA-Seq was carried out using a strand-specific RNA library preparation followed by target enrichment of the coding region of the human transcriptome without relying on the presence of poly-A tail. RNA sequencing libraries were prepared starting from a minimal amount of 20 ng of total RNA based on the DV200 metric assessment. The library pools were single-end sequenced on the Illumina HiSeq 2500 instrument at the length of 65bp. The resulting sequences were mapped to the human reference genome (build 38) using TopHat v2.1. Tophat was guided by using a transcriptome index from Ensembl (version 77). The HTSeq-count tool was used to generate the total number of uniquely mapped reads for each gene. Gene expressions were normalized with Count Per Million (CPM) normalization and log2 transformed afterwards. Microarray data of the sample were available for analysis comparison. Results: On average, we obtained 22 million reads assigned to gene per sample (min=15M, max=28M). The number of reads assigned to genes vary from 61% to 70% of the total number of reads. Between 80% and 90% of the reads assigned to genes mapped to protein coding genes which is comparable to fresh frozen material. The 70-gene signature was successfully mapped to the RNA-Seq genes. A median raw read-count of 384 was observed for the 70-gene profile among the samples. Importantly, we observed a high concordance (R2 Pearson correlation=0.97) between the MammaPrint index calculated using the RNA-Seq data and the correspondent Microarray MammaPrint index. Additionally, the BluePrint profile, a microarray diagnostic test for breast cancer molecular subtyping, was successfully translated to the RNA-Seq platform. As with the MammaPrint profile, BluePrint showed high concordance between the two technologies with high correlation values for each of the subtypes (Luminal R2 Pearson correlation=0.98, Basal R2 Pearson correlation=0.97, HER2 R2 Pearson correlation=0.77). Conclusions: Next Generation RNA-sequencing is a feasible technology to assess diagnostic signatures, such as the 70 gene MammaPrint and BluePrint profiles. Citation Format: Lorenza Mittempergher, Jacob B. Spangler, Mireille H. Snel, Leonie J. Delahaye, Iris de Rink, Sun Tian, Annuska M. Glas, Rene Bernards. Assessment of the MammaPrint 70-gene profile using RNA sequencing technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5409. doi:10.1158/1538-7445.AM2017-5409

Diagnostic microarray for 14 water and foodborne pathogens using a flatbed scanner

Parallel detection approaches are of interest to many researchers interested in identifying multiple water and foodborne pathogens simultaneously. Availability and cost-effectiveness are two key factors determining the usefulness of such approaches for laboratories with limited resources. In this study, we developed and validated a high-density microarray for simultaneous screening of 14 bacterial pathogens using an approach that employs gold labeling with silver enhancement (GLS) protocol. In total, 8887 probes (50-mer) were designed using an in-house database of virulence and marker genes (VMGs), and synthesized in quadruplicate on glass slides using an in-situ synthesis technology. Target VMG amplicons were obtained using multiplex polymerase chain reaction (PCR), labeled with biotin, and hybridized to the microarray. The signals generated after gold deposition and silver enhancement, were quantified using a flatbed scanner having 2-μm resolution. Data analysis indicated that reliable presence/absence calls could be made, if: i) over four probes were used per gene, ii) the signal-to-noise ratio (SNR) cutoff was greater than or equal to two, and iii) the positive fraction (PF), i.e., number of probes with SNR≥2 for a given VMG was greater than 0.75. Hybridization of the array with blind samples resulted in 100% correct calls, and no false positive. Because amplicons were obtained by multiplex PCR, sensitivity of this method is similar to PCR. This assay is an inexpensive and reliable technique for high throughput screening of multiple pathogens.

Application of the AMLprofiler Diagnostic Microarray in the South African Setting.

Acute myeloid leukemia (AML) is characterized by proliferation of the myeloid lineage and accumulation of immature hematopoietic cells in the bone marrow and is typified by marked heterogeneity both in response to treatment and survival. AMLprofiler is a qualitative in vitro diagnostic microarray incorporating seven molecular biomarkers used to diagnose and predict posttherapy survival rates. In this study, we compared AMLprofiler to routine AML diagnostic methodologies employed in South Africa, focusing on consistency of the results, cost, and time to result. RNA was isolated from bone marrow and peripheral blood samples from patients with de novo AML and was processed using Affymetrix Gene Profiling Reagent kits. The results from AMLprofiler and standard methodologies were highly comparable. In addition, many samples were determined to be positive for biomarkers not routinely investigated in South Africa, namely, CEBPA double mutants, NPM1 variants, and altered expression levels of BAALC and EVI1. 38% of samples presented with no positive biomarker; AMLprofiler nonetheless enabled 26% of AML patients to be classified into either favorable or poor prognostic categories. This study highlights the comprehensive nature of the microarray. Decreased time to result and refinement of risk stratification are notable benefits.

AMLprofiler: A Diagnostic and Prognostic Microarray for Acute Myeloid Leukemia.

Acute myeloid leukemia is characterized by the proliferation and accumulation of immature hematopoietic cells of the myeloid lineage in the bone marrow. The disease is typified by diverse genetic abnormalities and marked heterogeneity both with regard to response to treatment and survival. The AMLprofiler is a qualitative in vitro diagnostic microarray developed by SkylineDx for use with Affymetrix technology. The AMLprofiler makes use of RNA chemistry and incorporates seven separate assays based on three different technologies-cytogenetics, mutation, and expression analysis-to predict post-therapy survival rates in patients with acute myeloid leukemia. The assay has been validated for processing of bone marrow samples from which RNA is isolated within 48 h. The samples are subsequently processed using Affymetrix GeneChip reagent kits and analyzed on the Affymetrix GeneChip 3000Dx v2 system. The scanned AMLprofiler data is sent to a centralized server of SkylineDx via a secured Internet connection, and a diagnostic report is generated within 15 min. We have performed several AMLprofiler assays in our laboratory and found the data generated via this assay to be consistent with standard modalities.