by Prof. Godfrey Grech, Dr Stefan Jellbauer and Dr Hilary Graham
Understanding the molecular characteristics of tumour heterogeneity and the dynamics of progression of disease requires the simultaneous measurement of multiple biomarkers. Of interest, in colorectal cancer, clinical decisions are taken on the basis of staging and grade of the tumour, resulting in highly variable clinical outcomes. Molecular classification using sensitive and precise multiplex assays is required. In this article we shall explain the use of innovative methodologies using signal amplification and bead-based technologies as a solution to this unmet clinical need.
Introduction
Cancer is the leading cause of death globally, accounting for 9.6-million deaths in 2018, with 70% of cancer-related mortality occurring in low- and middle-income countries. In 2017, only 26% of low-income countries provided evidence of full diagnostic services in the public sector, contributing to late-stage presentation [1]. There are various aspects that negatively affect the survival rate of patients, including but not limited to:
(a) highly variable clinical outcome mainly due to lack of molecular classification;
(b) treatment of advanced stage of the disease mainly due to lack of, or reluctance to, screening programmes, resulting in treatment of symptomatic disease that is already in advanced stage;
(c) heterogeneity of the tumours that are undetected using representative biopsies of the tumour at primary diagnostics; and
(d) lack of surveillance of patients to detect early progression of disease and metastasis, mainly due to clinically inaccessible tumour tissue and the need of sensitive technologies to measure early metastatic events.
Colorectal cancer (CRC) represents the second most common cause of cancer-related deaths, with tumour metastasis accounting for the majority of cases. To date, treatment decisions in CRC are based on cancer stage and tumour location, resulting in highly variable clinical outcomes. Only recently, a system of consensus molecular subtype (CMS) was proposed based on gene expression profiling of primary CRC samples [2]. Organoid cultures derived from CRC samples were used in various studies to adapt the CMS signature (CMS1–CMS4) to preclinical models, to study heterogeneity and measure response to therapies. Of interest, the epidermal growth factor receptor (EGFR) and receptor tyrosine-protein kinase erbB-2 (HER2) inhibitors were selective and have a strong inhibitory activity on CMS2, indicating that subtyping provides information on potential first-line treatment [3]. In CRC, copy number variations are associated with the adenoma-to-carcinoma progression, metastatic potential and therapy resistance [4]. Our recent studies using primary and matched metastatic tissue showed that TOP2A (encoding DNA topoisomerase II alpha) and CDX2 (encoding caudal type homeobox 2) gene amplifications are associated with disease progression and metastasis to specific secondary sites. Hence, introducing robust and clinically-friendly molecular assays to enable measurement of multiple biomarkers to assess matched resected material and tumour-derived cells or cell vesicles in blood during therapy and beyond, has become a necessity to overcome this deadly toll. In addition, to support diagnostics in remote countries, the assays should allow measurement in low input, low quality tissue material.
To enable precise future diagnosis and patient classification and surveillance, we developed innovative methodologies (Innoplex assays) measuring expression of multiple marker panels representing the primary tumour heterogeneity and the dynamic changes associated with disease progression. We optimized these Molecular Diagnostics Sensitive and precise multiplex assays enable accurate classification and surveillance of tumours April/May 2020 21 | methodologies for multiplex digitalized readout using various sample sources ranging from archival formalin-fixed paraffinembedded (FFPE) tissues and characterization of gene amplifications in blood-derived exosomes. In this article we summarize the Innoplex assays based on the xMAP Luminex Technology and the Invitrogen QuantiGene™ Plex Assay, the research outputs from the University of Malta in terms of the biomarker panels and the commercialization of the assays through Omnigene Medical Technologies Ltd.
Molecular profiling technology and workflow
The Innoplex multiplex assays are based on two components, namely (a) the integration of the Invitrogen QuantiGene™ Plex Assay (Thermo Fisher Scientific) and the xMAP Luminex technology enabling multiplexing of the technique, and (b) the novel panel of biomarkers developed by the Laboratory of Molecular Oncology at the University of Malta, headed by Professor Godfrey Grech. The technologies and the research output provides the versatility of the assays. To date a breast cancer molecular classification panel and a CRC metastatic panel were developed and are currently being optimized for the clinical workflow by Omnigene Medical Technologies Ltd through the miniaturization and automation of the RNA-bead plex assay.
The Innoplex RNA-bead plex assays use the Quantigene branched- DNA technology that runs on the Luminex xMAP technology. Specific probes are conjugated to paramagnetic microspheres (beads) that are internally infused with specific portions of red and infrared fluorophores, used by the Luminex optics (first laser/ detector) to identify the specific beads known to harbour specific probes. The Quantigene branched-DNA technology builds a molecular scaffold on the specifically bound probe-target complex to amplify the signal that is read by a second laser/LED [5].
The workflow of the assay can be divided into a pre-analytical phase involving the lysis/homogenization of the tissue or cells, and the analytical phase that involves hybridization, pre-amplification and signal amplification with a total hands-on time of 2|h. This is comparable to the time required to prepare a 5-plex quantitative real-time (qRT)-PCR reaction. Increased multiplexing within a reaction will result in an increase in hands-on time for qRT-PCR, while the same 2|h are retained for the Innoplex assays. As shown by Scerri et al. [5], qRT-PCR 40-plex reactions will require 9|h to prepare as compared to the bead-based assay which retains a 2|h workflow. Hence, the bead-based assays have the advantage for high-throughput analysis in multiplex format.
Performance and applications
We have shown in previous studies, using breast cancer patient material, that gene expression can be measured using our RNA-based multiplex assays in FFPE patient archival material that was of low quality and low input [6]. Using a 22-plex assay, inter-run regression analysis using RNA extracted from cell lines performed well with an r2>0.99 in our hands. These assays were also evaluated by other groups using snap-frozen and FFPE tissues derived from patient and xenograft samples. In comparison with the reference methods, the bead-based multiplex assays outperformed the qRT-PCR when using FFPE-tissue-derived RNA, giving reliability coefficients of 99.3–100% as compared to 82.4–95% for qPCR results, indicating a lower assay variance [5].
One main advantage of the Innoplex assays is the direct measurement of gene expression on lysed/homogenized tissues and cells, providing a simplified workflow without RNA extraction, cDNA synthesis and target amplification. In addition, due to its chemistry and use of beads, gene expression can be measured in a multiplex format (up to 80 genes) using low input and low quality material. This enables the use of the assay in remote laboratories, and as detailed below for stained microdissected material and to measure multiple markers in low abundance material, such as blood-derived circulating tumours cells.
Comparison of gene expression data from homogenized and lysed patient tissue derived from either unstained or hematoxylin and eosin (H&E)-stained sections shows a high correlation (r2>0.98). This provides an advantage when studying heterogeneous tumours that are microdissected from H&E stained slides. In fact, using this methodology, an estrogen-receptor-positive tumour was analysed and one of the tumour foci had a more advanced tumour expressing the mesenchymal marker, FN1 (fibronectin). This was only possible by running a 40-plex assay on minimal input material (microdissected from 20|μm section) representing markers for molecular classification, epithelial to mesenchymal transition, and proliferation markers [7]. A recent audit on breast cancer diagnosis, indicates clearly that heterogeneous cases characterized using the bead-based multiplex assays on resection tumour samples are not represented in matched biopsies used for patient diagnosis. In fact, only 3.5% of 97 intra-tumour heterogeneous cases were detected in a cohort of 570 patients at diagnosis. The advantage of the digitalized result of the Innoplex assays is to avoid increasing the workload of pathologists when resected samples are re-analysed to characterize multiple sites within a tumour.
Multiplexing provides both sensitivity and versatility in biomarker validation and was instrumental in our hands to measure gene amplifications in cancer-derived exosomes (tumour-derived vesicles in blood) using plasma from CRC patients. Of interest, these methods have been optimized using cancer cell lines to measure RNA transcripts in cells at low abundance, mimicking the isolation of circulating tumour cells from blood [5]. In this study we show that measurement of transcripts of EPCAM (encoding epithelial cell adhesion molecule), KRT19 (encoding keratin, type I cytoskeletal 19), ERBB2 (encoding HER2) and FN1 maintain a linear signal down to 15 cells or less. In addition, the simple workflow with direct measurement using lysed cells enables this assay to be translated more efficiently to the clinical setting. Absolute quantification of transcripts presents alternative endpoint methods to the Invitrogen QuantiGene™ Plex Assay. Droplet digital PCR (dPCR) and Nanostring’s nCounter® technology are precise and sensitive methods. Multiplexing in dPCR is limiting and RNA studies are hindered by reverse transcription inefficiency. The nCounter® technology requires multiple target enrichment (PCR-based pre-amplification) to measure low input RNA, which introduces amplification bias and risk for false positive results.
Summary
In conclusion, the innovative multiplex assays indicate a shift from reactive medicine (treating patients based on average risks) towards predictive, precise and personalized treatment that takes into account heterogeneity of primary tumour, progression of tumour during therapy and the metastatic surveillance of the individual patient. The versatility of the method allows the development of various assays to support different applications (Figs|1 & 2). Our first innovative methods were developed for the molecular classification of luminal and basal breast cancer and to predict sensitivity to specific therapy in triple-negative breast cancer subtype [8]. As discussed above, the multiplex assays have a wide range of possible applications in the diagnosis of tumours and surveillance of tumours during therapy. The main advantages of these methods include:
(a) implementation of high-throughput analysis which has a positive impact on remote testing and implementation of such assays in patient surveillance and clinical trials;
(b) the digitalized result excludes subjectivity and equivocal interpretation, which are common events in image-based measurements, and also eliminates the need for highly specialized facilities and human resources;
(c) accurate and precise detection of multiple targets in one assay, minimizing the use of precious patient samples; and
(d) enables the measurement of gene expression in heterogeneous tumours and low input / low quality patient material. The method is streamlined with the current pathology laboratory practices resulting in a workflow that is cost-effective and with minimal turnaround time.
The authors
Godfrey Grech*1,2 PhD, Stefan Jelbauer3 PhD, Hilary Graham4 PhD
1 Department of Pathology, Faculty of Medicine & Surgery, University of Malta
2 Scientific Division, Omnigene Medical Technologies Ltd, Malta
3 Thermo Fisher Scientific, Carlsbad, CA 92008, United States
4 Licensed Technologies Group, Luminex Corporation, Austin, Texas

*Corresponding author
E-mail: godfrey.grech@um.edu.mt

by Dr Huub H. van Rossum
Recently, significant improvements have been made in understanding and applying moving average quality control (MA QC) that enable its practical implementation. These include the description of new and laboratory-specific MA QC optimization and validation methods, the online availability thereof, insights into operational requirements, and demonstration of practical implementation.
Introduction
Moving average quality control (MA QC) is the process of algorithmically averaging obtained test results and using that average for (analytical) quality control purposes. MA QC is generally referred to as patient-based real-time quality control (PBRTQC) because it is one of various methods (e.g. limit checks, delta checks, etc) that use patient results for (real-time) quality control. MA QC was first described over half a century ago as ‘average of normals’ [1]. Since then, it has evolved into a more general MA QC concept not necessarily based on using mean calculations of the obtained ‘normal’ test results [2]. Although MA QC has been available for a few decades, its adoption by laboratories has been limited due to the complexity of setting up the necessary procedures, operational challenges and a lack of evidence to justify its application and demonstrate its value. During the past 5|years, however, significant improvements have been made in the field of MA QC, and research studies have addressed all these issues. Consequently, true practical application of validated MA QC procedures to support analytical quality control in medical laboratories is now possible. Furthermore, the recent improvements may well change the way we perform daily analytical quality control in medical laboratories in the near future.
MA QC optimization and validation
The recent significant improvements in the field of MA QC include, first and foremost, the description of new methods to design and optimize laboratory-specific MA QC procedures and to enable validation of their actual error-detection performance [2–5]. These methods use realistic MA QC simulations based on laboratory-specific datasets and thus provide objective insights into MA QC error detection [2]. To enable practical implementation, the requirement that the number of MA QC alarms must be manageable is now acknowledged as essential and has been fulfilled when setting up MA QC [2, 6]. The newly developed methods use a novel metric to determine the error-detection performance: that is, the mean or median number of test results needed for error detection. One of the new methods presents these simulation results in bias-detection curves so that the optimal MA QC procedure can be selected, based on its overall error-detection performance [5]. An example of a bias-detection curve and its application is presented in Figure 1. After selecting the optimal MA QC settings, an MA validation chart can be used to obtain objective insights into the overall error-detection performance and the uncertainty thereof. Therefore, this chart can be seen as a validation of the MA QC procedure. An example of an MA validation chart is presented in Figure 2 and shows that the MA QC procedure will almost always (with 97.5% probability) detect a systematic error of −4% (or larger negative errors) within 20 test results.
Importantly, this method has become available to laboratories via the online MA Generator application, enabling them to design their own optimized and validated MA QC procedures [7]. Laboratories can now upload their own datasets of historical results, study potential MA QC settings using this simulation analysis and obtain their own laboratory-specific MA QC settings and MA validation charts. Several laboratories have demonstrated that this tool has enabled them to obtain relevant MA QC settings and thus implement MA QC [8, 9].
Integration of MA QC with internal QC
Measurement of internal quality control (iQC) samples is still the cornerstone of analytical quality control as performed in medical laboratories. For many tests, iQC alone is sufficient to assure and control the quality of obtained test results. For some tests, however, iQC itself is insufficient. The reasons for this are related to certain fundamental characteristics of iQC that include: lack of available (stable) control materials, its scheduled character, the risk of using non-commutable control samples and tests with a sigma metric score of ≤4. For several reasons, PBRTQC or, more specifically, MA QC is a particularly valuable and powerful way to support quality assurance in such cases.
First, if no (stable) QC materials are available it is impossible, or it becomes complicated, to use iQC. This is, for example, relevant for the erythrocyte sedimentation rate, serum indices or hemocytometry tests including erythrocyte mean corpuscular volume in particular. MA QC is possible as long as patient results are available. Second, the scheduled character of iQC becomes a limitation and a risk when temporary assay failures or rapid onset of critical errors occur between scheduled iQC. Because a new MA QC value can be calculated for each newly obtained test result, MA QC can be designed as a continuous and real-time QC tool. In this context, detection of temporary assay failure by MA QC between scheduled iQC has been demonstrated for a sodium case [10], and several examples of MA QC detection of rapid onset of critical errors have been published for both chemistry and hematological tests [11]. Third, because PBRTQC methods such as MA QC use obtained patient results, by design there is no commutability issue. Fourth, and finally, for some tests iQC is intrinsically limited in its ability to detect relevant clinical errors, due to the low ratio of biological variations to analytical variations, as reflected in low sigma metric values. Such tests require frequent iQC analysis and application of stringent control rules. However, even with such an intensive and strict iQC set-up, the probability of detecting clinically relevant errors remains limited [12]. In contrast, MA QC has the best error-detection performance for tests with a low sigma value [13].
For all these reasons, MA QC is ideal for supplementing analytical quality control by iQC. Recently, an approach was presented that integrated MA QC into the QC plan when iQC was found to be insufficient [9]. This approach was based on first determining whether one of the abovementioned iQC limitations applied to a test. If so, then iQC alone was considered insufficient and MA QC was studied, using the online MA Generator tool (www.huvaros.com) to obtain optimal MA QC settings and MA QC procedures to support the analytical quality control [7, 9]. The MA QC error-detection performance was validated using MA validation charts. These latter insights into MA QC error detection also enabled iQC measurements to be reduced. The MA QC procedures alone provided significant error-detection performance, so running iQC measures multiple times a day would add only limited error-detection performance. Therefore, it was decided to run the iQC only once a day and add the obtained MA QC procedures to the QC plan.
Others have taken this a step further and studied MA QC not only for tests with limited iQC performance but also for a much larger test selection, in order to reduce the number of iQC measures and more efficiently schedule and apply iQC [4]. This approach has been shown to be successful for a large commercial laboratory with high production numbers. Since the MA QC error-detection performance improves with an increasing number of test results and benefits from a small number of pathological test results, this approach may be particularly valuable to the larger commercial laboratories. For such an approach, the key is objective insights into the error-detection performance of MA QC procedures such as obtained using MA validation charts.
Implementation and application of MA QC for real-time QC in medical laboratories
The final aspect in which there have been significant improvements in recent years relates to the practical application of MA QC in medical laboratories. Recently, an International Federation of Clinical Chemistry and Laboratory Medicine working group was founded that summarized medical laboratories’ experiences of practically applying MA QC and formulated several recommendations for both MA QC software suppliers and medical laboratories that are working on, or are interested in, implementation of MA QC [14, 15]. Also, a step-by-step roadmap has recently been published to enable MA QC implementation [9]. The first two steps of this roadmap – i.e. selection of tests and obtaining MA QC settings for them – were discussed in the previous two paragraphs.
The next step would be to set up and configure the software used to implement MA QC in medical laboratories. If you are interested in applying MA QC in your laboratory, it is important to review the available software (e.g. analyser, middleware, LIS, third party) and to decide which will be used to run and apply MA QC. Your decision depends not only on the availability of suitable software in or for the laboratory, but also on the actual MA QC functionality present in the software packages.
The minimum software features that are necessary to enable practical implementation have been formulated [2, 15]. In my view, key elements would be that the software supports: exclusion of specified samples (non-patient materials, QC results, extreme results, etc), calculation of relevant MA QC algorithms, applying SD-based as well as non-statistical control limits (plain lower and upper control limits), proper real-time alarming and – depending on the MA QC optimization method – presentation of MA QC in a Levey–Jennings or accuracy graph. Figure 3 presents an example of MA QC in an accuracy graph as operated for real-time QC in my laboratory. To enable effective implementation of MA QC, all of these software features should be configured.
The final implementation step I wish to address here is the design of laboratory protocols for working up MA QC alarms, which determines the extent to which an error detected by an MA QC alarm is acknowledged. An important requirement is that all MA QC alarms should be worked up by means of this protocol.
As previously indicated, because MA QC can generate many more QC results and alarms than iQC, a critical requirement of every MA QC procedure is a manageable number of alarms. As a result, when an MA QC alarm occurs there is a reasonable chance of detecting error.
A first common action as part of the MA QC alarm protocol would be to run iQC. This provides a quick insight into the size of the error and enables rapid confirmation of large errors. As a second step, re-running of recently analysed samples (in addition to running iQC) enables temporary assay failures to be detected and can confirm or exclude errors not necessarily detectable by iQC. Also, finally, a review of recently analysed test results to identify a pre-analytical cause or a single patient with extreme but valid test results is often very useful as part of the MA QC alarm protocol. All these aspects have recently been discussed in greater detail [10, 14].
Conclusions
Altogether, the recent developments in the field of PBRTQC and, more specifically, MA QC now – finally – enable true practical implementation of MA QC in medical laboratories and allow more effective and efficient QC plans to be designed.
The authors
Huub H. van Rossum1,2 PhD
1 Department of Laboratory Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands
2 Huvaros, Amsterdam, The Netherlands

E-mail: h.v.rossum@nki.nl