Advances in circulating biomarker research have led to the use of blood samples to characterize cancer patients’ tumour DNA where a lack of tumour tissue prevents molecular testing. This is critical for non-small cell lung cancer (NSCLC) patients who require tumour molecular characterization in order to access life-extending treatments that would be denied without biopsy. Here we describe a new liquid biopsy diagnostic service for NSCLC patients at the All Wales Medical Genetics Service, Cardiff, UK.
by Dr Angharad Williams, Dr Daniel Nelmes, Helen Roberts and Dr Rachel Butler

Liquid biopsies and cell-free circulating tumour DNA (ctDNA) in clinical practice
The term ‘liquid biopsy’ comes from the sampling of a cancer patient’s tumour DNA from a simple, non-invasive blood test rather than an invasive surgical biopsy. This circulating tumour DNA (ctDNA) is a small fraction of the total cell-free circulating DNA (cfDNA) and consists of short strands of DNA shed by degrading tumour cells directly into a patient’s bloodstream. The levels of ctDNA present will vary greatly based on clinical factors such as proximity of sampling to chemotherapy or radiotherapy, as well as the burden and activity of the tumour [1].

Genetic mutations within the patient’s tumour are detectable at extremely low levels in the ctDNA in the blood [2]. The detection of such mutations provides many potential uses for ctDNA as a biomarker in disease diagnosis and screening, monitoring of therapy response and resistance and detection of minimal residual disease and relapse [3–6].

The many advantages for using ctDNA as a biomarker rest on the fact that ctDNA can be simply extracted from blood; therefore, invasive biopsy procedures can be avoided. Such simple blood sampling is beneficial if the patient is too ill for invasive surgery and is also useful if biopsy-based tumour analysis has failed; thus, unnecessary re-biopsies can be averted. Another benefit of the use of ctDNA over biopsies is that serial blood samples can be taken to replace the need for a re-biopsy to monitor a patient’s response to therapy in ‘real-time’ in the clinic. Practically, blood samples can be arranged, taken and sent for processing at a much faster pace than surgery, gaining valuable time for patients who are in need of urgent cancer-related treatments.

There are, however, potential pitfalls in using ctDNA as a diagnostic biomarker that should be considered prior to setting up a ctDNA-based diagnostic service as well as when interpreting genetic results from ctDNA (summarized in Figure 1). The greatest concern is the fragile nature of cfDNA molecules [7], which means that cfDNA will degrade in a blood sample to undetectable levels the longer that the blood is left unprocessed. Efficient centrifugation and separation of the blood to plasma and storage at −80 °C can be used to halt degradation of cfDNA. In cases where analysis of the cfDNA sample identifies no genetic mutations, this raises the important question of whether the patient was actually shedding ctDNA at the time of the blood sampling or did the ctDNA degrade prior to sample processing? This indicates the unfortunate possibility of false negative results when using ctDNA in the diagnostic setting. Another important factor to consider is that the level of a mutation in the ctDNA, which can quite often be as low as ≤1% mutated ctDNA to wild-type patient cfDNA [8]. Thus, only highly sensitive molecular analysis options should be considered for diagnostic testing strategies using ctDNA.

Molecular analysis of the epidermal growth factor receptor gene in non-small cell lung cancer patients
The epidermal growth factor receptor gene (EGFR) encodes the EGRF protein, a signalling protein that is part of the cellular pathways that control normal cell growth, differentiation and angiogenesis [9]. Approximately 10–20% of ethnically Caucasian non-small cell lung cancer (NSCLC) patients with the adenocarcinoma histological subtype will have a DNA mutation in the EGFR gene, which will activate abnormal constitutive signalling and tumorigenesis [10].

The most common sensitizing EGFR mutations, which represent 85% of known activating EGFR mutations in NSCLC, are the exon 21 point mutation c.2573T>G (p.Leu858Arg) and in-frame deletions in exon 19 [9]. These activating mutations provide a convenient target for first and second generation tyrosine kinase inhibitor (TKI) treatments such as gefitinib (Iressa®, AstraZeneca) [11–13] and act as positive predictive biomarkers for response to these drugs. Traditionally, for patients to access these TKI treatments, tumour biopsy in the form of a formalin-fixed sample is tested for evidence of these activating EGFR mutations at clinical genetic testing centres, such as the All Wales Medical Genetics Service (AWMGS) in Cardiff. However, preservation of the tumour biopsy as formalin-fixed paraffin-embedded (FFPE) tissue leads to a number of issues with genetic analysis including poor quality and yields of DNA (noted in Figure 1). Additionally, a large proportion of NSCLC patients are not well enough to have a biopsy taken and so genetic analysis of tumour DNA and subsequent access to TKI treatments is not possible. This inequity in service provision indicated a clinical need to expand current testing options for NSCLC patients to reach those patients who cannot access TKI-based stratified medicine treatment options. To address this clinical need, a ctDNA-based diagnostic NHS service was developed within AWMGS to detect activating EGFR mutations from patient blood samples in order to alleviate the need for biopsy.

In addition to the availability of first and second generation TKIs, a new third generation TKI, osimertinib (Tagrisso®, AstraZeneca), has recently been made available to a specific group of NSCLC patients. Approximately 50% of patients on first and second generation TKIs will develop an EGFR resistance mutation, c.2369C>T (p.Thr790Met) (commonly known as T790M), leading to disease progression [6]. Since October 2016, osimertinib (Tagrisso®, AstraZeneca), has been available to UK patients shown to harbour the T790M mutant in their tumour via either biopsy or ctDNA analysis through the NHS Cancer Drugs Fund [14]. CtDNA testing has become a popular method of testing for resistance mutations as it mitigates the need for a second invasive biopsy for the patient and, also, serial blood samples can be used to track the patient’s response over a period of time [15].

Establishing the ctDNA-based NSCLC stratified medicine service in the All Wales Medical Genetics Service
Since 2009, the AWMGS has been providing stratified medicine services for NSCLC patients, as well as metastatic colorectal cancer patients, melanoma and gastrointestinal stromal tumour patients in Wales. Though all of these services are based on FFPE tumour analysis, we have developed a wealth of experience in using ctDNA from blood in the field of clinical trials. By 2015, following a number of successful ctDNA-based feasibility studies by laboratory staff and research students, we were confident that we had the knowledge and expertise to bring ctDNA into service, and were one of the first laboratories in the UK to do so.

Owing to the inherent shortcomings of using ctDNA as a biomarker, discussed previously, the following questions were deliberated during validation to find the most appropriate testing methods for the diagnostic service:

• How do we best protect ctDNA in blood during sampling and shipping to the lab, to ensure that the sample that reaches us is faithful to the patient’s real mutation status?
• As important mutations in ctDNA can be at very low levels, how do we ensure we can detect a low enough range of mutations to be clinically relevant to interpretation?

To guarantee sample quality and maintain sufficient levels of ctDNA, we have imposed stringent quality measures on the blood collection and dispatch. The main requirement is that blood samples should be taken in a specialist preservative tube such as CellSave Preservative Tubes (Janssen Diagnostics) or Cell-Free DNA BCT® (Streck) and must reach the laboratory for processing within a strict 96-hour window. This was decided on after discussion with other research groups and internal investigations on the stability of ctDNA in blood and the use of preservative tubes [7].

The sensitivity of the molecular ctDNA assay was paramount in our decision to use the recently developed technology droplet digital polymerase chain reaction (ddPCR) by Bio-Rad (Bio-Rad Laboratories, Inc, California, USA). ddPCR is a highly sensitive fluorescence-based PCR method with an extreme lower limit of detection of 0.0001% of mutant DNA in a wild-type background, which makes it the superior choice over other technologies such as next-generation sequencing and quantitative PCR (qPCR). Practically, in the service, we have detected EGFR mutations in patient ctDNA at an abundance as low as 0.7%.

The AWMGS now provides ctDNA-based testing of the EGFR gene in NSCLC patients at both first-line testing for sensitizing mutations and for resistance mutation testing on patient progression on TKIs (Figure 2). The service launched across Wales in April 2016 and has since been expanded to provide testing for certain centres in the South West of England with funding from AstraZeneca. A year on, over 100 patients have been tested, the majority (approximately 60%) of patient referrals have been for T790M progression testing to avoid repeat biopsies for patients. Six patients, for whom TKIs were previously inaccessible due to failed FFPE-based testing or inability to biopsy, were successfully tested through the ctDNA service and are now receiving first-line EGFR TKI therapy following the detection of activating EGFR mutations in ctDNA.

Ongoing and future developments
The field of liquid biopsies is steadily gaining pace in the UK and abroad with a number of centres now providing EGFR ctDNA testing. New circulating biomarkers, such as exosomes and circulating tumour cells, are coming through from translation research and have vast potential in the field of stratified medicine. At the AWMGS, we aim to expand our current liquid biopsy testing in the near future with targets for both metastatic colorectal cancer and metastatic melanoma.

References
1. Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer 2011; 11: 426–437.
2. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, Bartlett BR, Wang H, Luber B, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 2014; 6: 224ra24.
3. Chen KZ, Lou F, Yang F, Zhang JB, Ye H, Chen W, Guan T, Zhao MY, Su XX, et al. Circulating tumor DNA detection in early-stage non-small cell lung cancer patients by targeted sequencing. Sci Rep 2016; 6: 31985.
4. Dawson S-J, Tsui DWY, Murtaza M, Biggs H, Rueda OM, Chin SF, Dunning MJ, Gale D, Forshew T, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med 2013; 368: 1199–1209.
5. Forshew T, Murtaza M, Parkinson C, Gale D, Tsui DW, Kaper F, Dawson SJ, Piskorz AM, Jimenez-Linan M, et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 2012; 4: 136ra68.
6. Murtaza M, Dawson SJ, Tsui DW, Gale D, Forshew T, Piskorz AM, Parkinson C, Chin SF, Kingsbury Z, et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 2013; 497: 108–112.
7. Rothwell DG, Smith N, Morris D, Leong HS, Li Y, Hollebecque A, Ayub M, Carter L, Antonello J, et al. Genetic profiling of tumours using both circulating free DNA and circulating tumour cells isolated from the same preserved whole blood sample. Mol Oncol 2016; 10: 566–574.
8. Heitzer E, Ulz P, Geigl JB. Circulating tumor DNA as a liquid biopsy for cancer. Clin Chem 2015; 61: 112–123.
9. Jänne PA, Engelman JA, and Johnson BE. Epidermal growth factor receptor mutations in non–small-cell lung cancer: implications for treatment and tumor biology. J Clin Onc 2005; 23: 3227–3234.
10. Li T, Kung H-J, Mack PC, Gandara DR. Genotyping and genomic profiling of non-small-cell lung cancer: implications for current and future therapies. J Clin Onc 2013; 31: 1039–1049.
11. Douillard JY, Ostoros G, Cobo M, Ciuleanu T, Cole R, McWalter G, Walker J, Dearden S, Webster A, et al. Gefitinib treatment in EGFR mutated Caucasian NSCLC: circulating-free tumor DNA as a surrogate for determination of EGFR status. J Thorac Oncol 2014; 9: 1345–1353.
12. Goto K, Ichinose Y, Ohe Y, Yamamoto N, Negoro S, Nishio K, Itoh Y, Jiang H, Duffield E, et al. Epidermal growth factor receptor mutation status in circulating free DNA in serum: from IPASS, a phase III study of gefitinib or carboplatin/paclitaxel in non-small cell lung cancer. J Thorac Oncol 2012; 7: 115–121.
13. National Institute for Health and Care Excellence (NICE). Gefitinib for the first-line treatment of locally advanced or metastatic non-small-cell lung cancer. Technology appraisal guidance [TA192] 2010. [https: //www.nice.org.uk/guidance/ta192]
14. NICE. Osimertinib for treating locally advanced or metastatic EGFR T790M mutation-positive non-small-cell lung cancer. Final appraisal determination [TA10022] 2016. [https: //www.nice.org.uk/guidance/GID-TA10022/documents/final-appraisal-determination-document]
15. Sundaresan TK, Sequist LV, Heymach JV, Riely GJ, Jänne PA, Koch WH, Sullivan JP, Fox DB, Maher R, et al. Detection of T790M, the acquired resistance EGFR mutation, by tumor biopsy versus noninvasive blood-based analyses. Clin Cancer Res 2016; 22: 1103–1110.

The authors
Angharad Williams¹ PhD, Daniel Nelmes^2,3 PhD, Helen Roberts¹ BSc and Rachel Butler*¹ FRCPath
¹All Wales Medical Genetics Service,
NHS Wales, The Institute of Medical
Genetics, Cardiff and Vale University LHB,
University Hospital of Wales, Cardiff
CF14 4XW, Wales, UK
²School of Medicine, Cardiff University, Cardiff CF14 4XN, Wales, UK
³Velindre Cancer Centre, Cardiff
CF14 2TL, Wales, UK

*Corresponding author
E-mail: Rachel.Butler@wales.nhs.uk

The DxN VERIS Molecular Diagnostics System* from Beckman Coulter is a real-time PCR analyser for accurate and precise quantitative detection of both RNA and DNA targets. Single sample random access offers workflow flexibility and automation benefits to the laboratory. The design features of the DxN VERIS System and performance characteristics of the VERIS HCV, HIV-1, HBV, and CMV viral load assays enable laboratories to develop Quality Control (QC) programmes tailored to their unique needs. Methods: A QC programme was developed by the Virology lab at the Rennes University Hospital, France. The laboratory evaluated the performance levels of the DxN VERIS System as well as the total number of VERIS HIV-1, HBV, and CMV tests performed over a period of five months. Results: The precision observed over the five-month study period was less than 5.8% CV with standard deviation (SD) within 0.16 log IU/ml. Based on these results the laboratory concluded that performing three levels of QC (negative, low, high) two times per week would provide an acceptable level of system control while significantly reducing QC costs and hands-on time.

Introduction
Consistency in reporting quantitative viral load results is critically important to clinical laboratories, physicians, and the patients they serve. The use of quantitative tests to measure viral load levels in patient samples is especially important for monitoring treatment. With the advent of new quantitative PCR (qPCR) assays for viral load testing, physicians are better able to manage diseases with antiretroviral therapy (ART).
Clinical laboratories are challenged to achieve stringent Quality Control (QC) objectives for viral load testing in an effective and economical manner. The use of external quality controls (EQC) provides laboratories with a means of monitoring variation in the analytical process as well as environmental factors that can affect patient results. In addition, EQC can assist laboratories in identifying when errors are occurring that can impact the utility of viral load assays. For these reasons, manufacturers of qPCR systems may recommend the use of EQC as part of the analytical process for viral load testing.
The DxN VERIS System and VERIS viral load assays are designed to deliver a high standard of clinical performance while providing rapid, convenient, and cost effective QC alternatives to the laboratory.

Quality control for quantitative diagnostic systems – a statistical approach
Statistical QC is defined as a procedure in which stable samples are measured and the observed results compared with limits that describe the variation expected when the measurement method is working properly[2]. Statistical QC is important to ensure the quality of the test results produced by any measurement method. An important concept in statistical QC is the definition of an “analytical run”. With many modern analytical systems, the definition of a run is not always clear. For example, many molecular diagnostics analysers available to laboratories today perform testing in “batch” mode, wherein each run corresponds to a single batch of several tests. While these methods can provide efficiencies in some testing environments (e.g. high volume labs) they can result in delayed results while the laboratory waits to accrue sufficient samples to complete the batch. In addition, batch systems lack the flexibility to adapt to fluctuating testing demand driven by sample volume and clinical needs in the laboratory. New qPCR systems are now available that provide “random access” capability; enabling labs to test individual samples at the precise time that they are most needed. In addition to providing more timely results for physicians and patients, these systems can also increase laboratory work flow efficiency, resulting in less hands-on time. For random access systems, an analytical run can be better understood in terms of the time or number of measurements for which the measurement is stable[2]. Statistical guidance for molecular assays typically suggests that quality control samples should be run at least once during each user-defined analytical run.

The DxN VERIS system
The DxN VERIS System is a fully automated molecular diagnostic system that integrates nucleic acid extraction, reaction setup, real-time PCR amplification and detection, and results interpretation into one system; saving space and time. The system provides single sample random access capability which allows the laboratory to run the right viral load test at the right time for physicians and patients. The DxN VERIS System provides time and workflow advantages compared to batch systems which require the laboratory to accrue a number of patient samples prior to each run.

Designed for quality and accuracy
The DxN VERIS System is engineered to deliver a high level of reliability and process control. The system provides a comprehensive range of individual process checks throughout the analytical process, from sample introduction to result reporting. Listed below are key features of the DxN VERIS System that ensure consistent performance and process control. Collectively, these capabilities may serve to reduce risk of analytical error within run and between runs.

Sample introduction

• Sample obstruction detection
• Liquid level sensing of sample and key reagents
• Fully automated end to end processing, which minimizes sample handling

Nucleic acid extraction

• Unitized extraction and purification (EP) cartridges to minimize contamination risk
• Internal thermal control to enable repeatable clinical results across the specified range of laboratory conditions
• Same hardware same pathway for extraction of all samples (transfers, pumps, motors)
• Dedicated pipette tip for each reagent to minimize risk of contamination

Real-time PCR amplification and detection

• Precise thermal control of PCR vessel during PCR processing
• One-wire chip located in each assay reagent pack (ARP) to ensure reagent life and calibration
• Uracil-DNA-glycosylase (UDG) enzyme in the PCR reaction mix. UDG digests uracil-containing amplicons created in previous PCR reactions

An internal process control (PC) is run with each sample to monitor the reaction. The PC may be a plasmid or an inactivated virus that contains a selected or engineered target sequence and is designed to mimic the behaviour of the assay target throughout the extraction, purification, and PCR process.
In addition to these quality features, the DxN VERIS System displays QC results in chart format to provide a graphical view of the data. Depending on the characteristics of the data, the system uses a Levy-Jennings chart or a Shewhart chart. Multiple data sets can be viewed simultaneously in an overlay chart, or in up to four individual charts. On- board QC management software flags when QC is out of range.

QC procedure for VERIS viral load assays
Beckman-Coulter’s QC procedure provides a method of monitoring system performance while minimizing hands-on time and QC cost to the laboratory. Beckman Coulter recommends that Quality Control should be run in each 24-hour period in which test samples are run until variability limits have been established on the DxN VERIS System. Reduced frequency of control testing should be based on data as determined by the individual laboratory. Quality control materials should incorporate the analyte and a negative control.
Each laboratory should establish mean values and acceptable ranges to assure proper performance. Quality control results that do not fall within acceptable ranges may indicate invalid test results. It is recommended that laboratories examine all test results generated after obtaining the last acceptable quality control test point for this analyte.
In some countries or geographic locations, government regulation may define specific requirements that dictate frequency and number of QC data points and specimens used. Each laboratory should establish its own QC protocol based on data as determined by the laboratory in accordance with accrediting organizations and government regulations, as applicable[2].

Customer case study – Rennes University Hospital
Quality Control programmes utilizing the DxN VERIS System have been successfully implemented at customer laboratories across Europe. Described below is an example of a QC protocol developed in the Virology laboratory at Rennes University Hospital, France.
Rennes University Hospital is a 2,000 bed facility serving the Brittany region of France. The Virology lab processes approximately 133,000 analyses per year including 8,000 qPCR tests for HIV-1, HBV, and CMV viral load monitoring. The laboratory adopted the DxN VERIS system in 2016 based on the system’s workflow advantages and assay performance.
The analytical performance of the DxN VERIS System enabled the laboratory to consider the possibility of reducing the frequency of QC testing required to monitor routine patient analyses. In order to determine an appropriate QC frequency for viral load testing, the laboratory evaluated the performance characteristics of the analyser as well as the number of tests performed over a period time. Based on this assessment the laboratory concluded that performing three levels of QC (negative, low, high) two times per week would provide an acceptable level of system control while significantly reducing QC costs and hands-on time. This level of QC testing was appropriate based on the volume of tests performed by the laboratory. For labs that perform a higher volume of tests, QC may need to be performed more frequently in order to provide a sufficient level of QC relative to the number of tests performed. In case of QC out-of  range, a procedure has been set-up in Rennes in order to re-test all samples analysed between the two QC measurement times. To validate the twice-weekly QC protocol, the lab evaluated the precision performance of each assay over 5 months. The precision observed over this time frame was less than 5.8% CV with standard deviation (SD) within 0.16 log IU/ml. No values were observed outside of the expected range. These data, summarized below, were determined by Rennes to be sufficient to support the twice-weekly QC protocol.
The Virology laboratory at Rennes University Hospital has been following the twice-weekly QC protocol since April 2016. This has resulted in a reduction in the cost per reportable result and simplified the QC process without impacting the quality of results produced by the lab.

Conclusion
Beckman Coulter’s DxN VERIS System provides a high level of assay performance, ease of use, and workflow efficiency. Effective quality control programs can be developed based on the unique testing requirements of each laboratory, resulting in a high level of system control while reducing hands-on time and QC cost.

* DxN VERIS products are CE-marked IVDs. DxN VERIS product line has not been submitted to U.S. FDA and is not available in the U.S. market. DxN VERIS Molecular Diagnostics System is also known as VERIS MDx Molecular Diagnostics System and VERIS MDx System.

The authors
J. Wyatt, P. Le Roux, V. Thibault¹
Beckman Coulter Diagnostics | Brea, CA
¹ Rennes University Hospital, France
² CLSI. Statistical Quality Control for Quantitative Measurement Procedures:
Principles and Definitions; Approved
Guideline-Third Edition. CLSI document C24-A3. Wayne, PA: Clinical and
Laboratory Standards Institute.

www.beckmancoulter.com

Recent years have witnessed the growing use of mass spectrometry (MS) in the clinical laboratory. MS provides massive improvements in the sensitivity and specificity of clinical tests. It does this by using an ionized molecule’s mass/charge (m/z) ratio for identification.
MS has its roots in the screening and fingerprinting of molecules in drugs of abuse. Over the years, the technology has rapidly evolved. Today, it is routinely used to screen for diseases and to precisely identify causes of infections for targeted therapies. The analysis of proteins is also accelerating, with special potential demonstrated by biomarkers such as thyroglobulin.Alongside, limitations to immunoassays have also driven adoption of MS. For example, no immunoassays were approved for the immunosuppressant sirolimus and this compelled laboratories to turn to MS. Another advantage lay in improved assay quality, such as the measurement of testosterone in patients with low endogenous concentrations, such as women and children.

Enticing advantages
One of the most enticing set of advantages of mass spectrometry is that it provides clinically relevant information from relatively small sample volumes, and does this both rapidly and at a reduced cost. Gas chromatography (GC), liquid chromatography (LC) and ion mobility spectrometry (IMS) separation now allow targeting of ever-smaller analyte concentrations. LC-MS/MS (liquid chromatography-tandem mass spectrometry), on its part, offers scope to cut costs further, while continuing to improve accuracy. Other technology trends include integrating MS with low-flow chromatography, ultra-high pressure chromatography and online/multi-dimensional  chromatography.
Nevertheless, MS also adds a new layer of complexity. As a result, close and well-structured communication between laboratories and clinicians is a vital component for the effective use of MS.

A three-step process
Today, there are three principal steps for conducting an analysis by MS: sample preparation, separation by gas-chromatography (GC) or liquid-chromatography (LC), and mass spectrometric analysis.
In MS, a sample ‘matrix’ refers to everything present in a sample, excluding analytes of interest. Differences in behaviour between analytes and matrix components determines the choice of sample preparation. Although sample preparation requires more labour than immunoassays, in-house mass spectrometry-based assays are now considered cost-effective, even for smaller labs.

Sample preparation
The preparation of samples and their subsequent separation by chromatography both use mechanisms which first position molecules (the stationary phase) and then separate analytes from matrix components (the mobile phase).
Preparation firstly depends on the sample type selected for analysis (e.g. blood/serum or urine). Analytes from serum (including blood fractions) require the maximum care in preparation, owing to a relatively low ratio in the concentration of analytes to matrix components. On the other hand, urine analytes are often compatible with simple dilution protocols, due to the concentrating effect of kidneys in the production of urine.

Typical techniques in preparing samples include solid-phase extraction (SPE), immunoextraction and dilution. The choice depends principally on whether the analytes are acidic or alkaline, and if they are heavily protein-bound.

Solid-phase extraction
SPE is based on combining a solid stationary phase with a liquid mobile phase.
Analytes of interest (and matrix components) remain in the liquid phase or associate only temporarily with the solid stationary phase. The amount of time taken up by the latter is based on characteristics such as charge and polarity of the analytes versus matrix components. A binding-and-wash solvent (different from the elution solvent), provides a relatively crude separation of analytes from the (unwanted) components.

Immunoextraction
Immunoextraction (also known as immunoaffinity purification) is based on the use of antibodies in a solid phase. This separates antibody-bound analytes from free matrix components.

Dilution
When analytes are present in high concentrations, dilution provides a simple and effective methodology to reduce matrix components. Dilution (often called ‘dilute-and-shoot’) is a common method of sample preparation for comprehensive screening and for confirmatory testing for drugs in urine.

Separation
Gas chromatography
GC chromatography uses hydrogen or helium to push molecules into a column (known as the stationary phase). Modifying the column temperature then changes the affinity of molecules in the stationary phase, thereby separating analytes from matrix components (known as the mobile phase). Though largely relevant for volatile, heat-stable compounds, ‘derivatization’ via chemical modification can increase compatibility with GC.
GC mass spectrometry (GC-MS) remains the most common method for comprehensive drug screening in the clinical laboratory.

Liquid chromatography
LC chromatography is largely used for separation of samples before MS analysis. This is largely due to a wide range of LC-compatible analytes and a reduced need for derivatization. The mobile phase in LC uses a combination of organic solvents and water. Adjustments to the ratio between the organics and water redistributes components between the mobile and stationary phases.

Ionization techniques: APCI and electrospray
MS detects charged analytes in the gaseous phase alone. Ionization is required to convert liquid-phase analytes for analysis. The two most common methods in the clinical laboratory consist of atmospheric pressure chemical ionization (APCI) and electrospray ionization.
APCI produces ions by using heat to evaporate the solvent and plasma to ionize the sample. Physical interaction with gaseous analytes leads to formation of negative or positive ions.
Electrospray ionization, on its part, combines electricity, air and heat to produce successively smaller and concentrated droplets from the liquid which elutes off a chromatographic column. This leads to a dramatic increase in charge per unit volume. Ions on the droplet surface desorb from liquid to gas phase, and the latter is introduced into the mass spectrometer.

Sample transfer to mass spectrometer
There are several choices for introducing samples into a mass spectrometer. These range from direct infusion to multidimensional chromatographic separation. The latter enables the staggered delivery of analytes and matrix components. This permits more effective utilization of analyser time by limiting analysis to fractions containing analytes of interest.

Methods of analysis
MS analysis is largely based on quadrupole analysers, time-of-flight (TOF) analysers and tandem mass spectrometers, as well as combinations of the three.

Quadrupole analysers
Linear quadrupole analysers are currently the most common type of mass spectrometer in a clinical laboratory. Called quadrupoles due to the presence of four parallel rods in a square, one pair of (diagonally opposed) rods is positively charged, while the other is negatively charged. The charges are optimized and alternated based on the analyte of specific interest. Via sequential attraction and repulsion, an ion of interest can be programmed to maintain a stable flight path between the rods. The charge and frequency can moreover be rapidly altered to sequentially detect different analytes. Quadrupole analysers have high sensitivity and mass accuracy. On the other hand, they have a limited range in mass/charge (m/z) ratios – which, as noted previously, is a unique identifier for a particular ion.

Time-of-flight analysers
Time-of-flight (TOF) mass spectrometers are based on using an electric field which accelerates gas phase ions to a detector. The time taken for this travel is based on an ion’s m/z ratio, with low m/z ions travelling faster than higher ones.
TOF analysers have an essentially unlimited m/z range and high sensitivity and accuracy, but users face limits in their dynamic range.

Key challenges in clinical MS
Tandem mass spectrometry
Successful identification of an analyte by m/z alone does not always confer specificity. One good example is morphine and hydromorphone. Though the two are distinct, they have identical positive ions, with 286 m/z.
Tandem mass spectrometers (MS/MS) use multiple quadrupoles in series. One typical configuration is to use three quadrupoles. The first and third (denoted Q1 and Q3) use combinations of charge and frequencies as described above (see section on ‘Quadrupole Analysers’). The second quadrupole, denoted q2 (in smaller case), serves as a collision cell with an inert gas (e.g. nitrogen). On entry into q2, ions collide with the inert gas, and fragment into smaller product ions which then pass through Q3 and hit a detector.
In the case of morphine and hydromorphone, q2 entry produces stable product ions (m/z of 153 for the former and 157 for the latter). After this, setting the Q3 charge and frequency to first transmit the product ion for morphine and then change the charge/frequency settings to transmit the  hydromorphone product ion results in a way to measure and differentiate between the two compounds. This approach is also known as multiple reaction monitoring (MRM) and allows a mass spectrometer to scan faster – by targeting specific m/z set points rather than a broad range.

Ion suppression/enhancement and ion ratios
Ion suppression and enhancement are two of the most common problems facing MS. These occur when a substance in a sample interferes with the ionization process of the analytes. These can range from matrix molecules to co-eluting compounds. For example, components in the sample with lower volatility can reduce the efficiency of solvent evaporation, resulting in reduced ion formation.
There are several options to reduce or eliminate such interference, including mobile phase additives to aid ionization.
Another approach is to use ion ratios. When analytes of interest are present alongside structurally similar compounds in complex matrices, interference risks rise due to co-eluting molecules with identical mass. However, ion ratios seek to monitor multiple m/z transitions for each analyte and determine ratios of chromatographic peak area for more abundant fragments to less abundant ones. The use of ion ratios further enhances the specificity of MS.

The future
Endocrinology
Although industry has sought to use antibody-mediated detection to overcome the inherent limitations of immunoassays in identifying proteins and small molecules, these have yet to be meaningfully eradicated.
Rising interest in (regular and more-frequent) testing for vitamin D has also driven implementation of LC-MS/MS (liquid chromatography-tandem mass spectrometry), which separates vitamin D2 from vitamin D3 and provide information on its epimeric form. This is not possible with existing immunoassays.
Meanwhile, although steroid hormone assays for diagnostic and forensic testing continue to grow, a lack of specificity and accuracy at low concentrations has hampered the diagnosis of endocrine disorders. This has led several medical groups to recommend mass spectrometry as the preferred method of analysis, in spite of the high degree of technical competence, skill and experience required to achieve meaningful results.

Metabolomics
Measurements of the genome and proteome need to be accompanied by quantified data on the metabolome to comprehend differences between disease and healthy status, and provide meaningful diagnosis and monitoring of disease. One of the fastest growing areas for MS in metabolomics is the screening of newborns.

Protein analysis
The success of MS in precise measurement of small molecules has driven interest in using it for peptide and protein analysis for diagnostic testing. In spite of some challenges, quantitative proteomics (covering factors such as isotope dilution and m/z transitions) is an especially exciting application for mass spectrometry.

Alterations of the microbiome are associated with colorectal cancer. Research suggests that microbiome data could improve colorectal cancer screening. Analysis of the microbiome directly from existing screening methods offers the opportunity to rapidly translate this research into practice, with the potential to develop a multifactorial colorectal cancer screening tool.

by Dr Caroline Young and Professor Philip Quirke

Current colorectal cancer screening methods
Different countries have adopted various approaches to colorectal cancer screening. They share a common goal: detection of asymptomatic adenomas or early stage carcinomas, as detection and treatment at an earlier stage is associated with improved survival [1]. Two main screening methods are in use: detection of fecal occult blood and visualization of the colon. Stool DNA testing has recently been approved but is currently prohibitively expensive.

Detection of fecal occult blood can be achieved using the guaiac fecal occult blood test (gFOBT) or an immunochemical method, fecal immunochemical test (FIT). The gFOBT method requires participants to apply stool to a gFOBT card on three occasions and return this to a screening centre through the post. Hydrogen peroxide is applied and if heme is present, blue discolouration occurs. This method has been shown to reduce mortality by 16 % [2]. The FIT method requires participants to insert a FIT probe into stool and return this to a screening centre through the post. An antibody-based assay is used to detect globin. FIT is more sensitive and specific, can be analysed quantitatively and has improved acceptability [3]. Participants in whom fecal occult blood is detected above a threshold, by either method, are referred for colonoscopy.

Alternatively, direct visualization of the colon by colonoscopy/sigmoidoscopy can be undertaken as first-line screening. Limitations include procedural risks, associated costs, workforce capacity and reduced acceptability [4].

The microbiome and colorectal cancer
The microbiome can be characterized using a number of technologies: next generation sequencing (NGS) of bacterial 16SrRNA, whole genome shotgun metagenomics of bacterial communities or the analysis of fecal metabolites (metabolomics). These techniques have enabled an appreciation of the diversity and function of the microbiome in health and disease.

Epidemiological studies demonstrate that the incidence of colorectal cancer is highest in countries with a Western culture, which encompasses Western diet, sanitation and hygiene, medication use, urbanization, etc. [5]. Migrant populations to such countries acquire the increased risk, suggesting an environmental risk factor. African Americans, who typically have a high incidence of colorectal cancer, have been shown to have different microbiomes to Native Africans, who have a low incidence of colorectal cancer [6] and the diets typical of these two groups have been shown to differentially influence the microbiome [7].

Numerous studies have found differences in the microbiome, ‘dysbiosis’, of patients with colorectal adenomas or carcinomas compared to healthy controls [8]. In general, dysbiosis is characterized by a decrease of short chain fatty acid-producing bacteria, an increase of bacteria that produce bile salts or hydrogen sulphide, an increase of pathogenic bacteria and inflammation [9]. In particular, the species Fusobacterium nucleatum, a Gram-negative oral commensal, has been associated with colorectal carcinoma in many studies.

Animal models have explored potential mechanisms [10] and interestingly show that risk is transferable with transplant of dysbiotic microbiomes. This suggests that dysbiosis may be causative or promotional of the development of colorectal cancer, rather than merely associative.

Given the association between dysbiosis and colorectal cancer, researchers have considered whether the microbiome could be used as a screening tool.

The microbiome compared to gFOBT
Several studies have compared the accuracy of the microbiome as a screening tool to gFOBT. Amiot et al. showed that a screening model combining age plus microbiome (typed by qPCR) was no better than a model combining age plus gFOBT [11]. However, metabolomic analysis [by 1(H)-NMR spectroscopy] was more accurate than gFOBT [12]. Zeller et al. created a screening model that combined metagenomic data with gFOBT results, which lead to an increase in sensitivity compared to gFOBT alone. This model was subsequently validated in a cohort of a different nationality. It showed some ability to distinguish colorectal cancer from a distinct bowel condition (inflammatory bowel disease) and could be extrapolated to NGS of 16SrRNA (a cheaper method) [13].

Zackular et al. used 16SrRNA analysis of the microbiome to create models combining microbiome data and patient metadata that were more accurate than models based on metadata alone [14]. A model comprising BMI, microbiome data and gFOBT was more accurate at distinguishing adenoma from carcinoma than gFOBT alone. Yu et al. used metagenomics to identify two discriminatory bacterial genes that they then validated as biomarkers by qPCR (a cheaper method) in a cohort of a different nationality. The area under the receiver operating characteristic (ROC) curve for discriminating carcinoma from controls was 0.84, although gFOBT or FIT screening was not performed for comparison [15].

The microbiome compared to FIT
As FIT is replacing gFOBT in many screening programmes and has a higher sensitivity, comparing the accuracy of the microbiome as a screening tool with FIT is more appropriate.

Baxter et al. used 16SrRNA to create a screening model that combined microbiome data and FIT to discriminate healthy controls from cases with either adenoma or carcinoma [16]. This model was more sensitive but less specific than FIT alone; it detected 70% of cancers and 37% of adenomas which were missed by FIT. Liang et al. [17] identified four bacterial species (one being F. nucleatum) by qPCR that could distinguish colorectal carcinoma from healthy controls with greater accuracy than FIT. Combining microbiome and FIT data afforded greater accuracy still.

Goedert et al. [18] analysed the microbiome by 16SrRNA in patients with a positive FIT result at baseline. The microbiome data gave an area under the ROC curve for discriminating between healthy controls and colorectal adenoma of 0.767.

Limitations of current research
The studies mentioned above show promise for the microbiome as a potential colorectal cancer screening tool. However, they should be interpreted with a degree of caution, owing to a number of limitations which mean that aspects of the studies do not realistically reflect screening conditions. Several of the studies assessed participants at increased risk of colorectal cancer or who were symptomatic. Some collected stool samples following bowel preparation and colonoscopy; one study found that this did not affect the significance of results [16], whereas another found that it did [15]. Several studies included adenomas <10 mm within their control groups. Many of the studies created models that distinguished adenomas from carcinomas or carcinomas from healthy controls; few designed models to discriminate between healthy controls and participants with any colorectal lesion (i.e. either adenoma or carcinoma).

All of the studies used whole stool samples that were refrigerated or frozen by participants at home or delivered within a limited time window to research centres. This method of sample collection would not translate to national screening programmes, which already struggle with poor participant uptake. In light of this, researchers have, therefore, investigated whether the microbiome can be analysed directly from the existing screening tools, gFOBT or FIT.

Analysing the microbiome directly from existing screening tools
Sinha et al. emphasize the need to assess reproducibility, stability over time and how accurately results reflect the gold standard (fresh or immediately frozen stool) when analysing different methods of microbiome sample collection [19]. They found that 16SrRNA microbiome results were similar when analysed from unprocessed or processed gFOBT cards and, in addition to Dominianni et al. [20], showed stability after storage at room temperature for several days. This work was extended by Taylor et al. [21] who demonstrated that the microbiome is stable when analysed by 16SrRNA from processed gFOBT cards stored at room temperature for up to 3 years.

Lotfield et al. showed that metabolomic assessment of the microbiome by ultra-performance liquid chromatography and high resolution/tandem mass spectrometry was stable and accurate (albeit with a degree of bias affecting certain metabolite groups) when analysed directly from gFOBT samples but not from FIT samples [22]. This suggests that different methods of sample collection may be more or less appropriate dependent upon the method of microbiome analysis.

These studies have assessed methods of microbiome sample collection from healthy volunteers. Baxter et al. [23] have analysed the microbiome directly from processed FIT from subjects with normal bowels, colorectal adenomas or carcinomas. Their study comes with the caveat that some of the stool samples were collected after bowel preparation and colonoscopy; samples were stored at −80 °C before being thawed and transferred to FIT; FIT was refrigerated for up to 2 days, processed, then stored at −20 °C before being thawed for microbiome analysis. The study demonstrated that a screening model to discriminate between healthy controls and subjects with any colonic lesion had a similar area under the ROC curve whether microbiome analysis was performed directly from FIT samples or whole stool samples.

As an alternative to stool, Westenbrink et al. analysed microbiome-related volatile organic compounds from urine [24] and described a similar sensitivity for the detection of colorectal cancer as gFOBT or FIT.

Conclusion
Research suggests that there is potential for microbiome analysis to both augment and to be integrated with existing screening methods. The landscape of colorectal cancer screening is changing [25]; it seems likely that a more sophisticated, multifactorial screening tool will be adopted. Microbiome analysis is likely to contribute and may even offer information beyond that of screening, e.g. prevention or treatment targets [26]. Furthermore, collection of longitudinal, population-based microbiome data via national screening programmes will transform the field of microbiome research.

References
1. Cancer Research UK (http://www.cancerresearchuk.org).
2. Hewitson P, Glasziou PP, Irwig L, Towler B, Watson E. Screening for colorectal cancer using the faecal occult blood test, Hemoccult. Cochrane Database Syst Rev. 2007; DOI: 10.1002/14651858.CD001216.pub2
3. Schreuders EH, Grobbee EJ, Spaander MC, Kuipers EJ. Advances in fecal tests for colorectal cancer screening. Curr Treat Options Gastroenterol. 2016; 14(1): 152–162.
4. US Preventive Services Task Force, Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW Jr, García FA, Gillman MW, Harper DM, et al. Screening for colorectal cancer: US preventive services task force recommendation statement. JAMA 2016; 315(23): 2564–2575.
5. Haggar FA, Boushey RP. colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin Colon Rectal Surg. 2009; 22(4): 191–197.
6. Ou J, Carbonero F, Zoetendal EG, DeLany JP, Wang M, Newton K, Gaskins HR, O’Keefe SJ. Diet, microbiota, and microbial metabolites in colon cancer risk in rural Africans and African Americans. Am J Clin Nutr. 2013; 98(1): 111–120.
7. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014; 505(7484): 559–563.
8. Borges-Canha M, Portela-Cidade JP, Dinis-Ribeiro M, Leite-Moreira AF, Pimentel- Nunes P. Role of colonic microbiota in colorectal carcinogenesis: a systematic review. Rev Esp Enferm Dig. 2015; 107(11): 659–671.
9. Sun J, Kato I. Gut microbiota, inflammation and colorectal cancer. Genes Dis. 2016; 3(2): 130–143.
10. Keku TO, Dulal S, Deveaux A, Jovov B, Han X. The gastrointestinal microbiota and colorectal cancer. Am J Physiol Gastrointest Liver Physiol. 2015; 308(5): G351–363.
11. Amiot A, Mansour H, Baumgaertner I, Delchier JC, Tournigand C, Furet JP, Carrau JP, Canoui-Poitrine F, Sobhani I; CRC group of Val De Marne. The detection of the methylated Wif-1 gene is more accurate than a fecal occult blood test for colorectal cancer screening. PLoS One 2014; 9(7): e99233.
12. Amiot A, Dona AC, Wijeyesekera A, Tournigand C, Baumgaertner I, Lebaleur Y, Sobhani I, Holmes E. (1)H NMR spectroscopy of fecal extracts enables detection of advanced colorectal neoplasia. J Prot Res. 2015; 14(9): 3871–3881.
13. Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, Amiot A, Böhm J, Brunetti F, et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol Syst Biol. 2014; 10: 766.
14. Zackular JP, Rogers MA, Ruffin MT 4th, Schloss PD. The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev Res (Phila). 2014; 7(11): 1112–1121.
15. Yu J, Feng Q, Wong SH, Zhang D, yi Liang Q, Qin Y, et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 2015; DOI: 10.1136/gutjnl-2015-309800.
16. Baxter NT, Ruffin MT 4th, Rogers MA, Schloss PD. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med. 2016; 8(1): 37.
17. Liang JQ, Chiu J, Chen Y, Huang Y, Higashimori A, Fang JY, Brim H, Ashktorab H, Ng SC, et al. Fecal bacteria act as novel biomarkers for non-invasive diagnosis of colorectal cancer. Clin Cancer Res. 2016; DOI: 10.1158/1078-0432.CCR-16-1599.
18. Goedert JJ, Gong Y, Hua X, Zhong H, He Y, Peng P, Yu G, Wang W, Ravel J, et al. Fecal microbiota characteristics of patients with colorectal adenoma detected by screening: a population-based study. EBioMedicine 2015; 2(6): 597–603.
19. Sinha R, Chen J, Amir A, Vogtmann E, Shi J, Inman KS, Flores R, Sampson J, Knight R, Chia N. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol Biomarkers Prev. 2016; 25(2): 407–416.
20. Dominianni C, Wu J, Hayes RB, Ahn J. Comparison of methods for fecal microbiome biospecimen collection. BMC Microbiol. 2014; 14: 103.
21. Taylor M, Wood H, Halloran S, Quirke P. Examining the potential use and long term stability of guaiac faecal occult blood test cards for microbial DNA 16srRNA sequencing. J Clin Pathol. Accepted for publication.
22. Loftfield E, Vogtmann E, Sampson JN, Moore SC, Nelson H, Knight R, Chia N, Sinha R. Comparison of collection methods for fecal samples for discovery metabolomics in epidemiologic studies. Cancer Epidemiol Biomarkers Prev. 2016; 25(11): 1483–1490.
23. Baxter NT, Koumpouras CC, Rogers MA, Ruffin MT 4th, Schloss P. DNA from fecal immunochemical test can replace stool for microbiota-based colorectal cancer screening. Microbiome 2016; 4(1): 59.
24. Westenbrink E, Arasaradnam RP, O’Connell N, Bailey C, Nwokolo C, Bardhan KD, Covington JA. Development and application of a new electronic nose instrument for the detection of colorectal cancer. Biosens Bioelectron. 2015; 67: 733–738.
25. Nguyen MT, Weinberg DS. Biomarkers in colorectal cancer screening. J Natl Compr Canc Netw. 2016; 14(8): 1033–1040.
26. Pitt JM, Vetizou M, Waldschmitt N, Kraemer G, Chamaillard M, Boneca IG, Zitvogel L. Fine-tuning cancer immunotherapy: optimizing the gut microbiome. Cancer Research 2016; 76(16): 4602–4607.

The authors
Caroline Young* MA, BMBCh; Philip Quirke BM, PhD, FRCPath, FMedSci
Wellcome Trust Brenner Building, St James University Hospital, Leeds LS9 7TF, UK

*Corresponding author
E-mail: caroline.young4@nhs.net