The current prognostic staging of colorectal cancer (CRC) by the tumour, node, metastasis method, alongside the minimal core data set, provides good prognostic information for patient populations but is less accurate for the individual. Reporting of additional histopathological features can improve individualized prognostic staging, but manual microscopic surveillance often results in observer variability and there is a lack of consensus on standardized quantification methods. Automated image analysis can standardize the quantification of prognostic features, can personalize CRC prognosis and augment clinical staging.
by Dr Peter Caie
Introduction
Colorectal cancer (CRC) incidence is extremely high in the developed world, where it is the third most common cancer in men and women in both the UK and USA. There will be an estimated 134 500 new CRC cases diagnosed, and just under 50 000 deaths will have been caused by the disease, in the USA alone. Although the incidence rate has decreased only slightly in the last decade, the change in mortality has dropped significantly between 2000 and 2010, with ~50% of patients across all disease stages surviving for at least 5 years post-diagnosis. Reasons for the decrease in mortality include life style change (47% of CRC cases in the UK could be prevented from healthier lifestyle choice) early detection of disease (e.g. home testing such as fecal occult blood kits), targeted therapy as the result of ‘omics’ research, novel prognostic factors coupled with more accurate pathological and clinical staging of disease and advances in surgical technique. These factors culminate in a more effective treatment of patients at an early stage and at a personalized level. Survival rates in early stage cancer, where the tumour is localized, are extremely good with ~90% of patients experiencing 5-year disease-free survival. Upon spread to localized lymph nodes survival decreases to 50–70% and if distant metastasis has occurred survival is only 12% [1, 2].
Current prognostic staging of CRC
Although multiple CRC subtypes exist, with both molecular and histopathological variances, 90% of CRCs are adenocarcinomas and prognosis is determined through the international tumour, node, metastasis (TNM) staging system alongside the minimal core data set. The TNM staging is based on gross observation and analysis of histopathological tissue sections under the microscope which revolves around the depth of local invasion (T), presence of cancer within the lymph nodes (N) and if the cancer has metastasized (M). TNM staging is excellent at returning prognostic information on a population of patients; however, it is less specialized at predicting prognosis at the level of the individual [3]. A patient’s prognosis is worse the higher the stage they are classified within, however, the TNM system does not differentiate between good and poor outcome of patients within the same stage [4]. There are defined adjuvant treatment guidelines associated with the various stages of CRC [5]. Stage 0 and I cancers will not routinely receive adjuvant chemotherapy and surgical resection is considered curative. Adjuvant therapy is recommended for stage III and IV patients, however, there remains ambiguity about whether to treat all, a subset or no stage II patients with adjuvant chemotherapy [6]. Around 30% of stage II CRC patients will succumb to their disease after surgical resection and, therefore, an accurate and more personalized identification and stratification of high-risk stage II patients, some of whom have comparable or worse outcomes than stage III patients [7], is therefore imperative to increase disease free survival rates. In the UK, pathologists collect a minimal core data set for each patient which helps to identify high-risk stage II CRC cases [8]. Although some parameters within the data set are disregarded in clinical decision making for the management of stage II CRC patients, some features, if present, may invoke the decision to treat the patient: high grade/poor differentiation, pT4 local spread and extramural lymphovascular invasion [9]. However, there is little evidence to date to show the advantage of adjuvant therapy for stage II patients with additional high-risk factors. Furthermore, there are promising histopathological features listed in the literature that have been significantly correlated with poor prognosis but which rarely feature in final clinical reports. There is, for example, growing evidence that immune cell infiltrate and perineural invasion are strongly correlated with poor patient outcome, whereas lymphatic vessel invasion (LVI) and the invasive growth pattern, including tumour budding, are two of the most promising histopathological features that have been significantly associated with lymph node metastasis and disease-specific survival.
Manual reporting yields observer variability
Although histopathological features such as nuclear grade have long been established in the core data sets for CRC prognosis and features such as immune infiltrate, invasive pattern, LVI, lymphatic vessel density (LVD) and the tumour-to-stroma ratio are associated with poor prognosis in the literature, they have also been associated with observer variability in their reporting. Observer variability is an inevitable occurrence when reporting histopathological features by eye under microscopic surveillance; however, it is increased by certain features being obscure under H&E stained tissue with associated retraction artefact, difficult to accurately quantify or if they are rare events. This is particularly true when the calculation of areas in manually determined ‘hot-spots’ is required, such as for LVD and tumour-to-stroma ratio calculations which are both very prone to observer bias and variability. Furthermore, a general consensus on standardized quantification methods is lacking for many of these candidate histopathological features and for this reason they, apart from grading, have not translated into the minimal core data sets of CRC pathological reporting. Although nuclear grading has been reported as a minimal core data set for many years, there have been studies which have also found it to be non-significant [10]. Therefore, there is now a consensus for nuclear grading in CRC to move toward a two-tiered scoring system of ‘well differentiated’ and ‘poorly differentiated’ which eliminates the ‘moderately differentiated’ class and attempts to increase standardization.
Automated image analysis can standardize the quantification of prognostic features
Digital pathology and associated image analysis technology is becoming increasingly sophisticated. It is now possible to create image analysis algorithms that can automatically segment and quantify histopathological features within digital tissue sections with high accuracy. There are multiple advantages to applying image analysis to histopathology reporting that overcome the associated observer variability of manual scoring. Automated image analysis uses standardized algorithms and objectively reports on the features it is programmed to quantify. It does so in a robust manner across all patient samples being tested. Applying image analysis allows a higher degree of accuracy when reporting on the number, area or ratio of specific features across a whole tissue section and so negates the need of manually assigned hot-spots. Furthermore continuous data is captured when image analysis is applied, which allows more accurate clinical cut-offs to be used resulting in a more personalized assessment of a patient’s condition than the more traditional categorical reporting of, e.g. 1+, 2+, 3+ for immunohistochemistry, or ‘well’ or ‘poorly’ differentiated cases. Finally, rare and obscure events that may be missed by the eye are able to be reported with higher confidence when the computer assesses the entire issue section. Jeremy Jass in the late 1980s reported a novel grading system for CRC that included the reporting of the immune infiltrate and the pattern of invasive margin [11]; however, his promising results were not translated into the clinic due to poor reproducibility. Recently, two groups have used image analysis to quantify the immune infiltrate in the form of the immunoscore [12] and the tumour’s infiltrative pattern in the form of tumour budding [13] (Fig. 1A) in a manner that allows standardization, and have shown both features to be prognostically significant. Image analysis has further been used to quantify, amongst others, additional set histopathological features such as for nuclear grading [14], vasculature hot-spots [15] and LVI and LVD (Fig. 1B) [13].
Automated image analysis personalizes CRC prognosis
CRC is a specifically heterogeneous disease and the tumour microenvironment is also a heterogeneous and heterotypic ecology. Therefore, quantifying a single histopathological aspect of a patient’s tumour may not be sufficient for an accurate prognosis across a large population. A recent study by our group used multiplexed immunofluorescence to quantify a suite of histopathological features co-registered across a single CRC tissue section for each patient. Furthermore, the algorithm exported data captured across each nucleus (Fig. 2) in each sample to create a large and complex personalized multi-parametric data set for each patient in the stage II CRC study [16]. A single standardized image analysis algorithm was run across a training set of patients which quantified continuous data from the invasive front of stage II CRC on the number, shape and extent of: tumour buds, poorly differentiated clusters, LVI, LVD, tumour-to-stroma ratio, tumour gland morphology as well as multiple measurements across each nucleus to increase the accuracy of nuclear grading. The resultant ‘big-data’ was distilled through machine learning to identify the optimal parameter set to stratify patients into a high or low risk of disease-specific death. The result was the identification of a novel feature that was independently significant and where the addition of any other measured feature into the model added no further significance to patient stratification. This feature was the mean area of poorly differentiated clusters (area PDC) across the invasive front. The data from the training cohort was validated across a larger independent cohort and again the novel feature held more significance for patient risk stratification in stage II CRC than any of the other more established histopathological feature measured in the study. Furthermore, mathematical modelling was employed to identify if any of the parameters from the clinical pathology report added value to the prediction of disease specific death. By performing this analysis it was found that pT stage and differentiation added further value and were incorporated into a Novel Prognostic Index alongside the area PDC. This novel index outperformed the clinical gold standard of pT staging by almost twofold (Fig. 3).
Big-data and personalized pathology augments clinical staging
The idea behind big-data pathology is to include as much data as possible about each single patient and so to move towards a more personalized prognosis. The acquisition of quantitative data through image analysis and molecular pathology lends itself very well to big-data pathology, where the vast data sets can be mined through sophisticated machine learning algorithms to identify the optimal parameters to answer the clinical question. However, clinical staging and reporting has stood the test of time and it is imperative to include data such as this in any integrative model. As it becomes easier and cheaper to acquire large, reproducible and standardized data sets, modern pathology will become more personalized and patient outcome will improve due to tailored treatment regimens directed at individual patients.
References
1. Siegel R, Ward E, Brawley O, Jemal A. Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin. 2011; 61(4): 212–236.
2. Shah R, Jones E, Vidart V, Kuppen PJ, Conti JA, Francis NK. Biomarkers for early detection of colorectal cancer and polyps: systematic review. Cancer Epidemiol Biomarkers Prev. 2014; 23(9):1712–1728.
3. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet 2014; 383(9927):1490–1502.
4. Lea D, Haland S, Hagland HR, Soreide K. Accuracy of TNM staging in colorectal cancer: a review of current culprits, the modern role of morphology and stepping-stones for improvements in the molecular era. Scand J Gastroenterol. 2014; 49(10):1153–1163.
5. Poston GJ, Tait D, O’Connell S, Bennett A, Berendse S. Diagnosis and management of colorectal cancer: summary of NICE guidance. BMJ 2011; 343:d6751.
6. Dotan E, Cohen SJ. Challenges in the management of stage II colon cancer. Semin Oncol. 2011; 38(4):511–520.
7. Urquhart R, Bu J, Grunfeld E, Dewar R, MacIntyre M, Porter GA. Examining stage IIB survival in a population-based cohort of patients with colorectal cancer. Cancer 2012; 118(23):5973–5981.
8. Loughrey MB, Quirke P, Shephard NA. Standards and datasets for reporting cancers. Dataset for colorectal cancer histopathology reports July 2014. The Royal College of Pathologists 2014. The cancer datasets are a combination of textual guidance, educational information and reporting proformas to enable consistent grading and staging. (https://www.google.co.uk/?gws_rd=ssl#q=Standards+and+datasets+for+reporting+cancers+Dataset+for+colorectal+cancer+histopathology+reports+July+2014)
9. Morris EJ, Maughan NJ, Forman D, Quirke P. Who to treat with adjuvant therapy in Dukes B/stage II colorectal cancer? The need for high quality pathology. Gut 2007; 56(10):1419–1425.
10. Ratto C, Sofo L, Ippoliti M, Merico M, Doglietto GB, Crucitti F. Prognostic factors in colorectal cancer. Literature review for clinical application. Dis Colon Rectum 1998; 41(8):1033–1049.
11. Jass JR, Love SB, Northover JM. A new prognostic classification of rectal cancer. Lancet 1987; 1(8545):1303–1306.
12. Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, Lugli A, Zlobec I, Hartmann A, et al. Towards the introduction of the Immunoscore in the classification of malignant tumors. J Pathol. 2014; 232(2):199–209.
13. Caie PD, Turnbull AK, Farrington SM, Oniscu A, Harrison DJ. Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer. J Transl Med. 2014; 12:156.
14. Rathore S, Hussain M, Aksam Iftikhar M, Jalil A. Novel structural descriptors for automated colon cancer detection and grading. Comput Methods Programs Biomed. 2015; 121(2):92–108.
15. Kather JN, Marx A, Reyes-Aldasoro CC, Schad LR, Zollner FG, Weis CA. Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images. Oncotarget 2015; 6(22):19163–19176.
16. Caie PD, Zhou Y, Turnbull AK, Oniscu A, Harrison DJ. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget 2016; doi: 10.18632/oncotarget.10053 [Epub ahead of print].
Acknowledgment
This article is based on the author’s recently published paper: Caie PD, Zhou Y, Turnbull AK, Oniscu A, Harrison DJ. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget 2016; doi: 10.18632/oncotarget.10053 [16].
The author
Peter Caie PhD
Quantitative and Digital Pathology, School of Medicine, University of St Andrews, North Haugh, St Andrews, UK
*Corresponding author
E-mail: Pdc5@st-andrews.ac.uk
UPLC-MS/MS measurement of prednisolone in adrenal insufficiency
, /in Featured Articles /by 3wmediaPrednisolone is an attractive once-daily option to treat adrenal insufficiency. Its prior association to osteoporosis and diabetes is possibly due to widespread over-replacement. With the availability of an ultra-performance liquid-chromatography tandem mass spectrometry (UPLC-MS/MS) method to detect serum concentrations and guide treatment, we can assess the true effects of long-term low-dose prednisolone therapy.
by Dr Sirazum Choudhury and Dr Emma Williams
Introduction
Prednisolone is a pioneering synthetic corticosteroid synthesized by Arthur Nobile in 1950 as an anti-arthritic treatment [1, 2]. Sharing a similar structure to cortisol, prednisolone benefits from a longer half-life and increased potency compared to endogenous steroids, owing to a double bond found between C1 and C2 on the first carbocyclic ring (Fig. 1). Prednisolone has proven to be an indispensable anti-inflammatory drug and has long been used in the treatment of many conditions including asthma, inflammatory bowel disease and rheumatoid arthritis.
Use of prednisolone for adrenal insufficiency
More recently prednisolone is gaining traction as an option for glucocorticoid replacement therapy in adrenal insufficiency. There are an estimated 8400 individuals living with the condition in the UK, with an annual incidence of 4.4–6 cases per million in Europe [3]. The challenges of adrenal insufficiency are well characterized. In the era prior to the availability of effective treatment, the associated mortality was 85% in 2 years, and up to 100% in 5 years [4]. Over the last half-century, our increasing understanding of steroids has meant that patients are living longer, with a life expectancy approaching that of the normal population. However, a mortality gap does remain, which may in part be due to incorrect replacement of glucocorticoids, concurrently increasing the risk of diabetes, osteoporosis and cancer.
Oral hydrocortisone is the most commonly prescribed treatment for adrenal insufficiency, but is perhaps not the most ideal [5]. Due to the relatively short half-life, hydrocortisone must be administered three times daily, which can hinder compliance. For this reason it is our experience that some patients tend to omit the last dose of the day. Moreover, the price of hydrocortisone has been rising in the UK, costing £76 for a 1-month supply of 10 mg. This contrasts to a 1-month supply of 5 mg prednisolone tablets, which costs £0.88. With prednisolone offering a once-daily solution to adrenal insufficiency, it now features in the Endocrine Society clinical practice guidelines from earlier this year as an alternative to thrice-daily hydrocortisone therapy [6].
Prednisolone dose and adverse effects
The biggest obstacle to the widespread acceptance of prednisolone as a viable therapy has been its association with adverse metabolic effects such as osteoporosis. This is as a result of multiple studies purporting to show that ‘low dose’ prednisolone has a negative impact of the markers of bone turnover and bone absorptiometry [7]. Based on an assumed bioequivalence ratio of 4 : 1, 7.5 mg of prednisolone was judged to be equivalent to 30 mg of hydrocortisone and was considered ‘low dose’. The basis of this ratio is difficult to ascertain but was probably calculated from data on anti-inflammatory doses of prednisolone, which are significantly higher than the doses likely to be needed in steroid replacement therapy. More recently, a study comparing prednisolone to hydrocortisone in 44 children with congenital adrenal hyperplasia found that a lower dose of prednisolone than expected was required to control the condition [8]. Using objective biological markers, such as growth velocity, and hormonal markers such as androstenedione and 17-hyroxyprogesterone, the group discovered that prednisolone is 1.5 to 2 times more potent than previously thought, suggesting that a more appropriate prednisolone replacement dose is in fact 3 mg to 5 mg, and not as high as 7.5 mg.
To facilitate this shift towards the use of even lower doses of prednisolone, it is important to provide reassurance to both clinicians and patients that the lowest necessary dose of prednisolone is used to maintain an appropriate trough level towards the end of the day. This would be in keeping with the diurnal rhythm of cortisol. Our ability to more accurately and efficiently report serum prednisolone concentrations using an ultra-performance liquid-chromatography (UPLC) tandem mass spectrometry (MS/MS) technique provides this confidence.
Measurement of plasma prednisolone concentrations
Historically, the first assays to measure plasma prednisolone concentrations were competitive protein binding assays and radio-immunoassays [9]. The protein binding assays were designed to use cortisol binding globulin and were therefore non-specific to prednisolone. The early radio-immunoassays were prone to interference from other endogenous steroids and intermediaries, making them unreliable especially if patients continued to produce subclinical levels of cortisol. Specificity could be improved with the addition of a thin layer chromatography preparatory step; however, the lower limit of detection remained as high as 20 µg/L.
In the 1970s, high-performance liquid-chromatography (HPLC) methods gained popularity [10]. Offering greater specificity for prednisolone, the method involved a time-consuming liquid–liquid-extraction sample-preparation step. The extracted organic phase would be dried before being reconstituted with mobile phase and passed through a normal phase hydrophilic interaction chromatography HPLC column. Prednisolone concentrations were detected with ultraviolet absorbance spectrophotometry. Although this method could identify different corticosteroids, it proved to be cumbersome with retention times of up to 8 minutes for prednisolone, and 20 minutes for other steroids. With 76% recovery and a lower limit of detection of 25 µg/L, this technique is not suitable to assess trough levels of prednisolone, with a high likelihood of reporting undetectable results at the lower end, potentially facilitating over-replacement in patients.
Using a UPLC-MS/MS method, we are able to overcome the obstacles that have plagued prednisolone assays in the past (reference awaiting PubMed identifier). Serum samples are prepared using a protein precipitation method, involving zinc sulphate and the addition of deuterated (D6) prednisolone as internal standard. Following preparation, the extract is combined with both methanol and water based mobile phases before being passed through a C-18 chromatography column, which employs a reversed phase partition process. Prednisolone is eluted at approximately 1.0 minutes, before being detected by multiple reaction monitoring using electrospray ionization in positive ion mode. An example of the observed chromatograms can be found in Figure 2.
This method of measuring plasma prednisolone concentrations is linear to prednisolone concentrations of 1000 µg/L (Fig. 3), with an inter- and intra-assay co-efficient of variance at 50 µg/L of 4.1% and 2.5% respectively. The technique has proven more sensitive than HPLC with the lower limit of quantification at 10 µg/L without the HPLC recovery issues, and is equally as specific to prednisolone. By using a protein precipitation method, the preparation step is now significantly shorter. Additionally, with reduced prednisolone retention times, a prepared sample can now be analysed in 3.5 minutes before the next sample is immediately run. As a result, the UPLC-MS/MS technique is better suited to the modern clinical biochemistry laboratory being able to reliably cope with larger numbers of patient samples in shorter times than previously thought possible.
Measuring serum prednisolone concentrations has proven extremely valuable in monitoring glucocorticoid replacement therapy. There is observable variability in prednisolone metabolism between individuals, with terminal half-lives routinely varying between 1.75 and 3.75 hours. We currently measure a trough level at 8 hours post-prednisolone administration aiming for a concentration of 10–20 µg/L to ensure adequate replacement throughout the day and preserve an overnight corticosteroid nadir. The results are used clinically to inform the decision either to increase or decrease prednisolone doses as appropriate but also serve as objective proof to patients who are anxious about a reduction. The assay is also clinically useful in confirming patient compliance with their prescribed medication.
Future perspectives
Beyond the clinical utility in quantifying serum prednisolone levels, there is significant research potential for this assay. Addisonian crises are currently responsible for up to 15% of deaths in patients with adrenal insufficiency [11]. Our understanding of the disease process is limited by the urgency to provide treatment with either intravenous or intramuscular hydrocortisone, before a blood sample is taken. As this is detected by cortisol assays, it is difficult to interpret whether the pre-crisis hydrocortisone concentration was inadequate (suggesting non-compliance or reduced absorption) or appropriate (suggesting that the level was insufficient to match requirement). In patients treated with prednisolone who present with Addisonian crises, the assay will allow us to assess the pre-treatment serum prednisolone concentrations, even if the blood sample is taken after treatment with hydrocortisone.
More importantly in the immediate setting, it is anticipated that the previously accepted long-term effects of ‘low dose’ prednisolone can be explored. The availability of a reliable and specific assay will result in a greater number of patients on prednisolone who are appropriately treated and not over-replaced. In time, as more data becomes available, we will gain a clearer picture of the true effects of prednisolone.
References
1. Nobile A. The discovery of the delta 1,4-steroids, prednisone, and prednisolone at the Schering Corporation (USA). Steroids 1994; 59(3): 227–230.
2. Herzog HL, Nobile A, Tolksdorf S, Charney W, Hershberg EB, Perlman PL. New antiarthritic steroids. Science 1955; 121(3136): 176.
3. Charmandari E, Nicolaides NC, Chrousos GP. Adrenal insufficiency. Lancet 2014; 383(9935): 2152–2167.
4. Dunlop D. Eighty-six cases of Addison’s disease. Br Med J. 1963; 2(5362): 887–891.
5. Groves RW, Toms GC, Houghton BJ, Monson JP. Corticosteroid replacement therapy: twice or thrice daily? J R Soc Med. 1988; 81(9): 514–516.
6. Bornstein SR, Allolio B, Arlt W, Barthel A, Don-Wauchope A, Hammer GD, et al. Diagnosis and treatment of primary adrenal insufficiency: an Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2016; 101(2): 364–389.
7. Jodar E, Valdepenas MP, Martinez G, Jara A, Hawkins F. Long-term follow-up of bone mineral density in Addison’s disease. Clin Endocrinol. (Oxf) 2003; 58(5): 617–620.
8. Caldato MC, Fernandes VT, Kater CE. One-year clinical evaluation of single morning dose prednisolone therapy for 21-hydroxylase deficiency. Arq Bras Endocrinol Metabol. 2004; 48(5): 705–712.
9. Wilson CG, Ssendagire R, May CS, Paterson JW. Measurement of plasma prednisolone in man. Br J Clin Pharmacol. 1975; 2(4): 321–325.
10. Loo JC, Butterfield AG, Moffatt J, Jordan N. Analysis of prednisolone in plasma by high-performance liquid chromatography. J Chromatogr 1977; 143(3): 275–280.
11. Erichsen MM, Lovas K, Fougner KJ, Svartberg J, Hauge ER, Bollerslev J, et al. Normal overall mortality rate in Addison’s disease, but young patients are at risk of premature death. Eur J Endocrinol. 2009; 160(2): 233–237.
The authors
Sirazum Choudhury BSc, MBBS, MRCP
and Emma Williams* BSc, PhD, FRCPath
Charing Cross Hospital, Fulham Palace Road, London W6 8RF, UK
*Corresponding author
E-mail: emma.walker@imperial.nhs.uk
Ensuring high quality viral load results, reduced turnaround times and enhanced laboratory workflows
, /in Featured Articles /by 3wmediaThis article describes the experiences of the Virology Department at Toulouse University Hospital, France, in the evaluation of a new, fully automated molecular diagnostics system for the quantitative determination of nucleic acid targets, such as cytomegalovirus (CMV) DNA and human immunodeficiency virus type 1 (HIV-1) RNA.
by Prof. Jacques Izopet
The 3000-bed Toulouse University Hospital is one of the leading medical facilities in France with a number of research specialties, including immunology and infectious diseases, cardiovascular and metabolic diseases, and oncology. The hospital’s department of biomedical sciences, which employs over 120 medical biologists and 450 engineers and technicians, performs around 6.6 million tests every year. Among these, the department of virology performs a range of culture, serology and molecular biology investigations.
The virology department’s molecular biology laboratory faces a number of challenges in performing viral load analyses for targets, such as cytomegalovirus (CMV) and human immunodeficiency virus type 1 (HIV-1). For CMV, optimal automated quantitative molecular methods are needed to monitor infection, especially among immune-suppressed patients. Similarly, for HIV-1, sensitive biological tools are needed to quantify HIV-1 RNA and to characterize persistent viremia in patients receiving antiretroviral therapy.
These investigations require robust instrumentation and high quality analytical performance. Currently, the laboratory’s viral load measurements are performed using multiple separate instruments for the aliquoting of samples, nucleic acid extraction and amplification/detection. The existing method requires samples to be processed in batches; involves skilled personnel; and is associated with long turnaround times.
Evaluation of a new, fully automated platform
Recently, the laboratory evaluated a new, automated, random access platform for viral load analyses. The DxN VERIS Molecular Diagnostics System (Beckman Coulter) is fully automated from sample entry to result, consolidating DNA or RNA extraction, nucleic acid amplification, quantification and detection onto a single instrument for a number of molecular targets, including CMV, HIV-1, hepatitis B virus (HBV) and hepatitis C virus (HCV).
The aim of the evaluation was to assess the analytical performances of the VERIS CMV assay (for the quantitative determination of CMV DNA in human plasma) and VERIS HIV-1 assay (for the quantitative determination of HIV-1 RNA), and to compare them to the laboratory’s existing method for CMV and HIV-1 (COBAS® AmpliPrep/COBAS® TaqMan® [Roche] coupled to a Hamilton liquid handling system). The laboratory also investigated differences in workflow, comparing the fully automated DxN VERIS System to the existing, semi-automated method.
CMV performance results
The analytical performance of the VERIS CMV assay system was very good. It demonstrated very high sensitivity and specificity, very good intra/inter-assay reproducibility (both with high viral loads and also when CMV-DNA loads were close to the threshold used to initiate therapy) and a wide analytical range (see Table 1) [1-4].
The clinical performance of the VERIS CMV assay was compared to the laboratory’s existing method for CMV viral load measurement using 169 CMV-positive clinical samples. The two methods were concordant for 88.2% of samples [3,4]. There was good agreement for positive clinical specimens tested by both techniques [1,3,4]. Bland-Altman analysis showed that mean viral loads obtained using the VERIS CMV assay were higher than those obtained using the existing method, with a standard deviation of 0.41 log10IU/mL [4] (Figure 1).
For discordant results, 18/20 (90%) samples tested positive with the VERIS CMV assay and negative with the existing method [3], confirming the very high sensitivity of the VERIS assay [4].
Both assays were also compared for patient monitoring, using four successive samples collected from 17 immunosuppressed patients. This comparison revealed similar trends between the two assays, with overlapping patterns and higher viral loads obtained with the VERIS CMV assay [1,3,4] (Figure 2).
HIV-1 performance results
The VERIS HIV-1 assay demonstrated excellent analytical performance with high sensitivity and specificity, excellent intra/inter-assay reproducibility, and very good linearity across a broad analytical range (table 1) [5,6]. Preliminary data also indicates that there is no influence of HIV-1 subtype on the quantification [6].
The clinical performance of the VERIS HIV-1 assay was assessed using 114 HIV-1 positive samples (mostly HIV-1 subtype B) from Toulouse University Hospital. Passing-Bablok analysis demonstrated that the clinical performance of the VERIS HIV-1 assay correlated well with the existing HIV-1 viral load method, with a small bias for high concentrations (figure 3) [5]. Bland-Altman analysis revealed that the mean difference of HIV-1 RNA concentration obtained using the VERIS HIV-1 assay compared to the existing method was 0.41 log copies/mL (Figure 4) [5,6].
The performance of the two assays was also compared using a panel of 252 HIV-1 positive samples from the Saint-Louis Hospital, Paris, which contained both B (121 samples) and non-B (131 samples) subtypes. Passing-Bablok analysis showed good correlation between the assays, with a small bias for high concentrations (for B and non-B subtypes; for B subtypes only; and for non-B subtypes only) [5]. At very low concentrations (<400 copies/mL), the difference between VERIS and Roche assays was very small (<0.2 log copies/mL) [5].
Workflow efficiencies
The DxN VERIS Molecular Diagnostics System is fully-automated with single sample random access and availability of results as soon as each test is complete (i.e. the first result is available in around 70 minutes for DNA tests and around 100 minutes for RNA tests, with subsequent results every 2.5 minutes). Consolidation of sample extraction and amplification/detection in a single automated platform reduces the number of instruments required for viral load determination from three to just one [5]. It also reduces hands-on time, improving sample security and standardization, and offers a more streamlined workflow [4]. With just 4 steps required for operation (loading of samples onto a rack; placing the rack in the DxN VERIS System; starting the run; reading the auto-verified results), the DxN VERIS System has the potential to revolutionize laboratory practice [7], while the capability to interface with the Laboratory Information System (LIS) ensures ASTM compliance in this respect.
In a workflow analysis for HIV-1 viral load testing at the Toulouse laboratory, the DxN VERIS System was found to reduce complexity of use, with fewer steps (daily maintenance, pre-analytical and post-analytical) and fewer consumables (reduced from >10 to 5) compared to the existing method [5].
The DxN VERIS System also reduced turnaround times for results. The difference in turnaround times between the DxN VERIS System and the existing method was over 25 hours in favour of the DxN VERIS System when the weekend was not taken into account, and over 49 hours in favour of the DxN VERIS System when the weekend was taken into account (figure 5) [5].
Conclusions
In the evaluation at Toulouse University Hospital, the DxN VERIS System demonstrated good analytical and clinical performances in the quantitative determination of CMV DNA and HIV-1 RNA in plasma samples [1-7], comparing well to the laboratory’s existing methodology [1-7] and satisfying quality requirements for the routine monitoring of viral loads in plasma samples [2,4]. It is a completely automated platform, from primary patient sample to result, making it easy-to-use and reliable [1], and offering major improvement in laboratory workflows [5].
The simplified workflow and reduced manual intervention saves staff time, allowing them to focus on other tasks, such as research and innovation [7]. In addition, the single sample random access capabilities of the DxN VERIS System allow laboratories to process samples whenever they are required, without the need for batching, which allows faster results and provides a better service for clinicians and patients [7].
References
1. Mengelle, C, Sauné, K, Haslé, C et al (2014) VERIS/MDx System CMV Assay: a new automated molecular method for quantifying cytomegalovirus-DNA in plasma. Poster presentation, RICAI 2014.
2. Mengelle, C, Sauné, K, Haslé, C (2015) Performance of a completely automated system for monitoring CMV DNA in plasma. Poster presentation, ECCMID, Copenhagen, 2015.
3. Izopet, J, Mengelle, C, Sauné, K (2015) Performance of a new completely automated system for monitoring CMV DNA, HBV DNA, HCV** and HIV** RNA in plasma*. Presented at ECCMID 2015.
4. Mengelle, C, Sandres-Sauné, K, Mansuy, J et al. (2016) Performance of a completely automated system for monitoring CMV DNA in plasma. Journal of Clinical Virology 79: 25–31.
5. Izopet, J (2016) Quantifying HIV-1 RNA with DxN VERIS, a new fully-automated system. Presented at ECCMID 2016.
6. Sauné, K, Haslé, C, Boineau, J (2015) Analytical performance of VERIS MDx system HIV assay for quantifying HIV RNA. Poster presentation, ESCV, Edinburgh, 2015.
7. Izopet, J (2015) Workflow Transformed: A New Fully-automated System for Molecular Diagnostics. Presented at EuroMedLab, Paris , 2015.
The author
Professor Jacques Izopet, Department of Virology, Institut Fédératif de Biologie, CHU Toulouse, France.
Assay kit for the assessment of Lp(a) levels in serum or plasma with minimum apo (a) size related bias
, /in Featured Articles /by 3wmediaLp(a) is a complex macromolecule synthesized in the liver, it presents similarities with Low Density Lipoprotein (LDL) as it possesses a cholesterol-phospholipid core and a closely associated protein, apoB100. Lp(a) differs however from LDL in that each lipoprotein particle contains one copy of the glycoprotein apolipoprotein (a) [apo (a)] covalently bound to apoB100 by a single disufide bond [1]. Studies indicate the association of elevated levels of plasma Lp(a) and risk of coronary heart disease [2,3]. A study reported that elevated plasma Lp(a) and small apolipoprotein (a) increased the risk of recurrent arterial ischemic stroke in children [4]. Moreover, the structural similarities between apo(a) and plasminogen highlighted the importance of Lp(a) in both atherosclerosis and thrombogenesis [5-7]. The intra and inter-individual size heterogeneity of apo(a) is genetically determined, this size variation constitutes a challenge for the immunochemical measurement of Lp(a) in plasma. The use of a 5-point calibrator which take into account the heterogeneity of Lp(a) for each of the levels would reduce result discrepancies. This study reports the performance characteristics of an immunoturbidimetric assay for the determination of Lp(a) in serum or plasma with minimum apo(a) size related bias.
Methodology
In this immunoturbidimetric assay agglutination occurs due to an antigen-antibody reaction between Lp(a) in a sample and anti-Lp(a) antibody adsorbed to latex particles. The agglutination is detected as an absorbance change at 700 nm proportional to the concentration of Lp(a) in the sample. The reagents are stable and ready to use. Lp(a) Calibrator Series, Lp(a) Control and Lipid Controls were used (Randox Laboratories Limited, Crumlin, UK). The assay is applicable to a variety of clinical chemistry analysers, for the results of this study the RX Daytona Plus analyser was used (Randox Laboratories Limited, Crumlin, UK).
Results
Assay range
This immunoturbidimetric assay presented a reportable range of 3 to 106 mg/dL.
Sensitivity
The Limit of Quantitation (LOQ), the Limit of Detection (LOD) and the Limit of Blank were determined consistent with CLSI guidelines EP17-A (table 1).
Prozone
Antigen excess effects were not noted until Lp(a) levels approached 493 mg/dL.
Within run and total precision
Within run precision and total precision, expressed as CV(%), were <4.00 and <4.5 respectively (Table 2). Serum/plasma comparison
The assay was used to compare serum to plasma samples (n=56) collected into tubes containing Li heparin, Na heparin, Na EDTA, K EDTA or citrate. The data was subjected to linear regression analysis and in all cases the correlation coefficient (r) was > 0.996.
Conclusion
The immunoturbidimetric assay reported here for the determination of Lp(a) in serum/plasma with minimum apo (a) size related bias showed optimal analytical performance. The assay utilises ready- to –use stable reagents which facilitates the application in test settings by simplifying the experimental procedure and reducing handling errors. Its applicability to different automated analysers ensures the reliability, the accuracy of the measurements and facilitates the testing procedure. This represents an excellent analytical tool to facilitate clinical investigation.
References
1. Utermann G, Weber W. Protein composition of Lp(a) lipoprotein from human plasma. FEBS Lett. 1983; 154: 357-361.
2. Bennet A, Di Angelantonio E, Erqou S, Eirikdottir G, Sigurdsson G, Woodward M, Rumley A, Lowe G.D, Danesh J, Gudnason V. Lipoprotein (a) levels and risk of future coronary heart disease: large-scale prospective data. Arch. Intern. Med. 2008; 168: 598-608.
3. Emerging Risk Factors Collaboration, Erqou S, Kaptoge S, Perry P.L, Di Angelantonio E, Thompson A, White I.R, Marcovina S.M, Collins R, Thompson S.G, Danesh J. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA. 2009; 302: 412-423.
4. Goldenberg N.A, Bernard T.J, Hillhouse J, Armstrong-Wells J, Galinkin J, Knapp-Clevenger R, Jacobson, L, Marcovina S.M, Manco-Johnson M.J. Elevated lipoprotein(a), small apolipoprotein (a), and the risk of arterial ischemic stroke in North American children. Haematol. 2013; 98: 802-807.
5. McLean J.W, Tomlinson J.E, Kuang W.J, Eaton D.L, Chen E.Y, Fless G.M, Scanu A.M, Lawn R.M. cDNA sequence of human apolipoprotein(a) is homologous to plasminogen. Nature. 1987; 300: 132-137.
6. Tate J.R, Rifai N, Berg K, Couderc R, Dati F, Kostner G.M, Sakurabayashi I, Steinmetz A. International Federation of Clinical Chemistry standardization project for the measurement of lipoprotein(a). Phase I. Evaluation of the analytical performance of lipoprotein(a) assay systems and commercial calibrators. Clin Chem. 1998; 44: 1629-1640.
7. Hajjar K.A, Nachman R.L. The role of lipoprotein(a) in atherogenesis and thrombosis. Annu Rev Med. 1996; 47: 423-442.
Randox Laboratories Limited, Diamond Road, Crumlin, County Antrim, N. Ireland, BT29 4QY, UK
www.randox.com
Rio 2016: will there be health repercussions?
, /in Featured Articles /by 3wmediaWhen Dr Margaret Chan, Director-General of WHO, stated that the health risks for both participants and spectators at the Rio Olympic games were “low and manageable” she was referring to possible exposure to Zika virus. But the other major health concern raised was the quality of the water, particularly for the one to two thousand athletes who competed in aquatic events.
Both the CDC and WHO provided advice on managing the risk of Zika virus infection whilst in Brazil, including use of insect repellent and wearing light clothing covering most of the body. Visitors were also urged to stay in air-conditioned accommodation so that open windows would not admit mosquitoes, and to avoid impoverished areas where lack of suitable sanitation encourages Zika vectors to breed. Abstention or the correct and consistent use of condoms was also advocated whilst in Brazil and for at least eight weeks after returning. Unfortunately we now know that the virus can persist in semen for much longer than eight weeks; two recent cases in men who contacted symptomatic Zika infection (and around 80% of cases are estimated to be asymptomatic) still had virus in their semen after 181 and 188 days respectively.
There was a rather bizarre occurrence during the games, when water in certain dedicated swimming pools turned from blue to green overnight. However the Fédération Internationale de Natation (FINA) Sports Medicine Committee confirmed that this resulted from “some of the chemicals used in the water treatment process running out”, causing the pH to be outside the normal range. FINA’s assurance that there was no risk to the health and safety of athletes was trusted; hopefully there was sufficient supporting evidence. However the potentially most serious health threat was the quality of the natural recreational water used for many aquatic events, water that is still significantly contaminated with untreated sewage. Athletes and visitors were urged to have vaccinations such as typhoid and Hepatitis A before travelling to Brazil, to avoid swallowing recreational water and to shower after being exposed to it.
But the risks to visiting athletes and participants were hopefully only transient compared with the enduring health hazards faced daily by the poorer citizens of Brazil. The best possible legacy of the Rio Olympic games would not be an increased interest in athletics but rather continued intensive Aedes mosquito control, widely disseminated information on sexual and vertical transmission of Zika virus, the sustained treatment of raw sewage and the provision of safe drinking water.
Spatial intratumoral proliferative heterogeneity in neuroendocrine tumours of the pancreas: assessment and impact
, /in Featured Articles /by 3wmediaInteractions of neoplastic cells with each other and the microenvironment are complex. The main factors contributing to intratumoral heterogeneity may be reflected in biomarker expression. The aim is to investigate the spatial intratumoral heterogeneity of Ki-67 immunostains in whole sections of pancreatic neuroendocrine neoplasms. The extent and range of heterogeneity has potential as a prognostic marker.
by Dr Nektarios A. Valous, Dr Frank Bergmann and Dr Niels Halama
Overview
Tumour heterogeneity means that a neoplasm comprises distinct cellular subpopulations that can vary in histology and growth rate. Phenotypic and functional heterogeneity arise among cancer cells within the neoplasm because of genetic change, environmental differences and reversible changes in cell properties [1, 2]. Genomic instability arises through various routes, leaving distinct genomic footprints and differentially affecting tumour evolution and patient outcome [3]. In addition, heterologous cell types within tumours can influence therapeutic response and shape resistance [4]. The interactions of neoplastic cells with each other and the microenvironment are complex. Clinicians assess complex cytological, histological, and morphological characteristics of tissues often in a semi-quantitative manner. In order to understand intratumoral heterogeneity, potentially subtle differences within neoplasms should be quantified.
The main factors contributing to intratumoral heterogeneity include the ischemic gradient within the neoplasm, the action of the microenvironment, mechanisms of intercellular transfer of genetic information, and differential mechanisms of modifications of genetic material/proteins [5]. This may be reflected in the expression of biomarkers and their clinical utility in the context of prognosis/stratification. A rigorous approach for assessing the spatial intratumoral heterogeneity of histological biomarker expression with accuracy and reproducibility is required, since patterns in immunohistochemical images exhibit scale-dependent changes in structure and can be challenging to identify and describe [6–8]. The aim is to determine the implications and prognostic value of observed variations, in a host of clinically relevant neoplastic properties [9].
Case study
It is recognized that proliferative heterogeneity is common in many tumours, including pancreatic neuroendocrine neoplasms [10]. These represent a rather heterogeneous group regarding histology, hormone secretion and functional activity, as well as biological behaviour. Pancreatic neuroendocrine neoplasms are classified as tumours (grade G1 and G2) and carcinomas (grade G3), based on proliferation activity. The latter is determined using a mitotic count in ten high-power fields or determination of fraction of Ki-67 positive cells within a highly proliferative area. In resected neoplasms, the proliferation-dependent tumour grade is shown to be a strong prognostic marker, whereas patient clinical management largely depends on proliferation activity. Key treatment decisions rely on the robust classification of these tumours.
Method
Earlier work has shown the advantages of using spatial statistics in histopathology [11]. A quantitative method from the fields of complex systems and image analysis that is particularly useful for characterizing complex irregular structures is lacunarity. The term lacunarity itself stems from the field of fractal geometry referring to a measure of how patterns fill space [12]. The approach has several theoretical and practical advantages for the assessment of spatial heterogeneity [13]. It is a multiscale technique, its computation is simple to implement, it exhaustively samples the image to quantify scaling changes, the analysis can be used for very sparse data, and the decay of the lacunarity index as a function of window size follows characteristic patterns for random, self-similar and structured spatial arrangements. Compared to previous approaches, the proposed methodology quantifies directly the distributional landscape of proliferative cells and not the textural content of the histological slide, thus providing a more realistic measure of heterogeneity within the sample space of the tumour region.
Workflow
Figure 1 provides an overview of the workflow for measuring the spatial intratumoral heterogeneity of proliferation in pancreatic neuroendocrine neoplasms. Immunohistochemical (IHC) staining for Ki-67 is performed on whole-slide sections taken from original tissue blocks using the avidin-biotin-peroxidase detection system on a fully automated staining facility. On IHC stains for Ki-67, the brown reaction product at the antigen site is in the cell nucleus. The slide is counterstained with hematoxylin to allow evaluation and assessment of staining localization. Glass slides are automatically imaged in bright-field mode. The resulting virtual slides are reviewed by a pathologist for determining the locality of the neoplastic region; areas are marked and large representative sections are cropped. The automated proliferative cell nuclei segmentation workflow is based on background removal, stain vector extraction/color deconvolution, and post-processing operations. Hence, for every virtual slide (large section of tumour region), a segmented image depicting proliferative nuclei is produced.
For measuring the spatial heterogeneity of proliferation using lacunarity, an automated sampling scheme is employed prior to analysis allowing better scrutiny and interpretation, thus keeping computation times manageable. The sampling method aims at capturing the spatial variability of Ki-67 positive cells in the images. Then, lacunarity is computed and subsequently visualized in a double log plot as a function of scale. These plots explicitly characterize the spatial organization of images and measure space filling capacity. From the plots, mean, median, and mode lacunarity curves are computed. These metrics capture the variability (or lack of) of the curves for the sampled sections. To ascertain that lacunarity describes the spatial organization of proliferation, the three metrics are used for partitioning the neoplasms into conceptually meaningful clusters. This is achieved by performing unsupervised learning using the k-means algorithm. First, their Mahalanobis distance is computed and then using principal component analysis vectors are decorrelated by projection into a subspace that minimizes reconstruction error in the mean squared sense. Finally, a phenomenological heterogeneity index is computed from the spatial distribution in order to provide direct numerical values.
Impact and outlook
Phenotypic heterogeneity that stems from genetic/non-genetic determinants constitutes a major source of therapeutic resistance, and is an important clinical obstacle [14]. Tissue architecture is generally not reflected in molecular assays, rendering this information underused. Heterogeneity in histological expression of biomarkers has been noted in earlier studies. However, experimental exploration has been limited by a lack of conceptual framework and tools. The critical bottleneck has become the development of computational methods to analyse, integrate, and connect data to prognostic and actionable clinical information [15].
The architectural complexity of immunohistological images has shown that single measurements are often insufficient for characterization. The selection of a region of interest as a surrogate for the complete complex structure is prey to selection bias and subsequent loss of reproducibility and precision. Especially for the neuroendocrine pancreatic tumours, the inhomogeneity of distribution depends not only on percentage content of proliferation phase but also on how the phase fills the space. An increased degree of spatial proliferative heterogeneity is observed in certain neoplasms comparing to others with similar histological grade. Whether this is a sign of different tumour biology and subsequently an association with a more benign or malignant clinical course needs to be investigated further. The approach provides an increased level of granularity for discerning different levels of spatial heterogeneity in biomarker expression, since manual inspection can be cumbersome and may not resolve finer differences. The extent and range of heterogeneity has the potential to be evaluated as a prognostic marker, e.g. for the evaluation of the clinical course utilizing the construction of survival curves. This heterogeneity is also most likely to play a role in different aspects of the clinical course. Clinical trials should incorporate such studies on heterogeneity so that the impact on therapeutic effectiveness can be understood. For practical reasons, the heterogeneity index outputted in the final step of the analysis is desirable when a single numerical value is required, e.g. in direct assessments or comparisons for the automated ranking of cases in order of increasing/decreasing heterogeneity.
In summary, the lacunarity morphometric provides information about the distribution of Ki-67 immunolabelling that corresponds to the degree of spatial organization of proliferation. This reflects a general approach with potential importance in clinical work, which is relevant to other solid tumours and a vast array of biomarkers. The additional level of understanding the distributional patterns of specific biomarkers holds promise for providing valuable information in a clinical setting. Drawing upon the richness of histopathological information and merits of computational biomedicine, the approach removes qualitative ambiguities and uncovers salient features for use in future studies of clinical relevance.
References
1. Hölzel M, Bovier A, Tüting T. Plasticity of tumour and immune cells: a source of heterogeneity and a cause for therapy resistance. Nat Rev Cancer 2013; 13: 365–376.
2. Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature 2013; 501: 328–337.
3. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 2013; 501: 338–345.
4. Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 2013; 501: 346–354.
5. Diaz-Cano SJ. Tumor heterogeneity: mechanisms and bases for a reliable application of molecular marker design. Int J Mol Sci. 2012; 13: 1951–2011.
6. Halama N, Zoernig I, Spille A, Michel S, Kloor M, Grauling-Halama S, Westphal K, Schirmacher P, Jäger D, Grabe N. Quantification of prognostic immune cell markers in colorectal cancer using whole slide imaging tumor maps. Anal Quant Cytol. 2010; 32: 333–340.
7. Keim S, Zoernig I, Spille A, Lahrmann B, Brand K, Herpel E, Grabe N, Jäger D, Halama N. Sequential metastases of colorectal cancer: immunophenotypes and spatial distributions of infiltrating immune cells in relation to time and treatments. Oncoimmunology 2012; 1: 593–599.
8. Halama N, Zoernig I, Berthel A, Kahlert C, Klupp F, Suarez-Carmona M, Suetterlin T, Brand K, Krauss J, et al. Tumoral immune cell exploitation in colorectal cancer metastases can be targeted effectively by anti-CCR5 therapy in cancer patients. Cancer Cell 2016; 29: 587–601.
9. Brooks FJ, Grigsby PW. Quantification of heterogeneity observed in medical images. BMC Med Imaging 2013; 13: 7.
10. Yang Z, Tang LH, Klimstra DS. Effect of tumor heterogeneity on the assessment of Ki-67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: Implications for prognostic stratification. Am J Surg Pathol. 2011; 35: 853–860.
11. Nawaz S, Heindl A, Koelble K, Yuan Y. Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer. Mod Pathol. 2015; 28: 766–777.
12. Smith TG Jr, Lange GD, Marks WB. Fractal methods and results in cellular morphology—dimensions, lacunarity and multifractals. J Neurosci Methods 1996; 69: 123–136.
13. Plotnick RE, Gardner RH, Hargrove WW, Prestegaard K, Perlmutter M. Lacunarity analysis: a general technique for the analysis of spatial patterns. Phys Rev E. 1996; 53: 5461–5468.
14. Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer. Nat Rev Cancer 2012; 12: 323–334.
15. Alizadeh AA, Aranda V, Bardelli A, Blanpain C, Bock C, Borowski C, Caldas C, Califano A, Doherty M, et al. Toward understanding and exploiting tumor heterogeneity. Nat Med. 2015; 21: 846–853.
The authors
Nektarios A. Valous*1 PhD, Frank Bergmann2 MD, Niels Halama3 MD, PhD
1Applied Tumor Immunity Clinical
Cooperation Unit, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
2Institute of Pathology, Heidelberg
University Hospital, Heidelberg, Germany
3Department of Medical Oncology, National Center for Tumor Diseases,
Heidelberg University Hospital, Heidelberg, Germany
*Corresponding author
E-mail: nek.valous@nct-heidelberg.de
Automated image analysis personalizes colorectal cancer prognosis
, /in Featured Articles /by 3wmediaThe current prognostic staging of colorectal cancer (CRC) by the tumour, node, metastasis method, alongside the minimal core data set, provides good prognostic information for patient populations but is less accurate for the individual. Reporting of additional histopathological features can improve individualized prognostic staging, but manual microscopic surveillance often results in observer variability and there is a lack of consensus on standardized quantification methods. Automated image analysis can standardize the quantification of prognostic features, can personalize CRC prognosis and augment clinical staging.
by Dr Peter Caie
Introduction
Colorectal cancer (CRC) incidence is extremely high in the developed world, where it is the third most common cancer in men and women in both the UK and USA. There will be an estimated 134 500 new CRC cases diagnosed, and just under 50 000 deaths will have been caused by the disease, in the USA alone. Although the incidence rate has decreased only slightly in the last decade, the change in mortality has dropped significantly between 2000 and 2010, with ~50% of patients across all disease stages surviving for at least 5 years post-diagnosis. Reasons for the decrease in mortality include life style change (47% of CRC cases in the UK could be prevented from healthier lifestyle choice) early detection of disease (e.g. home testing such as fecal occult blood kits), targeted therapy as the result of ‘omics’ research, novel prognostic factors coupled with more accurate pathological and clinical staging of disease and advances in surgical technique. These factors culminate in a more effective treatment of patients at an early stage and at a personalized level. Survival rates in early stage cancer, where the tumour is localized, are extremely good with ~90% of patients experiencing 5-year disease-free survival. Upon spread to localized lymph nodes survival decreases to 50–70% and if distant metastasis has occurred survival is only 12% [1, 2].
Current prognostic staging of CRC
Although multiple CRC subtypes exist, with both molecular and histopathological variances, 90% of CRCs are adenocarcinomas and prognosis is determined through the international tumour, node, metastasis (TNM) staging system alongside the minimal core data set. The TNM staging is based on gross observation and analysis of histopathological tissue sections under the microscope which revolves around the depth of local invasion (T), presence of cancer within the lymph nodes (N) and if the cancer has metastasized (M). TNM staging is excellent at returning prognostic information on a population of patients; however, it is less specialized at predicting prognosis at the level of the individual [3]. A patient’s prognosis is worse the higher the stage they are classified within, however, the TNM system does not differentiate between good and poor outcome of patients within the same stage [4]. There are defined adjuvant treatment guidelines associated with the various stages of CRC [5]. Stage 0 and I cancers will not routinely receive adjuvant chemotherapy and surgical resection is considered curative. Adjuvant therapy is recommended for stage III and IV patients, however, there remains ambiguity about whether to treat all, a subset or no stage II patients with adjuvant chemotherapy [6]. Around 30% of stage II CRC patients will succumb to their disease after surgical resection and, therefore, an accurate and more personalized identification and stratification of high-risk stage II patients, some of whom have comparable or worse outcomes than stage III patients [7], is therefore imperative to increase disease free survival rates. In the UK, pathologists collect a minimal core data set for each patient which helps to identify high-risk stage II CRC cases [8]. Although some parameters within the data set are disregarded in clinical decision making for the management of stage II CRC patients, some features, if present, may invoke the decision to treat the patient: high grade/poor differentiation, pT4 local spread and extramural lymphovascular invasion [9]. However, there is little evidence to date to show the advantage of adjuvant therapy for stage II patients with additional high-risk factors. Furthermore, there are promising histopathological features listed in the literature that have been significantly correlated with poor prognosis but which rarely feature in final clinical reports. There is, for example, growing evidence that immune cell infiltrate and perineural invasion are strongly correlated with poor patient outcome, whereas lymphatic vessel invasion (LVI) and the invasive growth pattern, including tumour budding, are two of the most promising histopathological features that have been significantly associated with lymph node metastasis and disease-specific survival.
Manual reporting yields observer variability
Although histopathological features such as nuclear grade have long been established in the core data sets for CRC prognosis and features such as immune infiltrate, invasive pattern, LVI, lymphatic vessel density (LVD) and the tumour-to-stroma ratio are associated with poor prognosis in the literature, they have also been associated with observer variability in their reporting. Observer variability is an inevitable occurrence when reporting histopathological features by eye under microscopic surveillance; however, it is increased by certain features being obscure under H&E stained tissue with associated retraction artefact, difficult to accurately quantify or if they are rare events. This is particularly true when the calculation of areas in manually determined ‘hot-spots’ is required, such as for LVD and tumour-to-stroma ratio calculations which are both very prone to observer bias and variability. Furthermore, a general consensus on standardized quantification methods is lacking for many of these candidate histopathological features and for this reason they, apart from grading, have not translated into the minimal core data sets of CRC pathological reporting. Although nuclear grading has been reported as a minimal core data set for many years, there have been studies which have also found it to be non-significant [10]. Therefore, there is now a consensus for nuclear grading in CRC to move toward a two-tiered scoring system of ‘well differentiated’ and ‘poorly differentiated’ which eliminates the ‘moderately differentiated’ class and attempts to increase standardization.
Automated image analysis can standardize the quantification of prognostic features
Digital pathology and associated image analysis technology is becoming increasingly sophisticated. It is now possible to create image analysis algorithms that can automatically segment and quantify histopathological features within digital tissue sections with high accuracy. There are multiple advantages to applying image analysis to histopathology reporting that overcome the associated observer variability of manual scoring. Automated image analysis uses standardized algorithms and objectively reports on the features it is programmed to quantify. It does so in a robust manner across all patient samples being tested. Applying image analysis allows a higher degree of accuracy when reporting on the number, area or ratio of specific features across a whole tissue section and so negates the need of manually assigned hot-spots. Furthermore continuous data is captured when image analysis is applied, which allows more accurate clinical cut-offs to be used resulting in a more personalized assessment of a patient’s condition than the more traditional categorical reporting of, e.g. 1+, 2+, 3+ for immunohistochemistry, or ‘well’ or ‘poorly’ differentiated cases. Finally, rare and obscure events that may be missed by the eye are able to be reported with higher confidence when the computer assesses the entire issue section. Jeremy Jass in the late 1980s reported a novel grading system for CRC that included the reporting of the immune infiltrate and the pattern of invasive margin [11]; however, his promising results were not translated into the clinic due to poor reproducibility. Recently, two groups have used image analysis to quantify the immune infiltrate in the form of the immunoscore [12] and the tumour’s infiltrative pattern in the form of tumour budding [13] (Fig. 1A) in a manner that allows standardization, and have shown both features to be prognostically significant. Image analysis has further been used to quantify, amongst others, additional set histopathological features such as for nuclear grading [14], vasculature hot-spots [15] and LVI and LVD (Fig. 1B) [13].
Automated image analysis personalizes CRC prognosis
CRC is a specifically heterogeneous disease and the tumour microenvironment is also a heterogeneous and heterotypic ecology. Therefore, quantifying a single histopathological aspect of a patient’s tumour may not be sufficient for an accurate prognosis across a large population. A recent study by our group used multiplexed immunofluorescence to quantify a suite of histopathological features co-registered across a single CRC tissue section for each patient. Furthermore, the algorithm exported data captured across each nucleus (Fig. 2) in each sample to create a large and complex personalized multi-parametric data set for each patient in the stage II CRC study [16]. A single standardized image analysis algorithm was run across a training set of patients which quantified continuous data from the invasive front of stage II CRC on the number, shape and extent of: tumour buds, poorly differentiated clusters, LVI, LVD, tumour-to-stroma ratio, tumour gland morphology as well as multiple measurements across each nucleus to increase the accuracy of nuclear grading. The resultant ‘big-data’ was distilled through machine learning to identify the optimal parameter set to stratify patients into a high or low risk of disease-specific death. The result was the identification of a novel feature that was independently significant and where the addition of any other measured feature into the model added no further significance to patient stratification. This feature was the mean area of poorly differentiated clusters (area PDC) across the invasive front. The data from the training cohort was validated across a larger independent cohort and again the novel feature held more significance for patient risk stratification in stage II CRC than any of the other more established histopathological feature measured in the study. Furthermore, mathematical modelling was employed to identify if any of the parameters from the clinical pathology report added value to the prediction of disease specific death. By performing this analysis it was found that pT stage and differentiation added further value and were incorporated into a Novel Prognostic Index alongside the area PDC. This novel index outperformed the clinical gold standard of pT staging by almost twofold (Fig. 3).
Big-data and personalized pathology augments clinical staging
The idea behind big-data pathology is to include as much data as possible about each single patient and so to move towards a more personalized prognosis. The acquisition of quantitative data through image analysis and molecular pathology lends itself very well to big-data pathology, where the vast data sets can be mined through sophisticated machine learning algorithms to identify the optimal parameters to answer the clinical question. However, clinical staging and reporting has stood the test of time and it is imperative to include data such as this in any integrative model. As it becomes easier and cheaper to acquire large, reproducible and standardized data sets, modern pathology will become more personalized and patient outcome will improve due to tailored treatment regimens directed at individual patients.
References
1. Siegel R, Ward E, Brawley O, Jemal A. Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin. 2011; 61(4): 212–236.
2. Shah R, Jones E, Vidart V, Kuppen PJ, Conti JA, Francis NK. Biomarkers for early detection of colorectal cancer and polyps: systematic review. Cancer Epidemiol Biomarkers Prev. 2014; 23(9):1712–1728.
3. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet 2014; 383(9927):1490–1502.
4. Lea D, Haland S, Hagland HR, Soreide K. Accuracy of TNM staging in colorectal cancer: a review of current culprits, the modern role of morphology and stepping-stones for improvements in the molecular era. Scand J Gastroenterol. 2014; 49(10):1153–1163.
5. Poston GJ, Tait D, O’Connell S, Bennett A, Berendse S. Diagnosis and management of colorectal cancer: summary of NICE guidance. BMJ 2011; 343:d6751.
6. Dotan E, Cohen SJ. Challenges in the management of stage II colon cancer. Semin Oncol. 2011; 38(4):511–520.
7. Urquhart R, Bu J, Grunfeld E, Dewar R, MacIntyre M, Porter GA. Examining stage IIB survival in a population-based cohort of patients with colorectal cancer. Cancer 2012; 118(23):5973–5981.
8. Loughrey MB, Quirke P, Shephard NA. Standards and datasets for reporting cancers. Dataset for colorectal cancer histopathology reports July 2014. The Royal College of Pathologists 2014. The cancer datasets are a combination of textual guidance, educational information and reporting proformas to enable consistent grading and staging. (https://www.google.co.uk/?gws_rd=ssl#q=Standards+and+datasets+for+reporting+cancers+Dataset+for+colorectal+cancer+histopathology+reports+July+2014)
9. Morris EJ, Maughan NJ, Forman D, Quirke P. Who to treat with adjuvant therapy in Dukes B/stage II colorectal cancer? The need for high quality pathology. Gut 2007; 56(10):1419–1425.
10. Ratto C, Sofo L, Ippoliti M, Merico M, Doglietto GB, Crucitti F. Prognostic factors in colorectal cancer. Literature review for clinical application. Dis Colon Rectum 1998; 41(8):1033–1049.
11. Jass JR, Love SB, Northover JM. A new prognostic classification of rectal cancer. Lancet 1987; 1(8545):1303–1306.
12. Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, Lugli A, Zlobec I, Hartmann A, et al. Towards the introduction of the Immunoscore in the classification of malignant tumors. J Pathol. 2014; 232(2):199–209.
13. Caie PD, Turnbull AK, Farrington SM, Oniscu A, Harrison DJ. Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer. J Transl Med. 2014; 12:156.
14. Rathore S, Hussain M, Aksam Iftikhar M, Jalil A. Novel structural descriptors for automated colon cancer detection and grading. Comput Methods Programs Biomed. 2015; 121(2):92–108.
15. Kather JN, Marx A, Reyes-Aldasoro CC, Schad LR, Zollner FG, Weis CA. Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images. Oncotarget 2015; 6(22):19163–19176.
16. Caie PD, Zhou Y, Turnbull AK, Oniscu A, Harrison DJ. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget 2016; doi: 10.18632/oncotarget.10053 [Epub ahead of print].
Acknowledgment
This article is based on the author’s recently published paper: Caie PD, Zhou Y, Turnbull AK, Oniscu A, Harrison DJ. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget 2016; doi: 10.18632/oncotarget.10053 [16].
The author
Peter Caie PhD
Quantitative and Digital Pathology, School of Medicine, University of St Andrews, North Haugh, St Andrews, UK
*Corresponding author
E-mail: Pdc5@st-andrews.ac.uk
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