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
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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, /in Featured Articles /by 3wmediaHigh-sensitivity cardiac troponin: scope for improvement?
, /in Featured Articles /by 3wmediaAcute chest pain remains the most common reason for emergency hospital admissions in the West, accounting for around 10% of visits. The majority of these patients do not have a life-threatening condition, but around 17% will be diagnosed with acute myocardial infarction (AMI). A physical examination and an ECG or serial ECGs remain essential. Diagnosis is straightforward in patients with typical cardiac symptoms and notable ST-segment deviation, but biomarker testing is necessary in patients with atypical symptoms and non-diagnostic ECGs. Despite huge and sustained efforts by the scientific community during the last six decades, a perfect cardiac biomarker to detect which of these patients have AMI has not yet been found. The cardiac troponin immunoassay, first developed in 1989, has now given rise to a fifth generation hs-cTn immunoassay that is currently used to facilitate the triage of chest pain patients, but is there any scope for improvement in either cardiac biomarker tests or their role in patient management?
The perfect cardiac biomarker would be present in significant concentration in the myocardium but not in any other tissues, and be released rapidly into the blood when MI occurs. It would persist for sufficient duration to allow diagnosis via a rapid and relatively inexpensive assay. Current hs-cTn assays can detect cardiac troponin release within 3 hours and MI can be ruled out in the approximately 60% of chest pain patients who have undetectable levels, or levels below the 99th percentile upper reference limit of a healthy population; the negative predictive value is nearly 100%. Tests are cost-effective and fairly rapid: central labs are able to provide results within an hour, and POC test results can be available within 10 to 20 minutes. However, predictably, the increased sensitivity of hs-cTn assays lowers specificity, resulting in values above normal in patients with conditions other than MI, including atrial fibrillation, hypertension, hepatic and renal disorders, acute and chronic pulmonary disease and even some allergic reactions. Using the currently set diagnostic cut-off for MI, the low positive predictive value results in approximately 22% of chest pain patients without MI remaining in hospital under observation.
Is cTn the best cardiac biomarker that will ever be available, or is it possible that an ever-increasing knowledge of the pathophysiology of acute cardiac disease together with current technological advances may eventually discover the perfect biomarker? Until this happens hs-cTn assays, with probable refinements in their use, will remain an integral part of suspected MI patient management.
The use of growth stimulation expressed gene 2 (sST2) as a heart failure biomarker
, /in Featured Articles /by 3wmediaThere is a need for cardiac biomarkers for the diagnosis of heart failure and to assist risk stratification and monitoring of therapy. Natriuretic peptides are currently widely used to assist diagnosis. The new marker sST2 has potential to provide prognostic information and to monitor therapy.
by Dr Stuart J. Bennett and Dr Ruth M. Ayling
Introduction
Heart failure (HF) is a complex clinical syndrome of symptoms and signs that suggest impairment of the heart supporting physiological circulation and affects approximately 900 000 people in the UK [1]. The incidence and prevalence increase with age and the prevalence is expected to rise in future as a result of the ageing population and improved survival of people with ischaemic heart disease. The symptoms (e.g. dyspnoea, fatigue and ankle swelling) and signs (e.g. pulmonary crackles) are not sensitive or specific for HF so diagnosis remains challenging. Investigations such as chest X-ray and echocardiogram are used for this purpose but attention is increasingly being focused on cardiac biomarkers as a tool to assist diagnosis and management.
An ideal biomarker would enable underlying, and hence potentially reversible, causes of HF to be identified and would differentiate between the presence and absence of HF. It would also enable an estimation of severity and disease prognosis and could be used for monitoring of treatment. In addition, an ideal marker should be widely available at reasonable cost and short notice and the assay should be appropriately robust. Various cardiac biomarkers have been proposed and represent a wide range of pathophysiological mechanisms of cardiovascular disease, for example markers of myocardial stress (natriuretic peptides), myocyte injury (troponin), inflammation (C-reactive protein) and myocyte remodelling (galectin-3, sST2). Of these, only the natriuretic peptides are in routine clinical use as markers of HF and this review will describe their use in more detail, together with that of the new biomarker, sST2.
The natriuretic peptides
The natriuretic peptides B-type natriuretic peptide (BNP) and N-terminal (NT)-proBNP are currently the most commonly used markers of HF. BNP is derived from a 134 amino acid precursor, preproBNP, which is synthesized in cardiac myocytes in response to ventricular stretch and stress. On release, a 26 amino acid signal peptide is cleaved from the N-terminus to produce proBNP, which is then further cleaved by a membrane-bound protease into a 76 amino acid N-terminal-proBNP (NT-proBNP) and the active C-terminal 32 amino acid hormone (BNP). The most common use of natriuretic peptides is in the diagnosis of HF. In the ‘breathing not properly’ trial, BNP was found to have a sensitivity of 90% and a specificity of 76%, using a cut-off of 100 ng/L, for diagnosing HF in patients presenting to the emergency department with breathlessness [2]. The National Institute for Health Care Excellence (NICE) suggests that in new suspected acute HF, a BNP concentration <100 ng/L or NT-proBNP concentration <300 ng/L are appropriate thresholds to rule out the diagnosis [3]. In chronic HF, a BNP concentration <100 ng/L or NT-proBNP of <400 ng/L makes HF unlikely [4]. Defining rule-in cut-offs for HF is more complicated; a cut-off of >400 ng/L has been proposed for BNP and age-related cut-offs for NT-proBNP of >450 ng/L for <50 years, >900 ng/L for 50–75 years and >1800 ng/L for >75 years [5]. As natriuretic peptide concentrations can provide prognostic information, NICE advise referral for further investigation within 6 weeks if BNP is 100–400 ng/L or NT-proBNP is 400–2000 ng/L or within 2 weeks if BNP is >400 ng/L or NT-proBNP is >2000 ng/L. There is some evidence to suggest that measurement of natriuretic peptides may be of use in monitoring therapy. A meta-analysis of six randomized controlled trials found a reduction in all-cause mortality with natriuretic-peptide-guided therapy [6]. However, optimal monitoring schedules and targets are not yet established.
NT-proBNP may have certain practical advantages as, unlike BNP, it can be measured in serum as well as plasma and has superior stability. However, BNP and NT-proBNP concentrations should always be interpreted with due regard to the clinical setting. In addition to age and female sex, factors other than HF that may increase baseline concentrations include myocardial ischemia, left ventricular hypertrophy, pulmonary embolism, liver failure, sepsis and renal failure. Conversely, BNP and NT-proBNP concentrations may be lowered in the presence of obesity (BMI >35kg/m2) and some medications (e.g. angiotensin converting enzyme inhibitors, β-blockers, angiotensin receptor blockers, aldosterone antagonists).
Soluble ST2
ST2 (growth stimulating expressed gene 2) is a member of the interleukin (IL)-1 receptor family and has both membrane-bound (ST2L) and soluble (sST2) forms, both forms can bind IL-33, which is released in response to stretch. The source of circulating sST2 was presumed to be the myocardium but it may be that in cardiac disease the major source is vascular endothelium. When circulating sST2 is low, its ligand, IL-33, binds to ST2L which has a protective effect. When sST2 concentrations are raised there is competitive binding to IL-33, with less binding to ST2L reducing the amount available for cardioprotection. This leads to fibrosis and hypertrophy with reduced cardiac function. Binding to ST2L promotes signalling that protects against fibrosis and hypertrophy, whereas binding to sST2 acts as a decoy receptor, tending to promote fibrosis and hypertrophy. The potential clinical use of sST2 was first highlighted in animal studies by the findings of induced sST2 mRNA in cultured heart muscle after mechanical strain and raised circulating concentrations after myocardial infarction [7]. In human subjects, raised sST2 was associated with poor outcome after myocardial infarction but was not of value for diagnosis of the condition [8], leading to a focus on its use as a biomarker for HF.
Various methods of measurement have been described for sST2. The Presage® assay (Critical Diagnostics, CA, USA) has been extensively evaluated [9] and has received FDA and CE approval. However, neither this nor other commercially available assays are rigorously standardized. The Presage® assay is a quantitative sandwich ELISA using two monoclonal antibodies to mouse ST2. Either serum or plasma is suitable for analysis and samples remain suitable for analysis if stored for up to 48 hours at 20°C. A suggested cut-off for sST2 in chronic HF is 35 ng/mL but more recently sex-related differences, higher in males, have been reported [10]. A recent development is the availability of a point-of-care test, allowing rapid sST2 testing.
In the PRIDE study, sST2 was not found to be a useful tool for the diagnosis of HF but does have potential for risk stratification in undiagnosed dyspnoea [11]. A number of studies have examined the use of sST2 in acute HF and found it to be associated with the severity of HF and with poor outcome and to provide independent and additive prognostic information in addition to other markers, e.g. natriuretic peptides and troponins [12]. In chronic HF, elevated sST2 concentrations are strongly associated with HF severity and with increased risk of cardiac death, cardiovascular events and hospitalization [13]. sST2 has been shown to be equivalent to natriuretic peptides in classifying risk in chronic HF and if used in addition improves risk stratification [14].
The usefulness of serial measurements of sST2 has been examined for monitoring HF [15], suggesting it correlates with clinical course and has potential as a marker for monitoring response to therapy. sST2 appears not to have particular advantages in the diagnosis of HF but can add value in identifying patients at high risk and in whom advanced disease management may be advantageous.
Conclusion
The importance of biochemical tests in contributing to HF diagnosis is evidenced by the incorporation of BNP and NT-proBNP into current NICE guidelines. There is a desire to find suitable markers for use in prognosis and monitoring enabling intensified management of high risk patients and tailoring of treatment regimens. sST2 is a marker of myocardial fibrosis and cardiac stretch and data exist to demonstrate its prognostic value, alone or in combination with natriuretic peptides. The existence of an ELISA method that can be used on a standalone analyser in the setting of a clinical laboratory means its routine use in HF management is now a distinct possibility and the advent of a point-of-care assay is likely to lead to further clinical opportunities for its use.
References
1. Cleland J, Dargie H, Hardman S, McDonagh T, Mitchell P. National Heart Failure Audit. British Society for Heart Failure. 2012; https://www.ucl.ac.uk/nicor/audits/heartfailure/documents/annualreports/hfannual11-12.pdf.
2. Maisel AS, Krishnaswamy, Nowak RM, McCord J, Hollander JE, Duc P, Omland T, Storrow AB, Abraham WT, et al. Rapid measurement of B-type natriuretic peptide in emergency diagnosis of heart failure. N Eng J Med. 2002; 347: 161–167.
3. NICE. Acute Heart Failure: diagnosis and management. 2014; https://www.nice.org.uk/guidance/cg187.
4. NICE. Chronic heart failure in adults: management. 2010; https://www.nice.org.uk/Guidance/CG108.
5. Maisel A, Mueller C, Adams K Jr, Anker SD, Aspromonde N, Cleland JG, Cohen-Solal A, Dahlstrom U, DeMaria A, et al. State of the art: using natriuretic peptide levels in clinical practice. Eur J Heart Fail. 2008; 10: 824–839.
6. Felker GM, Hasseblad V, Hernandez A, O’Connor CM. Biomarker-guided therapy in chronic heart failure: a meta-analysis of randomized controlled trials. Am Heart J. 2009; 158: 422–430.
7. Weinberg EO, Shimpo M, DeKeulenaer GW, MacGillivray C, Shin-Ichi T, Solomon SD, Rouleau JL, Lee RT. Expression and regulation of ST2, an interleukin-1 receptor family member, in cardiomyocytes and myocardial infarction. Circulation 2002; 106: 2961–2966.
8. Brown Am, Wu AH, Clopton P, Robey JL, Hollander JE. ST2 in emergency department chest pain patients with potential acute coronary syndromes. Ann Emerg Med. 2007; 50: 153–158.
9. Dieplinger B, Januzzi JL Jr, Steinmair M, Gabriel C, Poelz W, Haltmeyer M, Mueller T. Analytical and clinical evaluation of a novel high-sensitivity assay for measurement of soluble ST2 in human plasma–the Presage ST2 assay. Clin Chim Acta 2009; 409: 33–40.
10. Coglianese EE, Larson MG, Vasan RS, Ho JE, Ghorbani A, McCabe EL. Cheng S, Fradley MG, Kretschman D. et al. Distribution and clinical correlates of the interleukin receptor family member soluble ST2 in the Framingham Heart Study. Clin Chem. 2012; 58: 1673–1681.
11. Januzzi JL Jr, Peacock WF, Maisel AS, Chae CU, Jesse RL, Baggish AL. Measurement of the interleukin family member ST2 in patients with acute dyspnea: results from the PRIDE (Pro-Brain Natriuretic Peptide Investigation of Dyspnea in the Emergency Department) study. Am Coll Cardiol. 2007; 50: 607–613.
12. Pascual-Figal DA, Manzano-Fernandez S, Boronat M, Casa T, Garrido IP, Bonaque JC. Soluble ST2, high-sensitivity troponin T- and N-terminal pro-B-type natriuretic peptide: complementary role for risk stratification in acutely decompensated heart failure. Eur J Heart Fail. 2011; 13: 718–25.
13. Dieplinger B, Mueller T. Soluble ST2 in heart failure. Clin Chim Acta 2015; 443: 57–70.
14. Ky B, French B, McCloskey K, Rame JE, McIntosh E, Shahi P, et al. High-sensitivity ST2 for prediction of adverse outcomes in chronic heart failure. Circ Heart Fail .2011; 4: 180–187.
15. Bayes-Genis A, Pascual-Figal D, Januzzi JL, Maisel A, Casas T, Valdas Chavarri M. Soluble ST2 monitoring provides additional risk stratification for outpatients with decompensated heart failure. Rev Esp Cardiol. 2010; 63: 1171–1178.
The authors
Stuart J. Bennett PhD, Ruth M. Ayling* PhD, FRCP, FRCPath
Department of Clinical Biochemistry, Pathology and Pharmacy Building, Royal London Hospital, Bart’s Health NHS Trust, London, UK
*Corresponding author
E-mail: Ruth.Ayling@bartshealth.nhs.uk