The analysis of histopathology slides is routinely performed in a manual, semi-quantitative manner which is open to observer variability. This article summarizes how technological advances in image analysis software allow the objective and standardized quantification of such samples while driving pathology towards a more personalized medicine.

by Dr Peter Caie

Introduction
The assessment of stained tissue sections by manual observation down a microscope has been, and still is, the steadfast manner in which histopathologists observe diseased tissue architecture in order to report on a patient’s prognosis. The tissue, for example the tumour microenvironment, is complex, highly heterogeneous and heterotypic. Although specific stains exist to aid in the identification and semi-quantification of histopathological features or biomarkers, the empirical field is subjective and therefore open to observer variability. In colorectal cancer (CRC) this can be the case for reporting items from the minimal core clinical data set such as differentiation [1] or promising histopathological features such as tumour budding [2] and lymphovascular invasion [3]. Similarly, in breast cancer discrepancies exist in the reproducibilty of manual reporting of human epidermal receptor protein-2 (HER2) by fluorescence in situ hybridization (FISH) or immunohistochemistry and the scoring of estrogen receptor (ER), both of which have predictive implications for patient treatment strategies [4]. Some reproducibility issues may be overcome through molecular pathology and the objective automated quantification of molecular biomarkers extracted from patient tissue samples. Modern methodology in quantitative pathology, spanning the classical ‘omics’ fields, has the ability to create a wealth of complex big data. Indeed, the field of molecular pathology has seen an explosion of big data specifically in translational genomics, transcriptomics and proteomics and which has the ability to map aberrant molecular pathways with direct impact on clinical decisions. The automated and standardized extraction of large data sets from tissue, has been termed ‘tissue datafication’. The automated quantification of molecular pathology, such as next-generation sequencing (NCS), gene-chip transcriptomics and reverse phase protein arrays may still suffer from reproducibility issues. These may occur from poor and small sample sizes or tissue artefacts which can stem from multiple sources: surgical ischemia, fixation and sample preparation. Standardization is therefore the key to accurate tissue datafication in order to report reproducible results which translate to the clinic. Tissue heterogeneity, both inter-patient and intra-patient, poses a very real problem for the effective personalized treatment decisions for patients. Tissue is often homogenized in order to extract the DNA, RNA or protein required for many molecular pathology techniques. In doing so the tissue heterogeneity (both subpopulation and spatial heterogeneity) is invariably lost and a single end-point is reported from the most dominant signal within the complex sample. A patient may therefore initially respond to a targeted treatment such as cetuximab in CRC but relapse within a set time period because of the existence of resistant KRAS and BRAF mutated subpopulations within the tumour [5]. Effective personalized combination therapy must rely on the capture of molecular end-points across the heterogeneous disease. Quantitative pathology must take into account the imperfection of the tissue sample as well as its heterogeneity in order to produce standardized and reproducible results. With the advent of digital pathology and associated image analysis solutions, histopathology has joined the ranks of molecular pathology with the ability to generate robust and standardized quantitative big data. Image analysis can also capture the heterogeneity across a patient sample by digitally segmenting the tumour subpopulations while extracting quantitative hierarchical morphological or biomarker data (Fig. 1). This review will discuss datafication of the tissue section through image analysis and its benefits as well as some of the challenges within the field.

Quantitative pathology through image analysis
Image analysis has been well established in order to quantify in vitro cell-based assays [6, 7] but has been slow to translate to molecular pathology and histopathology. This is in part due to the more complex and heterogeneous nature of the tissue as well as the need for extensive validation for clinical research compared with cell culture work. Advances in both whole-slide scanners and analysis software are now making the translation of image analysis to clinical research a reality. The use of standardized and automated image analysis solutions overcomes the reproducibility issues associated with manual semi-quantitative scoring of tissue as it negates observer variability. Image analysis has many uses within quantitative histopathology where it can report biomarker expression at sub-cellular resolution, quantify set histopathological features, identify heterogeneous subpopulations or the spatial heterogeneity of tumour and host interaction as well as identify novel histopathological features. Standardization is always the key to reproducible results and the field of image analysis is no different. Standardization and validation must be present throughout the entire process from tissue section cutting, mounting, labelling and digitizing. There are a growing number of whole-slide imagers on the market but it is paramount that these allow the use of identical image capture profiles and associated image quality across all the patient samples used in a study. Once the tissue is digitized in a standardized manner the image analysis algorithms themselves must be of a high enough quality in order to deal with the complex and heterogeneous tissue. Simplified algorithms have their use for basic biomarker quantification but may report false results or classifications owing to heterogeneous cell populations or inter-patient heterogeneity. Autofluorescence or non-specific staining in the sample may result in the reporting of false positives or inaccurate parameters when quantifying histopathological features in the complex tumour microenvironment. The image analysis workflow must therefore be robust enough to take into account or build in quality control steps to negate tissue labelling artefact [8].

Image analysis can quantify biomarkers
Whole-slide image analysis of molecular biomarkers labelled via antibodies or probes such as in FISH, avoids the contamination of signals from heterogeneous subpopulations that occur when the tissue is homogenized (Fig. 2A). This has advantages over destructive assays as the tissue structure, spatial orientation and sub-localization of molecules are retained [9] and heterogeneity can be compartmentalized and quantified while providing insight into cellular interactions within the tumour and its microenvironment. In order to quantify the biomarker in question the algorithm must segment the cells and nuclei within a region of interest, e.g. the tumour or stroma (Fig. 2B). This gives a further advantage to automated image analysis as morphometric and texture parameters may be captured and co-registered to the cell’s expression of the desired biomarker. This additional information can be used to identify a morphological surrogate to a biomarker or to capture a more definitive result that reduces false positives. When immunofluorescence is applied to biomarker quantification a continuous data capture across the dynamic range of intensity can be reported. The intensity of the fluorophore signal directly correlates to the level of protein expression and therefore returns a more accurate result than the classical 1+, 2+, 3+ manual scoring of chromogenic assays. This continuous data can be used to calculate robust cut-off points for positive and negative expression, or for patient categorization, in software such as X-Tile[ 10] or TMA Navigator [11].

Image analysis can quantify histopathological features
Image analysis may also be employed for the quantification of histopathological features. Observer variability occurs when manual semi-quantification of certain set histopathological features across tissue sections stained with hematoxylin and eosin (H&E) are reported [1–3]. Automated image analysis with the aid of specific labels negates observer variability and introduces standardization which is applicable across heterogeneous patient cohorts. In this manner tumour buds, lymphatic vessel density and invasion were co-registered upon the same tissue section and all quantified using the same algorithm across a CRC patient cohort [8]. This methodology allowed the computer-based algorithm to quantify small lymphatic vessels that were invaded by up to five cancer cells and which often go unreported because of their obscurity in H&E stained sections (Fig. 3). The results showed that these so called ‘occult lymphatic invasion’ events were independently predictive of poor prognosis in stage II CRC patients.

Similarly image analysis may be employed to quantify the host response to the tumour and not just the tumour itself; such as the lymphocytic infiltration within the cancer microenvironment. The immunoscore in CRC uses image analysis to quantify CD3+ and CD8+ lymphocytes at either the invasive front or the centre of the tumour section [12]. The automated quantification of lymphocytes and their spatial heterogeneity have also been shown to be prognostic in breast cancer [13].

Image analysis can identify novel features
Research pathologists apply their extensive experience to identify novel or significant prognostic features within the tissue section. Automated segmentation of digitized tissue sections now allows the quantification and standardization of complex and subtle morphological features or signatures in a continuous data capture manner. These features are extracted from every possible computer segmented object within the image. This image analysis methodology quantifies and profiles the complex phenome of the tumour’s microenvironment in an a priori ‘measure-everything big-data’ approach. Parameters extracted from single objects segmented across the digitized tissue section include morphometrics, texture and spatial heterogeneity. This is performed in an attempt to identify and quantify novel clinically relevant histopathological objects or predictive features from large exported image based multi-parametric big data sets. This emerging methodology has been termed ‘Tissue Phenomics’ by Gerd Binnig a Nobel Laureate and expert in image analysis. These objects may represent single or combinations of morphometrically quantifiable histological features, which may prove too subtle to observe by eye but which could prove prognostic or predictive. Beck et al. demonstrated this technique in breast cancer and found the stromal microenvironment to be specifically relevant to prognosis [14]. The big data created by image analysis approaches such as these needs to be distilled in order to identify the significant parameters which answer the clinical question being investigated. Bioinformatics must be applied which allows redundant parameters to be discarded and clinically relevant cut-offs to be applied to the remaining significant features. The reduced end result of a few significant parameters from potentially thousands of captured features should form a clinically translatable test which must then be validated across multiple international cohorts.

Future developments and challenges to the field
Technological advances in both image capture and analysis are beginning to see the translational of automated big data from the realm of academic research to clinical tests. Further technological advances such as co-registering of tissue sections and the ability to multiplex numerous biomarkers on a single tissue section will add greater value to the field. This multiplexed, next-generation immunohistochemistry [15] approach coupled with automated quantification may allow whole molecular pathways to be mapped at the single cell level. There are, however, challenges within the field. The automated quantification of pathology requires expensive whole-slide scanners as well as image analysis workstations alongside associated IT infrastructure to archive and keep secure the images and associated analysis. Fast Ethernet connections are also essential to recall these images in a time dependent manner. Another challenge is the acceptance of automated analysis within the clinical environment. This challenge will need to be overcome by validating the standardized and automated image analysis algorithms across multiple cohorts. The many applications of the field, such as objective, standardized and reproducible quantification of biomarkers, histopathological features and the profiling of a tumour’s heterogeneity hold advantages for both the pathologist and the patient. The negating of observer variability should increase the accuracy of patient results as should the application of clinically relevant categorical cut-offs across a continuous data set captured per patient. The capture of the molecular and histopathological prognostic and predictive signatures across heterogeneous subpopulations as the potential to turn traditional population based statistics into a more personalized one which informs the optimal treatment regimen for the individual patient.

References
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The author
Peter Caie PhD
School of Medicine, University of St Andrews, St Andrews KY16 9TF, UK

E-mail: Pdc5@st-andrews.ac.uk

Cervical cancer is a major burden worldwide with significant mortality, especially in developing countries. Human papillomavirus (HPV) analysis is gaining ground as the primary screening modality for the early diagnosis and prevention of cervical carcinoma. Direct pathogen detection allows an infection to be identified before cell changes have even taken place. Thus, interventional measures can be applied before the cancer even develops, helping to reduce the overall incidence and mortality rates. The EUROArray HPV molecular diagnostic microarray provides highly sensitive detection and typing of all known high- and low-risk anogenital HPV in one reaction. With fully automated data analysis it is particularly well suited to the high-throughput requirements of routine screening.

Human papillomaviruses
Human papillomaviruses are uncoated double-stranded DNA viruses which infect epithelial cells of the skin and mucous membranes. They are transmitted by sexual contact. Infection is assumed to occur via tiny lesions in the basal cells of the epithelium. Thus, the most frequent place of infection is the transformation zone of the cervix, where dividing basal cells lie near to the surface. The size of the cells, their histology and the duration of the lesion can influence the number of cells infected. The course and outcome of the infection depends on the HPV type, the anatomy of the infection site and the differentiation status of the host cells.
Infections with HPV are always local and are not accompanied by viremia. Following infection, the viral DNA is replicated in the host cell nuclei. Viral proteins produced in the infected cells can trigger uncontrolled tumour-like growth of the cells. This is, depending on the infecting HPV subtype, mostly benign, leading to warts at the site of infection. However, some HPV types can induce malignant changes, particularly cervical cancer. A significant proportion of vaginal, penile, anal and head and neck carcinomas are also assumed to be caused by HPV infection.
HPV are the most frequent sexually transmitted viruses. The worldwide prevalence of HPV infection is estimated to be 2 to 44% in women and 4 to 45% in men, with regional variations depending on culture and the corresponding sexual activity. Viral transmission from mother to newborn during birth can also occur, even with subclinical infections. HPV infection does not lead to life-long immunity and reinfection with the same virus is possible.

HPV subtypes
Around 130 types of HPV have so far been described of which 30 infect exclusively the skin and mucous membranes in the anogenital area. HPV are divided into two groups according to their oncogenic potential. High-risk HPV cause cervical carcinoma. Low-risk HPV alone do not induce tumours, but cause non-malignant tissue changes. Concurrent infections with multiple HPV subtypes are common and known to increase the risk of malignant cell transformations.
Of the high-risk anogenital types, HPV 16 and HPV 18 are responsible for around 70% of cervical carcinomas. HPV 16 is found in 50 to 60% of cases and HPV 18 in 10 to 20%. Other types classified as high-risk by the WHO are 31, 33, 35, 39, 45, 51, 52, 56, 58, 59 and 66. Types 26, 53, 68, 73 and 82 have also been detected in cervical carcinoma and should be considered as high-risk types.
Of the low-risk types, HPV 6 and 11 are the main causative agents of genital warts (condylomata acuminata, fig warts). Further low-risk types are 40, 42, 43, 44, 54, 61, 70, 72, 81 and 89.

Cervical carcinoma
HPV infection is a prerequisite for the development of cervical carcinoma. However, HPV infection does not necessarily lead to cancer. Most infected women eliminate the virus within two years. If the virus remains detectable for longer than 18 months, the infection is considered to be persistent. A persistent infection, in particular with a high-risk HPV subtype, increases the risk of developing cervical carcinoma by around 300-fold.
HPV infections are often asymptomatic and tend to remain unnoticed. The initial stages of cervical carcinoma also proceed without pain, and the only symptom may be light bleeding. With increased tumour size, the cancer manifests with a blood-tinged, sweet smelling discharge.

Around 528,000 new cases of cervical carcinoma occur annually worldwide, making it the fourth most frequent cancer in women after breast, colorectal and lung cancers. It is also the fourth most common cause of cancer mortality, causing approximately 266,000 deaths in 2012 (International Agency for Research on Cancer).
In the early stages, treatment involves removal of the altered tissue by conisation. In later stages of the disease, the uterus and surrounding tissue must be removed.

Role of HPV detection and typing
Along with the current diagnostic gold standard, the Papanicolaou (Pap) test, HPV direct detection plays an important role in the early diagnosis of cervical carcinoma. In contrast to the Pap test, which is used to investigate cervical cells for pathological changes, PCR-based methods detect viral nucleic acids directly, and can thus identify an HPV infection at a very early stage before morphological cell changes have even occurred. Moreover, while the Pap test is based on subjective evaluation, HPV detection represents an objective as well as extremely sensitive test method.
In HPV screening it is crucial to differentiate between high- and low-risk types and also to discriminate between different high-risk viruses. A positive result for high-risk HPV indicates an increased risk for cervical carcinoma, which can then be minimized by more frequent follow-up examinations to detect morphological cell changes at an early stage. A positive result for low-risk HPV can help to clarify uncomfortable and embarrassing symptoms for patients. Since low-risk HPV can also cause mild dysplasia, HPV subtyping is also useful for excluding a high-risk HPV infection and a corresponding risk of cervical cancer in these cases. Women who are HPV negative can forgo Pap smears for a longer time interval, based on the recommendations of the respective professional societies.
The PCR detection strategy is a critical aspect of direct HPV analysis. Tests with primer or probe systems based on conserved genes like L1 may yield false negative results in some cases due to loss of these genes during integration of the viral DNA into the host DNA. The highest possible detection sensitivity is achieved using the viral oncogenes E6/E7. Detection of variable sequences in these genes enables differentiation of the different HPV subtypes.

Microarray for complete HPV typing
A standardized microarray based on PCR detection of E6/E7 has been developed for complete HPV typing in routine diagnosis. Using an extensive panel of specific primers and probes, the EUROArray HPV detects all thirty genitally relevant HPV subtypes in one test, distinguishing eighteen high-risk subtypes that may trigger cancer (16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82) and twelve low-risk subtypes that cause benign warts (6, 11, 40, 42, 43, 44, 54, 61, 70, 72, 81, 89). Multiple infections are reliably identified, and primary and persistent infections can be differentiated.

Simple procedure with automated evaluation
The EUROArray procedure (Figure 1) is extremely easy to perform and does not require any in-depth molecular biology knowledge. DNA prepared from patient cervical smear samples is first amplified by a single multiplex polymerase chain reaction (PCR). The fluorescently-labelled PCR products are then incubated with biochip microarray slides (Figure 2) containing immobilized complementary DNA probes. Specific binding (hybridization) of the PCR products to their corresponding microarray spots is detected using a specialized microarray scanner.
In contrast to manually evaluated tests, the results are evaluated (Figure 3) and interpreted fully automatically by user-friendly software (EUROArrayScan). A detailed result report (Figure 4) is produced for each patient and all data is documented and archived. Meticulously designed primers and probes, ready-to-use PCR components and integrated controls all contribute to the reliability of the analysis. The entire EUROArray process from sample arrival to report release is IVD validated and CE registered, supporting quality management in diagnostic laboratories.

Conclusion
As evidence mounts about the efficacy of HPV testing for primary cervical cancer screening, multiplex microarrays are poised to become a major tool in prevention programmes worldwide. The EUROArray HPV, in particular, is ideally positioned for high-throughput HPV screening, providing fast and sensitive detection of all high- and low-risk anogenital HPV types combined with fully automated data analysis.

The author
Jacqueline Gosink, PhD
EUROIMMUN AG
Seekamp 31
23560 Luebeck
Germany
E-mail: j.gosink@euroimmun.de