Characterization and Colocalization of Tissue-Based Biomarker Expression by Quantitative Image Analysis:

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UNIVERSITÉ LIBREDEBRUXELLES

Characterization and Colocalization of Tissue-Based Biomarker Expression by Quantitative Image Analysis:

Development and Extraction of Novel Features

Dissertation submitted for the degree of Doctor in Engineering Sciences

Xavier Moles Lopez

Thesis supervisor

Professor Christine Decaestecker Co-Promotor

Professor Olivier Debeir

Service

Laboratory of Image, Signal processing and Acoustics

Academic year

2013 - 2014

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Xavier Moles Lopez:Characterization and Colocalization of Tissue-Based Biomarker Expression by Quantitative Image Analysis: Development and Extraction of Novel Features, , © March 2014

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In every conceivable manner, the family is link to our past,

bridge to our future.

— Alex Haley

Dedicated to the loving memory of Antonio Moles Ibañez.

1927–2008

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A B S T R A C T

Proteins are the actual actors in the (normal or disrupted) physiological processes and immunohistochemistry (IHC) is a very efficient mean of visualizing and locating protein expression in tissue samples. By comparing pathologic and normal tissue,IHC is thus able to evidence protein expression alterations. This is the reason whyIHCplays a growing role to evidence tissue-based biomarkers in clinical pathology for diagnosing various diseases and directing personalized therapy. Therefore,IHC biomarker evaluation significantly impacts the adequacy of the therapeutic choices for patients with serious pathologies, such as cancer. However, this evaluation may be time-consuming and difficult to apply in practice due to the absence of precise positive cut-off values as well as staining (i.e. protein expression) heterogeneity intra- and inter-samples. Quantifying

IHC staining patterns has thus become a crucial need in histopathology. For this task, automated image analysis has multiple advantages, such as avoiding the evidenced effects of human subjectivity. The recent introduction of whole-slide scanners opened a wide range of possibilities for addressing challenging image analysis problems, including the identification of tissue-based biomarkers. Whole-slide scanners are devices that are able to image whole tissue slides at resolutions up to 0.1 micrometers per pixels, often referred to as virtual slides. In addition to quantification ofIHC staining patterns, virtual slides are invaluable tools for the implementation of digital pathology workflows. The present work aims to make several contributions towards this current digital shift in pathology. Our first contribution was to propose an automated virtual slide sharpness assessment tool. Although modern whole-slide scanner devices resolve most image standardization problems, focusing errors are still likely to be observed, requiring a sharpness assessment procedure. Our proposed tool will ensure that images provided to subsequent pathologist examination and image analysis are correctly focused. Virtual slides also enable the characterization of biomarker expression heterogeneity. Our second contribution was to propose a method to characterize the distribution of densely stained regions in the case of nuclearIHCbiomarkers, with a focus on the identification of highly proliferative tumor regions by analyzing Ki67-stained tissue slides. Finally, as a third contribution, we propose an efficient mean to register virtual slides in order to characterize biomarker colocalization on adjacent tissue slides. This latter contribution opens new prospects for the analysis of more complex questions at the tissue level and for finely characterizing disease processes and/or treatment responses.

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R É S U M É

Les protéines sont les véritables acteurs des processus physiologiques (normaux ou per- turbés) et l’immunohistochimie (IHC) est un moyen efficace pour visualiser et localiser leur expression au sein d’échantillons histologiques. En comparant des échantillons de tissus pathologiques et normaux, l’IHC permet de révéler des altérations dans des profils d’expression protéique. C’est pourquoi l’IHC joue un rôle de plus en plus important pour mettre en évidence des biomarqueurs histologiques intervenant dans le diagnostic de diverses pathologies et dans le choix de thérapies personnalisées. L’évaluation de l’expression de biomarqueurs révélés par IHC a donc des répercussions importantes sur l’adéquation des choix thérapeutiques pour les patients souffrant de pathologies graves, comme le cancer. Cependant, cette évaluation peut être chronophage et difficile à appliquer en pratique, d’une part, à cause de l’hétérogénéité de l’expression protéique intra- et inter-échantillon, d’autre part, du fait de l’absence de critères de positivité bien définis. Il est donc devenu crucial de quantifier les profils d’expression de marquages IHC en histopathologie. A cette fin, l’analyse d’image automatisée possède de multiples avantages, comme celui d’éviter les effets de la subjectivité humaine, déjà démontrés par ailleurs. L’apparition récente des numériseurs de lames histologiques complètes, ou scanners de lames, a permis l’émergence d’un large éventail de possibilités pour traiter des problèmes d’analyse d’image difficiles menant à l’identification de biomarqueurs histologiques. Les scanners de lames sont des dispositifs capables de numériser des lames histologiques à une résolution pouvant atteindre 0,1 micromètre par pixel, expliquant la dénomination de "lames virtuelles" des images ainsi acquises. En plus de permettre la quantification des marquages IHC, les lames virtuelles sont des outils indis- pensables pour la mise en place d’un flux de travail numérique en pathologie. Le travail présenté ici vise à fournir plusieurs contributions au récent changement de cap vers une numérisation de la discipline médicale qu’est l’anatomie pathologique. Notre première contribution consiste en un outil permettant d’évaluer automatiquement la netteté des lames virtuelles. En effet, bien que les scanners de lames résolvent la plupart des pro- blèmes liés à la standardisation de l’acquisition, les erreurs de focus restent fréquentes, ce qui nécessite la mise en place d’une procédure de vérification de la netteté. L’outil que nous proposons assurera la netteté des images fournies à l’examen du pathologiste et à l’analyse d’image. Les lames virtuelles permettent aussi de caractériser l’hétérogénéité de l’expression de biomarqueurs. Ainsi, la deuxième contribution de ce travail repose sur une méthode permettant de caractériser la distribution de régions densément marquées par des biomarqueurs IHC nucléaires. Pour ce travail, nous nous sommes concentrés sur l’identification de régions tumorales présentant une forte activité proliférative en analysant des lames virtuelles révélant l’expression de la protéine Ki67. Finalement, la troisième contribution de ce travail fut de proposer un moyen efficace de recaler des

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lames virtuelles dans le but de caractériser la colocalisation de biomarqueurs IHC révé- lés sur des coupes de tissu adjacentes. Cette dernière contribution ouvre de nouvelles perspectives pour l’analyse de questions complexes au niveau histologique ainsi que la caractérisation fine de processus pathologiques et de réponses thérapeutiques.

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A C K N O W L E D G M E N T S

I would like to express my deepest gratitude to Christine Decaestecker and Isabelle Salmon, for providing the interesting biological problems motivating the tools and methods presented in this work, for their scientific insight and for the kindness they have shown for me and my family. I also thank Olivier Debeir for his continued support and his advices which were of invaluable help for the achievement of this thesis.

My warmest thanks go to Sandrine Rorive, Nicky D’Haene, Calliope Maris, Laurine Verset, Anne-Laure Trépant, the pathologists who generously offered their time during the sample selection, diagnostic assessment and validation phases of the experiments conducted in this work. I would like to extend my thanks to Isabelle Roland, Marie Le Mercier, Audrey Verellen and all the scientific and technical staff of the Laboratory of Pathology for their expertise and cautious work during the preparation of the experiments. My transdisciplinary work in the Laboratory of Pathology has been very enriching and I thank Myriam Remmelink, Pieter Demetter, Delfyne Hastir, Françoise Hulet and all the members of the laboratory with whom I collaborated and who partici- pate in creating such a friendly environment.

The time I spent in the LISA was no less enjoyable and I would like to thank Ivan Adanja and Frederic Degroef for sharing their insight on good practices for software development (among other interesting topics), Rudy Ercek for his excellent technical support and all my colleagues from LISA for the interesting discussions and seminars.

I had the chance to assist to the creation of a new transdisciplinary laboratory, DIAP- ath, as part of the Center for Microscopy and Molecular Imaging (CMMI) dedicated to preclinical imaging. It was a extraordinary experience for which I would like to thank Paul Barbot, Etienne D’Andrea, Sebastien Sauvage, Justine Allard, and all the current and former members of DIAPath for the great collaborations that were made during the course of this work.

I would also like to acknowledge the Télévie program of the FNRS and Yvonne Boël funds for their research grants, which financed me and the equipments needed through- out my thesis, as well as the European Regional Development Fund and the Walloon Region who financed the CMMI.

Although they did not participated directly to this work, I would like to express my gratitude to my parents, who kindly provided help and support when most needed, to Fabienne and Philippe, for their generous support, and to my friends for their encour- agements.

Last but not least, I would like to thank my love, Charlotte, for her patience, care and the most lovely motivation I could hope: Juliette, our daughter.

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C O N T E N T S

1 introduction 1

1.1 Digital Pathology: a technological revolution for pathologist’s work 2 1.2 Techniques to evidence and to evaluate tissue-based biomarkers 5 1.3 Digital shift 8

1.4 How engineering can contribute to digital pathology 10

1.5 Specific challenges in (brightfield) WSI and aims of the thesis 11 2 sample preparation and image acquisition 14

2.1 Immunohistological slide preparation 15 2.2 Whole slide imaging technology 16

2.3 Automated sharpness assessment of virtual slides 22 2.4 Discussion and Conclusions 34

3 tissue-based biomarker analysis 36

3.1 Conventional biomarker expression analysis 37 3.2 Novel features for biomarker expression analysis 38 3.3 Discussion and Conclusions 51

4 biomarker colocalization 53

4.1 Colocalization of IHC protein expressions in tissue slides 54 4.2 Multiresolution registration of serial slides 55

4.3 Discussion and Conclusions 78 5 conclusions 80

5.1 Digital Pathology: Promises and Challenges 81 5.2 Improvements to the image analysis workflow 82 5.3 Future works 84

bibliography 88 Appendices 98

a list of publications 99

b description of the color deconvolution process 102

c requirements for the valid quantification of immunostains on tissue microarray materials using image analysis 104

d graph-based methods to detect ki-67 hot-spots on glioblastoma tissue sections 122

e supplementary material for chapter 2 127 f supplementary material for chapter 3 131 g supplementary material for chapter 4 140

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L I S T O F F I G U R E S

Figure1 Comparison between traditional and digital pathology workflows.

Illustration from [45] 2

Figure2 Principles of theIHCandin situhybridization (ISH) methods. 5 Figure3 Examples of IHC-revealed protein expressions showing different

cellular locations (nucleus: Ki67, cytoplasm: EGFRand E-cadherin, or membrane:HER2) at10X magnification. 6

Figure4 human epidermal growth factor receptor2(HER2) scoring in breast cancer. Adapted from [14]. 7

Figure5 Typical histologicalVSs image analysis workflow. Steps (a) to (f) indicate current challenges in WSI that are tackled in the present work. 13

Figure6 Synthetic workflow for histological slide preparation including the additional whole-slide imaging step. Rectangles and paral- lelograms are used to symbolize processes and outputs, respectively. Possible alterations are listed in relation to the different preparation steps at which they can occur. 17

Figure7 Pyramidal file structure proposed by the Digital Image and Com- munication in Medicine (DICOM) Standards Committee Working Group26on virtual pathology for storing virtual slide (VS)s. From [19] 19

Figure8 Light paths of a microscope adjusted for Köhler illumination.

Note that the specimen or object plane is conjugated with the retina on the left diagram, while the lamp filament plane is conjugated with the front focal plane of the condenser and the iris diaphragm of the eye (i.e. is out of focus), providing even, or Köhler, illumination. The lamp, collector lens, condenser, objective, and eyepiece must be adjusted to obtain this configuration.

Illustration from [48] 20

Figure9 Scanning technologies. a) Tiling method: the VS is imaged by acquiring high-magnification field of view (FOV)s, or tiles. Some whole slide scanner (WSS)s acquire overlapping tiles and use an image stitching method to form the VS. To increase speed, it is possible to skip empty tiles determined using a low resolution image of the entire slide. b) Scanning method: theVSis imaged by a lineal sensor acquiring non-overlapping bands. 21

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Figure10 Example of a pre-scanning step result with automatic tissue detection, focal plane division (maximum 1.5 mm length horizon- tally or vertically) and focusing point positioning (9 per focal plane). 35

Figure11 Contribution of our sharpness assessment tool in the biomarker analysis workflow. The green box indicates the step for which our developments were made, while the gray box shows the step which benefits from these developments. 35

Figure12 TwoFOVs of Ki67-stained tissue samples with close labeling index (LI) values (respectively 20.2% and20.5%) although the staining patterns are very different. 38

Figure13 Contribution of our hot-spot (HS) detection method in the biomarker analysis workflow. The green box indicates the step for which our developments were made, while the gray boxes show steps which benefit from these developments. Here, ourHSdetection method offers a new mean of characterizing nuclear biomarkers, but also to automatically define region of interest (ROI)s as indicated by the green arrow. 52

Figure14 FOVs of approximately the same tissue region shown on adjacent slides. 55

Figure15 Contribution of our image registration method in the biomarker analysis workflow. The green box indicates the step for which our developments were made, while the gray box shows the step which benefits from these developments. 79

Figure16 Contribution of our different methods to the biomarker image analysis workflow. The green boxes indicate steps for which our developments were made, while the gray boxes show steps which benefit from these developments. 84

Figure17 Illustration of the detection of1Dstructures on a circular colonic tissue sample (TMA core of 0.6 mm of diameter). A) Original image. B) Segmented image: background is shown in blue (label 0), cytoplasm/stroma in green (label 1) and nuclei in red (label 2). Six hundred thousand 1D ROIs with variable lengths (mean:

90 pixels, standard deviation: 30 pixels) are then randomly located and oriented on the image. A vector of 10 elements is regularly sampled from eachROIand compared to matching pattern [0011112221] using the Ratcliff-Obershelp string comparison method (similarity measure taking values in the interval [0, 1]).

Only the vectors showing similarity measure of at least0.80with the matching pattern are shown in light gray (white dots indicate their origin). C) Horizontal concatenation of the100 vectors (of size10) that are the most similar to the matching pattern (using the color legend of B). 86

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acronyms xi

A C R O N Y M S

1D one-dimensional

AP anatomic pathology

API application programming interface

CISH chromogenicin situhybridization

CT computed tomography

DAB 3,3’-diaminobenzidine

DICOM Digital Image and Communication in Medicine

EGFR epidermal growth factor receptor

EHR electronic health record

ER estrogen receptor

FDA Food and Drug Administration

FFPE formalyn-fixed paraffin-embedded

FISH fluorescentin situhybridization

FOV field of view

GPU graphics processing unit

H&E hematoxylin and eosin

HER2 human epidermal growth factor receptor2

HS hot-spot

IF immunofluorescence

IHC immunohistochemistry

ISH in situhybridization

LI labeling index

LIS laboratory information system

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acronyms xii

MI mean intensity

MM motorized microscope

mpp micrometers per pixel

MRF Markov random field

MRI magnetic resonance imaging

PACS picture archiving and communications system

PR progesterone receptor

QS quick score

ROI region of interest

SIMPLE sequential immunoperoxidase labelling and erasing

TII total integrated intensity

VS virtual slide

WSS whole slide scanner

WSI whole slide imaging

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1

I N T R O D U C T I O N

This transdisciplinary work results from the strong collaboration between two laboratories: the Image unit of the Laboratories of Image, Signal processing and Acoustics (LISA-image, Ecole polytechnique de Bruxelles) and the Laboratory of Pathology of the Erasme University Hospital in Brussels. During the course of this work, a new interfac- ulty research unit, Digital Image Analysis in Pathology (DIAPath), emerged from this collaboration and was integrated into the Center for Microscopy and Molecular Imaging (CMMI, Biopark of Gosselies). Therefore, it is not surprising that this work is centered on problems commonly encountered by pathologists in their daily clinical practice and their research activities. Pathology is a medical specialty which addresses the four components of disease: the cause of the disease, the mechanisms of development, the morphological changes induced, and the clinical manifestations. In particular, pathologists are responsible of the definitive diagnosis of disease on the basis tissue samples resected from a patient. The diagnosis relies on gross and microscopic examination of the tissue combined with the results of additional tests, such as immunohistochemistry.

This work was driven by the willingness to provide solutions based on an engineering approach. Proposed solutions to these problems are both original and pragmatic (i.e. applicable within the existing environments of the Laboratory of Pathology and of DIAPath).

Comprehensive understanding of the problems encountered in both the clinical and research activities of pathologists is therefore of utmost importance. In the following section we will describe the new context of pathologist’s work brought by the digital shift.

We will then provide additional details on different aspects which are more directly concerned by this thesis.

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1.1 digital pathology: a technological revolution for pathologist’s work 2

TRADITIONAL PATHOLOGY

Slide is sent to primary pathologist (subjective analysis)

Slide is scanned

(subjective analysis)

Digitized slide is screened by computer

(objective analysis)

Electronic document with patient history and various test results can be linked to the specimen file

Multiple reviewers can simultaneously see and discuss digitized slides and supporting documents DIGITAL

PATHOLOGY

Slide may be sent, in series by mail,

to one or more consultants, delaying diagnosis

Traditional slide preparation:

Tissue sample sectioned and stained

Figure1: Comparison between traditional and digital pathology workflows. Illustration from [45]

1.1 digital pathology: a technological revolution for pathologist’s work

Surgical pathology is entering the digital era, allowing rapid exchanges through expert pathologist networks and large-scale computer-assisted diagnostics. Indeed, considerable improvements in telecommunication technologies as well as the digitization of medical data and images enabled the advent of an increasing number of computer- assisted medical applications, among which, digital pathology. In the last decade, digital pathology has benefited from the developments of image acquisition enabling the rapid conversion of glass slides into digital images for the microscopic examination of histological or cytological samples (as further detailed in section 2.2) and from the worldwide image distribution via the Internet. This niche is rapidly evolving in the pathology domain and its popularity should keep growing at the rate of the technological improvements. Tomorrow’s pathologists will thus work without microscope. As illustrated in Figure 1 digital pathology fosters efficient communication between general and specialized pathologists. These new communication media facilitate second opinion requests and enable more precise diagnostics for difficult cases, among other advantages. Patients thus benefit from a faster diagnostic, sent to the clinician through a secured web platform. Faster and better diagnostic, and therefore treatment, could reduce health-care costs. In addition, samples from rare cancer cases could be scanned and archived in the Belgian Virtual Tumorbank, which would constitute a gold mine for teaching and research.

The classification of cancers is based on morphology and is supposed to define groups that have similar clinical evolutions. Nowadays, the pathologist is also asked to specifically evaluate each patient’s cancer in order to guide the therapeutic choices. This

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personalized medicine, summarized by the sentence " the right drug to the right patient", requires the development of new diagnostic methods. An increasing number of targeted therapies are made available in association with a companion test able to select patients that will benefit from this therapy. The digital slides enable applications in clinical practice of image analysis algorithms, e.g., designed to quantitatively evaluate the expression of a tissue-based biomarker involved in a companion test.

A notable example of biomarkers used to guide therapy is human epidermal growth factor receptor 2 (HER2), which is a prognostic and theranostic biomarker (i.e. able to predict disease evolution and therapy response, respectively) for the patients suffering of breast cancers. The amplification of gene HER2, which is accompanied by an overexpression of the corresponding protein, occurs in 10 to 30% of breast cancer cases and is associated with poor prognosis [84]. HER2 is the target of Trastuzumab (Her- ceptin, Genentech), a monoclonal antibody which improves the prognosis of patients suffering fromHER2-overexpressing breast cancers if associated with chemotherapy [84].

Therefore, given this association between HER2 over-expression and Trastuzumab effi- cacy, HER2 status must be evaluated for all new breast cancers. The strong impact of this evaluation on the therapeutic choice requires robust and standardized testing methods. HER2 status can be evaluated by2techniques: the gene amplification can be evidenced by the fluorescentin situhybridization (FISH) technique whereas the protein over-expression can be evidenced by immunohistochemistry (IHC) (as detailed in section 1.2). The result of theFISHtechnique, which is based on the counting of signals by nucleus, has the advantage of being objective and to have internal controls (presence of2

HER2 signals in non-neoplastic cells). However, this technique requires the use of fluorescence microscopy, which is expensive. Because the use ofIHC is widespread in the pathology departments and is cheap, this technique is used first to identify the patients that could potentially benefit from the treatment. However, HER2 evaluation usingIHC

relies on a semi-quantitative assessment (also referred to as "scoring") performed by a pathologist observing the stained tissue slide with a microscope, such as described in section1.2.

Different studies recently evidenced that HER2 was over-expressed in approximately 20% of gastric cancers [27], also in association with poor prognosis [24,53,71]. Given these results, a phase III clinical trial (ToGA) was conducted to study the effects of Trastuzumab for patients suffering from gastric cancers withHER2over-expression. This study evidenced a benefit in terms of survival when Trastuzumab was administered in association with standard chemotherapy to patients with HER2 over-expressing tumors [77]. Based on these results, Trastuzumab treatment for patients suffering from metastatic and HER2-positive gastric cancers has been approved by the European Com- mission in January 2010. Similarly to breast cancers, the protein over-expression can be evidenced by IHCandHER2gene amplification by FISH. It should be noted that the

IHC scoring system for gastric cancers is different than the one in use for breast cancers. In addition, these scoring systems are difficult to apply in clinical practice due to subjectivity.

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For each new breast cancer case, it is also recommended to evaluate the status of estrogen receptor (ER) and progesterone receptor (PR), which are prognostic factors [Har- ris2007]. They also predict the response to hormonal therapies such as Tamoxifène® [30].

The evaluation of the expression of these hormonal receptors is performed byIHC. In the literature, different scoring systems have been proposed, such as the Allred score, which is based on the sum of the stained area (discretized in5categories) and of the staining intensity (discretized in 3categories). The HSCORE, which is another scoring system, corresponds to the sum of the percentage of stained area for each staining intensity level multiplied by that intensity using the scale: 0= absence of staining,1= weak staining, 2= moderate staining, and3= intense staining. Nevertheless, because these scores are evaluated differently and potentially responsible of confusion, it is recommended to report the percentage of stained cells, the staining intensity and the interpretation of the test (positive, negative, or inconclusive), the value of the combined score being optional [29].

In the majority of the laboratories, the pathologists perform all these evaluations by eyes, without concordance study. Universities in the USA and in Europe are developing tools to quantitatively evaluate these biomarkers. Experts agree that image analysis has the potential to improve intra- and inter-observer reproducibility. However, the implementation of image analysis in daily practice remains controversial [29].

Another example of application of tissue-based biomarkers relates to neuroendocrine tumors of the digestive tract. This term designate the group of tumors presenting similar morphological characteristics but appearing in different anatomical locations and presenting different evolutions from benign tumors (carcinoids) to highly malignant tumors (neuroendocrine carcinomas). Cell proliferation evaluation has recently been proposed as a prognostic biomarker [60]. This evaluation is based for one part on the counting of mitotic figures (i.e. the specific nuclear step where chromosomes are sep- arated into two identical sets of chromosomes), and on the other part on the index of proliferation evaluated using the anti-Ki67antibody (which labeled the nucleus of any cell being in the cell division process). The evaluation in three categories (<2%, >3% and

<20%, and >20%) of the expression of Ki67 is restricted to "hot-spot" regions [60]. We will propose in Chapter3a method to identify these Ki67hot-spots.

Many studies have evidenced intra- and inter-observer variations for the interpretation ofIHCstaining such as HER2, hormone receptors, or Ki67[55,56,59,61,70]. Given the strong clinical impact of these results, numerous efforts, such as automation and quality controls, have been made to optimize theIHCtechnique. There remains an urg- ing need to develop image analysis tools to quantify the expression ofIHC biomarkers that could be used in daily clinical practice in pathology departments. Together with digital pathology, this shift toward quantitative evaluation represent a huge step forward in the direction of better diagnostic and therapeutic choices in oncology.

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1.2 techniques to evidence and to evaluate tissue-based biomarkers 5

58 | IHC STAINING METHODS, FIFTH EDITION

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In a similar method the labeled streptavidin-biotin (LSAB) method also utilizes a biotinylated secondary antibody that links primary antibodies to a streptavidin-peroxidase conjugate (6). In both methods a single primary antibody is subsequently associated with multiple peroxidase molecules, and because of the large enzyme-to-antibody ratio, a considerable increase in sensitivity is achieved compared to direct peroxidase-conjugate methods.

LSAB Methodology

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Because avidin is a glycoprotein and has an isoelectric point (pI) of 10, it has a propensity to bind non-specifically to lectin-like and negatively charged tissue components at physiological pH. In contrast to avidin, streptavidin has a more neutral isoelectric point and lacks the carbohydrate moieties. These differences result in less nonspecific tissue binding.

Polymer-Based Immunohistochemistry

Although many of these streptavidin-biotin methods are still in widespread use, there are certain limitations characteristic of these methods. The presence of endogenous biotin in tissues can lead to significant background staining in certain circumstances. Formalin fixation and paraffin embedding has been shown to significantly reduce the expression of endogenous biotin, but residual activity can still be observed in tissues such as liver and kidney. Furthermore, with the advent of heat-induced antigen retrieval, the recovery of endogenous biotin can appear as an unwanted side effect. Methods to block endogenous biotin are partially effective, but add another layer of complexity to an already complex procedure. These limitations are further exacerbated by the use of frozen tissue sections, in which levels of endogenous biotin are usually even higher than those encountered in paraffin-embedded specimens.

Because of these limitations, polymer-based immunohistochemical methods that do not rely on biotin have been introduced and are gaining popularity (5). These methods utilize a unique technology based on a polymer backbone to which multiple antibodies and enzyme molecules are conjugated. In the EPOS (Enhanced Polymer One Step)* system, as many as 70 enzyme molecules and about 10 primary antibodies were conjugated to a dextran backbone. This allowed the entire immunohistochemical staining procedure, from primary antibody to enzyme, to be accomplished in a single step (6).

On the other hand, one limitation of this method was its restriction to a select group of primary antibodies provided by the manufacturer, and not suitable for user-supplied primary antibodies.

To overcome this limitation a new type of dextran polymer, EnVision^™ *, was introduced. This polymer system contained a dextran backbone to which multiple enzyme molecules were attached. However, unlike Immunohistochemistry Staining Methods

* A proprietary methodology developed by Dako.

(a) Illustration of theIHCtechnique when using a secondary antibody and the avidin-biotin complex as labeling technique. Adapted from [36].

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(b) Illustration of the ISHtechnique when using a fluorescent label (fluorochrome). Adapted from [85].

Figure2: Principles of the^IHCand^ISHmethods.

1.2 techniques to evidence and to evaluate tissue-based biomarkers Biomarkers are biological markers, which are indicators of biological states, either healthy or pathological. In surgical pathology biomarkers are used for diagnostic, prognostic and therapeutic purposes. These biomarkers can target cell or tissue morphology (e.g.

involving structural changes of microarchitecture), as well as gene and protein expression, which can be evidenced in tissue samples by using specific staining techniques, such as IHC and in situ hybridization (ISH) [31]. Figure 2 illustrates the principles of these techniques. TheIHCtechnique is used to label proteins specifically using antigen - antibody reactions. A widely used IHC method uses a primary antibody targeting the studied protein and a secondary antibody targeting the primary antibody. Sec- ondary antibodies are biotinylated (i.e. conjugated to biotin) and behave as a bridge between primary antibodies and enzyme-labeled complexes (e.g. (strept)avidin-biotin complex). The staining is obtained by a chromogenic reaction occurring when the enzyme is brought into presence of its substrate (e.g.,3,3’-diaminobenzidine (DAB) giving a dark-brown staining). In contrast, the ISHtechnique is used to label a specific DNA or RNA sequence using a complementary DNA or RNA probe in tissue sections (i.e. in situ). The probe is labeled with a fluorochrome (FISH) or a chromogen (chromogenicin situ hybridization (CISH)). This technique quantifies the number of gene copies in the cell nucleus, hence enabling the evaluation of gene amplification or deletion.

Brightfield or fluorescent chromogens can be used to evidence gene or protein expression. Concerning tissue sample processing and staining, the brightfield-based approach is easier to implement. Indeed, it can be performed on formalyn-fixed paraffin- embedded (FFPE) tissue samples, which are easier to store and to slice, and offer better tissue morphology preservation than frozen sections, which are preferably used for fluorescence labeling [31]. In contrast, fluorescence-based labeling has some advantages in colocalizing antigens (e.g. regarding multilabeling facility and individual labeling

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Figure3: Examples ofIHC-revealed protein expressions showing different cellular locations (nucleus: Ki67, cytoplasm:EGFRand E-cadherin, or membrane:HER2) at10X magnification.

detection) but presents some disadvantages in practice (such as labeling fading, tissue autofluorescence and cost of the image acquisition equipment). Consequently, brightfield IHC staining on histological slides from FFPE tissue sample is more appropriate for clinical routine practice as well as for high-throughput tissue sample processing performed in preclinical (i.e. on animal models) and clinical research. Brightfield IHC

staining is universally adopted in surgical pathology laboratories because it is at the same time economical and convenient [31]. The adoption is such that the complete sample processing workflow can now be fully automated. Indeed, laboratory equipment manufacturers sell devices able to perform tissue fixation, paraffin embedding, tissue block sectioning, slide staining and slide covering almost without human intervention.

Similarly, the recent CISH technology utilizes conventional enzymatic reactions which are applicable on FFPE tissues and visualized under brightfield microscopy, enabling gene status evaluation in a morphological (i.e. tissue-based) context.

Proteins are the actual actors in the (normal or disrupted) physiological processes and

IHCis a very efficient mean of visualizing and locating protein expression in tissue samples (cf. Figure3). This is the reason whyIHCplays a growing role in surgical pathology for diagnosing various diseases and guiding personalized therapy. Indeed, IHC is able to evidence protein expression alterations, which can be then targeted in therapy. There- fore, biomarker evaluation significantly impacts the adequacy of the therapeutic choices for patients with serious pathologies, such as cancer.

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G U ID EL INES

Guidelines for Scoring

Use of the attached scoring system has proved reproducible both within and among laboratories. Dako recommends that scoring always be performed within the context of the pathologist’s past experience and best judgment in interpreting IHC stains. Only patients with invasive breast carcinoma should be scored. In cases with carcinoma in situ and invasive carcinoma in the same specimen, only the invasive component should be scored.

HER2 Protein

Score to Overexpression Staining Report Assessment Pattern***

0 Negative No staining is observed, or membrane staining is observed in

<10% of the tumor cells.

1+ Negative A faint/barely perceptible membrane staining is detected in >10%

of tumor cells. The cells exhibit incomplete membrane staining.

2+ Weakly Positive* A weak to moderate complete membrane staining is observed in (Equivocal) >10% of tumor cells.

3+ Strongly Positive** A strong complete membrane staining is observed in >10% of tumor cells.

Score: 0 (40x) Score: 1+ (40x)

Score: 2+ (40x) Score: 3+ (40x)

* Weakly positive cases (2+): May be considered equivocal and reflexed to FISH testing.

** Strongly positive cases (3+): Based on recent testing guidelines a 30 percent cut-off for reporting positivity is recommended. Scoring guidelines for the FDA-approved HercepTest^TM recommend a 10 percent cut-off for reporting positivity. Patient outcome for cases resulting between 10 percent and 30 percent positivity has not been defined. FISH may be used as a complementary test in these instances.

*/** Wolff, A, Hammond, E, et al: American Society of Clinical Oncology/College of American Pathologists Guideline Recomendations for Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer. Arch Pathol Lab Med 2007 January; 131: 18-43.

*** ASCO/CAP recommends a 30percent cut-off, Dako’s clinical data refers to a 10 percent cut-off.

Figure 9

HercepTest^TM Interpretation Manual - Breast 15

G U ID EL INES

HER2 Protein

Score: 2+ (40x) Score: 3+ (40x)

Figure 9

G U ID EL INES

HER2 Protein

Score: 2+ (40x) Score: 3+ (40x)

Figure 9

G U ID EL INES

HER2 Protein

Score: 2+ (40x) Score: 3+ (40x)

Figure 9

0 1

2 3

Figure4:HER2scoring in breast cancer. Adapted from [14].

As mentioned in the previous section, Trastuzumab has been approved by the Food and Drug Administration (FDA) for patients showing an overexpression of HER2 protein. This therapeutic agent inhibits theHER2/neupathway which was evidenced as an adverse prognostic factor in breast [65] and gastric cancers [9]. To evaluate the overexpression ofHER2, HercepTest™, which is a commercialIHCtest forHER2/neuexpression evaluation [14,72,82], is available. For this test, pathologists must attribute a score be- tween0(negative staining) to3(more than10% of tumor cells had complete membrane staining of strong intensity)[33]. Figure4illustratesIHCstaining patterns corresponding to these scores. It should be noted that scores 2 and 3 differ only by staining intensity [14]; that is why very strictIHC staining protocols and quality control methods are provided with the testing kit [14]. Patients with HercepTest score of 2should also be evaluated with aFISHtest to confirmHER2overexpression, while Trastuzumab treatment will be unconditionally provided to those with a score of3. SimilarIHC testing kits are available such asERandPR, which are also scored manually between0to3 [72]. This scoring process is sometimes referred to as "semi-quantitative" evaluation [22] because it uses the ordinal scale.

In addition to the technical guidelines mentioned above,IHC testing kit manufacturers also provide strong interpretation guidelines because companion tests are still performed manually by pathologists. The scoring criteria depend on the studied biomarker and its cellular compartment (e.g. membranous, cytoplasmic, or nuclear). All these scores have in common that they require an evaluation of the percentage of stained cells and the intensity of the staining. In addition, the cellular location of the staining may also play an important role in the scoring. For example, in the cases of HER2 and epidermal growth factor receptor (EGFR), only the staining located on the cell membrane

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1.3 digital shift 8

(i.e. necessary location to mediate a biological response) must be considered [13,14,33].

For these examples, the relevance of the IHC technique is obvious: with strict quality controls, this method enables to quantitatively estimate the expression of the studied biomarker and, at the same time, its cellular location. This latter information is not provided with other tests evaluating global protein expression in cells or tissue samples (such as western blotting or enzyme-linked immunosorbent assay (ELISA)).

However, the scoring ofIHCtests can be time-consuming and difficult due to tumor heterogeneity, absence of precise cut-off values and variable staining patterns, even when the IHC testing kits are provided with a cell-line standard (i.e. sample of cells for which the protein expression has been characterized) and instruction about how to interpret the staining patterns. Despite these variations, this evaluation is in use for the most common types of cancer, such as pulmonary, colonic, breast, head and neck, renal and bladder cancers, and the drugs developed in this context have blockbuster status.

QuantifyingIHCstaining patterns has thus become a crucial need in pathology practice. For this task, automated image analysis has multiple advantages, such as avoiding the effects of human subjectivity evidenced in visual evaluation [82]. In clinical research as well, more standardized and robust biomarker quantification methods are needed to avoid any bias possibly due to tissue preparation and/or subjective evaluation. Staining image analysis also increases the range of quantitative information that can be extracted from complex IHC staining patterns, such as those often encountered in tumor tissue slides due to high tissue heterogeneity. Glass slide digitization, detailed in the next section, constitutes an essential technological step to respond to the need in quantitative characterization of tissue-based biomarkers.

1.3 digital shift

Whole slide scanners (WSSs) are new devices capable of complete digitization of glass slides, a process known as whole slide imaging (WSI). Compared to the older technology based on a motorized microscope (MM), they feature a simpler configuration which enables a simplified user interface and much faster image acquisition of a complete slide. For high-throughput facilities, a whole slide scanner (WSS) featuring a slide loader should be preferred to scan batches of hundreds of histological slides. Although

WSSs capture complete slides faster thanMMs, they are less versatile and cannot allow a pathologist to freely move the stage for direct examination of the slide. However, once the scan is completed, the file containing the image of the scanned slide can be shared or viewed remotely with the use of image servers [43]. Entire slide images, also known as virtual slides (VSs), enable the user to navigate within slides at magnifications up to 100X [19].

Imaging an entire histological slide at high magnification is now achieved in a few minutes, which allows image acquisition of large-scale studies involving cell- or tissue- based biomarkers. Image analysis tools can then be used to extract objective and quantitative features from these digital slides (orVSs) in order to characterize these biomarkers.

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1.3 digital shift 9

Data analysis and machine learning can then be applied to explore and validate the pre- dictive impact of these quantitative features in terms of diagnosis, prognosis and therapy response [23].

This digital shift is implemented in the DIAPath research facility, which develops an integrated approach for the identification, characterization and validation of protein biomarkers in animal tissue samples as well as on human tissue (in close collaboration with the Pathology Department of the Erasme hospital). The developed methodology involves histological andIHCtechniques (CISHare currently under development) as well as image analysis, biostatistics and data mining (in close collaboration with LISA-image).

Databases are then set up on large sample series by merging the multiple quantitative features characterizingIHCstains and clinical and/or pharmaceutical data, depending of the study context. These data are submitted to multivariate data analysis and machine learning methods. The general aim is to extract information useful in understanding pathological processes and therapy responses, as well as to identify and to validate new biomarkers beneficial for diagnostic, prognostic and therapeutic procedures [42,44,81].

Such research on tissue-based biomarkers requires the processing and the analysis of large sets of tissue samples, and so additional standardization steps (detailed in Chap- ter2). In contrast with clinical applications, for which the tissue sample is freshly pro- cessed, tissue samples used in research may have several years and come from different laboratories. Tissue sample preparation and storage conditions, which may introduce variations in the IHC staining, must therefore be standardized in order to avoid any experimental bias.

Recently, WSSs were extended for immunofluorescence (IF) using fluorescent dyes to evidence protein or gene expression. As mentioned above (cf. section 1.2), fluorescent chromogens facilitate the detection of multiple proteins and quantification using monochromatic images that focus on specific chromogen emission wavelengths [8]. In contrast, brightfieldIHC, using chromogen such asDAB, utilizes cost- and time-effective techniques that are routinely used in daily practice pathology and allow for useful morphological controls (thanks to tissue counterstaining), making quantitative IHC more readily accessible to most laboratories. However, the spectral characteristics ofIHC tissue staining, usually combined with counterstaining to visualize the morphological context (e.g.,DABchromogen and hematoxylin, respectively), cause difficulties with respect to quantification using brightfield-based microscopy and color cameras. Multispectral imaging can assist in this task [78] although the high cost of the associated acquisition technology often prohibits its use in more standard applications. Furthermore, this acquisition technology is not yet implemented inWSSs. In brightfield WSI, specific image processing techniques, such as color deconvolution, should thus be used to easily detect and evaluate the contribution of the different stains [67,68]. This aspect will be detailed in Chapter3.

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1.4 how engineering can contribute to digital pathology 10

1.4 how engineering can contribute to digital pathology

The current digital imaging process in pathology includes four key steps, none of which being standardized: (1) image acquisition, (2) storage and management, (3) manipulation and annotation, and (4) visualization and transmission [51]. Despite the standardization efforts made for each of these key steps [15, 19], the adoption of digital pathology in routine clinical practice is still hindered by the poor ergonomics of the proposed solutions [51]. Pathologists are reluctant to abandon the speed and ease of use of their microscope, althoughWSI has been demonstrated as a valid replacement of light microscopes for diagnostic [52], education [83] and research [23] purposes.

Ergonomics could be improved by providing an integrated digital pathology diagnosing station, also referred to as the pathologist’s "cockpit" [51], from which the pathologist could visualizeVSs on high-definition and calibrated monitors [18], using dedicated devices to navigate theVSs. This diagnosing station should be connected to the laboratory information system (LIS), the electronic health record (EHR) and the picture archiving and communications system (PACS) of the institution to provide access to complete patient- related information. The LIS contains the information related to the pathology department such as patient data, tissue processing steps, slide staining steps and anatomic pathology (AP) report, theEHRcontains the medical history of the patient, and thePACS

stores the medical images of the patient. Such information is useful to establish a correct diagnostic and aVSvisualization platform dedicated to diagnostic should integrate this information for rapid retrieval. Additionally, by connecting the station to the Internet, workflows such as those illustrated in1are enabled.

Adequate hardware and software components are very important factors to provide ergonomic working stations to pathologists. In order to create good software, a comprehensive business modeling as well as correctly defined workflows for image creation, storage, management, manipulation and visualization are needed [15]. In these models, the major output of the pathology department is theAPreport, that should also include theVSs and the quantitative image analysis results, as part of theEHR. These enhance- ments of digital pathology are needed to improve the standardization, reproducibility, and traceability of patient care while facilitating collaboration between pathologists as well as between pathologists and clinicians.

Furthermore, with the advent of companion tests, such as the HercepTest™, the need for more objective and accurate interpretation becomes crucial. Digital pathology enabled the introduction of FDA-cleared image analysis tools aimed at objectively interpreting HER2 levels [45]. However, as more companion tests are to be expected [22], image analysis algorithms aimed at characterizing other IHC biomarker expression remain to be implemented andFDA-cleared for routine clinical practice. These new algorithms should not necessarily follow the same guidelines as "manual" scoring, which is still often performed by a pathologist estimating the score based on the observation of a few field of view (FOV)s, but could rather compute complex scores based on the analysis of the entire slide [6]. Novel features will be needed to reliably estimate IHC

biomarker expression and, additionally provide new discriminant features to develop

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1.5 specific challenges in (brightfield) wsi and aims of the thesis 11

robust companion tests. As a sequel, the image analysis algorithms involved in these new companion tests will be easier to validate [22].

Digital pathology also enabled applications such as characterizingIHCbiomarker colocalization. Although these applications already showed to be valuable in research [46], their use in clinic as part of a companion test involving several biomarkers remains to be tested and validated. New technologies, such as multispectral imaging might become necessary to conduct such analysis [23].

In addition to the quantitative evaluation of expression of one or several biomarkers, digital pathology also enabled the application of image analysis algorithms to characterize the morphology of the tissue in order to compute a morphological score indicative of health or disease levels and/or to highlight particular regions of the slides that the pathologist should not miss to provide a correct diagnostic [45,28].

1.5 specific challenges in (brightfield) wsi and aims of the thesis Digital pathology is a valuable tool to address reproducibility issues encountered in current clinical practice. Indeed, collaborative applications play an important role to improve reproducibility by promoting the submission of difficult cases to a specialist or a board of experts and by improving the training of younger pathologists. Furthermore, image analysis algorithms are intended to objectively evaluateIHCbiomarkers or tissue morphology in order to guide therapy. Because the cancer prevalence tends to augment whereas the number of pathologists is decreasing, these applications are necessary to ensure care quality while alleviating pathologists’ increasing workload.

Despite the strong technological advances observed recently, some challenges remain regarding the image acquisition process and, more particularly, image format, size and quality management. Image quality assessment and, more particularly, sharpness evaluation, is essential for ensuring the quality of subsequent analyses, making a slide review process mandatory. This time-consuming task requires the scanner operator to carefully assess the entire slide image, whose size can reach 80,000 x60,000pixels. Automated methods forWSIquality assessment are therefore strongly needed for providing image quality feedback in order to help the operator to identify blurred regions in the images [40] (see Figure5, step a).

In addition to the determination of specific regions of interest (ROIs), useful for e.g.

limiting subsequent analyses and morphological characterization (Figure5steps b and c), efficient image visualization methods combined with annotation tools are also needed for biomarker research purposes. In particular, these tools play essential roles in automatic identification of cell types, based on features extraction and machine learning approaches applied to cytological images [43]. From a few cells labeled by an expert according to some predefined phenotypes, cell features can be extracted and submitted to machine learning methods in order to infer the phenotype classes of unlabeled cells remaining on the images [43]. A similar approach can be used to recognize particular histological structures in tissue sample images. However, this latter problem appears

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1.5 specific challenges in (brightfield) wsi and aims of the thesis 12

more challenging because of the high variability of the morphological and architectural features characterizing pathological tissue samples [11,12].

Another challenge is the extraction of original features to better characterize protein expression patterns evidenced by IHC and, more specifically, their heterogeneous distribution across whole pathological tissue slides [80] (Figure 5 step d). Furthermore, extracting relevant information from complex biological processes involved in pathologies, such as cancers, requires to target multiple proteins simultaneously and locate their respective expression in tissue slides. Such information can be extracted from imaging and registering adjacent tissue sections on which expression patterns of different proteins are evidenced (Figure5steps e and f). This approach also enables to characterize with greater accuracy cellular heterogeneity in tissue samples.

In the context of DIAPath’s research activities, we tested several commercial solutions that are available for some of the tasks of the image analysis workflow presented in Figure5. A good image analysis solution should propose an integrated approach that is at same time powerful and intuitive, but, in our experience, no commercial solution satisfies all these requirements. The most intuitive solutions propose simple classification tools that classify pixels according to some intensity-based criteria. In contrast, more powerful tools propose a pre-segmentation step that segment the image into objects whose pixels satisfy some homogeneity criteria, enabling the application of more complex classification rules. Although the latter approach offers more possibilities than the former by creating a "graph" of classified objects, the pre-segmentation is critical to obtain valid results and the choice of its parameters is delegated to the - often in- experienced - user. Furthermore, both the intuitive and the powerful solutions lack the possibility to easily test and validate segmentation and classification parameters on various samples that were not used during parametrization and training. Image analysis solution vendors often propose modules specialized for a specific image analysis problem, providing the user with a simplified interface and pre-validated parameters requiring only small adjustments. But even with these specialized modules, the possibility to manually review and adjust the intermediary results are often lacking. Commercial solutions propose a limited range of available image features and defining custom image features is not always possible. Finally, most of these features are computed per tile and summarized by summing or averaging feature values to get results per region of interest (ROI) or slide. However, the process leading to a value perROIor slide is rarely well documented and it is often necessary to manually compute the result per case (patient or animal) for certain features, such as the commonly used labeling index (LI), quick score (QS) and mean intensity (MI) defined in Chapter3.

The present work is involved in the development of the DIAPath unit and focuses on brightfieldWSIfor analyzing tissue-based biomarker expression. All these challenging problems motivated us to develop efficient methods for image sharpness assessment as well as original feature extraction and marker colocalization, as detailed in the next Chapters.

In this work, we focus on the quantitative characterization ofIHC biomarker expression involved in clinical research, i.e. on human tissue samples. Direct applications to