Leonie L. Zeune

Multiscale Segmentation via Bregman Distances and Nonlinear Spectral Analysis

In biomedical imaging reliable segmentation of objects (e.g. from small cells up to large organs) is of fundamental importance for automated medical diagnosis. New approaches for multi-scale segmentation can considerably improve performance in case of natural variations in intensity, size and shape. This paper aims at segmenting objects of interest based on shape contours and automatically finding multiple objects with different scales. The overall strategy of this work is to combine nonlinear segmentation with scales spaces and spectral decompositions recently introduced in literature. For this we generalize a variational segmentation model based on total variation using Bregman distances to construct an inverse scale space. This offers the new model to be accomplished by a scale analysis approach based on a spectral decomposition of the total variation. As a result we obtain a very efficient, (nearly) parameter-free multiscale segmentation method that comes with an adaptive regularization parameter choice. The added benefit of our method is demonstrated by systematic synthetic tests and its usage in a new biomedical toolbox for identifying and classifying circulating tumor cells. Due to the nature of nonlinear diffusion underlying, the mathematical concepts in this work offer promising extensions to nonlocal classification problems.

Improving the CellSearch® system

ABSTRACT Introduction: The CellSearch® CTC test enumerates tumor cells present in 7.5 ml blood of cancer patients. improvements, extensions and different utilities of the cellsearch system are discussed in this paper. Areas covered: This paper describes work performed with the CellSearch system, which go beyond the normal scope of the test. All results from searches with the search term ‘CellSearch’ from Web of Science and PubMed were categorized and discussed. Expert commentary: The CellSearch Circulating Tumor Cell test captures and identifies tumor cells in blood that are associated with poor clinical outcome. How to best use CTC in clinical practice is being explored in many clinical trials. The ability to extract information from the CTC to guide therapy will expand the potential clinical utility of CTC.

Circulating tumor cells, tumor-derived extracellular vesicles and plasma cytokeratins in castration-resistant prostate cancer patients

Purpose The presence of Circulating Tumor Cells (CTCs) in Castration-Resistant Prostate Cancer (CRPC) patients is associated with poor prognosis. In this study, we evaluated the association of clinical outcome in 129 CRPC patients with CTCs, tumor-derived Extracellular Vesicles (tdEVs) and plasma levels of total (CK18) and caspase-cleaved cytokeratin 18 (ccCK18). Experimental Design CTCs and tdEVs were isolated with the CellSearch system and automatically enumerated. Cut-off values dichotomizing patients into favorable and unfavorable groups of overall survival were set on a retrospective data set of 84 patients and validated on a prospective data set of 45 patients. Plasma levels of CK18 and ccCK18 were assessed by ELISAs. Results CTCs, tdEVs and both cytokeratin plasma levels were significantly increased in CRPC patients compared to healthy donors (HDs). All biomarkers except for ccCK18 were prognostic showing a decreased median overall survival for the unfavorable groups of 9.2 vs 21.1, 8.1 vs 23.0 and 10.0 vs 21.5 months respectively. In multivariable Cox regression analysis, tdEVs remained significant. Conclusions Automated CTC and tdEV enumeration allows fast and reliable scoring eliminating inter- and intra- operator variability. tdEVs provide similar prognostic information to CTC counts.

Quantifying HER-2 expression on Circulating Tumor Cells by ACCEPT

Circulating tumor cells (CTCs) isolated from blood can be probed for the expression of treatment targets. Immunofluorescence is often used for both the enumeration of CTC and the determination of protein expression levels related to treatment targets. Accurate and reproducible assessment of such treatment target expression levels is essential for their use in the clinic. To enable this, an open source image analysis program named ACCEPT was developed in the EU-FP7 CTCTrap and CANCER-ID programs. Here its application is shown on a retrospective cohort of 132 metastatic breast cancer patients from which blood samples were processed by CellSearch® and stained for HER-2 expression as additional marker. Images were digitally stored and reviewers identified a total of 4084 CTCs. CTC’s HER-2 expression was determined in the thumbnail images by ACCEPT. 150 of these images were selected and sent to six independent investigators to score the HER-2 expression with and without ACCEPT. Concordance rate of the operators’ scoring results for HER-2 on CTCs was 30% and could be increased using the ACCEPT tool to 51%. Automated assessment of HER-2 expression by ACCEPT on 4084 CTCs of 132 patients showed 8 (6.1%) patients with all CTCs expressing HER-2, 14 (10.6%) patients with no CTC expressing HER-2 and 110 (83.3%) patients with CTCs showing a varying HER-2 expression level. In total 1576 CTCs were determined HER-2 positive. We conclude that the use of image analysis enables a more reproducible quantification of treatment targets on CTCs and leads the way to fully automated and reproducible approaches.

Toward a real liquid biopsy in metastatic breast and prostate cancer: Diagnostic LeukApheresis increases CTC yields in a European prospective multicenter study (CTCTrap): Toward a real liquid biopsy in metastatic breast and prostate cancer

Frequently, the number of circulating tumor cells (CTC) isolated in 7.5 mL of blood is too small to reliably determine tumor heterogeneity and to be representative as a “liquid biopsy”. In the EU FP7 program CTCTrap, we aimed to validate and optimize the recently introduced Diagnostic LeukApheresis (DLA) to screen liters of blood. Here we present the results obtained from 34 metastatic cancer patients subjected to DLA in the participating institutions. About 7.5 mL blood processed with CellSearch® was used as “gold standard” reference. DLAs were obtained from 22 metastatic prostate and 12 metastatic breast cancer patients at four different institutions without any noticeable side effects. DLA samples were prepared and processed with different analysis techniques. Processing DLA using CellSearch resulted in a 0–32 fold increase in CTC yield compared to processing 7.5 mL blood. Filtration of DLA through 5 μm pores microsieves was accompanied by large CTC losses. Leukocyte depletion of 18 mL followed by CellSearch yielded an increase of the number of CTC but a relative decrease in yield (37%) versus CellSearch DLA. In four out of seven patients with 0 CTC detected in 7.5 mL of blood, CTC were detected in DLA (range 1–4 CTC). The CTC obtained through DLA enables molecular characterization of the tumor. CTC enrichment technologies however still need to be improved to isolate all the CTC present in the DLA.

How to Agree on a CTC: Evaluating the Consensus in Circulating Tumor Cell Scoring: How to Agree on a CTC

For using counts of circulating tumor cells (CTCs) in the clinic to aid a physicians decision, its reported values will need to be accurate and comparable between institutions. Many technologies have become available to enumerate and characterize CTCs, thereby showing a large range of reported values. Here we introduce an Open Source CTC scoring tool to enable comparison of different reviewers and facilitate the reach of a consensus on assigning objects as CTCs. One hundred images generated from two different platforms were used to assess concordance between 15 reviewers and an expert panel. Large differences were observed between reviewers in assigning objects as CTCs urging the need for computer recognition of CTCs. A demonstration of a deep learning approach on the 100 images showed the promise of this technique for future CTC enumeration.

Classification of Cells in CTC-Enriched Samples by Advanced Image Analysis

In the CellSearch® system, blood is immunomagnetically enriched for epithelial cell adhesion molecule (EpCAM) expression and cells are stained with the nucleic acid dye 4′6-diamidino-2-phenylindole (DAPI), Cytokeratin-PE (CK), and CD45-APC. Only DAPI+/CK+ objects are presented to the operator to identify circulating tumor cells (CTC) and the identity of all other cells and potential undetected CTC remains unrevealed. Here, we used the open source imaging program Automatic CTC Classification, Enumeration and PhenoTyping (ACCEPT) to analyze all DAPI+ nuclei in EpCAM-enriched blood samples obtained from 192 metastatic non-small cell lung cancer (NSCLC) patients and 162 controls. Significantly larger numbers of nuclei were detected in 300 patient samples with an average and standard deviation of 73,570 ± 74,948, as compared to 359 control samples with an average and standard deviation of 4191 ± 4463 (p < 0.001). In patients, only 18% ± 21% and in controls 23% ± 15% of the nuclei were identified as leukocytes or CTC. Adding CD16-PerCP for granulocyte staining, the use of an LED as the light source for CD45-APC excitation and plasma membrane staining obtained with wheat germ agglutinin significantly improved the classification of EpCAM-enriched cells, resulting in the identification of 94% ± 5% of the cells. However, especially in patients, the origin of the unidentified cells remains unknown. Further studies are needed to determine if undetected EpCAM+/DAPI+/CK-/CD45- CTC is present among these cells.

Combining contrast invariant L1 data fidelities with nonlinear spectral image decomposition

This paper focuses on multi-scale approaches for variational methods and corresponding gradient flows. Recently, for convex regularization functionals such as total variation, new theory and algorithms for nonlinear eigenvalue problems via nonlinear spectral decompositions have been developed. Those methods open new directions for advanced image filtering. However, for an effective use in image segmentation and shape decomposition, a clear interpretation of the spectral response regarding size and intensity scales is needed but lacking in current approaches. In this context, \(L^1\) data fidelities are particularly helpful due to their interesting multi-scale properties such as contrast invariance. Hence, the novelty of this work is the combination of \(L^1\)-based multi-scale methods with nonlinear spectral decompositions. We compare \(L^1\) with \(L^2\) scale-space methods in view of spectral image representation and decomposition. We show that the contrast invariant multi-scale behavior of \(L^1-TV\) promotes sparsity in the spectral response providing more informative decompositions. We provide a numerical method and analyze synthetic and biomedical images at which decomposition leads to improved segmentation.

Abstract LB-250: Liquid biopsy in NSCLC: EpCAM+ and EpCAM- circulating tumor cells, tumor derived extracellular vesicles and cell-free circulating tumor DNA

Introduction The need for a liquid biopsy in non-small cell lung cancer (NSCLC) patients is rapidly increasing as more and more targeted therapies become available. Presence in blood of circulating tumor cells (CTC), tumor derived extracellular vesicles (tdEV) and cell-free circulating tumor DNA (ctDNA) measured with different approaches are being explored for their potential to represent a liquid biopsy in the European and Dutch CANCER-ID projects (https://www.cancer-id.eu/ & https://www.utwente.nl/tnw/cancer-id/). Here, we determine in just one 7.5 mL tube of blood the presence of CTC, tdEV and ctDNA and investigate the relation with survival of metastatic NSCLC patients. Methods In total 106 advanced NSCLC patients were enrolled in the study. In 86 patients EpCAM+ CTC, EpCAM- CTC & tdEV were enumerated and in 50 patients EpCAM+ CTC, EpCAM- CTC, tdEV & ctDNA, all from one CellSave blood tube. ctDNA from a separate plasma tube is available for all patients but not yet analyzed. Before placing the sample in the CellSearch system, plasma was aspirated and stored at -80°C. EpCAM+ CTC were enumerated by CellSearch and EpCAM- CTC after filtration of the EpCAM+ CTC depleted blood through 5µm pore filters, as described by de Wit et al. (Sci. Rep. doi: 10.1038/srep12270, 2015). tdEV were defined by a multidimensional gate as cytokeratin+/DAPI-/CD45- vesicles and identified in the CellSearch images, using the open source image analysis program ACCEPT. The stored plasma was used for ctDNA quantification with the FAST-SeqS approach, described by Belic et al. (ClinChem 61, 838, 2015). In several patients with EpCAM- CTC, fluorescent in situ hybridization was performed on the filter to establish the cancerous origin of the EpCAM- CTC. Results In 24% of the patients ≥1 EpCAM+ CTC as well as ≥1 EpCAM- CTC were detected in 7.5 mL of blood. In 30% of the patients tdEV were present at a frequency >45 per 7.5 mL. This frequency is based on the mean + 2SD from 42 healthy controls. In 20% of the patients >10% ctDNA load was found. No significant correlation was found between the presence of these biomarkers. Presence of all four biomarkers was detected in 6% of patients and at least one of four was found in 52% of patients. One or more EpCAM+ CTC were associated with poor overall survival (p=0.010 for 86 patients and p=0.019 for 50 patients), whereas EpCAM- CTC were not (p=0.495 n=86; p=0.571 n=50). The latter is surprising since some CTC were shown to be cytogenetically aberrant, conform the primary tumor. The presence of >45 tdEV (p=0.271 n=50) and >10% ctDNA (p=0.082 n=50) did not reach significance. Conclusions In this study EpCAM+ CTC, EpCAM- CTC, tdEV or ctDNA was detected in one tube of blood in 52% of the NSCLC patients. Only the presence of EpCAM+ CTC was associated with poor overall survival, raising the question whether or not the extraction of molecular information from these other biomarkers can be used to predict response to treatment in NSCLC. To increase the percentage of patients from which a liquid biopsy can be obtained, the analyzed blood volume will need to be increased. Citation Format: Sanne de Wit, Menno Tamminga, Joost F. Swennenhuis, Leonie L. Zeune, Ellen Heitzer, Michael Speicher, T Jeroen N. Hiltermann, Leon WMM Terstappen, Harry JM Groen. Liquid biopsy in NSCLC: EpCAM+ and EpCAM- circulating tumor cells, tumor derived extracellular vesicles and cell-free circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-250. doi:10.1158/1538-7445.AM2017-LB-250

Automated Identification of Circulating Tumor Cells by Image Analysis

In the field of Circulating Tumor Cell (CTC) research many new technologies are emerging to isolate CTCs. Some of them provide accompanying automated image analysis tools that present possible CTCs to the user. Others need fully manual image analysis. For all CTC isolation technologies the definition of a CTC based on the immuno-morphologic criteria is either customized to the specific platform or subjective to the user causing high interreader differences – a problem which may condemn many CTC-based clinical studies to failure. Thus, an important issue that the field is confronted with is the lack of a unified and standardized definition to classify a cellular object as a CTC. This problem is addressed within the European FP7 consortium CTCTrap and the Innovative Medicines Initiative (IMI) consortium CANCER-ID by the development of an open-source image analysis toolbox for CTC identification and enumeration. This toolbox is baptized ACCEPT (Automated CTC Classification, Enumeration and Phenotyping) and can process images generated by various CTC isolation technologies. The main software components are the Marker Characterization, the Full Detection and the Automatic Classification. The Marker Characterization tool aims at quantifying the antigens expressed by previously selected CTCs. The Full Detection tool is based on advanced mathematical methods to reliably detect all objects in the images, visualize the objects in scatter plots and enable the user to classify the cell types by the use of gates or selection of specific objects in the scatter plots or on the actual images. The Automatic Classification tool first detects all objects in the images followed by an automated classification approach that – as a result – presents found CTCs to the user. We demonstrate the effectiveness of these tools on two different datasets. The Marker Characterization tool was tested for Her2 expression on archived CTC images isolated and classified by the CellSearch system from patients with metastatic breast cancer. Investigators from three different institutes were asked to score these cells for Her2 positivity first on the images generated by the CellTracks Analyzer and afterwards using ACCEPT. We show that the improved CTC visualization provided in ACCEPT, combined with several measurements which we extract for each cell, can reduce the inter-user variability. The Full Detection and Automatic Classification tools of ACCEPT were tested on archived samples of patients with castration resistant prostate cancer processed with the CellSearch system as well as on microsieves obtained after filtration of the blood discarded by the CellSearch system. Results were compared with manually scored CTCs and showed the improvement of CTC classification by the availability of quantitative image analysis tools. The Open Source ACCEPT program will be available on the MCBP website (http://www.tnw.utwente.nl/mcbp). Citation Format: Leonie L. Zeune, Guus van Dalum, Francois-Clement Bidard, Jean-Yves Pierga, Tanja Fehm, Hans Neubauer, Brigitte Rack, Marianna Alunni-Fabbroni, Mateus Crespo, Johann de Bono, Leon W.M.M. Terstappen, Christoph Brune. Automated identification of circulating tumor cells by image analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1733. doi:10.1158/1538-7445.AM2017-1733