Interactions of neoplastic cells with each other and the microenvironment are complex. The main factors contributing to intratumoral heterogeneity may be reflected in biomarker expression. The aim is to investigate the spatial intratumoral heterogeneity of Ki-67 immunostains in whole sections of pancreatic neuroendocrine neoplasms. The extent and range of heterogeneity has potential as a prognostic marker.
by Dr Nektarios A. Valous, Dr Frank Bergmann and Dr Niels Halama
Tumour heterogeneity means that a neoplasm comprises distinct cellular subpopulations that can vary in histology and growth rate. Phenotypic and functional heterogeneity arise among cancer cells within the neoplasm because of genetic change, environmental differences and reversible changes in cell properties [1, 2]. Genomic instability arises through various routes, leaving distinct genomic footprints and differentially affecting tumour evolution and patient outcome . In addition, heterologous cell types within tumours can influence therapeutic response and shape resistance . The interactions of neoplastic cells with each other and the microenvironment are complex. Clinicians assess complex cytological, histological, and morphological characteristics of tissues often in a semi-quantitative manner. In order to understand intratumoral heterogeneity, potentially subtle differences within neoplasms should be quantified.
The main factors contributing to intratumoral heterogeneity include the ischemic gradient within the neoplasm, the action of the microenvironment, mechanisms of intercellular transfer of genetic information, and differential mechanisms of modifications of genetic material/proteins . This may be reflected in the expression of biomarkers and their clinical utility in the context of prognosis/stratification. A rigorous approach for assessing the spatial intratumoral heterogeneity of histological biomarker expression with accuracy and reproducibility is required, since patterns in immunohistochemical images exhibit scale-dependent changes in structure and can be challenging to identify and describe [6–8]. The aim is to determine the implications and prognostic value of observed variations, in a host of clinically relevant neoplastic properties .
It is recognized that proliferative heterogeneity is common in many tumours, including pancreatic neuroendocrine neoplasms . These represent a rather heterogeneous group regarding histology, hormone secretion and functional activity, as well as biological behaviour. Pancreatic neuroendocrine neoplasms are classified as tumours (grade G1 and G2) and carcinomas (grade G3), based on proliferation activity. The latter is determined using a mitotic count in ten high-power fields or determination of fraction of Ki-67 positive cells within a highly proliferative area. In resected neoplasms, the proliferation-dependent tumour grade is shown to be a strong prognostic marker, whereas patient clinical management largely depends on proliferation activity. Key treatment decisions rely on the robust classification of these tumours.
Earlier work has shown the advantages of using spatial statistics in histopathology . A quantitative method from the fields of complex systems and image analysis that is particularly useful for characterizing complex irregular structures is lacunarity. The term lacunarity itself stems from the field of fractal geometry referring to a measure of how patterns fill space . The approach has several theoretical and practical advantages for the assessment of spatial heterogeneity . It is a multiscale technique, its computation is simple to implement, it exhaustively samples the image to quantify scaling changes, the analysis can be used for very sparse data, and the decay of the lacunarity index as a function of window size follows characteristic patterns for random, self-similar and structured spatial arrangements. Compared to previous approaches, the proposed methodology quantifies directly the distributional landscape of proliferative cells and not the textural content of the histological slide, thus providing a more realistic measure of heterogeneity within the sample space of the tumour region.
Figure 1 provides an overview of the workflow for measuring the spatial intratumoral heterogeneity of proliferation in pancreatic neuroendocrine neoplasms. Immunohistochemical (IHC) staining for Ki-67 is performed on whole-slide sections taken from original tissue blocks using the avidin-biotin-peroxidase detection system on a fully automated staining facility. On IHC stains for Ki-67, the brown reaction product at the antigen site is in the cell nucleus. The slide is counterstained with hematoxylin to allow evaluation and assessment of staining localization. Glass slides are automatically imaged in bright-field mode. The resulting virtual slides are reviewed by a pathologist for determining the locality of the neoplastic region; areas are marked and large representative sections are cropped. The automated proliferative cell nuclei segmentation workflow is based on background removal, stain vector extraction/color deconvolution, and post-processing operations. Hence, for every virtual slide (large section of tumour region), a segmented image depicting proliferative nuclei is produced.
For measuring the spatial heterogeneity of proliferation using lacunarity, an automated sampling scheme is employed prior to analysis allowing better scrutiny and interpretation, thus keeping computation times manageable. The sampling method aims at capturing the spatial variability of Ki-67 positive cells in the images. Then, lacunarity is computed and subsequently visualized in a double log plot as a function of scale. These plots explicitly characterize the spatial organization of images and measure space filling capacity. From the plots, mean, median, and mode lacunarity curves are computed. These metrics capture the variability (or lack of) of the curves for the sampled sections. To ascertain that lacunarity describes the spatial organization of proliferation, the three metrics are used for partitioning the neoplasms into conceptually meaningful clusters. This is achieved by performing unsupervised learning using the k-means algorithm. First, their Mahalanobis distance is computed and then using principal component analysis vectors are decorrelated by projection into a subspace that minimizes reconstruction error in the mean squared sense. Finally, a phenomenological heterogeneity index is computed from the spatial distribution in order to provide direct numerical values.
Impact and outlook
Phenotypic heterogeneity that stems from genetic/non-genetic determinants constitutes a major source of therapeutic resistance, and is an important clinical obstacle . Tissue architecture is generally not reflected in molecular assays, rendering this information underused. Heterogeneity in histological expression of biomarkers has been noted in earlier studies. However, experimental exploration has been limited by a lack of conceptual framework and tools. The critical bottleneck has become the development of computational methods to analyse, integrate, and connect data to prognostic and actionable clinical information .
The architectural complexity of immunohistological images has shown that single measurements are often insufficient for characterization. The selection of a region of interest as a surrogate for the complete complex structure is prey to selection bias and subsequent loss of reproducibility and precision. Especially for the neuroendocrine pancreatic tumours, the inhomogeneity of distribution depends not only on percentage content of proliferation phase but also on how the phase fills the space. An increased degree of spatial proliferative heterogeneity is observed in certain neoplasms comparing to others with similar histological grade. Whether this is a sign of different tumour biology and subsequently an association with a more benign or malignant clinical course needs to be investigated further. The approach provides an increased level of granularity for discerning different levels of spatial heterogeneity in biomarker expression, since manual inspection can be cumbersome and may not resolve finer differences. The extent and range of heterogeneity has the potential to be evaluated as a prognostic marker, e.g. for the evaluation of the clinical course utilizing the construction of survival curves. This heterogeneity is also most likely to play a role in different aspects of the clinical course. Clinical trials should incorporate such studies on heterogeneity so that the impact on therapeutic effectiveness can be understood. For practical reasons, the heterogeneity index outputted in the final step of the analysis is desirable when a single numerical value is required, e.g. in direct assessments or comparisons for the automated ranking of cases in order of increasing/decreasing heterogeneity.
In summary, the lacunarity morphometric provides information about the distribution of Ki-67 immunolabelling that corresponds to the degree of spatial organization of proliferation. This reflects a general approach with potential importance in clinical work, which is relevant to other solid tumours and a vast array of biomarkers. The additional level of understanding the distributional patterns of specific biomarkers holds promise for providing valuable information in a clinical setting. Drawing upon the richness of histopathological information and merits of computational biomedicine, the approach removes qualitative ambiguities and uncovers salient features for use in future studies of clinical relevance.
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Nektarios A. Valous*1 PhD, Frank Bergmann2 MD, Niels Halama3 MD, PhD
1Applied Tumor Immunity Clinical
Cooperation Unit, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
2Institute of Pathology, Heidelberg
University Hospital, Heidelberg, Germany
3Department of Medical Oncology, National Center for Tumor Diseases,
Heidelberg University Hospital, Heidelberg, Germany