Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma

Whole slide imaging of tumour microenvironment in classical Hodgkin's lymphoma: development of a clinical prediction model based on programmed death-ligand 1 and tumorous Reed-Sternberg cells

AIMS

The prognostic impact of programmed death-ligand 1 (PD-L1) cells in classic Hodgkin lymphoma (cHL) tumour microenvironment remains undefined.

METHODS

Model development via Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines were followed. PD-L1+ and CD30+ tumoral Reed-Sternberg cells were quantified through whole slide imaging and digital image analysis in 155 digital histopathological slides of cHL. Univariate and multivariate survival analyses were performed. The analyses were reproduced for patients with advanced stages (IIB, III and IV) using the Advanced-stage cHL International Prognostic Index.

RESULTS

The PD-L1/CD30 ratio was statistically significantly associated with survival outcomes. Patients with a PD-L1/CD30 ratio above 47.1 presented a shorter overall survival (mean OS: 53.7 months; 95% CI: 28.7 to 78.7) in comparison with patients below this threshold (mean OS: 105.4 months; 95% CI: 89.6 to 121.3) (p=0.04). When adjusted for covariates, the PD-L1/CD30 ratio retained prognostic impact, both for the OS (HR: 1.005; 95% CI: 1.002 to 1.008; p=0.000) and the progression-free survival (HR: 3.442; 95% CI: 1.045 to 11.340; p=0.04) in a clinical and histopathological multivariate model including the male sex (HR: 3.551; 95% CI: 0.986 to 12.786; p=0.05), a percentage of tumoral cells ≥10.1% (HR: 1.044; 95% CI: 1.003 to 1.087; p=0.03) and high risk International Prognostic Score (≥3 points) (HR: 6.453; 95% CI: 1.970 to 21.134; p=0.002).

CONCLUSIONS

The PD-L1/CD30 ratio identifies a group of cHL patients with an increased risk of treatment failure. Its clinical application can be performed as it constitutes an easy to implement pathological information in the diagnostic work-up of patients with cHL.

Tissues as networks of cells: towards generative rules of complex organ development

Network analysis is a well-known and powerful tool in molecular biology. More recently, it has been introduced in developmental biology. Tissues can be readily translated into spatial networks such that cells are represented by nodes and intercellular connections by edges. This discretization of cellular organization enables mathematical approaches rooted in network science to be applied towards the understanding of tissue structure and function. Here, we describe how such tissue abstractions can enable the principles that underpin tissue formation and function to be uncovered. We provide an introduction into biologically relevant network measures, then present an overview of different areas of developmental biology where these approaches have been applied. We then summarize the general developmental rules underpinning tissue topology generation. Finally, we discuss how generative models can help to link the developmental rule back to the tissue topologies. Our collection of results points at general mechanisms as to how local developmental rules can give rise to observed topological properties in multicellular systems.

Holistic View on the Structure of Immune Response: Petri Net Model

The simulation of immune response is a challenging task because quantitative data are scarce. Quantitative theoretical models either focus on specific cell-cell interactions or have to make assumptions about parameters. The broad variation of, e.g., the dimensions and abundance between lymph nodes as well as between individual patients hampers conclusive quantitative modeling. No theoretical model has been established representing a consensus on the set of major cellular processes involved in the immune response. In this paper, we apply the Petri net formalism to construct a semi-quantitative mathematical model of the lymph nodes. The model covers the major cellular processes of immune response and fulfills the formal requirements of Petri net models. The intention is to develop a model taking into account the viewpoints of experienced pathologists and computer scientists in the field of systems biology. In order to verify formal requirements, we discuss invariant properties and apply the asynchronous firing rule of a place/transition net. Twenty-five transition invariants cover the model, and each is assigned to a functional mode of the immune response. In simulations, the Petri net model describes the dynamic modes of the immune response, its adaption to antigens, and its loss of memory.

Imaging bridges pathology and radiology

In recent years, medical disciplines have moved closer together and rigid borders have been increasingly dissolved. The synergetic advantage of combining multiple disciplines is particularly important for radiology, nuclear medicine, and pathology to perform integrative diagnostics. In this review, we discuss how medical subdisciplines can be reintegrated in the future using state-of-the-art methods of digitization, data science, and machine learning. Integration of methods is made possible by the digitalization of radiological and nuclear medical images, as well as pathological images. 3D histology can become a valuable tool, not only for integration into radiological images but also for the visualization of cellular interactions, the so-called connectomes. In human pathology, it has recently become possible to image and calculate the movements and contacts of immunostained cells in fresh tissue explants. Recording the movement of a living cell is proving to be informative and makes it possible to study dynamic connectomes in the diagnosis of lymphoid tissue. By applying computational methods including data science and machine learning, new perspectives for analyzing and understanding diseases become possible.

Identification of prognostic factors in classic Hodgkin lymphoma by integrating whole slide imaging and next generation sequencing

Digital pathology and genomics are increasingly used to improve our understanding of lymphoid neoplasms. Algorithms for quantifying cell populations in the lymph node and genetics can be integrated to identify new biomarkers with prognostic impact in classic Hodgkin lymphoma (cHL). In 16 cHL patients, we have performed whole slide imaging (WSI) analysis and quantification of CD30+, CD20+, CD3+ and MUM1+ cells in whole tissue slides, and Next Generation Sequencing (NGS) in formalin fixed paraffin-embedded (FFPE) tissue, using a widely used NSG panel (Oncomine® Focus Assay) to define genetic variants underlying tumor development. The different cell populations could be successfully identified in scanned slides of cHL, supporting the inclusion of WSI in the histopathological evaluation of cHL as an adequate method for the quantification of different cell populations. We also performed genetic profiling in FFPE samples of cHL leading to the identification of copy number variations in the Neurofibromin 1 gene (17q11.2) and the Androgen Receptor gene (Xq12) accompanied by chromosomal gains and losses in CDK4, KRAS and FGFR2 genes. Progression-free survival (PFS) was statistically significantly higher in cHL patients with amplification in the NF1 gene combined with CD3+ cells above 28.6% (p = 0.006) and MUM1+ cells above 21.8% (p < 0.001). Moreover, patients with MUM1+ cells above 21.8% showed a statistically significantly higher PFS when combined with amplification of the AR gene (p < 0.001) and wild-type KRAS (p < 0.001). The integration of WSI analysis and DNA sequencing could be useful to improve our understanding of the biology of cHL and define risk subgroups.

Whole-slide image analysis identifies a high content of Hodgkin Reed-Sternberg cells and a low content of T lymphocytes in tumor microenvironment as predictors of adverse outcome in patients with classic Hodgkin lymphoma treated with ABVD

Classic Hodgkin lymphoma (cHL) constitutes the most frequent lymphoma in young adults. Its histopathology is unique as a scattered tumor population, termed Hodgkin Reed-Sternberg (HRS) cells is diluted in a prominent tumor microenvironment (TME) composed of T lymphocytes, B lymphocytes, macrophages, neutrophils, eosinophils and histiocytes. Traditionally, the identification of prognostic biomarkers in the cHL TME has required visual inspection and manual counting by pathologists. The advent of whole-slide imaging (WSI) and digital image analysis methods could significantly contribute to improve this essential objective in cHL research, as a 10-20% of patients are still refractory or relapsed after conventional chemotherapy. In this work, we have digitized a total of 255 diagnostic cHL slides and quantified the proportion of HRS cells (CD30), B cells (CD20) and T cells (CD3) by digital image analysis. Data obtained where then correlated with the overall survival (OS) and progression free survival (PFS) of cHL patients. Quantification of HRS cells, B cells and T cells reflects the biological heterogeneity of the different cHL histological subtypes analyzed. A percentage of 2.00% of HRS cells statistically significantly discriminated between patients achieving a complete metabolic response (CMR) and refractory or relapsed (R/R) patients both for the OS (P=0.001) and PFS (P=0.005). Furthermore, patients with a percentage of T cells below the 26.70% in the TME showed a statistically significantly shorter OS (P=0.019) and PFS (P=0.041) in comparison with patients above this threshold. A subgroup of patients with a low content of T cells and high content of HRS cells exhibited a special aggressive clinical course. Currently, there is the need to implement quantitative and easy scalable methods to enhance clinical translation, as the cHL TME plays a central role in the clinical course of the disease. The results of this study could contribute to the identification of prognostic biomarkers specifically looking at the cHL TME and their inclusion in future clinical trials.

Two-dimensional adaptive dynamics of evolutionary public goods games: finite-size effects on fixation probability and branching time

Public goods games (PGGs) describe situations in which individuals contribute to a good at a private cost, but others can free-ride by receiving a share of the public benefit at no cost. The game occurs within local neighbourhoods, which are subsets of the whole population. Free-riding and maximal production are two extremes of a continuous spectrum of traits. We study the adaptive dynamics of production and neighbourhood size. We allow the public good production and the neighbourhood size to coevolve and observe evolutionary branching. We explain how an initially monomorphic population undergoes evolutionary branching in two dimensions to become a dimorphic population characterized by extremes of the spectrum of trait values. We find that population size plays a crucial role in determining the final state of the population. Small populations may not branch or may be subject to extinction of a subpopulation after branching. In small populations, stochastic effects become important and we calculate the probability of subpopulation extinction. Our work elucidates the evolutionary origins of heterogeneity in local PGGs among individuals of two traits (production and neighbourhood size), and the effects of stochasticity in two-dimensional trait space, where novel effects emerge.