Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma

Abstract

Background: Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. Methods: A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. results: New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. conclusion: The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma. t o clearly distinguish the heterogeneous spectrum of clinical, histological, and molecular markers of cancer, and thereafter determine the markers essential to diagnosing the degree of malignancy and predicting response to therapy, remains a difficult challenge. These essential markers may be elucidated by considering the patient's organism and transformed tissues as holistically interconnected and dependent on microenvironment interactions via biochemical and biophysical signals (1). Mathematical models enable us to integrate measures made at different levels and generate from relatively simple to highly complex computational descriptors of the disease process of human tumors (2). Digitization of clinical data is necessary to deal with the complex hallmarks of cancer (3). To achieve personalized therapy, wellness and image-defined risk factors must be quantified (4). Hanahan and Weinberg have proposed that eight hallmarks of cancer together constitute an organizing principle that provides a logical framework for understanding the remarkable diversity of neoplastic diseases (5,6). A novel ninth hallmark which includes the aspect of physics has recently been emphasized (7). The identification of these hallmarks by quantifying the structural variations in tumor tissues, at diagnosis as well as during tumor progression and after treatment, using whole histological sections or tissue microarrays with the human eye is a challenging process (8). However, the difficulty can be overcome by forming morphometric data to represent the histological texture and classify the structural changes via sophisticated computational methods (9-11...

Neuroblastoma patient‐derived orthotopic xenografts retain metastatic patterns and geno‐ and phenotypes of patient tumours

“…Images were acquired using Olympus BX51 light microscope with CCD Olympus DP50 camera along with the CellA Analysis Image Processing Program. Ki67 index was calculated by automatic quantification using the Image Pro‐Plus software as described previously . Neural cell adhesion molecule (NCAM) positive metastatic cells in liver, lungs and bone marrow were manually quantified in 1–2 entire tissue sections/mouse at ×20 magnification.…”

Biotensegrity of the Extracellular Matrix: Physiology, Dynamic Mechanical Balance, and Implications in Oncology and Mechanotherapy

“…Statistical analysis of the quantitative data of fibers, GAGs, tumor cells, and immune system markers compared with the current parameters used to predict risk of relapse (stage, age, histopathology, state of MYCN oncogene, state of 11q region, overall genomic profile, and ploidy) (72–74) and other genetic markers of prognostic interest in a subset of 78 primary neuroblastic tumors has already been published by our group, and highlights the interest of studying ECM in neuroblastic tumors (50). The fact that ECM elements differ depending on the characteristics of the tumors and, more interestingly, the fact that the characteristics of ECM elements are related to prognosis (relapse or overall survival) advocates on behalf of the regulatory role of ECM biotensegrity in tumor progression.…”

“…In fact, the dynamic mechanical balance achieved through mechanosensors, cytoskeletal tensegrity, molecular biotensegral intra-cellular pathways, ECM with compressive and resistant elements, supportive cells (such as fibroblasts and multiple tumor-associated immune cells), and vascular and lymphatic vessels tensional structures, can be as important as the genetic instability of tumor cells in the pathogenesis and evolution of the malignant process (42, 49). In this regard, various studies have demonstrated the importance of cell–ECM biotensegrity in cancer (34, 48, 50). Indeed, a desmoplastic reaction is frequent in many solid tumors, such as breast, prostate, colon, or lung, in which high levels of TGF-b and PDGF are found.…”

“…A previous study by our group takes in account some structural elements of ECM that have the capacity to influence physical conditions and suggests that Schwannian stroma cells are not the only important factor in the histopathologic analysis of neuroblastic tumors (50). Specifically, multi-parametric analysis of other tumor stroma components (Ret F, Col F, GAG, and immune cells) detected by classic histochemistry (HC) and immunohistochemistry (IHC) techniques and incorporated into a quantitative morphological analysis would improve the value of the International NB Pathology Classification (62).…”

“…The use of tissue microarrays is advised for standardization purpose of background subtraction and color segmentation, given that these techniques tend to be dependent of the intensity of the staining and algorithms must be recalibrated with every change of intensity/ground noise/contrast staining, thus losing objectiveness. Further details regarding objective quantification of different cell and ECM elements and a flowchart of the analysis used by our group, are described elsewhere (50). …”