Computational pathology: Exploring the spatial dimension of tumor ecology

Abstract

Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment.

Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer

“…Such studies appear to suggest that the spatial organization of lymphocytic infiltration in the context of nearby cancer cells is an important prognostic hallmark of certain types of tumors. This suggests that the study of the immune response with respect to patient outcome should take into account not only the quantity of immune cells, but also the spatial arrangement of the cancerous and surrounding immune cells (20,21,23,35). Previous related studies have found a strong association between the spatial location of nuclei and surrounding cytoplasmic features with OS (20,21) and RFS (4,23) in patients with early-stage NSCLC.…”

A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images

“…Hence we can exactly extract each nucleus from the image and make it available for further analysis. For example, the features of the individual nucleus and the distribution of nuclei clusters can be used to grade and classify status of breast cancers [3,4]. Because of appearance variation such as color, shape, and texture, nuclei segmentation from histopathological images could be very challenging, as illustrated in Fig.1, in which it is very challenging even for human to recognize and segment all nuclei within the images.…”

PD1hi cells associate with clusters of proliferating B-cells in marginal zone lymphoma

“…cancer cells, lymphocytes and stromal cells, interact with one another. This work has demonstrated that patterns of lymphocyte infiltration are prognostic [ 10 ] and specifically that Tfh cell infiltration and gene signature predicted response in breast cancer [ 11 ].…”