Collagen organization of renal cell carcinoma differs between low and high grade tumors

Abstract

Background The traditional pathologic grading for human renal cell carcinoma (RCC) has low concordance between biopsy and surgical specimen. There is a need to investigate adjunctive pathology technique that does not rely on the nuclear morphology that defines the traditional grading. Changes in collagen organization in the extracellular matrix have been linked to prognosis or grade in breast, ovarian, and pancreatic cancers, but collagen organization has never been correlated with RCC grade. In this study, we used Second Harmonic Generation (SHG) based imaging to quantify possible differences in collagen organization between high and low grades of human RCC. Methods A tissue microarray (TMA) was constructed from RCC tumor specimens. Each TMA core represents an individual patient. A 5 μm section from the TMA tissue was stained with standard hematoxylin and eosin (H&E). Bright field images of the H&E stained TMA were used to annotate representative RCC regions. In this study, 70 grade 1 cores and 51 grade 4 cores were imaged on a custom-built forward SHG microscope, and images were analyzed using established software tools to automatically extract and quantify collagen fibers for alignment and density assessment. A linear mixed-effects model with random intercepts to account for the within-patient correlation was created to compare grade 1 vs. grade 4 measurements and the statistical tests were two-sided. Results Both collagen density and alignment differed significantly between RCC grade 1 and RCC grade 4. Specifically, collagen fiber density was greater in grade 4 than in grade 1 RCC ( p < 0.001). Collagen fibers were also more aligned in grade 4 compared to grade 1 ( p < 0.001). Conclusions Collagen density and alignment were shown to be significantly higher in RCC grade 4 vs. grade 1. This technique of biopsy sampling by SHG could complement classical tumor grading approaches. Furthermore it might allow biopsies to be more clinically relevant by informing diagnostics. Future studies are required to investigate the functional role of collagen organization in RCC.

Engineering T cells to enhance 3D migration through structurally and mechanically complex tumor microenvironments

“…Indeed, the complex stromal reaction in solid tumors can limit access and effective distribution of T cells creating antitumor immunityfree sanctuaries (Elahi-Gedwillo et al, 2019;Hartmann et al, 2014;Kuczek et al, 2019;Salmon et al, 2012;Stromnes et al, 2017). Furthermore, many solid tumors are rich with aligned extracellular matrix (ECM) networks (Best et al, 2019;Provenzano et al, 2006;Ray et al, 2017a) , which provide contact guidance for carcinoma cells (Provenzano et al, 2006(Provenzano et al, , 2008Tabdanov et al, 2018aTabdanov et al, , 2018b, and can also direct migration of infiltrated T cells in solid tumors (Hartmann et al, 2014;Salmon et al, 2012;Wolf et al, 2003). Yet, our understanding of how native and engineered T cells migrate through mechanically complex tumor microenvironments (TMEs) is quite incomplete.…”

RUNX1 Is a Driver of Renal Cell Carcinoma Correlating with Clinical Outcome

“…SERPINH1 also associates with enhanced TGFβ signalling and both RUNX1 and RUNX2 have been shown to be involved in TGFβ induced kidney fibrosis (23,59). The role of collagen in ccRCC is currently unclear, however collagen density and alignment have recently been shown to be significantly higher in patients with high grade tumours compared to low grade (60). The fatty acid metabolism enzyme CPT1A increased markedly on RUNX1 deletion suggesting a negative correlation.…”

Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images

“…We performed experiments to evaluate our RCNN approach on three previously published tissue microarray (TMA) cancer datasets, a breast TMA dataset consisting of images, 54 and a kidney TMA dataset with 381 images, 55 and a pancreatic TMA dataset with 180 images. 56 The datasets are summarized in Table 1.…”