Quantification of large scale DNA organization for predicting prostate cancer recurrence

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

In ovo protective effects of chicoric and rosmarinic acids against Thiacloprid-induced cytotoxicity, oxidative stress, and growth retardation on newly hatched chicks

Thiacloprid (TH) is a neonicotinoid insecticide employed in agriculture to protect fruits and vegetables against different insects. It showed different deleterious effects on the general health of non-target organisms including birds and animals, however, its developmental toxicity has yet to be fully elucidated. Chicoric (CA) and rosmarinic (RA) acids are polyphenolic compounds with a wide range of beneficial biological activities. In this study, the possible protective effects of CA and RA were investigated in chick embryos exposed in ovo to TH (1µg/egg) with or without CA (100 µg/egg) or RA (100 µg/egg) co-exposure. TH reduced the hatchling body weight, body weight/egg weight, and relative weight of bursa of Fabricius in the one-day-old hatchlings. Examination of the 7-day-old chicks revealed a decline in feed intake, daily weight gain, feed conversion ratio (FCR), and plasma levels of T3, T4, and growth hormone. Serum ALT, AST activities, and total cholesterol levels showed significant elevations. Hepatic MDA was increased with a reduction in SOD activity and GSH level and downregulation of the liver SOD and GST gene expression pattern. Serum IgG and IgM levels were reduced, and various histopathological alterations were noticed in the liver. Co-administration of CA or RA with TH mitigated the toxic effects on hatchlings. When both CA and RA are combined, they present a synergistic protective effect. CA and RA can be used as protective agents against TH toxicity as they improve growth performance and have hepatoprotective and immunostimulant effects in newly hatched chicks.

Dunaliella salina Microalga Restores the Metabolic Equilibrium and Ameliorates the Hepatic Inflammatory Response Induced by Zinc Oxide Nanoparticles (ZnO-NPs) in Male Zebrafish

Microalgae are rich in bioactive compounds including pigments, proteins, lipids, polyunsaturated fatty acids, carbohydrates, and vitamins. Due to their non-toxic and nutritious characteristics, these are suggested as important food for many aquatic animals. Dunaliella salina is a well-known microalga that accumulates valuable amounts of carotenoids. We investigated whether it could restore the metabolic equilibrium and mitigate the hepatic inflammation induced by zinc oxide nanoparticles (ZnO-NPs) using male zebrafish which were exposed to 1/5^th 96 h-LC₅₀ for 4 weeks, followed by dietary supplementation with D. salina at two concentrations (15% and 30%) for 2 weeks. Collectively, ZnO-NPs affected fish appetite, whole body composition, hepatic glycogen and lipid contents, intestinal bacterial and Aeromonas counts, as well as hepatic tumor necrosis factor- α (TNF-α). In addition, the mRNA expression of genes related to gluconeogenesis (pck1, gys2, and g6pc3), lipogenesis (srepf1, acaca, fasn, and cd36), and inflammatory response (tnf-α, tnf-β, nf-kb2) were modulated. D. salina reduced the body burden of zinc residues, restored the fish appetite and normal liver architecture, and mitigated the toxic impacts of ZnO-NPs on whole-body composition, intestinal bacteria, energy metabolism, and hepatic inflammatory markers. Our results revealed that the administration of D. salina might be effective in neutralizing the hepatotoxic effects of ZnO-NPs in the zebrafish model.

Predicting progression of low-grade oral dysplasia using brushing based DNA ploidy and Chromatin Organization analysis

Most oral cancers arise from oral potentially malignant lesions, which show varying grades of dysplasia. Risk of progression increases with increasing grade of dysplasia, however risk prediction among oral low-grade dysplasia (LGDs) i.e., mild and moderate dysplasia can be challenging as only 5-15% transform. Moreover, grading of dysplasia is subjective and varies with the area of the lesion being biopsied. To date, no biomarkers or tools are used clinically to triage oral LGDs. This study utilizes a combination of DNA ploidy and chromatin organization (CO) scores from cells obtained from lesion brushings to identify oral LGDs at high-risk of progression. A total of 130 lesion brushings from patients with oral LGDs were selected of which 16 (12.3%) lesions progressed to severe dysplasia or cancer. DNA ploidy and CO scores were analyzed from nuclear features measured by our in-house DNA image cytometry (DNA-ICM) system and used to classify brushings into low risk and high risk. A total of 57 samples were classified as high-risk of which 13 were progressors. High-risk DNA brushing was significant for progression (P = 0.001) and grade of dysplasia (P = 0.004). Multivariate analysis showed high-risk DNA brushing showed 5.1 to 8-fold increased risk of progression, a stronger predictor than dysplasia grading and lesion clinical features. DNA-ICM can serve as a non-invasive, high throughput tool to identify high-risk lesions several years prior to transformation. This will help clinicians focus on such lesions while low-risk lesions may be spared from unnecessary biopsies.

EpiGraph: an open-source platform to quantify epithelial organization

Summary

Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform Fiji. This makes EpiGraph very user friendly, with no programming skills required.

Availability and implementation

EpiGraph is available at https://imagej.net/EpiGraph and the code is accessible (https://github.com/ComplexOrganizationOfLivingMatter/Epigraph) under GPLv3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence

Purpose

Prostate cancer (PCa) with biopsy-based grade group (GG) 1 or 2 characteristics has a favorable outcome, yet some cases still progress after radical prostatectomy and present with biochemical recurrence (BCR). We hypothesized that the multi-scale tissue architecture (MSTA) analysis score would correlate with the aggressive PCa phenotype and could be used as a tool for risk assessment to improve the management of patients with favorable-risk PCa.

Materials and Methods

MSTA was evaluated in needle-biopsy samples from 115 patients with favorable-risk PCa, as defined by GG1 and GG2, a prostate-specific antigen (PSA) level of <10 ng/mL, a clinical stage of cT1c to cT2b, and general Gleason GG (GGG) and expert pathologist-assessed GG (EGG). Algorithms based on Voronoi diagrams were applied to all Feulgen-thionin-stained diagnostic areas. One hundred tissue architecture features were calculated and an MSTA score, a linear combination of the most discriminant features, was generated. Correlation of MSTA score with BCR and other clinical variables was investigated.

Results

In a univariate regression model, EGG, clinical stage, and MSTA were significant predictors of BCR (respective p-values: 0.0016, 0.016, and 0.028). Survival analysis showed that patients with a high MSTA score were more likely to experience BCR than were patients with a low MSTA score (odds ratio, 2.9). Combining MSTA with GG assessment resulted in a significant stratification of risk for BCR.

Conclusions

MSTA score could be used as an objective adjunct risk stratification tool to pathologist assessments and could improve the management of patients with favorable-risk PCa.

Strategy of Hepatic Metabolic Defects Induced by beclin1 Heterozygosity in Adult Zebrafish

Hepatic disorders have been increasing in recent years because of high carbohydrate diets. Hepatocytes depend mainly on the basal autophagy to maintain hepatic glucose/lipid homeostasis in mammals. However, the regulatory mechanisms of autophagy in hepatic energy metabolism are still unknown in fish species. Accordingly, mutant zebrafish lines of autophagy-related genes beclin1 and atg7 were generated by CRISPR/Cas9 gene-editing technology. Interestingly, unlike atg7+/-, male beclin1+/- zebrafish displayed liver defects in the morphology and histology, including abnormal hepatocyte proliferation, hemorrhagic and inflammatory phenotypes. A significant decrease in hepatocyte glycogen and an increase in hepatocyte lipids were detected in the histological assay that coincidence with the hepatic gene expression. Meanwhile, loss of heterozygosity for beclin1 creates a suitable microenvironment for hepatic tumorigenesis via phosphorylation of Akt kinase, which in turn affects liver autophagy. The reduction in autophagy activity in male beclin1+/- liver leads to a disturbance in the glucose/lipid metabolism and negatively regulates apoptosis accompanied by the induction of cellular proliferation and acute inflammatory response. Our findings highlight an important role of beclin1 in zebrafish liver development and energy metabolism, suggesting the crucial role of autophagy in maintaining homeostasis of the nutrient metabolism in fish species.

The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer-aided detection

Tumors are complex networks of constantly interacting elements: tumor cells, stromal cells, immune and stem cells, blood/lympathic vessels, nerve fibers and extracellular matrix components. These elements can influence their microenvironment through mechanical and physical signals to promote tumor cell growth. To get a better understanding of tumor biology, cooperation between multidisciplinary fields is needed. Diverse mathematic computations and algorithms have been designed to find prognostic targets and enhance diagnostic assessment. In this work, we use computational digital tools to study the topology of vitronectin, a glycoprotein of the extracellular matrix. Vitronectin is linked to angiogenesis and migration, two processes closely related to tumor cell spread. Here, we investigate whether the distribution of this molecule in the tumor stroma may confer mechanical properties affecting neuroblastoma aggressiveness. Combining image analysis and graph theory, we analyze different topological features that capture the organizational cues of vitronectin in histopathological images taken from human samples. We find that the Euler number and the branching of territorial vitronectin, two topological features, could allow for a more precise pretreatment risk stratification to guide treatment strategies in neuroblastoma patients. A large amount of recently synthesized VN would create migration tracks, pinpointed by both topological features, for malignant neuroblasts, so that dramatic change in the extracellular matrix would increase tumor aggressiveness and worsen patient outcomes.

Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence

Background

The tumor microenvironment (TME) is a complex mixture of tumor epithelium, stroma and immune cells, and the immune component of the TME is highly prognostic for tumor progression and patient outcome. In lung cancer, anti-PD-1 therapy significantly improves patient survival through activation of T cell cytotoxicity against tumor cells. Direct contact between CD8+ T cells and target cells is necessary for CD8+ T cell activity, indicating that spatial organization of immune cells within the TME reflects a critical process in anti-tumor immunity. Current immunohistochemistry (IHC) imaging techniques identify immune cell numbers and densities, but lack assessment of cell–cell spatial relationships (or “cell sociology”). Immune functionality, however, is often dictated by cell-to-cell contact and cannot be resolved by simple metrics of cell density (for example, number of cells per mm²). To address this issue, we developed a Hyperspectral Cell Sociology technology platform for the analysis of cell–cell interactions in multi-channel IHC-stained tissue.

Methods

Tissue sections of primary tumors from lung adenocarcinoma patients with known clinical outcome were stained using multiplex IHC for CD3, CD8, and CD79a, and hyperspectral image analysis determined the phenotype of all cells. A Voronoi diagram for each cell was used to approximate cell boundaries, and the cell type of all neighboring cells was identified and quantified. Monte Carlo analysis was used to assess whether cell sociology patterns were likely due to random distributions of the cells.

Results

High density of intra-tumoral CD8+ T cells was significantly associated with non-recurrence of tumors. A cell sociology pattern of CD8+ T cells surrounded by tumor cells was more significantly associated with non-recurrence compared to CD8+ T cell density alone. CD3+ CD8- T cells surrounded by tumor cells was also associated with non-recurrence, but at a similar significance as cell density alone. Cell sociology metrics improved recurrence classifications of 12 patients. Monte Carlo re-sampling analysis determined that these cell sociology patterns were non-random.

Conclusion

Hyperspectral Cell Sociology expands our understanding of the complex interplay between tumor cells and immune infiltrate. This technology could improve predictions of responses to immunotherapy and lead to a deeper understanding of anti-tumor immunity.

Electronic supplementary material

The online version of this article (10.1186/s40425-018-0488-6) contains supplementary material, which is available to authorized users.