SATB1, genomic instability and Gleason grading constitute a novel risk score for prostate cancer

Current prostate cancer risk classifications rely on clinicopathological parameters resulting in uncertainties for prognostication. To improve individual risk stratification, we examined the predictive value of selected proteins with respect to tumor heterogeneity and genomic instability. We assessed the degree of genomic instability in 50 radical prostatectomy specimens by DNA-Image-Cytometry and evaluated protein expression in related 199 tissue-microarray (TMA) cores. Immunohistochemical data of SATB1, SPIN1, TPM4, VIME and TBB5 were correlated with the degree of genomic instability, established clinical risk factors and overall survival. Genomic instability was associated with a GS ≥ 7 (p = 0.001) and worse overall survival (p = 0.008). A positive SATB1 expression was associated with a GS ≤ 6 (p = 0.040), genomic stability (p = 0.027), and was a predictor for increased overall survival (p = 0.023). High expression of SPIN1 was also associated with longer overall survival (p = 0.048) and lower preoperative PSA-values (p = 0.047). The combination of SATB1 expression, genomic instability, and GS lead to a novel Prostate Cancer Prediction Score (PCP-Score) which outperforms the current D’Amico et al. stratification for predicting overall survival. Low SATB1 expression, genomic instability and GS ≥ 7 were identified as markers for poor prognosis. Their combination overcomes current clinical risk stratification regimes.

PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer

Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (n = 253). It was validated according to a predefined protocol in an independent RP cohort (n = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry. In the primary analysis, automatically assessed dichotomized PTEN status was associated with time to biochemical recurrence (TTBCR) (hazard ratio (HR) = 3.32, 95% CI 2.05 to 5.38). Patients with both non-diploid tumors and PTEN-low had an HR of 4.63 (95% CI 2.50 to 8.57), while patients with one of these characteristics had an HR of 1.94 (95% CI 1.15 to 3.30), compared to patients with diploid tumors and PTEN-high, in univariable analysis of TTBCR in the validation cohort. Automatic PTEN scoring was strongly predictive of the PTEN status assessed by human experts (area under the curve 0.987 (95% CI 0.968 to 0.994)). This suggests that PTEN status can be accurately assessed using ML, and that the combined marker of automatically assessed PTEN and DNA ploidy status may provide an objective supplement to the existing risk stratification factors in prostate cancer.

Image3C, a multimodal image-based and label independent integrative method for single-cell analysis

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell cluster pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and to detect changes in cellular composition between different conditions. Therefore, Image3C expands the use of imaged-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

Development of an Image-Based HCS-Compatible Method for Endothelial Barrier Function Assessment

The recent renascence of phenotypic drug discovery (PDD) is catalyzed by its ability to identify first-in-class drugs and deliver results when the exact molecular mechanism is partially obscure. Acute respiratory distress syndrome (ARDS) is a severe, life-threatening condition with a high mortality rate that has increased in frequency due to the COVID-19 pandemic. Despite decades of laboratory and clinical study, no efficient pharmacological therapy for ARDS has been found. An increase in endothelial permeability is the primary event in ARDS onset, causing the development of pulmonary edema that leads to respiratory failure. Currently, the detailed molecular mechanisms regulating endothelial permeability are poorly understood. Therefore, the use of the PDD approach in the search for efficient ARDS treatment can be more productive than classic target-based drug discovery (TDD), but its use requires a new cell-based assay compatible with high-throughput (HTS) and high-content (HCS) screening. Here we report the development of a new plate-based image cytometry method to measure endothelial barrier function. The incorporation of image cytometry in combination with digital image analysis substantially decreases assay variability and increases the signal window. This new method simultaneously allows for rapid measurement of cell monolayer permeability and cytological analysis. The time-course of permeability increase in human pulmonary artery endothelial cells (HPAECs) in response to the thrombin and tumor necrosis factor α treatment correlates with previously published data obtained by transendothelial resistance (TER) measurements. Furthermore, the proposed image cytometry method can be easily adapted for HTS/HCS applications.

Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology

Several advances in recent decades in digital imaging, artificial intelligence, and multiplex modalities have improved our ability to automatically analyze and interpret imaging data. Imaging technologies such as optical coherence tomography, optical projection tomography, and quantitative phase microscopy allow analysis of tissues and cells in 3-dimensions and with subcellular granularity. Improvements in computer vision and machine learning have made algorithms more successful in automatically identifying important features to diagnose disease. Many new automated multiplex modalities such as antibody barcoding with cleavable DNA (ABCD), single cell analysis for tumor phenotyping (SCANT), fast analytical screening technique fine needle aspiration (FAST-FNA), and portable fluorescence-based image cytometry analyzer (CytoPAN) are under investigation. These have shown great promise in their ability to automatically analyze several biomarkers concurrently with high sensitivity, even in paucicellular samples, lending themselves well as tools in FNA. Not yet widely adopted for clinical use, many have successfully been applied to human samples. Once clinically validated, some of these technologies are poised to change the routine practice of cytopathology.

New Concept and Apparatus for Cytocentrifugation and Cell Processing for Microscopy Analysis

Cytocentrifugation is a common technique for the capture of cells on microscopic slides. It usually requires a special cytocentrifuge or cytorotor and cassettes. In the study presented here, we tested the new concept of cytocentrifugation based on the threaded connection of the lid and the sample holder to ensure an adjustable flow of solutions through the filters and the collection of the filtered solutions in the reservoir during centrifugation. To test this concept, we developed a device for the preparation of cell samples on circular coverslips. The device was tested for the capture and sample processing of both eukaryotic and prokaryotic cells, cell nuclei, and mitochondria for microscopy analysis including image cytometry. Moreover, an efficient procedure was developed for capturing formaldehyde-fixed cells on non-treated coverslips without cell drying. The results showed that the tested arrangement enables the effective capture and processing of all of the tested samples and the developed device represents an inexpensive alternative to common cytocentrifuges, as only the paper filter is consumed during sample processing, and no special centrifuge, cytorotor, or cassette is necessary. As no additional system of solution removal is required during sample staining, the tested concept also facilitates the eventual automation of the staining procedure.