Quantitative assessment of pancreatic cancer precursor lesions in IHC-stained tissue with a tissue image analysis platform

Bcl-xL Enforces a Slow-Cycling State Necessary for Survival in the Nutrient-Deprived Microenvironment of Pancreatic Cancer

Solid tumors possess heterogeneous metabolic microenvironments where oxygen and nutrient availability are plentiful (fertile regions) or scarce (arid regions). While cancer cells residing in fertile regions proliferate rapidly, most cancer cells in vivo reside in arid regions and exhibit a slow-cycling state that renders them chemoresistant. Here, we developed an in vitro system enabling systematic comparison between these populations via transcriptome analysis, metabolomic profiling, and whole-genome CRISPR screening. Metabolic deprivation led to pronounced transcriptional and metabolic reprogramming, resulting in decreased anabolic activities and distinct vulnerabilities. Reductions in anabolic, energy-consuming activities, particularly cell proliferation, were not simply byproducts of the metabolic challenge, but rather essential adaptations. Mechanistically, Bcl-xL played a central role in the adaptation to nutrient and oxygen deprivation. In this setting, Bcl-xL protected quiescent cells from the lethal effects of cell-cycle entry in the absence of adequate nutrients. Moreover, inhibition of Bcl-xL combined with traditional chemotherapy had a synergistic antitumor effect that targeted cycling cells. Bcl-xL expression was strongly associated with poor patient survival despite being confined to the slow-cycling fraction of human pancreatic cancer cells. These findings provide a rationale for combining traditional cancer therapies that target rapidly cycling cells with those that target quiescent, chemoresistant cells associated with nutrient and oxygen deprivation.

SIGNIFICANCE

The majority of pancreatic cancer cells inhabit nutrient- and oxygen-poor tumor regions and require Bcl-xL for their survival, providing a compelling antitumor metabolic strategy.

The AAV9 Variant Capsid AAV-F Mediates Widespread Transgene Expression in Nonhuman Primate Spinal Cord After Intrathecal Administration

Intrathecal delivery of AAV9 into the subarachnoid space has been shown to transduce spinal cord and brain and be less affected by preexisting antibodies, which are lower in cerebral spinal fluid. Still, efficiency of transduction needs to be improved. Recently, we identified a new capsid from a library selection in mice, called AAV-F, that allowed robust transduction of the spinal cord gray matter after lumbar injection. In this study, we test transduction of spinal cord by AAV-F (n = 3) compared to AAV9 (n = 2), using a reporter gene, in cynomolgus monkeys after lumbar intrathecal injection. Using an automated image analysis (IA) approach to sensitively quantitate reporter gene expression in spinal cord, we found that AAV-F capsid mediated slightly higher transgene expression (both in percentages of cells and intensity of immunostaining) in motor neurons and interneurons, in the lumbar and thoracic regions, compared to AAV9. Interestingly, although AAV-F mediated higher transgene expression in spinal cord, the number of genomes in spinal cord and periphery were on average lower for AAV-F than AAV9, which suggest that lower numbers of genomes were able to mediate higher transgene expression in spinal cord with this capsid. In contrast, dorsal root ganglion transduction efficiency was lower for AAV-F compared to AAV9 on average. Interestingly, we also observed transduction of Schwann cells in sciatic nerve in two nonhuman primates injected with AAV-F, but none with AAV9. Overall, our data demonstrate the utility of automated IA for quantitation of AAV transduction in the spinal cord and the favorable on-target:off-target transduction profile suggests that the AAV-F capsid be considered for gene therapy applications focused on treating the spinal cord after intrathecal delivery.

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence-based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist's assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

Concordance, intra- and inter-observer agreements between light microscopy and whole slide imaging for samples acquired by EUS in pancreatic solid lesions

BACKGROUND

No study has compared the performance of light microscopy (LM) and whole slide imaging (WSI) for endoscopic ultrasound (EUS) histological acquired tissue samples from pancreatic solid lesions (PSLs). We evaluated the concordance between LM and WSI and the inter- and intra-observer agreements among pathologists on PSLs EUS acquired samples.

METHODS

LM and WSI from 60 patients with PSLs were evaluated by five expert pathologists to define: diagnostic classification, presence of a core, number and percentage of lesional cells. Washout period between evaluations was 3 months. Time of the procedures was also assessed.

RESULTS

Forty-eight cell-block and 12 biopsy samples were evaluated. A high concordance between LM and WSI was found. Inter- and intra-observer agreements for diagnostic classification were substantial and complete, respectively. For all the other parameters, the inter-observer agreement was usually higher for LM. For the intra-observer, a substantial agreement was reached regarding the presence of tissue core and the number and the percentage of malignant cells. Median time for performing LM was significantly shorter than for WSI (p < 0.0001).

CONCLUSIONS

LM and WSI of cell-block and biopsy samples acquired by EUS in PSLs were highly concordant, with a substantial inter-observer and a complete intra-observer agreements regarding diagnostic classification.

Digital Microscopy, Image Analysis, and Virtual Slide Repository

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

Fundamental Concepts for Semiquantitative Tissue Scoring in Translational Research

Failure to reproduce results from some scientific studies has raised awareness of the critical need for reproducibility in translational studies. Macroscopic and microscopic examination is a common approach to determine changes in tissues, but text descriptions and visual images have limitations for group comparisons. Semiquantitative scoring is a way of transforming qualitative tissue data into numerical data that allow more robust group comparisons. Semiquantitative scoring has broad uses in preclinical and clinical studies for evaluation of tissue lesions. Reproducibility can be improved by constraining bias through appropriate experimental design, randomization of tissues, effective use of multidisciplinary collaborations, and valid masking procedures. Scoring can be applied to tissue lesions (eg, size, distribution, characteristics) and also to tissues through evaluation of staining distribution and intensity. Semiquantitative scores should be validated to demonstrate relevance to biological data and to demonstrate observer reproducibility. Statistical analysis should make use of appropriate tests to give robust confidence in the results and interpretations. Following key principles of semiquantitative scoring will not only enhance descriptive tissue evaluation but also improve quality, reproducibility, and rigor of tissue studies.

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

Non-invasive dynamic monitoring initiation and growth of pancreatic tumor in the LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) transgenic mouse model

The LSL-Kras^G12D/+;LSL-Trp53^R172H/+;Pdx-1-Cre (KPC) mouse is one of the most widely used transgenic models to evaluate tumor characteristics and to develop novel therapies for pancreatic ductal adenocarcinoma (PDAC). There is no report of the effective systemic evaluation of longitudinal KPC tumor imitation and growth. Therefore, we aimed to characterize the initiation and progression of pancreatic cancer in KPC mice using longitudinal multiparametric magnetic resonance imaging (MRI) approaches and overall survival. Ten KPC mice were used to develop spontaneous PDAC and monitored by MRI. Tumor growth was evaluated using weekly acquired MRI data. The relationship between diffusion-weighted MRI (DW-MRI) imaging biomarkers (apparent diffusion coefficient - ADC) and tumor fibrosis measurement by pathological methods was assessed by Pearson correlation coefficient. Six KPC mice developed spontaneously pancreatic tumors at the age of 20.0 ± 2.9 weeks with a relatively short life span (6.8 ± 1.8 weeks). The tumors could be detected by MRI with a minimum diameter of 3.88 ± 1.18 mm (range, 2.18-5.20 mm), showing a rapid growth curve according to both the longest diameter (1.63 ± 0.52 mm/week) and tumor volume (148.77 ± 80.87 mm³)/week. Pathological results confirmed that the tumors display histopathological features of human pancreatic cancer. A strong correlation between ADC values and fibrosis measurements was observed (R = -0.825, P = .043). Our results show that the initiation and progression of pancreatic tumor in KPC mice can be evaluated by longitudinally non-invasive dynamic MRI approaches. The findings will be the fundamental KPC background data for developing novel therapeutic approaches, in particular for evaluation of response to novel treatments.