Lysyl-Oxidase Dependent Extracellular Matrix Stiffness in Hodgkin Lymphomas: Mechanical and Topographical Evidence

Deep-learning based classification of a tumor marker for prognosis on Hodgkin's disease

PURPOSE

Hodgkin's disease is a common malignant disorder in adolescent patients. Although most patients are cured, approximately 10%-15% of patients experience a relapse or have resistant disease. Furthermore, there are no definitive molecular predictors for early identification of patients at high risk of treatment failure to first line therapy. The aim of this study was to evaluate the deep learning-based classifier model of medical image classification to predict clinical outcome that may help in appropriate therapeutic decisions.

METHODS

Eighty-three FFPE biopsy specimens from patients with Hodgkin's disease were stratified according to the patient's qPET scores, stained with picrosirius red dye and digitalized by whole slide image scanning. The resulting whole slide images were cut into tiles and annotated by two classes based on the collagen fibers' degree of coloring with picrosirius red. The neural network (YOLOv4) was then trained with the annotated data. Training was performed with 30 cases. Prognostic power of the weakly stained picrosirius red fibers was evaluated with 53 cases. The same neural network was trained with MMP9 stained tissue slides from the same cases and the quantification results were compared with the variant from the picrosirius red cases.

RESULTS

There was a weak monotonically increasing relationship by parametric ANOVA between the qPET groups and the percentages of weakly stained fibers (p = .0185). The qPET-positive cases showed an average of 18% of weakly stained fibers, and the qPET-negative cases 10%-14%. Detection performance showed an AUC of 0.79.

CONCLUSIONS

Picrosirius red shows distinct associations as a prognostic metric candidate of disease progression in Hodgkin's disease cases using whole slide images but not sufficiently as a prognostic device.

Priming of Colorectal Tumor-Associated Fibroblasts with Zoledronic Acid Conjugated to the Anti-Epidermal Growth Factor Receptor Antibody Cetuximab Elicits Anti-Tumor Vδ2 T Lymphocytes

Tumor-associated fibroblasts (TAF) exert immunosuppressive effects in colorectal carcinoma (CRC), impairing the recognition of tumor cells by effector lymphocytes, including Vδ2 T cells. Herein, we show that CRC-derived TAF can be turned by zoledronic acid (ZA), in soluble form or as antibody-drug conjugate (ADC), into efficient stimulators of Vδ2 T cells. CRC-TAF, obtained from patients, express the epidermal growth factor receptor (EGFR) and the butyrophilin family members BTN3A1/BTN2A1. These butyrophilins mediate the presentation of the phosphoantigens, accumulated in the cells due to ZA effect, to Vδ2 T cells. CRC-TAF exposed to soluble ZA acquired the ability to trigger the proliferation of Vδ2 T cells, in part represented by effector memory cells lacking CD45RA and CD27. In turn, expanded Vδ2 T cells exerted relevant cytotoxic activity towards CRC cells and CRC-TAF when primed with soluble ZA. Of note, also the ADC made of the anti-EGFR cetuximab (Cet) and ZA (Cet-ZA), that we recently described, induced the proliferation of anti-tumor Vδ2 T lymphocytes and their activation against CRC-TAF. These findings indicate that ZA can educate TAF to stimulate effector memory Vδ2 T cells; the Cet-ZA ADC formulation can lead to the precise delivery of ZA to EGFR⁺ cells, with a double targeting of TAF and tumor cells.

Evolution of 3D Cultures: Toward Tailored Preclinical Models

The identification and validation of simple, reliable and reproducible three dimensional (3D) in vitro culture systems represent a major challenge in the field of anticancer drug development [...].