A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data.

Dkk1 as a Prognostic Marker for Neoadjuvant Chemotherapy Response in Breast Cancer Patients.

Purpose: To investigate the role of Dkk1 as a predictor of response to NACT in BC patients.

Methods: This retrospective monocentric study included 145 women who had undergone NACT followed by breast surgery. Dkk1 protein expression was assessed using immunohistochemistry staining in core needle biopsies and mammary carcinoma specimens.

Th17 cells target the metabolic miR-142-5p-succinate dehydrogenase subunit C/D (SDHC/SDHD) axis, promoting invasiveness and progression of cervical cancers.

During cervical carcinogenesis, T-helper (Th)-17 cells accumulate in the peripheral blood and tumor tissues of cancer patients. We previously demonstrated that Th17 cells are associated with therapy resistance as well as cervical cancer metastases and relapse; however, the underlying Th17-driven mechanisms are not fully understood. Here, using microarrays, we found that Th17 cells induced an epithelial-to-mesenchymal transition (EMT) phenotype of cervical cancer cells and promoted migration and invasion of 2D cultures and 3D spheroids via induction of microRNA miR-142-5p.

Evaluation of Prognostic Parameters to Identify Aggressive Penile Carcinomas.

Background: Advanced penile carcinoma is characterized by poor prognosis. Most data on prognostic factors are based on small study cohorts, and even meta-analyses are limited in patient numbers. Therefore, there is still a lack of evidence for clinical decisions.