Ovarian cancer (OvCa) is one of the leading causes of mortality globally with an overall 5-year survival of 47%. The predominant subtype of OvCa is epithelial carcinoma, which can be highly aggressive. This review launches with a summary of the clinical features of OvCa, including staging and current techniques for diagnosis and therapy. Further, the important role of proteases in OvCa progression and dissemination is described. Proteases contribute to tumor angiogenesis, remodeling of extracellular matrix, migration and invasion, major processes in OvCa pathology. Multiple proteases, such as metalloproteinases, trypsin, cathepsin and others, are overexpressed in the tumor tissue. Presence of these catabolic enzymes in OvCa tissue can be exploited for improving early diagnosis and therapeutic options in advanced cases. Nanomedicine, being on the interface of molecular and cellular scales, can be designed to be activated by proteases in the OvCa microenvironment. Various types of protease-enabled nanomedicines are described and the studies that focus on their diagnostic, therapeutic and theranostic potential are reviewed.

Breast cancer liver metastases (BCLM) are hypovascular lesions that resist intravenously administered therapies and have grim prognosis. Immunotherapeutic strategies targeting BCLM critically depend on the tumor microenvironment (TME), including tumor-associated macrophages. However, a priori characterization of the BCLM TME to optimize therapy is challenging because BCLM tissue is rarely collected. In contrast to primary breast tumors for which tissue is usually obtained and histologic analysis performed, biopsies or resections of BCLM are generally discouraged due to potential complications. This study tested the novel hypothesis that BCLM TME characteristics could be inferred from the primary tumor tissue. Matched primary and metastatic human breast cancer samples were analyzed by imaging mass cytometry, identifying 20 shared marker clusters denoting macrophages (CD68, CD163, and CD206), monocytes (CD14), immune response (CD56, CD4, and CD8a), programmed cell death protein 1, PD-L1, tumor tissue (Ki-67 and phosphorylated ERK), cell adhesion (E-cadherin), hypoxia (hypoxia-inducible factor-1α), vascularity (CD31), and extracellular matrix (alpha smooth muscle actin, collagen, and matrix metalloproteinase 9). A machine learning workflow was implemented and trained on primary tumor clusters to classify each metastatic cluster density as being either above or below median values. The proposed approach achieved robust classification of BCLM marker data from matched primary tumor samples (AUROC ≥ 0.75, 95% confidence interval ≥ 0.7, on the validation subsets). Top clusters for prediction included CD68+, E-cad+, CD8a+PD1+, CD206+, and CD163+MMP9+. We conclude that the proposed workflow using primary breast tumor marker data offers the potential to predict BCLM TME characteristics, with the longer term goal to inform personalized immunotherapeutic strategies targeting BCLM. Significance: BCLM tissue characterization to optimize immunotherapy is difficult because biopsies or resections are rarely performed. This study shows that a machine learning approach offers the potential to infer BCLM characteristics from the primary tumor tissue.

This paper reviews biokinetic data for the Group VB elements vanadium, niobium, and tantalum, and presents biokinetic models describing their systemic behaviour. The model for systemic niobium in adults was developed earlier and described in Publication 134 of the International Commission on Radiological Protection. The model for niobium is used as a starting point for the development of models for vanadium and tantalum. Published biokinetic data for vanadium, including comparisons with niobium, indicate that the initial distribution of vanadium is broadly similar to that of niobium but that vanadium is less firmly fixed in most tissues and is excreted more rapidly than niobium. Biokinetic data for tantalum are more limited but suggest that its systemic behaviour closely resembles that of niobium at early times after administration. The model for niobium is proposed for application to tantalum in view of the suggested biological similarities of tantalum and niobium, their generally strong coherence in nature due to similar ionic radii and identical valence states, and the difficulties in developing parameter values directly from available data for tantalum. The proposed model for vanadium relies largely on vanadium-specific information and varies considerably from the model for niobium.

• Alpha-fetoprotein secreting tumors in gynecologic cancers have a broad differential. • Surgical pathology may provide a more comprehensive picture of tumor heterogeneity than initial biopsy. • Yolk Sac Tumor components intermixed with carcinoma points towards a somatic derivative rather than a collision tumor. • Alpha-fetoprotein can be trended over time to gauge response to treatment of cancers that produce the protein.