Osteosarcoma is a highly aggressive cancer for which treatment has remained essentially unchanged for more than 30 years. Osteosarcoma is characterized by widespread and recurrent somatic copy-number alterations (SCNA) and structural rearrangements. In contrast, few recurrent point mutations in protein-coding genes have been identifi ed, suggesting that genes within SCNAs are key oncogenic drivers in this disease. SCNAs and structural rearrangements are highly heterogeneous across osteosarcoma cases, suggesting the need for a genome-informed approach to targeted therapy. To identify patient-specifi c candidate drivers, we used a simple heuristic based on degree and rank order of copy-number amplifi cation (identifi ed by whole-genome sequencing) and changes in gene expression as identifi ed by RNA sequencing. Using patient-derived tumor xenografts, we demonstrate that targeting of patient-specifi c SCNAs leads to signifi cant decrease in tumor burden, providing a road map for genome-informed treatment of osteosarcoma. SIGNIFICANCE: Osteosarcoma is treated with a chemotherapy regimen established 30 years ago. Although osteosarcoma is genomically complex, we hypothesized that tumor-specifi c dependencies could be identifi ed within SCNAs. Using patient-derived tumor xenografts, we found a high degree of response for "genome-matched" therapies, demonstrating the utility of a targeted genome-informed approach.

CD138 IHC staining of endometrial biopsies in women with RPL provides increased sensitivity when screening for CE compared with H & E staining and morphological assessment alone. Untreated CE may contribute to poor pregnancy outcomes and deserves further investigation in a larger cohort.

Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.npj Digital Medicine (2020) 3:23 ; https://doi.

Pediatric tumors are relatively infrequent, but are often associated with significant lethality and lifelong morbidity. A major goal of pediatric cancer research has been to identify key drivers of tumorigenesis to eventually develop targeted therapies to enhance cure rate and minimize acute and long-term toxic effects. Here, we used genomic approaches to identify biomarkers and candidate drivers for fibrolamellar hepatocellular carcinoma (FL-HCC), a very rare subtype of pediatric liver cancer for which limited therapeutic options exist. In-depth genomic analyses of one tumor followed by immunohistochemistry validation on seven other tumors showed expression of neuroendocrine markers in FL-HCC. DNA and RNA sequencing data further showed that common cancer pathways are not visibly altered in FL-HCC but identified two novel structural variants, both resulting in fusion transcripts. The first, a 400 kb deletion, results in a DNAJB1-PRKCA fusion transcript, which leads to increased cAMP-dependent protein kinase (PKA) activity in the index tumor case and other FL-HCC cases compared with normal liver. This PKA fusion protein is oncogenic in HCC cells. The second gene fusion event, a translocation between the CLPTM1L and GLIS3 genes, generates a transcript whose product also promotes cancer phenotypes in HCC cell lines. These experiments further highlight the tumorigenic role of gene fusions in the etiology of pediatric solid tumors and identify both candidate biomarkers and possible therapeutic targets for this lethal pediatric disease.

A major drawback with current cancer therapy is the prevalence of unrequired dose-limiting toxicity to non-cancerous tissues and organs, which is further compounded by a limited ability to rapidly and easily monitor drug delivery, pharmacodynamics and therapeutic response. In this report, we describe the design and characterization of novel multifunctional “theranostic” nanoparticles (TNPs) for enzyme-specific drug activation at tumor sites and simultaneous in vivo magnetic resonance imaging (MRI) of drug delivery. TNPs were synthesized by conjugation of FDA-approved iron oxide nanoparticles ferumoxytol to an MMP-activatable peptide conjugate of azademethylcolchicine (ICT), creating CLIO-ICTs (TNPs). Significant cell death was observed in TNP-treated MMP-14 positive MMTV-PyMT breast cancer cells in vitro, but not MMP-14 negative fibroblasts or cells treated with ferumoxytol alone. Intravenous administration of TNPs to MMTV-PyMT tumor-bearing mice and subsequent MRI demonstrated significant tumor selective accumulation of the TNP, an observation confirmed by histopathology. Treatment with CLIO-ICTs induced a significant antitumor effect and tumor necrosis, a response not observed with ferumoxytol. Furthermore, no toxicity or cell death was observed in normal tissues following treatment with CLIO-ICTs, ICT, or ferumoxytol. Our findings demonstrate proof of concept for a new nanotemplate that integrates tumor specificity, drug delivery and in vivo imaging into a single TNP entity through attachment of enzyme-activated prodrugs onto magnetic nanoparticles. This novel approach holds the potential to significantly improve targeted cancer therapies, and ultimately enable personalized therapy regimens.