Search

Primary prostatic adenocarcinoma (pPC) undergoes genomic evolution secondary to therapy-related selection pressures as it transitions to metastatic noncastrate (mNC-PC) and castrate resistant (mCR-PC) disease. Next generation sequencing results were evaluated for pPC (n= 97), locally advanced disease (involving urinary bladder/rectum, n= 12), mNC-PC (n= 21), and mCR-PC (n= 54). We identified enrichment of TP53alterations in high-grade pPC, TP53/RB1alterations in HGNE disease, and ARalterations in metastatic and castrate resistant disease. Actionable alterations (MSI-H phenotype and HRR genes) were identified in approximately a fifth of all cases. These results help elucidate the landscape of genomic alterations across the clinical spectrum of prostate cancer.

A pathologist’s optical microscopic examination of thinly cut, stained tissue on glass slides prepared from a formalin-fixed paraffin-embedded tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of pathologists is dependent on their direct experience with common and rarer variant morphologies. Recently, deep learning approaches have been used to successfully show a high level of accuracy for such tasks. However, obtaining expert-level annotated images is an expensive and time-consuming task, and artificially synthesized histologic images can prove greatly beneficial. In this study, we present an approach to not only generate histologic images that reproduce the diagnostic morphologic features of common disease but also provide a user ability to generate new and rare morphologies. Our approach involves developing a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigated the ability of this framework in synthesizing realistic prostate and colon tissue images and assessed the utility of these images in augmenting the diagnostic ability of machine learning methods and their usability by a panel of experienced anatomic pathologists. Synthetic data generated by our framework performed similar to real data when training a deep learning model for diagnosis. Pathologists were not able to distinguish between real and synthetic images, and their analyses showed a similar level of interobserver agreement for prostate cancer grading. We extended the approach to significantly more complex images from colon biopsies and showed that the morphology of the complex microenvironment in such tissues can be reproduced. Finally, we present the ability for a user to generate deepfake histologic images using a simple markup of sematic labels.

Extensive archaeological excavations on well-preserved Roman deposits at Carlisle, Cumbria, have revealed much 'environmental' evidence. These data not only shed light on ancient building technology and the uses to which individual buildings were put, but they are of great importance in attempting to reconstruct ancient landscapes. Timber, wood, cereals, weeds of cultivation, invertebrate remains and animal bones from dendrochronologically-dated deposits are used to focus attention on specific elements of land use in a way which the technique of palynology, combined with radiocarbon dating, cannot achieve. It is concluded that the Romans had a considerable impact on the landscape, but the balances between arable, pasture and woodland cannot yet be determined.