RESUMO
OPINION STATEMENT: Prostate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen.
RESUMO
The human eye can distinguish as many as 10,000 different colours but is far less sensitive to variations in intensity1, meaning that colour is highly desirable when interpreting images. However, most biological samples are essentially transparent, and nearly invisible when viewed using a standard optical microscope2. It is therefore highly desirable to be able to produce coloured images without needing to add any stains or dyes, which can alter the sample properties. Here we demonstrate that colorimetric histology images can be generated using full-sized plasmonically active microscope slides. These slides translate subtle changes in the dielectric constant into striking colour contrast when samples are placed upon them. We demonstrate the biomedical potential of this technique, which we term histoplasmonics, by distinguishing neoplastic cells from normal breast epithelium during the earliest stages of tumorigenesis in the mouse MMTV-PyMT mammary tumour model. We then apply this method to human diagnostic tissue and validate its utility in distinguishing normal epithelium, usual ductal hyperplasia, and early-stage breast cancer (ductal carcinoma in situ). The colorimetric output of the image pixels is compared to conventional histopathology. The results we report here support the hypothesis that histoplasmonics can be used as a novel alternative or adjunct to general staining. The widespread availability of this technique and its incorporation into standard laboratory workflows may prove transformative for applications extending well beyond tissue diagnostics. This work also highlights opportunities for improvements to digital pathology that have yet to be explored.