Background: Systematic identification of data essential for outcome prediction in metastatic prostate cancer (mPC) would accelerate development of precision oncology.
Objective: To identify novel phenotypes and features associated with mPC outcome, and to identify biomarker and data requirements to be tested in future precision oncology trials.
Design Setting And Participants: We analyzed deep longitudinal clinical, neuroendocrine expression, and autopsy data of 33 men who died from mPC between 1995 and 2004 (PELICAN33), and related findings to mPC biomarkers reported in the literature.
IEEE J Biomed Health Inform
May 2021
Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the histological images and poses challenges for automated nucleus detection. Here, we studied the effect of histopathological sample fixation on the accuracy of a deep learning based nuclei detection model trained with hematoxylin and eosin stained images.
View Article and Find Full Text PDFAdvances in prostate cancer biology and diagnostics are dependent upon high-fidelity integration of clinical, histomorphologic, and molecular phenotypic findings. In this study, we compared fresh frozen, formalin-fixed paraffin-embedded (FFPE), and PAXgene-fixed paraffin-embedded (PFPE) tissue preparation methods in radical prostatectomy prostate tissue from 36 patients and performed a preliminary test of feasibility of using PFPE tissue in routine prostate surgical pathology diagnostic assessment. In addition to comparing histology, immunohistochemistry, and general measures of DNA and RNA integrity in each fixation method, we performed functional tests of DNA and RNA quality, including targeted Miseq RNA and DNA sequencing, and implemented methods to relate DNA and RNA yield and quality to quantified DNA and RNA picogram nuclear content in each tissue volume studied.
View Article and Find Full Text PDFWe report the first combined analysis of whole-genome sequence, detailed clinical history, and transcriptome sequence of multiple prostate cancer metastases in a single patient (A21). Whole-genome and transcriptome sequence was obtained from nine anatomically separate metastases, and targeted DNA sequencing was performed in cancerous and noncancerous foci within the primary tumor specimen removed 5 yr before death. Transcriptome analysis revealed increased expression of androgen receptor (AR)-regulated genes in liver metastases that harbored an AR p.
View Article and Find Full Text PDFMutations of the tumor suppressor TP53 are present in many forms of human cancer and are associated with increased tumor cell invasion and metastasis. Several mechanisms have been identified for promoting dissemination of cancer cells with TP53 mutations, including increased targeting of integrins to the plasma membrane. Here, we demonstrate a role for the filopodia-inducing motor protein Myosin-X (Myo10) in mutant p53-driven cancer invasion.
View Article and Find Full Text PDFAneuploidy, deviation from the normal chromosome number, and other chromosomal aberrations are commonly observed in cancer. Integrin-mediated adhesion and dynamic turnover of adhesion sites are required for successful cytokinesis of normal adherent cells and impaired cell division can lead to the generation of cells with abnormal chromosome contents. We find that repeated cytokinesis failure, due to impaired integrin traffic alone, is sufficient to induce chromosome aberrations resulting in the generation of aneuploid cells with malignant properties.
View Article and Find Full Text PDFSyndecans function as co-receptors for integrins on different matrixes. Recently, syndecan-1 has been shown to be important for α2β1 integrin-mediated adhesion to collagen in tumor cells by regulating cell adhesion and migration on two-dimensional collagen. However, the function of syndecans in supporting α2β1 integrin interactions with three-dimensional (3D) collagen is less well studied.
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