Investigation of the molecular mechanisms underlying metastasis in prostate cancer by gene expression profiling

271 Background: CTC RNA analysis currently involves single cell recovery that is laborious and expensive, or alternatively lysis of preserved whole blood which yields RNA predominantly from leukocytes which vastly outnumber CTCs. To effectively characterize gene expression in large patient cohorts, new enrichment methodologies are needed that yield high purity CTC populations while preserving RNA integrity. Here we describe a simple yet robust method for enrichment of prostate CTCs for gene expression analysis. Methods: Blood was drawn with informed consent under an IRB-approved protocol. For initial optimization, CFSE-stained PCa cells were spiked into healthy blood and recovered using various combinations of 2 methods: microfluidic enrichment (Parsortix™ system) and CD45 depletion. For assay qualification, a prostate-specific multiplexed qRT-PCR gene expression panel was developed. Enrichment and gene expression were tested initially using PCa cell lines spiked into healthy blood, then metastatic castrate resistant prostate cancer (mCRPC) blood samples in parallel with CellSearch enumeration. Results: Optimal enrichment of live cells was achieved with CD45 depletion followed by microfluidic enrichment, resulting in an average spiked cell recovery of 30% and approximately 100 contaminating background leukocytes. Using this enrichment method, prostate specific genes were detectable by multiplexed qRT-PCR down to 25 cells spiked into 7.5 ml whole blood, and transcripts were not measurable in matched healthy blood controls. When applied to mCRPC patient blood containing CTCs by CellSearch, multiplexed qRT-PCR successfully detected prostate specific genes in all samples. Conclusions: We developed a novel enrichment method capable of rapidly and efficiently recovering live CTCs with high purity, free of magnetic beads and with very few background leukocytes. Captured cells yielded high-quality RNA with high sensitivity and specificity for prostate-specific transcripts. This approach is applicable to high throughput gene expression profiling assays and offers an alternative to laborious single cell recovery or non-cancer-specific whole blood fixation.

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Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

Biomedicine Hub ◽

10.1159/000472146 ◽

2017 ◽

Vol 2 (2) ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

Zhaoyi Chen ◽

Travis Gerke ◽

Victoria Bird ◽

Mattia Prosperi

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Gene Expression Profiling ◽

Expression Profiling ◽

High Throughput Sequencing ◽

Prostate Cancer Risk ◽

Gene Profiling ◽

Cancer Risk Assessment ◽

Meta Analyses ◽

Study Designs

Objectives: The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management. Methods: We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included. Results: Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies. Conclusions: The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.

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What can we learn from gene expression profiling of mouse oocytes?

Reproduction ◽

10.1530/rep-07-0430 ◽

2008 ◽

Vol 135 (5) ◽

pp. 581-592 ◽

Cited By ~ 22

Author(s):

Toshio Hamatani ◽

Mitsutoshi Yamada ◽

Hidenori Akutsu ◽

Naoaki Kuji ◽

Yoshiyuki Mochimaru ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Expression Patterns ◽

Oocyte Quality ◽

Preimplantation Development ◽

Preimplantation Embryos ◽

Gene Expressions ◽

Oocyte Competence

Mammalian ooplasm supports the preimplantation development and reprograms the introduced nucleus transferred from a somatic cell to confer pluripotency in a cloning experiment. However, the underlying molecular mechanisms of oocyte competence remain unknown. Recent advances in microarray technologies have allowed gene expression profiling of such tiny specimens as oocytes and preimplantation embryos, generating a flood of information about gene expressions. So, what can we learn from it? Here, we review the initiative global gene expression studies of mouse and/or human oocytes, focusing on the lists of maternal transcripts and their expression patterns during oogenesis and preimplantation development. Especially, the genes expressed exclusively in oocytes should contribute to the uniqueness of oocyte competence, driving mammalian development systems of oocytes and preimplantation embryos. Furthermore, we discuss future directions for oocyte gene expression profiling, including discovering biomarkers of oocyte quality and exploiting the microarray data for ‘making oocytes’.

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