Clinical development of WEE1 inhibitors in gynecological cancers: A systematic review.

Introduction: The anti-tumor activity of WEE1 inhibitors (WEE1i) in gynecological malignancies has recently been demonstrated in clinical trials and its rationale is based on biological/molecular features of gynecological cancers. With this systematic review, we aim to outline the clinical development and current evidence regarding the efficacy and safety of these targeted agents in in this patient group.

Methods: Systematic literature review of trials including patients with gynecological cancers treated with a WEE1i.

Comparing genome-scale DNA methylation and CNV marks between adult human cultured ITGA6+ testicular cells and seminomas to assess in vitro genomic stability.

Autologous transplantation of spermatogonial stem cells is a promising new avenue to restore fertility in infertile recipients. Expansion of the initial spermatogonial stem cell pool through cell culturing is a necessary step to obtain enough cells for effective repopulation of the testis after transplantation. Since in vitro propagation can lead to (epi-)genetic mutations and possibly malignant transformation of the starting cell population, we set out to investigate genome-wide DNA methylation status in uncultured and cultured primary testicular ITGA6+ sorted cells and compare them with germ cell tumor samples of the seminoma subtype.

A pipeline to quantify serum and cerebrospinal fluid microRNAs for diagnosis and detection of relapse in paediatric malignant germ-cell tumours.

Background: The current biomarkers alpha-fetoprotein and human chorionic gonadotropin have limited sensitivity and specificity for diagnosing malignant germ-cell tumours (GCTs). MicroRNAs (miRNAs) from the miR-371-373 and miR-302/367 clusters are overexpressed in all malignant GCTs, and some of these miRNAs show elevated serum levels at diagnosis. Here, we developed a robust technical pipeline to quantify these miRNAs in the serum and cerebrospinal fluid (CSF).

Genome wide DNA methylation profiles provide clues to the origin and pathogenesis of germ cell tumors.

The cell of origin of the five subtypes (I-V) of germ cell tumors (GCTs) are assumed to be germ cells from different maturation stages. This is (potentially) reflected in their methylation status as fetal maturing primordial germ cells are globally demethylated during migration from the yolk sac to the gonad. Imprinted regions are erased in the gonad and later become uniparentally imprinted according to fetal sex.

An oncofetal and developmental perspective on testicular germ cell cancer.

Germ cell tumors (GCTs) represent a diverse group of tumors presumably originating from (early fetal) developing germ cells. Most frequent are the testicular germ cell cancers (TGCC). Overall, TGCC is the most frequent malignancy in Caucasian males (20-40 years) and remains an important cause of (treatment related) mortality in these young men.

DMRforPairs: identifying differentially methylated regions between unique samples using array based methylation profiles.

Background: Array based methylation profiling is a cost-effective solution to study the association between genome methylation and human disease & development. Available tools to analyze the Illumina Infinium HumanMethylation450 BeadChip focus on comparing methylation levels per locus. Other tools combine multiple probes into a range, identifying differential methylated regions (DMRs).

Targeted serum miRNA (TSmiR) test for diagnosis and follow-up of (testicular) germ cell cancer patients: a proof of principle.

Germ cell cancers (GCC) are the most frequent malignancy in young Caucasian males. GCC can consist of seminomas (SE) and non-seminomas (malignant NS: embryonal carcinoma (EC), yolk sac tumor (YS), choriocarcinoma (CH) and teratoma (TE)). Current serum-markers used for diagnosis and follow-up (AFP, hCG) are predominantly related to YS and CH and marker positivity can vary during disease.

miMsg: a target enrichment algorithm for predicted miR-mRNA interactions based on relative ranking of matched expression data.

Motivation: Algorithms predicting microRNA (miR)-mRNA interactions generate high numbers of possible interactions, many of which might be non-existent or irrelevant in a certain biological context. It is desirable to develop a transparent, user-friendly, unbiased tool to enrich miR-mRNA predictions.

Results: The miMsg algorithm uses matched miR/mRNA expression data to enrich miR-mRNA predictions.