miR-9-5p Exerts a Dual Role in Cervical Cancer and Targets Transcription Factor TWIST1

Squamous cell carcinoma (SCC) and adenocarcinoma (AC) represent the major cervical cancer histotypes. Both histotypes are caused by infection with high-risk HPV (hrHPV) and are associated with deregulated microRNA expression. Histotype-dependent expression has been observed for miR-9-5p, showing increased expression in SCC and low expression in AC. Here, we studied the regulation and functionality of miR-9-5p in cervical SCCs and ACs using cervical tissue samples and hrHPV-containing cell lines. Expression and methylation analysis of cervical tissues revealed that low levels of miR-9-5p in ACs are linked to methylation of its precursor genes, particularly miR-9-1. Stratification of tissue samples and hrHPV-containing cell lines suggested that miR-9-5p depends on both histotype and hrHPV type, with higher expression in SCCs and HPV16-positive cells. MiR-9-5p promoted cell viability and anchorage independence in cervical cancer cell lines SiHa (SCC, HPV16) and CaSki (metastasized SCC, HPV16), while it played a tumor suppressive role in HeLa (AC, HPV18). TWIST1, a transcription factor involved in epithelial-to-mesenchymal transition (EMT), was established as a novel miR-9-5p target. Our results show that miR-9-5p plays a dual role in cervical cancer in a histotype- and hrHPV type-dependent manner. MiR-9-5p mediated silencing of TWIST1 suggests two distinct mechanisms towards EMT in cervical cancer

Improved high-dimensional prediction with Random Forests by the use of co-data

Background: Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary 'co-data' can be used to improve the performance of a Random Forest in such a setting. Results: Co-data are incorporated in the Random Forest by replacing the uniform sampling probabilities that are used to draw candidate variables by co-data moderated sampling probabilities. Co-data here are defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate the co-data moderated Random Forest (CoRF) with two examples. In the first example we aim to predict the presence of a lymph node metastasis with gene expression data. We demonstrate how a set of external p-values, a gene signature, and the correlation between gene expression and DNA copy number can improve the predictive performance. In the second example we demonstrate how the prediction of cervical (pre-)cancer with methylation data can be improved by including the location of the probe relative to the known CpG islands, the number of CpG sites targeted by a probe, and a set of p-values from a related study. Conclusion: The proposed method is able to utilize auxiliary co-data to improve the performance of a Random Forest

The stimulatory effect of albumin on luteinizing hormone-stimulated Leydig cell steroid production depends on its fatty acid content and correlates with conformational changes

__Abstract__ The effects of purified albumin species and albumin fragments (0.2–1% w/v) on short-term (4 h) steroid secretion by immature rat Leydig cells, in the presence of a maximally stimulating dose of luteinizing hormone (LH), were investigated. Human albumin and the peptic fragment (comprising residues 1–387) enhanced pregnenolone production in isolated rat Leydig cells, whereas chicken albumin and the tryptic fragment (comprising residues 198–585) were not active. This stimulatory effect of human albumin and the peptic fragment correlated with the potential of these proteins to undergo a pH-dependent neutral-to-base transition as measured by circular dichroism. The tryptic fragment and chicken albumin did not have the potential to undergo such a transition. The pH-dependent conformational changes of albumin and fragments thereof occurred in parallel with a change in the binding affinity for testosterone and pregnenolone. The fatty acid oleic acid and the drug suramin, only when present in a molar ligand-to-albumin ratio equal to or higher than 2, inhibited the albumin-mediated stimulation of steroid production. These data show that the stimulatory effects of albumin species on LH-induced Leydig cell pregnenolone production depend on their fatty acid content and correlate with the potential of these molecules to undergo conformational changes. It is unknown via which mechanisms albumin exerts its stimulatory effect, but the LH action through the cyclic AMP pathway seems not to be affected

Inhibiting CDK4/6 in Breast Cancer with Palbociclib, Ribociclib, and Abemaciclib: Similarities and Differences

The cyclin-dependent kinase (CDK) 4/6 inhibitors belong to a new class of drugs that interrupt proliferation of malignant cells by inhibiting progression through the cell cycle. Three such inhibitors, palbociclib, ribociclib, and abemaciclib were recently approved for breast cancer treatment in various settings and combination regimens. On the basis of their impressive efficacy, all three CDK4/6 inhibitors now play an important role in the treatment of patients with HR+, HER2− breast cancer; however, their optimal use still needs to be established. The three drugs have many similarities in both pharmacokinetics and pharmacodynamics. However, there are some differences on the basis of which the choice for a particular CDK4/6 inhibitor for an individual patient can be important. In this article, the clinical pharmacokinetic and pharmacodynamic profiles of the three CDK4/6 inhibitors are reviewed and important future directions of the clinical applicability of CDK4/6 inhibitors will be discussed

Interconnectivity between molecular subtypes and tumor stage in colorectal cancer

BACKGROUND: There are profound individual differences in clinical outcomes between colorectal cancers (CRCs) presenting with identical stage of disease. Molecular stratification, in conjunction with the traditional TNM staging, is a promising way to predict patient outcomes. We investigated the interconnectivity between tumor stage and tumor biology reflected by the Consensus Molecular Subtypes (CMSs) in CRC, and explored the possible value of these insights in patients with stage II colon cancer. METHODS: We performed a retrospective analysis using clinical records and gene expression profiling in a meta-cohort of 1040 CRC patients. The interconnectivity of tumor biology and disease stage was assessed by investigating the association between CMSs and TNM classification. In order to validate the clinical applicability of our findings we employed a meta-cohort of 197 stage II colon cancers. RESULTS: CMS4 was significantly more prevalent in advanced stages of disease (stage I 9.8% versus stage IV 38.5%, p < 0.001). The observed differential gene expression between cancer stages is at least partly explained by the biological differences as reflected by CMS subtypes. Gene signatures for stage III-IV and CMS4 were highly correlated (r = 0.77, p < 0.001). CMS4 cancers showed an increased progression rate to more advanced stages (CMS4 compared to CMS2: 1.25, 95% CI: 1.08-1.46). Patients with a CMS4 cancer had worse survival in the high-risk stage II tumors compared to the total stage II cohort (5-year DFS 41.7% versus 100.0%, p = 0.008). CONCLUSIONS: Considerable interconnectivity between tumor biology and tumor stage in CRC exists. This implies that the TNM stage, in addition to the stage of progression, might also reflect distinct biological disease entities. These insights can potentially be utilized to optimize identification of high-risk stage II colo

High-throughput isolation of circulating tumor DNA

The emerging interest in circulating tumor DNA (ctDNA) analyses for clinical trials has necessitated the development of a high-throughput method for fast, reproducible, and efficient isolation of ctDNA. Currently, the majority of ctDNA studies use the manual QIAamp (QA) platform to isolate DNA from blood. The purpose of this study was to compare two competing automated DNA isolation platforms [Maxwell (MX) and QIAsymphony (QS)] to the current ‘gold standard’ QA to facilitate high-throughput processing of samples in prospective trials. We obtained blood samples from healthy blood donors and metastatic cancer patients for plasma isolation. Total cell-free DNA (cfDNA) quantity was assessed by TERT quantitative PCR. Recovery efficiency was investigated by quantitative PCR analysis of spiked-in synthetic plant DNA. In addition, a b-actin fragmentation assay was performed to determine the amount of contamination by genomic DNA from lysed leukocytes. ctDNA quality was assessed by digital PCR for somatic variant detection. cfDNA quantity and recovery efficiency were lowest using the MX platform, whereas QA and QS showed a comparable performance. All platforms preferentially isolated small (136 bp) DNA fragments over large (420 and 2000 bp) DNA fragments. Detection of the number variant and wild-type molecules was most comparable between QA and QS. However, there was no significant difference in variant allele frequency comparing QS and MX to QA. In summary, we show that the QS platform has comparable performance to QA, the ‘gold standard’, and outperformed the MX platform depending on the readout used. We conclude that the QS can replace the more laborious QA platform, especially when high-throughput cfDNA isolation is needed

Confirmation of a metastasis-specific microRNA signature in primary colon cancer

The identification of patients with high-risk stage II colon cancer who may benefit from adjuvant therapy may allow the clinical approach to be tailored for these patients based on an understanding of tumour biology. MicroRNAs have been proposed as markers of the prognosis or treatment response in colorectal cancer. Recently, a 2-microRNA signature (l et-7i and miR-10b) was proposed to identify colorectal cancer patients at risk of developing distant metastasis. We assessed the prognostic value of this signature and additional candidate microRNAs in an independent, clinically well-defined, prospectively collected cohort of primary colon cancer patients including stage I-II colon cancer without and stage III colon cancer with adjuvant treatment. The 2-microRNA signature specifically predicted hepatic recurrence in the stage I-II group, but not the overall ability to develop distant metastasis. The addition of miR-30b to the 2-microRNA signature allowed the prediction of both distant metastasis and hepatic recurrence in patients with stage I-II colon cancer who did not receive adjuvant chemotherapy. Available gene expression data allowed us to associate m iR-30b expression with axon guidance and l et-7i expression with cell adhesion, migration, and motility

Whole genome sequencing of metastatic colorectal cancer reveals prior treatment effects and specific metastasis features

In contrast to primary colorectal cancer (CRC) little is known about the genomic landscape of metastasized CRC. Here we present whole genome sequencing data of metastases of 429 CRC patients participating in the pan-cancer CPCT-02 study (NCT01855477). Unsupervised clustering using mutational signature patterns highlights three major patient groups characterized by signatures known from primary CRC, signatures associated with received prior treatments, and metastasis-specific signatures. Compared to primary CRC, we identify additional putative (non-coding) driver genes and increased frequencies in driver gene mutations. In addition, we identify specific genes preferentially affected by microsatellite instability. CRC-specific 1kb-10Mb deletions, enriched for common fragile sites, and LINC00672 mutations are associated with response to treatment in general, whereas FBXW7 mutations predict poor response specifically to EGFR-targeted treatment. In conclusion, the genomic landscape of mCRC shows defined changes compared to primary CRC, is affected by prior treatments and contains features with potential clinical relevance

Author Correction

The original version of this Article contained an error in Table 4, in which the coefficients of the LASSO regression model of treatment response corresponded to a version that was performed without non-coding genes. The new version of the table, which was generated during revision of the manuscript, contains the coefficients that were obtained after including potential non-coding driver genes in the model. Genomic features that became statistically significant after re-running the model were also added, which included: ‘nr of 10 kb–1Mb deletions’, ‘SBS41’, ‘Non-coding - LINC00672’, ‘Gain 7p12.3 - (PKD1L1)’, ‘Loss 4q22.1 - (CCSER1)’, and ‘Loss 18q23 - (NFATC1*)’. This has now been corrected in both the PDF and HTML versions of the Article. The original version of this Article also contained an error in the author affiliations. The affiliations of Job van Riet with Cancer Computational Biology Center, Erasmus MC Cancer Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands and Department of Urology, Erasmus MC Cancer Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands were inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article. The original version of this Article contained an error in the Methods, section “Whole-genome sequencing; identification of somatic changes”, which incorrectly read ‘GATK BQSR and Haplotype Caller v3.4.46 were used to call somatic mutations.’ The correct version is ‘GATK BQSR and Haplotype Caller v3.4.46 and Strelka v1.0.14 were used to call somatic mutations’. This has been corrected in both the PDF and HTML versions of the Article.</p