With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

A recent study has shown that increased activity of matrix metalloproteinases‐2 and metalloproteinases‐9 (MMP‐2 and MMP‐9) has detrimental effect on the brain after neonatal hypoxia. The present study determined the effect of maternal hypoxia on neuronal survivability and the activity of MMP‐2 and MMP‐9, as well as the expression of tissue inhibitors of metalloproteinase 1 and 2 (TIMP‐1 and TIMP‐2) in the brain of neonatal rats. Pregnant rats were exposed to 10.5% oxygen for 6 days from the gestation day 15 to day 21. Pups were sacrificed at day 0, 4, 7, 14, and 21 after birth. Body weight and brain weight of the pups were measured at each time point. The activity of MMP‐2 and MMP‐9 and the protein abundance of TIMP‐1 and TIMP‐2 were determined by zymography and Western blotting, respectively. The tissue distribution of MMPs was examined by immunofluorescence staining. The neuronal death was detected by Nissl staining. Maternal hypoxia caused significant decreases in body and brain size, increased activity of MMP‐2 at day 0, and increased MMP‐9 at day 0 and 4. The increased activity of the MMPs was accompanied by an overall tendency towards a reduced expression of TIMPs at all ages with the significance observed for TIMPs at day 0, 4, and 7. Immunofluorescence analysis showed an increased expression of MMP‐2, MMP‐9 in the hippocampus at day 0 and 4. Nissl staining revealed significant cell death in the hippocampus at day 0, 4, and 7. Functional tests showed worse neurobehavioral outcomes in the hypoxic animals. © 2009 Wiley Periodicals, Inc. Develop Neurobiol 2010

To understand chemoresistance in the context of cancer stem cells (CSC), a cisplatin resistance model was developed using a high-grade serous ovarian cancer patient-derived, cisplatin-sensitive sample, PDX4. As a molecular subtype-specific stem-like cell line, PDX4 was selected for its representative features, including its histopathological and BRCA2 mutation status, and exposed to cisplatin in vitro. In the cisplatin-resistant cells, transcriptomics were carried out, and cell morphology, protein expression, and functional status were characterized. Additionally, potential signaling pathways involved in cisplatin resistance were explored. Our findings reveal the presence of distinct molecular signatures and phenotypic changes in cisplatin-resistant PDX4 compared to their sensitive counterparts. Surprisingly, we observed that chemoresistance was not inherently linked with increased stemness. In fact, although resistant cells expressed a combination of EMT and stemness markers, functional assays revealed that they were less proliferative, migratory, and clonogenic–features indicative of an underlying complex mechanism for cell survival. Furthermore, DNA damage tolerance and cellular stress management pathways were enriched. This novel, syngeneic model provides a valuable platform for investigating the underlying mechanisms of cisplatin resistance in a clinically relevant context, contributing to the development of targeted therapies tailored to combat resistance in stem-like ovarian cancer.