Although an association between protein-truncating variants and breast cancer risk has been established for 11 genes, only alterations in BRCA1, BRCA2, TP53 and PALB2 have been reported in Asian populations. Given that the age of onset of breast cancer is lower in Asians, it is estimated that inherited predisposition to breast cancer may be more significant. To determine the potential utility of panel testing, we investigated the prevalence of germline alterations in 11 established and 4 likely breast cancer genes in a cross-sectional hospital-based cohort of 108 moderate to high-risk breast cancer patients using targeted next generation sequencing. Twenty patients (19%) were identified to carry deleterious mutations, of whom 13 (12%) were in the BRCA1 or BRCA2, 6 (6%) were in five other known breast cancer predisposition genes and 1 patient had a mutation in both BRCA2 and BARD1. Our study shows that BRCA1 and BRCA2 account for the majority of genetic predisposition to breast cancer in our cohort of Asian women. Although mutations in other known breast cancer genes are found, the functional significance and breast cancer risk have not yet been determined, thus limiting the clinical utility of panel testing in Asian populations.
We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland-Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.
Head and neck cancer (HNC)-derived cell lines represent fundamental models for studying the biological mechanisms underlying cancer development and precision therapies. However, mining the genomic information of HNC cells from available databases requires knowledge on bioinformatics and computational skill sets. Here, we developed a user-friendly web resource for exploring, visualizing, and analyzing genomics information of commonly used HNC cell lines. We populated the current version of GENIPAC with 44 HNC cell lines from 3 studies: ORL Series, OPC-22, and H Series. Specifically, the mRNA expressions for all the 3 studies were derived with RNA-seq. The copy number alterations analysis of ORL Series was performed on the Genome Wide Human Cytoscan HD array, while copy number alterations for OPC-22 were derived from whole exome sequencing. Mutations from ORL Series and H Series were derived from RNA-seq information, while OPC-22 was based on whole exome sequencing. All genomic information was preprocessed with customized scripts and underwent data validation and correction through data set validator tools provided by cBioPortal. The clinical and genomic information of 44 HNC cell lines are easily assessable in GENIPAC. The functional utility of GENIPAC was demonstrated with some of the genomic alterations that are commonly reported in HNC, such as TP53, EGFR, CCND1, and PIK3CA. We showed that these genomic alterations as reported in The Cancer Genome Atlas database were recapitulated in the HNC cell lines in GENIPAC. Importantly, genomic alterations within pathways could be simultaneously visualized. We developed GENIPAC to create access to genomic information on HNC cell lines. This cancer omics initiative will help the research community to accelerate better understanding of HNC and the development of new precision therapeutic options for HNC treatment. GENIPAC is freely available at http://genipac.cancerresearch.my/ .
Emerging biological and translational insights from large sequencing efforts underscore the need for genetically-relevant cell lines to study the relationships between genomic alterations of tumors, and therapeutic dependencies. Here, we report a detailed characterization of a novel panel of clinically annotated oral squamous cell carcinoma (OSCC) cell lines, derived from patients with diverse ethnicity and risk habits. Molecular analysis by RNAseq and copy number alterations (CNA) identified that the cell lines harbour CNA that have been previously reported in OSCC, for example focal amplications in 3q, 7p, 8q, 11q, 20q and deletions in 3p, 5q, 8p, 18q. Similarly, our analysis identified the same cohort of frequently mutated genes previously reported in OSCC including TP53, CDKN2A, EPHA2, FAT1, NOTCH1, CASP8 and PIK3CA. Notably, we identified mutations (MLL4, USP9X, ARID2) in cell lines derived from betel quid users that may be associated with this specific risk factor. Gene expression profiles of the ORL lines also aligned with those reported for OSCC. By focusing on those gene expression signatures that are predictive of chemotherapeutic response, we observed that the ORL lines broadly clustered into three groups (cell cycle, xenobiotic metabolism, others). The ORL lines noted to be enriched in cell cycle genes responded preferentially to the CDK1 inhibitor RO3306, by MTT cell viability assay. Overall, our in-depth characterization of clinically annotated ORL lines provides new insight into the molecular alterations synonymous with OSCC, which can facilitate in the identification of biomarkers that can be used to guide diagnosis, prognosis, and treatment of OSCC.