Project description:Whole genome sequencing of 10 HCLc tumor and matched-germline T cells. Genomic DNA from highly purified HCLc tumor and T cell populations were utilized for library preparation using NEBNext Ultra DNA library prep kit. Sequencing was performed as 150 bp paired end sequencing using four lanes of an Illumina HiSeq4000 to an average depth of 12X. Reads from each library were aligned to the human reference genome GRCh37 using BWA-MEM (v0.7.12). The analysis of somatic genetic alterations in WGS data from tumor-germline pair HCLc samples was divided based on the nature of the mutation, as follow: single-nucleotide variants (SNVs), indels, CNAs and SVs. Moreover, COSMIC mutational signatures and subclonal architecture was inferred for each tumor.
Project description:Large numbers of cells are generally required for quantitative global proteome profiling due to the significant surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations, such as circulating tumor cells (CTCs) and early metastatic cells. Herein we report a facile mass spectrometry (MS)-based single-cell proteomics method that capitalizes on a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, for greatly reducing the surface adsorption losses by ~20-fold for effective single-tube processing of single cells, thus significantly improving detection sensitivity for single-cell proteomic analysis. With standard MS platforms, the method allows for the first time precise, label-free, reliable quantification of hundreds of proteins from single human cells in a simple, convenient manner. When applied to a patient CTC-derived xenograft (PCDX) model, the method can reveal distinct protein signatures between primary tumor cells and early metastases to the lungs at the single-cell resolution. The approach paves the way for routine, precise quantitative single-cell proteomic analysis.
Project description:Intratumoral heterogeneity has been described for various tumor types and models of human cancer, and can have profound effects on tumor progression and drug resistance. This study describes transcriptional heterogeneity among subclonal populations (SCPs) derived from a single triple-negative breast cancer cell line.
Project description:Intratumoral heterogeneity has been described for various tumor types and models of human cancer, and can have profound effects on tumor progression and drug resistance. This study describes transcriptional heterogeneity among subclonal populations (SCPs) derived from a single triple-negative breast cancer cell line.
Project description:Intratumoral heterogeneity has been described for various tumor types and models of human cancer, and can have profound effects on tumor progression and drug resistance. This study describes transcriptional heterogeneity among subclonal populations (SCPs) derived from a single triple-negative breast cancer cell line.
Project description:This is a pathogenic mutation profile of colorectal patients specifically in 5 genes, i.e. APC, TP53, PIK3CA, KRAS, and MLH1. Single nucleotide variants identified were synchronized with patients’ characteristics.
Project description:Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing
Project description:Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing
Project description:Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing
