Project description:Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. This dataset comprises of the 10X genomics based single cell RNAseq on MDA-MB-231 xenografts and matched metastasis used to perform archetypal analysis to understand tumour hetegeneity as a phenotypic continuum.
Project description:Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. The dataset here comprises of the 10X genomics based spatial transcriptomics (Visium) on MDA-MB-231 xenografts to perform archetypal analysis and understand spatial aspects of tumour hetegeneity. scRNAseq datasets from matched models and metastasis is projected onto this spatial transcriptomics data to understand the spatial dependencies and characteriostics of these archetypes in the manuscript.
Project description:Dicer, RNase III endonuclease, is an essential enzyme in miRNA biogenesis that regulates target gene expression, and it has been reported that aberrant expressions of Dicer associate with the clinical outcomes of patients in various cancers. To explore the miRNA differencial expression regulated by Dicer in MDA-MB-231/E1A cells, the microarray profiling analysis was employed to conduct differentially expressed miRNAs in stable MDA-MB-231/vector, MDA-MB-231/E1A, and MDA-MB-231/E1A/shDicer cells.
Project description:RNA was isolated from ectopically sFRP1-expressing MDA-MB-231 cells and control MDA-MB-231 cells and as well from tumor lysates arising from these cells as nude mouse xenograft. Gene expression profiles for these samples were investigated using Affymetrix arrays.
Project description:Dicer, RNase III endonuclease, is an essential enzyme in miRNA biogenesis that regulates target gene expression, and it has been reported that aberrant expressions of Dicer associate with the clinical outcomes of patients in various cancers. To explore the miRNA differencial expression regulated by Dicer in MDA-MB-231/E1A cells, the microarray profiling analysis was employed to conduct differentially expressed miRNAs in stable MDA-MB-231/vector, MDA-MB-231/E1A, and MDA-MB-231/E1A/shDicer cells. The four groups including vector control, E1A-expressing and Dicer knockdown in E1A-expressing MDA-MB-231 cells were harvested and RNA were isolated. Two independent experiments were performed for each group.
Project description:Small-molecule Smac mimetics target inhibitor of apoptosis (IAP) proteins to induce TNFα-dependent apoptosis in cancer cells and several Smac mimetics have been advanced into clinical development as a new class of anticancer drugs. However, preclinical studies have shown that only a small subset of cancer cell lines are sensitive to Smac mimetics used as single agents and these cell lines are at risk of developing drug resistance to Smac mimetics. Thus, it is important to understand the molecular mechanisms underlying intrinsic and acquired resistance of cancer cells to Smac mimetics in order to develop effective therapeutic strategies to overcome or prevent Smac mimetic resistance. We established Smac mimetic resistant sublines derived from MDA-MB-231 breast cancer cells, which exhibit exquisite sensitivity to the Smac mimetic SM-164, and used microarrays to detail the global programme of gene expression underlying SM-164 resistance in MDA-MB-231 cells and identified differentially expressed genes in SM-164-resistant and -sensitive MDA-MB-231 cells. SCID mice with MDA-MB-231 xenograft tumors were treated with 5 mg/kg of SM-164 intravenously for 5 days/week for 2 weeks. SM-164-regressed MDA-MB-231 tumors regrew after treatment ended. Tumor cells from these regrown MDA-MB-231 tumors were isolated and total RNAs were prepared for microarray analysis.
Project description:Identification of genes that are involved in self-seeding by comparing gene expression profiles between parental MDA-MB-231 cells and seeder cells (MDA-231-S1a and S1b) 2 replicates from each sample (parental MDA-MB-231, MDA-MB-231 S1a and MDA-MB-231 S1b) were analyzed
Project description:Identification of genes that are involved in self-seeding by comparing gene expression profiles between parental MDA-MB-231 cells and seeder cells (MDA-231-S1a and S1b)