Project description:Mouse models have been developed to investigate colorectal cancer etiology and evaluate new anti-cancer therapies. While genetically engineered and carcinogen-induced mouse models have provided important information with regard to the mechanisms underlying the oncogenic process, xenograft models remain the standard for the evaluation of new chemotherapy and targeted drug treatments for clinical use. However, it remains unclear if drug efficacy data obtained from xenograft models translate into clinically-relevant treatment modalities. In this study, we have generated a panel of 28 patient-derived colorectal cancer explants (PDCCEs), an extension of our previous work, by direct transplantation of human colorectal cancer (CRC) tissues into NOD-SCID mice. A comprehensive histological and molecular evaluation of PDCCEs and their corresponding patient tumor demonstrates that PDCCEs maintain histological features and global biology through multiple passages. Furthermore, we demonstrate that in vivo sensitivity of PDCCEs to oxaliplatin can predict patient outcomes. Our findings suggest that PDCCEs maintain similarity to the patient tumor from which they are derived and can serve as a reliable preclinical model that can be incorporated into future strategies to optimize individual therapy for patients with CRC. 28 human primary colorectal and 37 mouse derived colorectal explant tumors
Project description:Mouse models have been developed to investigate colorectal cancer etiology and evaluate new anti-cancer therapies. While genetically engineered and carcinogen-induced mouse models have provided important information with regard to the mechanisms underlying the oncogenic process, xenograft models remain the standard for the evaluation of new chemotherapy and targeted drug treatments for clinical use. However, it remains unclear if drug efficacy data obtained from xenograft models translate into clinically-relevant treatment modalities. In this study, we have generated a panel of 28 patient-derived colorectal cancer explants (PDCCEs), an extension of our previous work, by direct transplantation of human colorectal cancer (CRC) tissues into NOD-SCID mice. A comprehensive histological and molecular evaluation of PDCCEs and their corresponding patient tumor demonstrates that PDCCEs maintain histological features and global biology through multiple passages. Furthermore, we demonstrate that in vivo sensitivity of PDCCEs to oxaliplatin can predict patient outcomes. Our findings suggest that PDCCEs maintain similarity to the patient tumor from which they are derived and can serve as a reliable preclinical model that can be incorporated into future strategies to optimize individual therapy for patients with CRC.
Project description:Pancreatic neuroendocrine tumor (PanNET) is relatively infrequent but is nevertheless metastatic. Seeking to extend a new paradigm of personalized medicine, we performed an integrative analysis of transcriptomic (mRNA and microRNA) and mutational profiles and defined three clinically relevant human PanNET subtypes. Importantly, cross-species analysis revealed two of these three subtypes in a well-characterized, genetically engineered mouse model (RIP1-Tag2) of PanNET and its cell lines. Each subtype share similarities to distinct cell types in pancreatic neuroendocrine development, features are reflected in their metabolic profiles. Subtype-specific molecular signatures metabolites are proposed to identify these subtypes. RNA was extracted from fresh frozen archival patient PanNET samples and hybridized on Affymetrix GeneChip human Gene 1.0 ST arrays. The CEL files were processed using R based bioconductor and normalized values were obtained using RMA.
Project description:Prostate cancer discovery and translational research are hampered by a lack of preclinical models which accurately reproduce the biological heterogeneity observed in patients. Accordingly, we have established a bank of transplantable patient-derived prostate tumor xenograft lines, using subrenal capsule grafting of human tumor tissue into immuno-deficient mice. This panel includes the first lines generated from primary prostate cancer tissue, and also new lines from metastatic tissue. Critically, the lines retained salient features of the original patient tumors, including histopathology, clinical marker expression, chromosomal aberration and gene expression profiles. Furthermore, they span major histopathological and molecular subtypes of prostate cancer, capturing diverse inter- and intra-tumoral heterogeneity. Host castration led to the development of castrate-resistant tumors, including the first model of complete neuroendocrine transdifferentiation. This publicly-available resource provides novel tools to advance mechanistic understanding of disease progression and response to therapy, and delivers clinically-relevant model systems for evaluation of preclinical drug efficacy. 3 primary tumors and 21 xenograft tumors
Project description:Prostate cancer discovery and translational research are hampered by a lack of preclinical models which accurately reproduce the biological heterogeneity observed in patients. Accordingly, we have established a bank of transplantable patient-derived prostate tumor xenograft lines, using subrenal capsule grafting of human tumor tissue into immuno-deficient mice. This panel includes the first lines generated from primary prostate cancer tissue, and also new lines from metastatic tissue. Critically, the lines retained salient features of the original patient tumors, including histopathology, clinical marker expression, chromosomal aberration and gene expression profiles. Furthermore, they span major histopathological and molecular subtypes of prostate cancer, capturing diverse inter- and intra-tumoral heterogeneity. Host castration led to the development of castrate-resistant tumors, including the first model of complete neuroendocrine transdifferentiation. This publicly-available resource provides novel tools to advance mechanistic understanding of disease progression and response to therapy, and delivers clinically-relevant model systems for evaluation of preclinical drug efficacy. 3 primary tumors and 22 xenograft tumors
Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated. Gene expression profiling experiments were conducted for 25 patient-derived xenograft glioblastoma samples using Affymetrix Human Gene 1.0 ST arrays according to manufacturer's protocol.
Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated. Gene expression profiling experiments were conducted for 25 patient-derived xenograft glioblastoma samples using Affymetrix Human Gene 1.0 ST arrays according to manufacturer's protocol.
Project description:Despite remarkable advances in cancer genomics and targeted therapy, cholangiocarcinoma (CCA) is still one of the deadliest cancers. Current translational approaches have focused on genomic alterations, while leaving proteomic alterations, that may more directly pinpoint therapeutic targets, unexplored. To address these knowledge gaps, we performed multiomic characterization of all three CCA subtypes, using whole exome sequencing, mRNA sequencing, and proteome/phosphoproteome profiling. Integrative dimensional reduction approaches revealed RNA, protein and phosphoprotein features driving tumor heterogeneity. These features defined three molecular clusters associated with unique pathways: immunomodulatory (cluster 1), metabolic (cluster 2), and chromosomal stability/apoptosis (cluster 3). We observed that cluster assignment was not related to anatomic subtype but was associated with overall survival after curative-intent resection. Further, we utilized a hierarchical all-against-all approach, which identified multi-omic features and pathways associated with overall survival and lymph node metastases, clinically relevant endpoints for selecting patient treatments. Kinase enrichment analysis of molecular features identified non-receptor tyrosine protein kinase TNK1 as a highly active kinase for cluster 2. We developed a radial support vector machine model that mapped to multiomically-characterized patient derived xenograft (PDX) models resulting in the selection of PDX models for each cluster. Importantly, we confirmed that treating with a selective TNK1 inhibitor significantly reduced tumor growth in a cluster 2 PDX, but not a cluster 1 or 3 PDX. Overall, we concluded that integrated multiomic characterization provides translational insights by defining unique molecular subtypes, identifying molecular features associated with clinical outcomes, and uncovering novel therapeutic targets.
Project description:Muscle-invasive bladder cancers are characterized by their distinct expression of luminal and basal genes, which could be used to predict key clinical features such as disease progression and overall survival. Transcriptionally, FOXA1, GATA3, and PPARg are shown to be essential for luminal subtype-specific gene regulation and subtype switching, while TP63, STAT3 and TFAP2 family members are critical for regulation of basal subtype-specific genes. Despite these advances, the underlying epigenetic mechanisms and 3D chromatin architecture responsible for subtype-specific regulation in bladder cancer remains unknown. Result: We determine the genome-wide transcriptome, enhancer landscape and transcription factor binding profiles of FOXA1 and GATA3 in luminal and basal subtypes of bladder cancer. Furthermore, we report the first-ever mapping of genome-wide chromatin interactions by Hi-C in both bladder cancer cell lines and primary patient tumors. We show that subtype-specific transcription is accompanied by specific open chromatin and epigenomic marks, at least partially driven by distinct transcription factor binding at distal-enhancers of luminal and basal bladder cancers. Finally, we identify a novel clinically relevant transcription factor, Neuronal PAS Domain Protein 2 (NPAS2), in luminal bladder cancers that regulates other subtype-specific genes and influences cancer cell proliferation and migration. Conclusion: In summary, our work identifies unique epigenomic signatures and 3D genome structures in luminal and basal urinary bladder cancers and suggests a novel link between the circadian transcription factor NPAS2 and a clinical bladder cancer subtype.
Project description:Pancreatic neuroendocrine tumor (PanNET) is relatively infrequent but is nevertheless metastatic. Seeking to extend a new paradigm of personalized medicine, we performed an integrative analysis of transcriptomic (mRNA and microRNA) and mutational profiles and defined three clinically relevant human PanNET subtypes. Importantly, cross-species analysis revealed two of these three subtypes in a well-characterized, genetically engineered mouse model (RIP1-Tag2) of PanNET and its cell lines. Each subtype share similarities to distinct cell types in pancreatic neuroendocrine development, features are reflected in their metabolic profiles. Subtype-specific molecular signatures metabolites are proposed to identify these subtypes. Gene expression data from different stages of RIP1-TAG2 genetically engineered PanNET mouse model RT2 mouse PanNET tumors, liver metastases, normal, hyperplastic, and angiogenic islets were dissected out or isolated. RNA was extracted and hybridized on Affymetrix GeneChip Mouse Gene 1.0 ST arrays. The CEL files were processed using aroma.affymetrix.