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  • American Association for Cancer Research (AACR)  (4)
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  • American Association for Cancer Research (AACR)  (4)
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  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 70, No. 8_Supplement ( 2010-04-15), p. 2620-2620
    Abstract: Cancer genome characterization efforts such as The Cancer Genome Atlas project are rapidly improving our knowledge of tumor genetic alterations. With the expanded use of massively parallel sequencing, the catalogue of known genetic alterations in cancer is expected to expand at an accelerating rate. In this context, the emphasis is shifting towards systematic identification of the genes and pathways targeted by recurrent genetic alterations, their functional impact in tumor biology, and the resulting cellular dependencies that might be exploited therapeutically. Anticipating the need for a companion resource to systematically probe tumor biology armed with cancer genomics knowledge, we have assembled a compendium of experimentally tractable cancer model systems consisting of ∼1000 human cancer cell lines and performed extensive genomic analysis (at the level of gene expression, DNA copy number and mutations) coupled with pharmacological profiling. This resource, which we call the Cancer Cell Line Encyclopedia (CCLE), is being used not only to identify the putative targets of prevalent genetic alterations, but also to systematically link the presence or absence of certain genetic alterations to drug sensitivity or resistance. To date, we have identified several previously unappreciated genomic predictors of response or intrinsic resistance to targeted anticancer agents. For instance, through integrative analysis, we have discovered additional mechanisms that may underlie sensitivity to MET inhibitors, beyond amplification of the MET receptor, highlighting the fact that response prediction in the clinic may require assessment of multiple variables. We have also broadened the potential relevance of known predictive biomarkers that might provide a rationale for future genotype-driven clinical trials. As an example, we have expanded on existing knowledge of resistance to receptor tyrosine kinase (RTK) inhibitors, showing that the presence of RAS mutations may predict lack of response to a broad spectrum of RTK inhibitors in addition to EGFR inhibitors. This work demonstrates that pharmacological profiling of large, genomically-annotated cancer model systems may uncover new tumor dependencies as well as positive and negative predictors of drug response. The results of this study are being made publicly available at a CCLE online portal, with the hope they will become a valuable resource for the cancer community to propel translation of the knowledge generated through in vitro integrative genomics into personalized cancer medicine. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2620.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2010
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    detail.hit.zdb_id: 410466-3
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  • 2
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 23, No. 11_Supplement ( 2017-06-01), p. AP19-AP19
    Abstract: BACKGROUND AND PURPOSE: Genetic heterogeneity is a hallmark of ovarian cancer (OvCa) biology and underlies treatment resistance. Macroscopic and or treatment resistant microscopic residual disease (MRD) after debulking surgery and chemotherapy are the source for disease recurrence and death in these patients. Current profiling methods are unable adequately reflect heterogeneity, and transcriptional programs associated with treatment resistance and MRD remain elusive. To elucidate transcriptional heterogeneity and potential mechanisms of drug-resistance, we isolated OvCa cells from patients with malignant ascites using flow-cytometry. We applied single-cell RNA-sequencing (sc-RNA-seq) to ascites-derived cells from patients with OvCa. We picked OvCa spheroids and profiled these separately. To investigate MRD, we used three PDX-models stably expressing mCherry, treated with carboplatin, and harvested tumor cells for sc-RNA-seq at three time points (pre-treatment, at time of MRD as determined by bio-luminescence imaging, and disease relapse). SUMMARY OF RESULTS: We successfully sequenced 770 single-cell transcriptomes from 6 individuals with treatment-resistant OvCa, including 3 patients with sequential samples. We mapped the landscape of chromosomal aberrations by inferred large-scale copy number variations (CNVs) at a single-cell level. We observed significant inter-tumor heterogeneity and started to deconstruct the genomic architecture of individual patients. Using experimentally validated gene sets, we determined the cell cycle state of individual cells and identified transcriptional programs related to the cell cycle as significant bias in publically available bulk RNA-sequencing data. Principal component analysis revealed the expression of a stem-ness signature, including CD133, ALDH1A and AXL, in a sub-set of non-cycling cells. An important driver of transcriptional heterogeneity common to patients included in this study was the expression of gene sets related to inflammatory pathways, such as the NFkB and JAK/STAT pathways. Hierarchical clustering of 42 spheroid profiles identified four major clusters, including a highly “inflamed” phenotype. Therapeutic inhibition of the STAT pathway abrogated the capacity of spheroid formation on an ultra-low attachment surface, indicating its importance for metastasis. We have successfully isolated thousands of individual cells for single-cell profiling from PDX models treated with carboplatin. These cells were collected at three time points, including at the MRD stage. We have successfully sequenced 100 cells from this collection and were able to generate whole-transcriptome data comparable to that of freshly isolated patient cells and thousands of single cells are currently undergoing sequencing. CONCLUSION: We have successfully applied single-cell RNA-sequencing to patient-derived ovarian cancer cells and PDX-models. Single-cell transcriptomes enabled inference of genomic information, genetic and transcriptional heterogeneity, cell cycle state, and programs related to stem-ness and inflammation, providing a unique and comprehensive perspective on ovarian cancer cell states. Ongoing profiling of carboplatin-resistant cells captured at the minimal residual disease stage in PDX-models will provide a unique opportunity to understand treatment resistance which ultimately leads to cancer recurrence. Citation Format: Benjamin Izar, Elizabeth Stover, Itay Tirosh, Asaf Rotem, Parin Shah, Chris Rodman, Sanjay Prakadan, Marc Wadsworth, Mei-Ju Su, Rachel Leeson, Sangeetha Palakurthi, Joyce Liu, Ursula Matulonis, Alex Shalek, Orit Rozenblatt-Rosen, Aviv Regev, Levi Garraway. SINGLE–CELL RNA–SEQUENCING OF PATIENT–DERIVED OVARIAN CANCER CELLS AND PATIENT–DERIVED XENOGRAFT MODELS [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr AP19.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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    detail.hit.zdb_id: 2036787-9
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 3037-3037
    Abstract: Background: Ovarian cancer (OvCa) is frequently associated with malignant effusions, which are complex ecosystems with heterogeneous populations of malignant cells and non-malignant cells. Bulk RNA-seq or whole-exome sequencing (WES) only reflect average cellular behavior and thereby mask intrinsic cell diversity with potential relevance for treatment resistance. Approach: To overcome some of these barriers, we applied single-cell RNA-sequencing (scRNA-seq) to malignant and non-malignant cells isolated from patients with platinum treatment resistant disease. Furthermore, we used patient-derived xenograft (PDX) cohorts, in which we isolated cells for scRNA-seq from vehicle tumors (VEH), treated the other models with carboplatin, and harvested cells at the time of minimal residual disease (MRD) or disease progression (PROG). Results: To date, we have profiled ~12000 single cells from 12 patients with treatment naïve (n=3) or platinum-resistant disease (n=9), including sequential sampling in 3 patients with resistant disease. We observed significant inter- and intra-individual transcriptional heterogeneity in malignant cells. A recurrent pattern across resistant patients was the differential expression of inflammatory pathways in a subset of cells. In a patient with three consecutive specimens, we observed increasing accumulation of cells expressing a cell state characterized by tumor necrosis factor alpha (TNF-a) signaling, Importantly, these cells were genetically identical to the entire population, supporting the hypothesis that non-encoded mechanisms conferred treatment resistance. In a BRCA-mutant patient, unbiased analysis identified a stemness program in a subpopulation of cells, which was genetically identical to other cells, indicating phenotypic conversion. To systemically interrogate mechanisms of resistance to platinum therapy, sequenced single cells isolated from PDX models at three time points (VEH, MRD and PROG). In a BRCA-WT PDX model, resistant cells isolated at MRD and PROG shared a transcriptional program that was dominated by expression of a STAT3 program. Ex vivo cultures from platinum-resistant patients were exquisitely sensitivity to JAK/STAT3-inhibitor. Live cell imaging revealed that STAT3-inhibition prevented spheroid formation, attachment and clearance through a mesothelial monolayer in vitro. Conclusion: Our results indicate that non-encoded mechanisms play an important role in the development of treatment resistance in ovarian cancer. Our initial studies indicate an important role of inflammatory pathways in treatment resistance, in particular STAT3 signaling, which can be overcome with specific inhibitors at nanomolar concentrations. These data suggests that single-cell profiling can be performed on clinical ovarian cancer specimens and may yield novel therapeutic avenues for patients with treatment-resistant ovarian cancer. Citation Format: Benjamin Izar, Itay Tirosh, Elizabeth Stover, Asaf Rotem, Parin Shah, Mike Cuoco, Chris Rodman, Joyce Liu, Ursula Matulonis, Orit Rozenblatt-Rosen, Levi Garraway, Aviv Regev. Dissecting treatment resistance in patients with ovarian cancer and PDX-models using single-cell RNA-sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3037. doi:10.1158/1538-7445.AM2017-3037
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 70, No. 8_Supplement ( 2010-04-15), p. 105-105
    Abstract: The Cancer Cell Line Encyclopedia (CCLE) represents a collaborative effort to assemble a comprehensive resource of human cancer models for basic and translational research. Thus far, the CCLE contains high-density SNP array data, gene expression microarray data and selected cancer gene mutation data for approximately 1,000 human cancer cell lines spanning many tumor types. Additionally, we are assessing the sensitivity of these same cell lines using a series of pharmacological compounds that represent both conventional cytotoxic and targeted agents. Another goal of the CCLE collaboration involves systematic integration of the genomic and pharmacologic datasets in order to identify putative targets of prevalent genetic alterations as well as predictors and modifiers of pharmacologic sensitivity and resistance. The availability of high-quality data generated by uniform criteria across hundreds of cell lines markedly enhances the statistical power to discover genetic alterations involved in carcinogenesis and molecular predictors of pharmacologic vulnerability. As proof of principle, we have carried out systematic nomination of putative targets of genetic alterations using integrative analyses. Here, significant regions of genomic gains and losses have been linked to expression and mutation data to find significant correlations at both single-gene and pathway levels. We have also begun to assemble systematic algorithms that identify genetic predictors of sensitivity or resistance to particular pharmacological compounds, taking advantage of the fact that the CCLE is a comprehensive resource with extensive genomic characterization. Toward this end, we integrated a preliminary sensitivity dataset for 28 compounds accurately profiled against more than 400 cell lines with all genomic data available in the CCLE. To enhance the robustness of our method, we reduced the number of significant genomic features for each cell line to a number that allows properly determined prediction of sensitivity. Expression data was converted to cell line-specific readouts of gene set expression; and DNA gains and losses are reduced to statistically significant regions using the GISTIC algorithm. These values were combined with critical oncogene mutations as inputs to a multifaceted prediction model for pharmacological sensitivity, the accuracy of which was assessed using cross-validation. Our results suggest that this integrative approach applied to a robust cancer cell line collection has considerable power to discover novel associations that augment ongoing basic research into cancer biology and drug discovery. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 105.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2010
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
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