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  • American Association for Cancer Research (AACR)  (3)
  • Bendall, Sean C.  (3)
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  • American Association for Cancer Research (AACR)  (3)
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  • 1
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2015
    In:  Clinical Cancer Research Vol. 21, No. 17_Supplement ( 2015-09-01), p. A32-A32
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 21, No. 17_Supplement ( 2015-09-01), p. A32-A32
    Abstract: Cells within a single tumor are known to display extensive phenotypic and functional heterogeneity. Many life-threatening features of cancer, including drug resistance, metastasis and relapse, are facets of intratumor heterogeneity. With emerging single-cell measurement technologies, the field is poised to make important strides in understanding and controlling this heterogeneity. However, these technologies require coordinated advances in analytical methods to interpret the complex data they produce. Acute myeloid leukemia (AML) is an aggressive bone marrow malignancy in which the importance of cellular heterogeneity has been well characterized. However, previous studies have only scraped the surface of the heterogeneity in this disease. Using mass cytometry, which measures single cells in ~40 simultaneous proteomic features, we developed novel methods for analyzing phenotypic heterogeneity in cancer. Our approach provides an extensive compendium of surface-marker and signaling phenotypes in AML that extends current boundaries of knowledge. The heart of our approach is a graph-based representation of the single-cell samples. In this representation, each cell is modeled by a node connected to its neighbors—the cells most phenotypically similar to it. Constructed by local rules connecting cells, the graph as a whole represents the phenotypic structure of the sample. The graph can be partitioned into subsets of densely interconnected nodes, called communities, which represent distinct phenotypic subpopulations. Unlike parametric methods such as mixture models, this method makes no assumption about the size, distribution, or number of subpopulations. Using our graph-based approach, we deconstructed several AML samples into discrete phenotypes. Comparing phenotypes across patients, we found a striking degree of order. Every phenotype identifiable phenotype was discoverable in multiple (but not all) patients, implying a constraint on the space of allowable AML phenotypes. For each phenotype we also identified cognate healthy cell types at different stages of bone marrow maturation, indicating a constraint that is linked to normal developmental programs. Our data contain measurements of under various environmental perturbations and we designed a method to statistically quantify evoked signaling responses, producing high-dimensional signaling phenotypes for each subpopulation, which we regard as a representation of cellular functional potential. We found a tight coupling between surface and signaling phenotypes in healthy cells that is disrupted in AML. We identified a primitive signaling phenotype, derived from healthy stem and progenitor cells, which was not correlated with the primitive surface marker profile typically used to define primitive cells in AML. Using single-cell frequencies to deconvolve existing bulk gene expression data, we identified genes associated with this primitive signaling phenotype. These genes are enriched for primitive hematopoietic annotations and produce a clinically predictive signature that is more powerful than genes associated with the primitive surface profile, validating the utility of our approach and indicating novel regulators of the primitive hematopoietic cell state. Citation Format: Jacob H. Levine, Erin F. Simonds, Sean C. Bendall, Garry P. Nolan, Dana Pe'er. Computational dissection of phenotypic and functional heterogeneity in acute myeloid leukemia. [abstract]. In: Proceedings of the AACR Special Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(17 Suppl):Abstract nr A32.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
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    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2013
    In:  Cancer Research Vol. 73, No. 19_Supplement ( 2013-10-01), p. IA30-IA30
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 73, No. 19_Supplement ( 2013-10-01), p. IA30-IA30
    Abstract: It is now well appreciated that intra-tumor heterogeneity is of critical importance. There is remarkable molecular variability between and within populations of tumor cells, driven by both genetic and epigenetic variation. We address the challenge of identifying and characterizing tumor sub-populations through a combined experimental and computational approach. In two examples, we will demonstrate approaches to address each type of heterogeneity. Seventy percent of melanoma tumors have activated, and dependent on, MAPK pathway. However, the phenotypic response to MAPK inhibition is heterogeneous, both in vitro and in patients. While this heterogeneity is well characterized, we attempted to better understand its underlying mechanism. We used gene expression data both before and after pathway inhibition in a panel of cell lines to decipher the network structure under MAPK, and to identify network rewiring that contribute to the phenotypic response. To our surprise, the targets of the MAPK pathway vary dramatically between cell lines. We found that most targets are context-specific, regulated by MAPK only in a subset of samples. We therefore developed an algorithm to detect context-specific targets, and we used those targets to identify the pathways that are being regulated by MAPK. We found that NFkB and STAT3 activation status is correlated with apoptosis levels induced by MAPK inhibition. Furthermore, we show that they protect cells from the cytotoxic effects of MAPK inhibition. We employ mass cytometry, which accurately measures the expression and phosphorylation states of more than forty proteins in thousands of single cells, including surface proteins and signaling molecules. We present results on both healthy bone marrow and bone marrow from both ALL and AML patients. To understand abnormal, we first built a more accurate map of normal, modeling B-cell development in the marrow at unprecedented resolution. We measured 8 healthy marrows with 42 antibody parameters with mass cytometry targeting a multitude of phenotypic markers, intracellular signaling molecules, hallmarks of cell cycle and apoptosis all in the context of in vitro perturbations relevant to B cell development (including IL-7 and BCR crosslinking). We developed a graph based trajectory algorithm (wanderlust) that can trace a continuous progression from the hematopoietic stem cells, through the progenitor cells, to the final, committed B-cells. Our derived map of healthy B-cell development revealed the order and timing of developmental events at unprecedented resolution. The resulting multidimensional data was modeled using wanderlust and the predicted trajectory was then used to inform a traditional ‘gating’ analysis of the data and provide a higher resolution view of human B cell development than previously published. This healthy map was used to help provide a backbone on which to further understand how these trajectories are dysregulated in ALL. Citation Format: El-ad David Amir, Oren Litvin, Jacob Levine, Sean C. Bendall, Kara L. Davis, Erin F. Simonds, Tanya Schild, Mark Rocco, Neal Rosen, Garry P. Nolan, Dana Pe'er. Towards rationale therapy: Dealing with intertumor and intratumor heterogeneity. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr IA30.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2013
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    Location Call Number Limitation Availability
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 73, No. 8_Supplement ( 2013-04-15), p. 3178-3178
    Abstract: Acute myeloid leukemia (AML) can present with multiple concurrent subclones at diagnosis. Subclone-specific mutations may confer resistance to molecular-targeted drugs through loss of antigen expression or rewiring of intracellular signaling pathways, leading to relapse. Deep sequencing approaches improve the detection of rare subclones, but the ability to prospectively identify subclones (i.e. without relapse material) is limited, and the effects of subclone-specific mutations on phenotype are poorly understood. In the course of a broader study of oligoclonal pediatric AML patients, we focused investigation on a diagnosis bone marrow sample from a patient harboring 3 presumed subclones (two with distinct NRAS-G12D, NRAS-G13D mutations) as determined by whole-genome sequencing (WGS). We employed a combination of 31-parameter mass cytometry, deep sequencing, FACS sorting, and computational modeling to produce a detailed profile of the subclonal genotypes and phenotypes in this patient. The 3 anticipated subclones did not correlate with a clear subset of surface markers in the mass cytometry analysis. Instead, we observed a single continuous ‘differentiation trajectory’ from progenitor-like to monocyte-like blasts. To dissect this trajectory, 7 distinct myeloid/progenitor subsets were FACS-sorted, plus T/B cells as a non-leukemic control. For each subset we performed capture-based deep sequencing of 307 tumor-specific variants (Tier 1: 13; Tier 2: 33; Tier 3: 261; median depth: 1420x). Shifts in allele frequencies among the FACS-sorted subsets provided critical information to a Bayesian mixture modeling algorithm, allowing identification of 5 subclones, as opposed to the 3 subclones anticipated by WGS. The inferred subclonal genotypes were validated and further refined by targeted single-cell Sanger sequencing of multiple Tier 3 loci. Although all 5 subclones were present throughout the differentiation trajectory, some were enriched in certain phenotypic states or ‘reservoirs’. For example, the NRAS-G12D subclone was enriched in a progenitor-like subset, but its daughter subclone, which harbored an additional mutation in RAC2 (implicated in HSC engraftment), was enriched in the more differentiated subsets, suggesting an opposing effect. Notably, the T/B cell population harbored 2 tumor-specific Tier 1 mutations at & gt;15% allele frequency, suggesting its presence in a preleukemic, multilineage-competent HSC. Taken together, subclone-specific mutations appear to skew cells toward either progenitor or mature phenotypes, but the developmental trajectory enforced by parental mutations is resistant to change. Furthermore, characterization of subclones is possible at diagnosis, and may improve the selection of targeted therapies. *Contributed equally: EFS, SCB & ALG Citation Format: Erin F. Simonds, Sean C. Bendall, Amanda L. Gedman, Jacob H. Levine, Kara L. Davis, Harris G. Fienberg, Astraea Jager, El-ad D. Amir, Ina Radtke, Wendy J. Fantl, Dana Pe'er, James R. Downing, Garry P. Nolan. Deep sequencing of immunophenotypically distinct subsets in acute myeloid leukemia reveals reservoirs of genetically distinct subclones along a conserved differentiation trajectory. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3178. doi:10.1158/1538-7445.AM2013-3178
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2013
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
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