Cytobank is a Web‐based application for storage, analysis, and sharing of flow cytometry experiments. Researchers use a Web browser to log in and use a wide range of tools developed for basic and advanced flow cytometry. In addition to providing access to standard cytometry tools from any computer, Cytobank creates a platform and community for developing new analysis and publication tools. Figure layouts created on Cytobank are designed to allow transparent access to the underlying experiment annotation and data processing steps. Since all flow cytometry files and analysis data are stored on a central server, experiments and figures can be viewed or edited by anyone with the proper permission, from any computer with Internet access. Once a primary researcher has performed the initial analysis of the data, collaborators can engage in experiment analysis and make their own figure layouts using the gated, compensated experiment files. Cytobank is available to the scientific community at http://www.cytobank.org. Curr. Protoc. Cytom. 53:10.17.1‐10.17.24. © 2010 by John Wiley & Sons, Inc.

Oncogenesis and tumour progression are supported by alterations in cell signalling. Using flow cytometry, it is now possible to track and analyse signalling events in individual cancer cells. Data from this type of analysis can be used to create a network map of signalling in each cell and to link specific signalling profiles with clinical outcomes. This form of 'single-cell proteomics' can identify pathways that are activated in therapy-resistant cells and can provide biomarkers for cancer diagnosis and for determining patient prognosis.

Summary Progress in understanding the molecular pathogenesis of human myeloproliferative disorders (MPDs) has led to guidelines incorporating genetic assays with histopathology during diagnosis. Advances in flow cytometry have made it possible to simultaneously measure cell type and signaling abnormalities arising as a consequence of genetic pathologies. Using flow cytometry, we observed a specific evoked STAT5 signaling signature in a subset of samples from patients suspected of having juvenile myelomonocytic leukemia (JMML), an aggressive MPD with a challenging clinical presentation during active disease. This signature was a specific feature involving JAK-STAT signaling, suggesting a critical role of this pathway in the biological mechanism of this disorder and indicating potential targets for future therapies. Significance Recent advances have enabled simultaneous measurement of cell type and cell signals in primary populations using flow cytometry. This technique enables the question, "Can we track oncogenic cell populations from diagnosis through disease evolution via signaling?" Doing so in an era of using specific inhibitors against components of key signal transduction pathways will be necessary to assess treatment effects in human patients and adapt as cancer cells alter their signaling in response to these treatments. This work uses such an approach to follow patients over time and shows that disease status in juvenile myelomonocytic leukemia (JMML) -- at diagnosis, remission, relapse, and transformation -- is indicated by a subset of cells with an abnormal signaling profile.

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Defining how cancer-associated mutations perturb signaling networks in stem/ progenitor populations that are integral to tumor formation and maintenance is a fundamental problem with biologic and clinical implications. Point mutations in RAS genes contribute to many cancers, including myeloid malignancies. We investigated the effects of an oncogenic Kras G12D allele on phosphorylated signaling molecules in primary c-kit ؉ lin ؊/low hematopoietic stem/progenitor cells. Comparison of wild-type and Kras G12Dc-kit ؉ lin ؊/low cells shows that K-Ras G12D expression causes hyperproliferation in vivo and results in abnormal levels of phosphorylated STAT5, ERK, and S6 under basal and stimulated conditions. Whereas Kras G12D cells demonstrate hyperactive signaling after exposure to granulocyte-macrophage colony-stimulating factor, we unexpectedly observe a paradoxical attenuation of ERK and S6 phosphorylation in response to stem cell factor. These studies provide direct biochemical evidence that cancer stem/ progenitor cells remodel signaling networks in response to oncogenic stress and demonstrate that multi-parameter flow cytometry can be used to monitor the effects of targeted therapeutics in vivo. This strategy has broad implications for defining the architecture of signaling networks in primary cancer cells and for implementing stem cell-targeted interventions. IntroductionRas proteins are signal-switch molecules that modulate cell fates by cycling between inactive GDP-bound and active GTP-bound conformations (Ras-GDP and Ras-GTP). When GTP-bound, Ras interacts productively with downstream effectors including the Ral-GDS, Raf/MEK/ERK, and phosphoinositol 3Ј (PI3) kinase/ Akt pathways. 1 Guanine nucleotide exchange factors promote Ras activation, and signaling is terminated when Ras-GTP is hydrolyzed to Ras-GDP by its intrinsic GTPase activity, which is markedly accelerated by GTPase-activating proteins (GAPs). [2][3][4] Somatic KRAS2, NRAS, and HRAS mutations are found in approximately 30% of human cancers and encode mutant proteins that accumulate in the GTP-bound conformation due to reduced intrinsic GTPase activity and resistance to GAPs. 5,6 Hematopoietic malignancies are tractable experimental systems for interrogating signaling networks in primary cells and for integrating biochemical, genetic, and cell biologic data. KRAS2 and NRAS are mutated frequently in myeloid malignancies, including acute myeloid leukemia (AML), chronic myelomonocytic leukemia (CMML), and juvenile myelomonocytic leukemia (JMML), which are classified as myeloproliferative disorders (MPDs). 7-9 An important role of hyperactive Ras in myeloid leukemias is further illustrated by the fact that many patients without RAS mutation harbor other genetic lesions that deregulate Ras signaling such as loss of the NF1 tumor suppressor, point mutations that activate the SHP-2 protein tyrosine phosphatase, and the BCR-ABL fusion protein. 10,11 However, despite the extensive evidence implicating hyperactive Ras in myeloid leukemogenesis, the biochemica...

Cytometry is used extensively in clinical and laboratory settings to diagnose and track cell subsets in blood and tissue. High-throughput, single-cell approaches leveraging cytometry are developed and applied in the computational and systems biology communities by researchers, who seek to improve the diagnosis of human diseases, map the structures of cell signaling networks, and identify new cell types. Data analysis and management present a bottleneck in the flow of knowledge from bench to clinic. Multi-parameter flow and mass cytometry enable identification of signaling profiles of patient cell samples. Currently, this process is manual, requiring hours of work to summarize multi-dimensional data and translate these data for input into other analysis programs. In addition, the increase in the number and size of collaborative cytometry studies as well as the computational complexity of analytical tools require the ability to assemble sufficient and appropriately configured computing capacity on demand. There is a critical need for platforms that can be used by both clinical and basic researchers who routinely rely on cytometry. Recent advances provide a unique opportunity to facilitate collaboration and analysis and management of cytometry data. Specifically, advances in cloud computing and virtualization are enabling efficient use of large computing resources for analysis and backup. An example is Cytobank, a platform that allows researchers to annotate, analyze, and share results along with the underlying single-cell data.

Purpose: Glioblastoma (GBM) is the most common primary malignant tumor in the central nervous system. Our recent preclinical work has suggested that PD-1/PD-L1 plays an important immunoregulatory role to limit effective antitumor T-cell responses induced by active immunotherapy. However, little is known about the functional role that PD-1 plays on human T lymphocytes in patients with malignant glioma. Experimental Design: In this study, we examined the immune landscape and function of PD-1 expression by T cells from tumor and peripheral blood in patients with malignant glioma. Results: We found several differences between PD-1 þ tumor-infiltrating lymphocytes (TIL) and patient-matched PD-1 þ peripheral blood T lymphocytes. Phenotypically, PD-1 þ TILs exhibited higher expression of markers of activation and exhaustion than peripheral blood PD-1 þ T cells, which instead had increased markers of memory. A comparison of the T-cell receptor variable chain populations revealed decreased diversity in T cells that expressed PD-1, regardless of the location obtained. Functionally, peripheral blood PD-1 þ T cells had a significantly increased proliferative capacity upon activation compared with PD-1 À T cells. Conclusions: Our evidence suggests that PD-1 expression in patients with glioma reflects chronically activated effector T cells that display hallmarks of memory and exhaustion depending on its anatomic location. The decreased diversity in PD-1 þ T cells suggests that the PD-1-expressing population has a narrower range of cognate antigen targets compared with the PD-1 nonexpression population. This information can be used to inform how we interpret immune responses to PD-1-blocking therapies or other immunotherapies.

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