Cue-Signal-Response Analysis of TNF-Induced Apoptosis by Partial Least Squares Regression of Dynamic Multivariate Data

Janes, Kevin A.; Kelly, Jason; Gaudet, Suzanne; Albeck, John G.; Sorger, Peter K.; Lauffenburger, Douglas A.

doi:10.1089/1066527041887258

Cited by 26 publications

(49 citation statements)

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“…The ability to reproducibly sample stimulated cells with 30 -90 s intervals enabled the use of instant derivatives as additional variables in the regression model; thus, the microfluidic device provided an additional benefit in enhancing the information content from the signaling data. Although others have reported derivatives of time courses to be less informative in PLSR analysis (27,47,48,70,71), these studies relied on signaling dynamics that extended for hours rather than minutes. Because the device can stimulate a small number of cells with high temporal resolution and subsequently lyse and fix them in parallel, we could seamlessly "stitch" together complementary measurements for our statistical analysis from populations of cells treated identically, not only from day to day, but also within a particular stimulation experiment.…”

Section: Discussionmentioning

confidence: 99%

“…PLSR models have been applied previously to understand complex signaling networks involving multiple inputs and multiple outputs, without prior knowledge of the network structure (27,47,48,70,71). Prior application of this type of modeling approach has demonstrated that antigenic information content is encoded within downstream phosphorylation events in T cells (48).…”

Section: Discussionmentioning

confidence: 99%

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Predicting Cytotoxic T-cell Age from Multivariate Analysis of Static and Dynamic Biomarkers

Rivet

Hill

et al. 2011

Molecular & Cellular Proteomics

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Adoptive T-cell transfer therapy relies upon in vitro expansion of autologous cytotoxic T cells that are capable of tumor recognition. The success of this cell-based therapy depends on the specificity and responsiveness of the T cell clones before transfer. During ex vivo expansion, CD8؉ T cells present signs of replicative senescence and loss of function. The transfer of nonresponsive senescent T cells is a major bottleneck for the success of adoptive T-cell transfer therapy. Quantitative methods for assessing cellular age and responsiveness will facilitate the development of appropriate cell expansion and selection protocols. Although several biomarkers of lymphocyte senescence have been identified, these proteins in isolation are not sufficient to determine the age-dependent responsiveness of T cells. We have developed a multivariate model capable of extracting combinations of markers that are the most informative to predict cellular age. To acquire signaling information with high temporal resolution, we designed a microfluidic chip enabling parallel lysis and fixation of stimulated cell samples on-chip. The acquisition of 25 static biomarkers and 48 dynamic signaling measurements at different days in culture, integrating single-cell and population based information, allowed the multivariate regression model to accurately predict CD8؉ T-cell age. From surface marker expression and early phosphorylation events following T-cell receptor stimulation, the model successfully predicts days in culture and number of population doublings with R 2 ‫؍‬ 0.91 and 0.98, respectively. Furthermore, we found that impairment of early signaling events following T cell receptor stimulation because of long term culture allows prediction of costimulatory molecules CD28 and CD27 expression levels and the number of population divisions in culture from a limited subset of signaling proteins. The multivariate analysis highlights the information content of both averaged biomarker values and heterogeneity metrics for prediction of cellular age within a T cell population.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Cytotoxic T-cell Age from Multivariate Analysis of Static and Dynamic Biomarkers

Rivet

Hill

et al. 2011

Molecular & Cellular Proteomics

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“…Using statistical approaches, such as partial least-squares regression, one could quantitatively assess the degree of variation in the cellular output that one would expect to be able to describe with a mechanistic model. Further discussion of this topic is the subject of an excellent set of papers (27)(28)(29).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Mechanistic Pathway Models in Drug Discovery: p38 Pathway

2008

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Mechanistic models of signal transduction have emerged as valuable tools for untangling complex signaling networks and gaining detailed insight into pathway dynamics. The natural extension of these tools is for the design of therapeutic strategies. We have generated a novel computational model of lipopolysaccharide-induced p38 signaling in the context of TNF-R production in inflammatory disease. Using experimental measurement of protein levels and phospho-protein time courses, populations of model parameters were estimated. With a collection of parameter sets, reflecting virtual diversity, we step through analysis of the p38 signaling pathway model to answer specific drug discovery questions regarding target prioritization, inhibitor simulation, model robustness and co-drugging. We demonstrate that target selection cannot be assessed independently from inhibitor mechanism of action and is also linked with robustness to cellular variability. Finally, we assert that in the face of parameter uncertainty one can still uncover consistent findings that can guide drug discovery efforts.

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“…(4,20) and not a single row vector as is the case here. It can then be that even when X is given, residual uncertainty about the output Y remains.…”

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confidence: 92%

“…This usage contrasts with the more common situation in which the approach of maximal entropy is applied to an underdetermined system. An additional motivation for the present application is the partial least squares regression-based seminal contributions to systems biology analysis by Janes et al (4,20). Although our general approach and various technical details differ, the spirit is the same.…”

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confidence: 99%

Maximal entropy inference of oncogenicity from phosphorylation signaling

Graeber

Heath

Skaggs

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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Point mutations in the phosphorylation domain of the Bcr-Abl fusion oncogene give rise to drug resistance in chronic myelogenous leukemia patients. These mutations alter kinase-mediated signaling function and phenotypic outcome. An information theoretic analysis of the correlation of phosphoproteomic profiling and transformation potency of the oncogene in different mutants is presented. The theory seeks to predict the leukemic transformation potency from the observed signaling by constructing a distribution of maximal entropy of site-specific phosphorylation events. The theory is developed with special reference to systems biology where high throughput measurements are typical. We seek sets of phosphorylation events most contributory to predicting the phenotype by determining the constraints on the signaling system. The relevance of a constraint is measured by how much it reduces the value of the entropy from its global maximum, where all events are equally likely. Application to experimental phospho-proteomics data for kinase inhibitor-resistant mutants shows that there is one dominant constraint and that other constraints are not relevant to a similar extent. This single constraint accounts for much of the correlation of phosphorylation events with the oncogenic potency and thereby usefully predicts the trends in the phenotypic output. An additional constraint possibly accounts for biological fine structure.high-throughput measurements | information theory | phospho proteomics | signal transduction networks | systems biology B iological systems use complex networks of molecular events, such as signaling and transcription, to regulate cellular outcome. Experimental biology is now able to measure up to thousands of these events from individual samples, inspiring efforts to identify both the events most contributory to cellular phenotypes and the nature of how multiple regulatory mechanisms are coordinated. Such understanding is crucial to the next generation of single agent and mixture molecularly targeted therapeutics. Kinase-mediated signaling is central to the execution of cellular programs and to communication with the environment and other cells. Aberrant signaling is implicated in many diseases and is a hallmark of cancer (1). Mass spectrometry-and antibody-based phospho proteomics allow the global profiling of the state of the signaling network (2-5). These approaches permit site-specific monitoring of phosphorylation and, thus, provide discrimination of the sometimes multiple and differential regulatory phosphorylation events on individual proteins. Here, we develop and apply an information theoretic maximal entropy analysis of the correlation of signaling events to cellular outcome to identify the dominant constraints that describe and predict the phenotypic output.There are equivalent ways of motivating the choice of a distribution of maximal entropy (6). From an information theoretic point of view it is the distribution that is consistent with the data at hand and is otherwise least informative. From ...

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Cue-Signal-Response Analysis of TNF-Induced Apoptosis by Partial Least Squares Regression of Dynamic Multivariate Data

Cited by 26 publications

References 0 publications

Predicting Cytotoxic T-cell Age from Multivariate Analysis of Static and Dynamic Biomarkers

Predicting Cytotoxic T-cell Age from Multivariate Analysis of Static and Dynamic Biomarkers

Analysis of Mechanistic Pathway Models in Drug Discovery: p38 Pathway

Maximal entropy inference of oncogenicity from phosphorylation signaling

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