A quantitative analysis of heterogeneities and hallmarks in acute myelogenous leukaemia.

Acute myelogenous leukaemia (AML) is associated with risk factors that are largely unknown and with a heterogeneous response to treatment. Here, we provide a comprehensive quantitative understanding of AML proteomic heterogeneities and hallmarks by using the AML Proteome Atlas, a proteomics database that we have newly derived from MetaGalaxy analyses, for the proteomic profiling of 205 patients with AML and 111 leukaemia cell lines. The analysis of the dataset revealed 154 functional patterns based on common molecular pathways, 11 constellations of correlated functional patterns and 13 signatures that stratify the outcomes of patients. We find limited overlap between proteomics data and both cytogenetics and genetic mutations. Moreover, leukaemia cell lines show limited proteomic similarities with cells from patients with AML, suggesting that a deeper focus on patient-derived samples is needed to gain disease-relevant insights. The AML Proteome Atlas provides a knowledge base for proteomic patterns in AML, a guide to leukaemia cell line selection, and a broadly applicable computational approach for quantifying the heterogeneities of protein expression and proteomic hallmarks in AML.

GAIN: A graphical method to automatically analyze individual neurite outgrowth.

BACKGROUND: Neurite outgrowth is a metric widely used to assess the success of in vitro neural stem cell differentiation or neuron reprogramming protocols and to evaluate high-content screening assays for neural regenerative drug discovery. However, neurite measurements are tedious to perform manually, and there is a paucity of freely available, fully automated software to determine neurite measurements and neuron counting. To provide such a tool to the neurobiology, stem cell, cell engineering, and neuroregenerative communities, we developed an algorithm for performing high-throughput neurite analysis in immunofluorescent images. NEW METHOD: Given an input of paired neuronal nuclear and cytoskeletal microscopy images, the GAIN algorithm calculates neurite length statistics linked to individual cells or clusters of cells. It also provides an estimate of the number of nuclei in clusters of overlapping cells, thereby increasing the accuracy of neurite length statistics for higher confluency cultures. GAIN combines image processing for neuronal cell bodies and neurites with an algorithm for resolving neurite junctions. RESULTS: GAIN produces a table of neurite lengths from cell body to neurite tip per cell cluster in an image along with a count of cells per cluster. COMPARISON WITH EXISTING METHODS: GAIN's performance compares favorably with the popular ImageJ plugin NeuriteTracer for counting neurons, and provides the added benefit of assigning neurites to their respective cell bodies. CONCLUSIONS: In summary, GAIN provides a new tool to improve the robust assessment of neural cells by image-based analysis.

p53 pathway dysfunction is highly prevalent in acute myeloid leukemia independent of TP53 mutational status.

TP53 mutations are associated with the lowest survival rates in acute myeloid leukemia (AML). In addition to mutations, loss of p53 function can arise via aberrant expression of proteins that regulate p53 stability and function. We examined a large AML cohort using proteomics, mutational profiling and network analyses, and showed that (1) p53 stabilization is universal in mutant TP53 samples, it is frequent in samples with wild-type TP53, and in both cases portends an equally dismal prognosis; (2) the p53 negative regulator Mdm2 is frequently overexpressed in samples retaining wild-type TP53 alleles, coupled with absence of p21 expression and dismal prognosis similar to that of cases with p53 stabilization; (3) AML samples display unique patterns of p53 pathway protein expression, which segregate prognostic groups with distinct cure rates; (4) such patterns of protein activation unveil potential AML vulnerabilities that can be therapeutically exploited.

IDENTIFYING CANCER SPECIFIC METABOLIC SIGNATURES USING CONSTRAINT-BASED MODELS.

Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.

Modeling transport kinetics with StarLogo.

StarLogo, an agent-based modeling and simulation platform, was used to simulate adsorption-mediated transcytosis of a molecule from the lumen side of a cell membrane to the abluminal extra-cellular fluid (ECF). The model contains small nondiffusible substrate molecules, transporters, and substrate-transporter agents. The "reaction" is a transporter combining with the substrate which then crosses the cell cytoplasm. The substrate that is deposited on the ECF side becomes the "product". Results showed characteristics consistent with Michaelis-Menten enzyme kinetics. The model can serve as an example of agent-based modeling and simulation.