Chenyue W. Hu

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

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.

Recapitulation and Modulation of the Cellular Architecture of a User-Chosen Cell of Interest Using Cell-Derived, Biomimetic Patterning.

Heterogeneity of cell populations can confound population-averaged measurements and obscure important findings or foster inaccurate conclusions. The ability to generate a homogeneous cell population, at least with respect to a chosen trait, could significantly aid basic biological research and development of high-throughput assays. Accordingly, we developed a high-resolution, image-based patterning strategy to produce arrays of single-cell patterns derived from the morphology or adhesion site arrangement of user-chosen cells of interest (COIs). Cells cultured on both cell-derived patterns displayed a cellular architecture defined by their morphology, adhesive state, cytoskeletal organization, and nuclear properties that quantitatively recapitulated the COIs that defined the patterns. Furthermore, slight modifications to pattern design allowed for suppression of specific actin stress fibers and direct modulation of adhesion site dynamics. This approach to patterning provides a strategy to produce a more homogeneous cell population, decouple the influences of cytoskeletal structure, adhesion dynamics, and intracellular tension on mechanotransduction-mediated processes, and a platform for high-throughput cellular assays.

Progeny Clustering: A Method to Identify Biological Phenotypes

Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset.

Histone Modification Patterns Using RPPA-Based Profiling Predict Outcome in Acute Myeloid Leukemia Patients

Posttranslational histone tail modifications are known to play a role in leukemogenesis and are therapeutic targets. A global analysis of the level and patterns of expression of multiple histone‐modifying proteins (HMP) in acute myeloid leukemia (AML) and the effect of different patterns of expression on outcome and prognosis has not been investigated in AML patients. Here we analyzed 20 HMP by reverse phase protein array (RPPA) in a cohort of 205 newly diagnosed AML patients. Protein levels were correlated with patient and disease characteristics, including survival and mutational state. We identified different protein clusters characterized by higher (more on) or lower (more off) expression of HMP, relative to normal CD34+ cells. On state of HMP was associated with poorer outcome compared to normal‐like and a more off state. FLT3 mutated AML patients were significantly overrepresented in the more on state. DNA methylation related mutations showed no correlation with the different HMP states. In this study, we demonstrate for the first time that HMP form recurrent patterns of expression and that these significantly correlate with survival in newly diagnosed AML patients.

Pinocembrin induces ER stress mediated apoptosis and suppresses autophagy in melanoma cells

Melanoma, one of the toughest tumors to treat, features high metastasis and high lethality. Pinocembrin is a natural flavanone with versatile biological and pharmacological activities. Here, we evaluated the anti-tumor effects of pinocembrin against melanoma in vitro and in vivo. In vitro, pinocembrin inhibited the proliferation of melanoma cells (B16F10 and A375) in a dose-dependent manner. It induced endoplasmic reticulum stress via IRE1α/Xbp1 pathway and triggered caspase-12/-4 mediated apoptosis in both cell lines. Furthermore, we discovered that pinocembrin suppressed autophagy through the activation of PI3K/Akt/mTOR pathway, which serves as a dual mechanism to enhance the pro-death effect of pinocembrin. In vivo, pinocembrin inhibited the growth of B16F10 by inducing apoptosis. Taken together, our results demonstrated that pinocembrin can induce ER stress mediated apoptosis and suppress autophagy in melanoma, indicating its application potential for melanoma therapy.

Shrinkage Clustering: A Fast and Size-Constrained Algorithm for Biomedical Applications

Motivation: Many common clustering algorithms require a two-step process that limits their efficiency. The algorithms need to be performed repetitively and need to be implemented together with a model selection criterion, in order to determine both the number of clusters present in the data and the corresponding cluster memberships. As biomedical datasets increase in size and prevalence, there is a growing need for new methods that are more convenient to implement and are more computationally efficient. In addition, it is often essential to obtain clusters of sufficient sample size to make the clustering result meaningful and interpretable for subsequent analysis. Results: We introduce Shrinkage Clustering, a novel clustering algorithm based on matrix factorization that simultaneously finds the optimal number of clusters while partitioning the data. We report its performances across multiple simulated and actual datasets, and demonstrate its strength in accuracy and speed in application to subtyping cancer and brain tissues. In addition, the algorithm offers a straightforward solution to clustering with cluster size constraints. Given its ease of implementation, computing efficiency and extensible structure, we believe Shrinkage Clustering can be applied broadly to solve biomedical clustering tasks especially when dealing with large datasets.

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.

Recognition of Recurrent Protein Expression Patterns in Pediatric Acute Myeloid Leukemia Identified New Therapeutic Targets

Heterogeneity in the genetic landscape of pediatric acute myeloid leukemia (AML) makes personalized medicine challenging. As genetic events are mediated by the expression and function of proteins, recognition of recurrent protein patterns could enable classification of pediatric AML patients and could reveal crucial protein dependencies. This could help to rationally select combinations of therapeutic targets. To determine whether protein expression levels could be clustered into functionally relevant groups, custom reverse-phase protein arrays were performed on pediatric AML (n = 95) and CD34+ normal bone marrow (n = 10) clinical specimens using 194 validated antibodies. To analyze proteins in the context of other proteins, all proteins were assembled into 31 protein functional groups (PFG). For each PFG, an optimal number of protein clusters was defined that represented distinct transition states. Block clustering analysis revealed strong correlations between various protein clusters and identified the existence of 12 protein constellations stratifying patients into 8 protein signatures. Signatures were correlated with therapeutic outcome, as well as certain laboratory and demographic characteristics. Comparison of acute lymphoblastic leukemia specimens from the same array and AML pediatric patient specimens demonstrated disease-specific signatures, but also identified the existence of shared constellations, suggesting joint protein deregulation between the diseases. Implication: Recognition of altered proteins in particular signatures suggests rational combinations of targets that could facilitate stratified targeted therapy. Mol Cancer Res; 16(8); 1275–86. ©2018 AACR. See related article by Hoff et al., p. 1263

Royal Jelly Reduces Cholesterol Levels, Ameliorates Aβ Pathology and Enhances Neuronal Metabolic Activities in a Rabbit Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia characterized by aggregation of amyloid β (Aβ) and neuronal loss. One of the risk factors for AD is high cholesterol levels, which are known to promote Aβ deposition. Previous studies have shown that royal jelly (RJ), a product of worker bees, has potential neuroprotective effects and can attenuate Aβ toxicity. However, little is known about how RJ regulates Aβ formation and its effects on cholesterol levels and neuronal metabolic activities. Here, we investigated whether RJ can reduce cholesterol levels, regulate Aβ levels and enhance neuronal metabolic activities in an AD rabbit model induced by 2% cholesterol diet plus copper drinking water. Our results suggest that RJ significantly reduced the levels of plasma total cholesterol (TC) and low density lipoprotein-cholesterol (LDL-C), and decreased the level of Aβ in rabbit brains. RJ was also shown to markedly ameliorate amyloid deposition in AD rabbits from Aβ immunohistochemistry and thioflavin-T staining. Furthermore, our study suggests that RJ can reduce the expression levels of β-site APP cleaving enzyme-1 (BACE1) and receptor for advanced glycation end products (RAGE), and increase the expression levels of low density lipoprotein receptor-related protein 1 (LRP-1) and insulin degrading enzyme (IDE). In addition, we found that RJ remarkably increased the number of neurons, enhanced antioxidant capacities, inhibited activated-capase-3 protein expression, and enhanced neuronal metabolic activities by increasing N-acetyl aspartate (NAA) and glutamate and by reducing choline and myo-inositol in AD rabbits. Taken together, our data demonstrated that RJ could reduce cholesterol levels, regulate Aβ levels and enhance neuronal metabolic activities in AD rabbits, providing preclinical evidence that RJ treatment has the potential to protect neurons and prevent AD.