Saumyadipta Pyne

Preclinical model of organotypic culture for pharmacodynamic profiling of human tumors

Predicting drug response in cancer patients remains a major challenge in the clinic. We have perfected an ex vivo, reproducible, rapid and personalized culture method to investigate antitumoral pharmacological properties that preserves the original cancer microenvironment. Response to signal transduction inhibitors in cancer is determined not only by properties of the drug target but also by mutations in other signaling molecules and the tumor microenvironment. As a proof of concept, we, therefore, focused on the PI3K/Akt signaling pathway, because it plays a prominent role in cancer and its activity is affected by epithelial–stromal interactions. Our results show that this culture model preserves tissue 3D architecture, cell viability, pathway activity, and global gene-expression profiles up to 5 days ex vivo. In addition, we show pathway modulation in tumor cells resulting from pharmacologic intervention in ex vivo culture. This technology may have a significant impact on patient selection for clinical trials and in predicting response to small-molecule inhibitor therapy.

Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions

Skew-normal and skew-t distributions have proved to be useful for capturing skewness and kurtosis in data directly without transformation. Recently, finite mixtures of such distributions have been considered as a more general tool for handling heterogeneous data involving asymmetric behaviors across subpopulations. We consider such mixture models for both univariate as well as multivariate data. This allows robust modeling of high-dimensional multimodal and asymmetric data generated by popular biotechnological platforms such as flow cytometry. We develop Bayesian inference based on data augmentation and Markov chain Monte Carlo (MCMC) sampling. In addition to the latent allocations, data augmentation is based on a stochastic representation of the skew-normal distribution in terms of a random-effects model with truncated normal random effects. For finite mixtures of skew normals, this leads to a Gibbs sampling scheme that draws from standard densities only. This MCMC scheme is extended to mixtures of skew-t distributions based on representing the skew-t distribution as a scale mixture of skew normals. As an important application of our new method, we demonstrate how it provides a new computational framework for automated analysis of high-dimensional flow cytometric data. Using multivariate skew-normal and skew-t mixture models, we could model non-Gaussian cell populations rigorously and directly without transformation or projection to lower dimensions.

A constitutively activated form of the p110β isoform of PI3-kinase induces prostatic intraepithelial neoplasia in mice

Recent work has shown that ablation of p110β, but not p110α, markedly impairs tumorigenesis driven by loss of phosphatase and tensin homolog (PTEN) in the mouse prostate. Other laboratories have reported complementary data in human prostate tumor lines, suggesting that p110β activation is necessary for tumorigenesis driven by PTEN loss. Given the multiple functions of PTEN, we wondered if p110β activation also is sufficient for tumorigenesis. Here, we report that transgenic expression of a constitutively activated p110β allele in the prostate drives prostate intraepithelial neoplasia formation. The resulting lesions are similar to, but are clearly distinct from, the ones arising from PTEN loss or Akt activation. Array analyses of transcription in multiple murine prostate tumor models featuring PI3K/AKT pathway activation allowed construction of a pathway signature that may be useful in predicting the prognosis of human prostate tumors.

AKT1 and MYC Induce Distinctive Metabolic Fingerprints in Human Prostate Cancer

Cancer cells may overcome growth factor dependence by deregulating oncogenic and/or tumor-suppressor pathways that affect their metabolism, or by activating metabolic pathways de novo with targeted mutations in critical metabolic enzymes. It is unknown whether human prostate tumors develop a similar metabolic response to different oncogenic drivers or a particular oncogenic event results in its own metabolic reprogramming. Akt and Myc are arguably the most prevalent driving oncogenes in prostate cancer. Mass spectrometry-based metabolite profiling was performed on immortalized human prostate epithelial cells transformed by AKT1 or MYC, transgenic mice driven by the same oncogenes under the control of a prostate-specific promoter, and human prostate specimens characterized for the expression and activation of these oncoproteins. Integrative analysis of these metabolomic datasets revealed that AKT1 activation was associated with accumulation of aerobic glycolysis metabolites, whereas MYC overexpression was associated with dysregulated lipid metabolism. Selected metabolites that differentially accumulated in the MYC-high versus AKT1-high tumors, or in normal versus tumor prostate tissue by untargeted metabolomics, were validated using absolute quantitation assays. Importantly, the AKT1/MYC status was independent of Gleason grade and pathologic staging. Our findings show how prostate tumors undergo a metabolic reprogramming that reflects their molecular phenotypes, with implications for the development of metabolic diagnostics and targeted therapeutics.

The ubiquitin-specific protease USP2a prevents endocytosis-mediated EGFR degradation

Ubiquitination of epidermal growth factor receptor (EGFR) is required for downregulation of the receptor by endocytosis. Impairment of this pathway results in constitutively active EGFR, which is associated with carcinogenesis, particularly in lung cancer. We previously demonstrated that the deubiquitinating enzyme ubiquitin-specific protease 2a (USP2a) has oncogenic properties. Here, we show a new role for USP2a as a regulator of EGFR endocytosis. USP2a localizes to early endosomes and associates with EGFR, stabilizing the receptor, which retains active downstream signaling. HeLa cells transiently expressing catalytically active, but not mutant (MUT), USP2a show increased plasma membrane-localized EGFR, as well as decreased internalized and ubiquitinated EGFR. Conversely, USP2a silencing reverses this phenotype. Importantly, USP2a prevents the degradation of MUT in addition to wild-type EGFR. Finally, we observed that USP2a and EGFR proteins are coordinately overexpressed in non-small cell lung cancers. Taken together, our data indicate that USP2a antagonizes EGFR endocytosis and thus amplifies signaling activity from the receptor. Our findings suggest that regulation of deubiquitination could be exploited therapeutically in cancers overexpressing EGFR.

Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data.

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

Spatial proximity to fibroblasts impacts molecular features and therapeutic sensitivity of breast cancer cells influencing clinical outcomes

Using a three-dimensional coculture model, we identified significant subtype-specific changes in gene expression, metabolic, and therapeutic sensitivity profiles of breast cancer cells in contact with cancer-associated fibroblasts (CAF). CAF-induced gene expression signatures predicted clinical outcome and immune-related differences in the microenvironment. We found that fibroblasts strongly protect carcinoma cells from lapatinib, attributable to its reduced accumulation in carcinoma cells and an elevated apoptotic threshold. Fibroblasts from normal breast tissues and stromal cultures of brain metastases of breast cancer had similar effects as CAFs. Using synthetic lethality approaches, we identified molecular pathways whose inhibition sensitizes HER2+ breast cancer cells to lapatinib both in vitro and in vivo, including JAK2/STAT3 and hyaluronic acid. Neoadjuvant lapatinib therapy in HER2+ breast tumors lead to a significant increase of phospho-STAT3+ cancer cells and a decrease in the spatial proximity of proliferating (Ki67+) cells to CAFs impacting therapeutic responses. Our studies identify CAF-induced physiologically and clinically relevant changes in cancer cells and offer novel approaches for overcoming microenvironment-mediated therapeutic resistance. Cancer Res; 76(22); 6495-506. ©2016 AACR.

A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues

MOTIVATION Monoclonal antibodies (mAbs) are among the most powerful and important tools in biology and medicine. MAb development is of great significance to many research and clinical applications. Therefore, objective mAb classification is essential for categorizing and comparing mAb panels based on their reactivity patterns in different cellular species. However, typical flow cytometric mAb profiles present unique modeling challenges with their non-Gaussian features and intersample variations. It makes accurate mAb classification difficult to do with the currently used kernel-based or hierarchical clustering techniques. RESULTS To address these challenges, in the present study we developed a formal two-step framework called mAbprofiler for systematic, parametric characterization of mAb profiles. Further, we measured the reactivity of hundreds of new antibodies in diverse tissues using flow cytometry, which we successfully classified using mAbprofiler. First, mAbprofiler fits a mAbs flow cytometric histogram with a finite mixture model of skew t distributions that is robust against non-Gaussian features, and constructs a precise, smooth and mathematically rigorous profile. Then it performs novel curve clustering of the fitted mAb profiles using a skew t mixture of non-linear regression model that can handle intersample variation. Thus, mAbprofiler provides a new framework for identifying robust mAb classes, all well defined by distinct parametric templates, which can be used for classifying new mAb samples. We validated our classification results both computationally and empirically using mAb profiles of known classification. AVAILABILITY AND IMPLEMENTATION A demonstration code in R is available at the journal website. The R code implementing the full framework is available from the author website - http://amath.nchu.edu.tw/www/teacher/tilin/software CONTACT saumyadipta_pyne@dfci.harvard.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

High resolution array CGH and gene expression profiling of alveolar soft part sarcoma

Purpose: Alveolar soft part sarcoma (ASPS) is a soft tissue sarcoma with poor prognosis, and little molecular evidence exists for its origin, initiation, and progression. The aim of this study was to elucidate candidate molecular pathways involved in tumor pathogenesis. Experimental Design: We employed high-throughput array comparative genomic hybridization (aCGH) and cDNA-Mediated Annealing, Selection, Ligation, and Extension Assay to profile the genomic and expression signatures of primary and metastatic ASPS from 17 tumors derived from 11 patients. We used an integrative bioinformatics approach to elucidate the molecular pathways associated with ASPS progression. FISH was performed to validate the presence of the t(X;17)(p11.2;q25) ASPL–TFE3 fusion and, hence, confirm the aCGH observations. Results: FISH analysis identified the ASPL–TFE3 fusion in all cases. aCGH revealed a higher number of numerical aberrations in metastatic tumors relative to primaries, but failed to identify consistent alterations in either group. Gene expression analysis highlighted 1,063 genes that were differentially expressed between the two groups. Gene set enrichment analysis identified 16 enriched gene sets (P < 0.1) associated with differentially expressed genes. Notable among these were several stem cell gene expression signatures and pathways related to differentiation. In particular, the paired box transcription factor PAX6 was upregulated in the primary tumors, along with several genes whose mouse orthologs have previously been implicated in Pax6 DNA binding during neural stem cell differentiation. Conclusion: In addition to suggesting a tentative neural line of differentiation for ASPS, these results implicate transcriptional deregulation from fusion genes in the pathogenesis of ASPS. Clin Cancer Res; 20(6); 1521–30. ©2014 AACR.

Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry.

BackgroundWhen flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past to study such changes, but high-dimensional experiments necessitate developing new computational approaches to this problem. A robust solution to this problem is to construct distinct templates to summarize all samples from a class, and then to compare these templates to study the changes across classes or conditions.ResultsWe designed a hierarchical algorithm, flowMatch, to first match the corresponding clusters across samples for producing robust meta-clusters, and to then construct a high-dimensional template as a collection of meta-clusters for each class of samples. We applied the algorithm on flow cytometry data obtained from human blood cells before and after stimulation with anti-CD3 monoclonal antibody, which is reported to change phosphorylation responses of memory and naive T cells. The flowMatch algorithm is able to construct representative templates from the samples before and after stimulation, and to match corresponding meta-clusters across templates. The templates of the pre-stimulation and post-stimulation data corresponding to memory and naive T cell populations clearly show, at the level of the meta-clusters, the overall phosphorylation shift due to the stimulation.ConclusionsWe concisely represent each class of samples by a template consisting of a collection of meta-clusters (representative abstract populations). Using flowMatch, the meta-clusters across samples can be matched to assess overall differences among the samples of various phenotypes or time-points.