Catherine M. Shachaf

MYC inactivation uncovers pluripotent differentiation and tumour dormancy in hepatocellular cancer

Hepatocellular carcinoma is generally refractory to clinical treatment. Here, we report that inactivation of the MYC oncogene is sufficient to induce sustained regression of invasive liver cancers. MYC inactivation resulted en masse in tumour cells differentiating into hepatocytes and biliary cells forming bile duct structures, and this was associated with rapid loss of expression of the tumour marker α-fetoprotein, the increase in expression of liver cell markers cytokeratin 8 and carcinoembryonic antigen, and in some cells the liver stem cell marker cytokeratin 19. Using in vivo bioluminescence imaging we found that many of these tumour cells remained dormant as long as MYC remain inactivated; however, MYC reactivation immediately restored their neoplastic features. Using array comparative genomic hybridization we confirmed that these dormant liver cells and the restored tumour retained the identical molecular signature and hence were clonally derived from the tumour cells. Our results show how oncogene inactivation may reverse tumorigenesis in the most clinically difficult cancers. Oncogene inactivation uncovers the pluripotent capacity of tumours to differentiate into normal cellular lineages and tissue structures, while retaining their latent potential to become cancerous, and hence existing in a state of tumour dormancy.

Genomic and Proteomic Analysis Reveals a Threshold Level of MYC Required for Tumor Maintenance

MYC overexpression has been implicated in the pathogenesis of most types of human cancers. MYC is likely to contribute to tumorigenesis by its effects on global gene expression. Previously, we have shown that the loss of MYC overexpression is sufficient to reverse tumorigenesis. Here, we show that there is a precise threshold level of MYC expression required for maintaining the tumor phenotype, whereupon there is a switch from a gene expression program of proliferation to a state of proliferative arrest and apoptosis. Oligonucleotide microarray analysis and quantitative PCR were used to identify changes in expression in 3,921 genes, of which 2,348 were down-regulated and 1,573 were up-regulated. Critical changes in gene expression occurred at or near the MYC threshold, including genes implicated in the regulation of the G(1)-S and G(2)-M cell cycle checkpoints and death receptor/apoptosis signaling. Using two-dimensional protein analysis followed by mass spectrometry, phospho-flow fluorescence-activated cell sorting, and antibody arrays, we also identified changes at the protein level that contributed to MYC-dependent tumor regression. Proteins involved in mRNA translation decreased below threshold levels of MYC. Thus, at the MYC threshold, there is a loss of its ability to maintain tumorigenesis, with associated shifts in gene and protein expression that reestablish cell cycle checkpoints, halt protein translation, and promote apoptosis.

Tumor Dormancy and MYC Inactivation: Pushing Cancer to the Brink of Normalcy

Upon MYC inactivation, tumors variously undergo proliferative arrest, cellular differentiation, and apoptosis and in some cases, apparently permanently revoking tumorigenesis. In liver tumor cells, we recently showed that MYC inactivation uncovers stem cell properties and triggers differentiation, but in this case, their neoplastic properties are restorable by MYC reactivation. Thus, whereas oncogene inactivation can push cancer to the brink of normalcy, some cells retain the latent capacity to turn cancerous again, arguing that they may exist in a state of tumor dormancy.

A Novel Method for Detection of Phosphorylation in Single Cells by Surface Enhanced Raman Scattering (SERS) using Composite Organic-Inorganic Nanoparticles (COINs)

Background Detection of single cell epitopes has been a mainstay of immunophenotyping for over three decades, primarily using fluorescence techniques for quantitation. Fluorescence has broad overlapping spectra, limiting multiplexing abilities. Methodology/Principal Findings To expand upon current detection systems, we developed a novel method for multi-color immuno-detection in single cells using “Composite Organic-Inorganic Nanoparticles” (COINs) Raman nanoparticles. COINs are Surface-Enhanced Raman Scattering (SERS) nanoparticles, with unique Raman spectra. To measure Raman spectra in single cells, we constructed an automated, compact, low noise and sensitive Raman microscopy device (Integrated Raman BioAnalyzer). Using this technology, we detected proteins expressed on the surface in single cells that distinguish T-cells among human blood cells. Finally, we measured intracellular phosphorylation of Stat1 (Y701) and Stat6 (Y641), with results comparable to flow cytometry. Conclusions/Significance Thus, we have demonstrated the practicality of applying COIN nanoparticles for measuring intracellular phosphorylation, offering new possibilities to expand on the current fluorescent technology used for immunoassays in single cells.

A pluripotency signature predicts histologic transformation and influences survival in follicular lymphoma patients

Histologic transformation (HT) of follicular lymphoma to diffuse large B-cell lymphoma (DLBCL-t) is associated with accelerated disease course and drastically worse outcome, yet the underlying mechanisms are poorly understood. We show that a network of gene transcriptional modules underlies HT. Central to the network hierarchy is a signature strikingly enriched for pluripotency-related genes. These genes are typically expressed in embryonic stem cells (ESCs), including MYC and its direct targets. This core ESC-like program was independent of proliferation/cell-cycle and overlapped but was distinct from normal B-cell transcriptional programs. Furthermore, we show that the ESC program is correlated with transcriptional programs maintaining tumor phenotype in transgenic MYC-driven mouse models of lymphoma. Although our approach was to identify HT mechanisms rather than to derive an optimal survival predictor, a model based on ESC/differentiation programs stratified patient outcomes in 2 independent patient cohorts and was predictive of propensity of follicular lymphoma tumors to transform. Transformation was associated with an expression signature combining high expression of ESC transcriptional programs with reduced expression of stromal programs. Together, these findings suggest a central role for an ESC-like signature in the mechanism of HT and provide new clues for potential therapeutic targets.

Electron Microscopy Localization and Characterization of Functionalized Composite Organic-Inorganic SERS Nanoparticles on Leukemia Cells

We demonstrate the use of electron microscopy as a powerful characterization tool to identify and locate antibody-conjugated composite organic-inorganic nanoparticle (COINs) surface enhanced Raman scattering (SERS) nanoparticles on cells. U937 leukemia cells labeled with antibody CD54-conjugated COINs were characterized in their native, hydrated state using wet scanning electron microscopy (SEM) and in their dehydrated state using high-resolution SEM. In both cases, the backscattered electron (BSE) detector was used to detect and identify the silver constituents in COINs due to its high sensitivity to atomic number variations within a specimen. The imaging and analytical capabilities in the SEM were further complemented by higher resolution transmission electron microscopy (TEM) images and scanning Auger electron spectroscopy (AES) data to give reliable and high-resolution information about nanoparticles and their binding to cell surface antigens.

A pathway analysis method for genome‐wide association studies

For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of interest (e.g., disease status). Although shifting the focus of analysis from individual genes to pathways has proven to improve the statistical power and provide more robust results, such methods tend to eliminate a large number of genes whose pathways are unknown. For these genes, we propose to use a Bayesian multinomial logit model to predict the associated pathways by using the genes with known pathways as the training data. Our hierarchical Bayesian model takes the uncertainty regarding the pathway predictions into account while assessing the significance of pathways. We apply our method to two independent studies on type 2 diabetes and show that the overlap between the results from the two studies is statistically significant. We also evaluate our approach on the basis of simulated data.