Functional synergies yet distinct modulators affected by genetic alterations in common human cancers

Response and resistance to BET bromodomain inhibitors in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a heterogeneous and clinically aggressive disease for which there is no targeted therapy. BET bromodomain inhibitors, which have shown efficacy in several models of cancer, have not been evaluated in TNBC. These inhibitors displace BET bromodomain proteins such as BRD4 from chromatin by competing with their acetyl-lysine recognition modules, leading to inhibition of oncogenic transcriptional programs. Here we report the preferential sensitivity of TNBCs to BET bromodomain inhibition in vitro and in vivo, establishing a rationale for clinical investigation and further motivation to understand mechanisms of resistance. In paired cell lines selected for acquired resistance to BET inhibition from previously sensitive TNBCs, we failed to identify gatekeeper mutations, new driver events or drug pump activation. BET-resistant TNBC cells remain dependent on wild-type BRD4, which supports transcription and cell proliferation in a bromodomain-independent manner. Proteomic studies of resistant TNBC identify strong association with MED1 and hyper-phosphorylation of BRD4 attributable to decreased activity of PP2A, identified here as a principal BRD4 serine phosphatase. Together, these studies provide a rationale for BET inhibition in TNBC and present mechanism-based combination strategies to anticipate clinical drug resistance.

CLK2 Is an Oncogenic Kinase and Splicing Regulator in Breast Cancer

Genetically activated kinases have been attractive therapeutic targets in cancer due to the relative ease of developing tumor-specific treatment strategies for them. To discover novel putative oncogenic kinases, we identified 26 genes commonly amplified and overexpressed in breast cancer and subjected them to a lentiviral shRNA cell viability screen in a panel of breast cancer cell lines. Here, we report that CLK2, a kinase that phosphorylates SR proteins involved in splicing, acts as an oncogene in breast cancer. Deregulated alternative splicing patterns are commonly observed in human cancers but the underlying mechanisms and functional relevance are still largely unknown. CLK2 is amplified and overexpressed in a significant fraction of breast tumors. Downregulation of CLK2 inhibits breast cancer growth in cell culture and in xenograft models and it enhances cell migration and invasion. Loss of CLK2 in luminal breast cancer cells leads to the upregulation of epithelial-to-mesenchymal transition (EMT)-related genes and a switch to mesenchymal splice variants of several genes, including ENAH (MENA). These results imply that therapeutic targeting of CLK2 may be used to modulate EMT splicing patterns and to inhibit breast tumor growth.

JARID1B is a luminal lineage-driving oncogene in breast cancer

Recurrent mutations in histone-modifying enzymes imply key roles in tumorigenesis, yet their functional relevance is largely unknown. Here, we show that JARID1B, encoding a histone H3 lysine 4 (H3K4) demethylase, is frequently amplified and overexpressed in luminal breast tumors and a somatic mutation in a basal-like breast cancer results in the gain of unique chromatin binding and luminal expression and splicing patterns. Downregulation of JARID1B in luminal cells induces basal genes expression and growth arrest, which is rescued by TGFβ pathway inhibitors. Integrated JARID1B chromatin binding, H3K4 methylation, and expression profiles suggest a key function for JARID1B in luminal cell-specific expression programs. High luminal JARID1B activity is associated with poor outcome in patients with hormone receptor-positive breast tumors.

Cancer develops, progresses and responds to therapies through restricted perturbation of the protein-protein interaction network

The products of genes mutated or differentially expressed in cancer tend to occupy central positions within the network of protein-protein interactions, or the interactome network. Integration of different types of gene and protein relationships has considerably increased the understanding of the mechanisms of carcinogenesis, while also enhancing the applicability of expression signatures. In this scenario, however, it remains unknown how cancer develops, progresses and responds to therapies in a potentially controlled manner at the systems level. Here, by applying the concepts of load transfer and cascading failures in power grids, we examine the impact and transmission of cancer-related gene expression changes in the interactome network. Relative to random perturbations, this study reveals topological robustness associated with all cancer conditions. In addition, experimental perturbation of a central cancer node, which consists of over-expression of the α-synuclein (SNCA) protein in MCF7 breast cancer cells, also reveals robustness. Conversely, a search for proteins with an opposite topological impact identifies the autophagy pathway. Mechanistically, the existence of smaller shortest paths among cancer-related proteins appears to be a topological feature that partially contributes to the restricted perturbation of the network. Together, the results of this study suggest that cancer develops, progresses and responds to therapies following controlled, restricted perturbation of the interactome network.

Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data

MOTIVATION

Selecting a small number of signature genes for accurate classification of samples is essential for the development of diagnostic tests. However, many genes are highly correlated in gene expression data, and hence, many possible sets of genes are potential classifiers. Because treatment outcomes are poor in advanced chronic myeloid leukemia (CML), we hypothesized that expression of classifiers of advanced phase CML when detected in early CML [chronic phase (CP) CML], correlates with subsequent poorer therapeutic outcome.

RESULTS

We developed a method that integrates gene expression data with expert knowledge and predicted functional relationships using iterative Bayesian model averaging. Applying our integrated method to CML, we identified small sets of signature genes that are highly predictive of disease phases and that are more robust and stable than using expression data alone. The accuracy of our algorithm was evaluated using cross-validation on the gene expression data. We then tested the hypothesis that gene sets associated with advanced phase CML would predict relapse after allogeneic transplantation in 176 independent CP CML cases. Our gene signatures of advanced phase CML are predictive of relapse even after adjustment for known risk factors associated with transplant outcomes.

Identification and chemoprevention in subjects at moderate risk of colorectal cancer

The risk of developing colorectal cancer (CRC) depends on both genetic factors and lifestyle-related factors. Chemoprevention's true contribution is dependent on lifetime CRC risk. There are clinical situations where chemoprevention for CRC is undoubtedly useful. There are other situations where the risk of CRC seems to be only moderately increased and in these situations, the true contribution of chemoprevention is questionable. A few specific studies assessing the effect of chemopreventive agents in these situations are available. In the present article, we will try to better define these particular situations and discuss the risk quantification and the expected chemoprevention contribution.