Safiyyah Ziyad

Subtype and pathway specific responses to anticancer compounds in breast cancer

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

Activity of the kinesin spindle protein inhibitor ispinesib (SB-715992) in models of breast cancer.

Purpose: Ispinesib (SB-715992) is a potent inhibitor of kinesin spindle protein, a kinesin motor protein essential for the formation of a bipolar mitotic spindle and cell cycle progression through mitosis. Clinical studies of ispinesib have shown a 9% response rate in patients with locally advanced or metastatic breast cancer and a favorable safety profile without significant neurotoxicities, gastrointestinal toxicities, or hair loss. To better understand the potential of ispinesib in the treatment of breast cancer, we explored the activity of ispinesib alone and in combination with several therapies approved for the treatment of breast cancer. Experimental Design: We measured the ispinesib sensitivity and pharmacodynamic response of breast cancer cell lines representative of various subtypes in vitro and as xenografts in vivo and tested the ability of ispinesib to enhance the antitumor activity of approved therapies. Results: In vitro, ispinesib displayed broad antiproliferative activity against a panel of 53 breast cell lines. In vivo, ispinesib produced regressions in each of five breast cancer models and tumor-free survivors in three of these models. The effects of ispinesib treatment on pharmacodynamic markers of mitosis and apoptosis were examined in vitro and in vivo, revealing a greater increase in both mitotic and apoptotic markers in the MDA-MB-468 model than in the less sensitive BT-474 model. In vivo, ispinesib enhanced the antitumor activity of trastuzumab, lapatinib, doxorubicin, and capecitabine and exhibited activity comparable with paclitaxel and ixabepilone. Conclusions: These findings support further clinical exploration of kinesin spindle protein inhibitors for the treatment of breast cancer. Clin Cancer Res; 16(2); 566–76

Integrated analysis of breast cancer cell lines reveals unique signaling pathways

BackgroundCancer is a heterogeneous disease resulting from the accumulation of genetic defects that negatively impact control of cell division, motility, adhesion and apoptosis. Deregulation in signaling along the EgfR-MAPK pathway is common in breast cancer, though the manner in which deregulation occurs varies between both individuals and cancer subtypes.ResultsWe were interested in identifying subnetworks within the EgfR-MAPK pathway that are similarly deregulated across subsets of breast cancers. To that end, we mapped genomic, transcriptional and proteomic profiles for 30 breast cancer cell lines onto a curated Pathway Logic symbolic systems model of EgfR-MAPK signaling. This model was composed of 539 molecular states and 396 rules governing signaling between active states. We analyzed these models and identified several subtype-specific subnetworks, including one that suggested Pak1 is particularly important in regulating the MAPK cascade when it is over-expressed. We hypothesized that Pak1 over-expressing cell lines would have increased sensitivity to Mek inhibitors. We tested this experimentally by measuring quantitative responses of 20 breast cancer cell lines to three Mek inhibitors. We found that Pak1 over-expressing luminal breast cancer cell lines are significantly more sensitive to Mek inhibition compared to those that express Pak1 at low levels. This indicates that Pak1 over-expression may be a useful clinical marker to identify patient populations that may be sensitive to Mek inhibitors.ConclusionsAll together, our results support the utility of symbolic system biology models for identification of therapeutic approaches that will be effective against breast cancer subsets.

A systems analysis of the chemosensitivity of breast cancer cells to the polyamine analogue PG-11047

BackgroundPolyamines regulate important cellular functions and polyamine dysregulation frequently occurs in cancer. The objective of this study was to use a systems approach to study the relative effects of PG-11047, a polyamine analogue, across breast cancer cells derived from different patients and to identify genetic markers associated with differential cytotoxicity.MethodsA panel of 48 breast cell lines that mirror many transcriptional and genomic features present in primary human breast tumours were used to study the antiproliferative activity of PG-11047. Sensitive cell lines were further examined for cell cycle distribution and apoptotic response. Cell line responses, quantified by the GI50 (dose required for 50% relative growth inhibition) were correlated with the omic profiles of the cell lines to identify markers that predict response and cellular functions associated with drug sensitivity.ResultsThe concentrations of PG-11047 needed to inhibit growth of members of the panel of breast cell lines varied over a wide range, with basal-like cell lines being inhibited at lower concentrations than the luminal cell lines. Sensitive cell lines showed a significant decrease in S phase fraction at doses that produced little apoptosis. Correlation of the GI50 values with the omic profiles of the cell lines identified genomic, transcriptional and proteomic variables associated with response.ConclusionsA 13-gene transcriptional marker set was developed as a predictor of response to PG-11047 that warrants clinical evaluation. Analyses of the pathways, networks and genes associated with response to PG-11047 suggest that response may be influenced by interferon signalling and differential inhibition of aspects of motility and epithelial to mesenchymal transition.See the related commentary by Benes and Settleman: http://www.biomedcentral.com/1741-7015/7/78

Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

BackgroundAn important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data.ResultsWe put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information.ConclusionsThe empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge.

Sparse combinatorial inference with an application in cancer biology

MOTIVATION Combinatorial effects, in which several variables jointly influence an output or response, play an important role in biological systems. In many settings, Boolean functions provide a natural way to describe such influences. However, biochemical data using which we may wish to characterize such influences are usually subject to much variability. Furthermore, in high-throughput biological settings Boolean relationships of interest are very often sparse, in the sense of being embedded in an overall dataset of higher dimensionality. This motivates a need for statistical methods capable of making inferences regarding Boolean functions under conditions of noise and sparsity. RESULTS We put forward a statistical model for sparse, noisy Boolean functions and methods for inference under the model. We focus on the case in which the form of the underlying Boolean function, as well as the number and identity of its inputs are all unknown. We present results on synthetic data and on a study of signalling proteins in cancer biology.

Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer

We report here on experimental and theoretical efforts to determine how best to combine drugs that inhibit HER2 and AKT in HER2+ breast cancers. We accomplished this by measuring cellular and molecular responses to lapatinib and the AKT inhibitors (AKTi) GSK690693 and GSK2141795 in a panel of 22 HER2+ breast cancer cell lines carrying wild type or mutant PIK3CA. We observed that combinations of lapatinib plus AKTi were synergistic in HER2+/PIK3CAmut cell lines but not in HER2+/PIK3CAwt cell lines. We measured changes in phospho-protein levels in 15 cell lines after treatment with lapatinib, AKTi or lapatinib + AKTi to shed light on the underlying signaling dynamics. This revealed that p-S6RP levels were less well attenuated by lapatinib in HER2+/PIK3CAmut cells compared to HER2+/PIK3CAwt cells and that lapatinib + AKTi reduced p-S6RP levels to those achieved in HER2+/PIK3CAwt cells with lapatinib alone. We also found that that compensatory up-regulation of p-HER3 and p-HER2 is blunted in PIK3CAmut cells following lapatinib + AKTi treatment. Responses of HER2+ SKBR3 cells transfected with lentiviruses carrying control or PIK3CAmut sequences were similar to those observed in HER2+/PIK3CAmut cell lines but not in HER2+/PIK3CAwt cell lines. We used a nonlinear ordinary differential equation model to support the idea that PIK3CA mutations act as downstream activators of AKT that blunt lapatinib inhibition of downstream AKT signaling and that the effects of PIK3CA mutations can be countered by combining lapatinib with an AKTi. This combination does not confer substantial benefit beyond lapatinib in HER2+/PIK3CAwt cells.

A Systems Analysis of Mitotic Apparatus Inhibitors Defines a Response Network for Breast Cancer.

Deregulation of aspects of the mitotic apparatus leads to increased genome instability, carcinogenesis and aggressive tumor behavior in human and rodent model systems 1 . This knowledge has stimulated development of inhibitors of elements of the mitotic apparatus as anticancer agents including PLK1, CENPE, and AURKB and several are now being tested for efficacy clincially 2-6 . These trials and eventual clinical use will benefit from molecular markers that predict response. In order to identify such markers, we assessed quantitative responses to the agents GSK461364, GSK923295 and GSK1070916 that target PLK1, CENPE and AURKB; respectively, in a panel of 50 breast cancer cell lines. This analysis showed that basal subtype cell lines were preferentially sensitive to all three agents and that responses among the lines to the three agents were strongly correlated. This may be explained by our discovery that components of the mitotic apparatus including PLK1, CENPE and AURKB form a transcriptionally co-regulated network comprised of more than 50 genes that is preferentially active in basal subtype of breast cell lines and primary tumors. Remarkably, this network also is activate in subsets of cancers of the lung, ovarian, prostate and brain, Wilms tumor, human blood malignancies and selected normal tissues. We then defined a mitotic apparatus network index (MANI) and showed that high MANI was associated with poor outcome clinically and with preferential responsive to GSK461364, GSK923295 and GSK1070916 in preclinical models. This suggests that measures of the MANI will identify poor outcome tumors that will likely respond well to mitotic apparatus network gene inhibitors as well as potential dose limiting normal tissues. Reference 1. Quigley, D.A. et al. Nature 458 , 505-8 (2009).2. Strebhardt, K. & Ullrich, A . Nat. Rev. Cancer 6 , 321-330 (2006).3. Toyoshima-Morimoto, F., Taniguchi, E., Shinya, N., Iwamatsu, A. & Nishida, E. Nature 410 , 215-20 (2001).4. Barr, F.A., Sillje, H.H. & Nigg, E.A. Nat. Rev. Mol. Cell. Biol. 5 , 429–440 (2004).5. McInnes, C. et al. Nat. Chem. Biol. 2 , 608–617 (2006).6. Yamada, S. et al. Oncogene 23 , 5901-5911(2004). Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2020.

Correction: Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer (PLoS ONE (2015) 10:7 (e0133219) DOI: 10.1371/journal.pone.0133219)

[This corrects the article DOI: 10.1371/journal.pone.0133219.].

Abstract 1979: Algebraic factorization of gene expression profiles reveals subtype-specific drug sensitivities among 54 breast cancer cell lines

Breast cancer is a heterogeneous disease, with reproducible and prognostically important subclasses. Breast cancer cell lines are widely used to study preclinical investigational agents, but the relationships, if any, between breast cancer subclass and drug response are not well understood. To help bridge this gap, we have profiled drug responses across a large panel of well-annotated breast cancer cell lines. In order to translate in vitro drug responses into clinically useful predictions, it is important that the cell line panel be organized into subtypes representative of the cancer subtype diversity found in the clinic. Recent studies have demonstrated that an algebraic clustering method known as “non-negative matrix factorization” (NMF) can be applied to gene expression profiles to resolve clinically meaningful cancer subtypes in greater detail than achievable using other clustering methods. To test whether NMF improves our ability to resolve drug sensitivities in breast cancer cell lines, we clustered pretreatment gene expression profiles of 54 breast cancer cell lines using two different methods: (1) NMF-based consensus clustering and (2) hierarchical consensus clustering. Using NMF-based consensus, we identified five robust subtypes (two Basal-A, one Basal-B and two Luminal classes). In contrast, using hierarchical consensus clustering, we could identify only three robust subtypes (one Basal-A, one Basal-B and one Luminal class). The drug response profiles were then segregated by subtype to determine whether either of the two clustering methods improves our ability to resolve drug sensitivities. Of the 67 drug compounds included in our study, the 5-subtype NMF-based classification scheme revealed three compounds (AG1024, CPT-11 and topotecan) exhibiting subtype-specific drug effects (p Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1979.