KDM5 inhibition offers a novel therapeutic strategy for the treatment of KMT2D mutant lymphomas.

Loss-of-function mutations in KMT2D are a striking feature of germinal center (GC) lymphomas, resulting in decreased histone 3 lysine 4 (H3K4) methylation and altered gene expression. We hypothesized that inhibition of the KDM5 family, which demethylates H3K4me3/me2, would reestablish H3K4 methylation and restore the expression of genes repressed on loss of KMT2D. KDM5 inhibition increased H3K4me3 levels and caused an antiproliferative response in vitro, which was markedly greater in both endogenous and gene-edited KMT2D mutant diffuse large B-cell lymphoma cell lines, whereas tumor growth was inhibited in KMT2D mutant xenografts in vivo. KDM5 inhibition reactivated both KMT2D-dependent and -independent genes, resulting in diminished B-cell signaling and altered expression of B-cell lymphoma 2 (BCL2) family members, including BCL2 itself. KDM5 inhibition may offer an effective therapeutic strategy for ameliorating KMT2D loss-of-function mutations in GC lymphomas.

Reproducible pharmacogenomic profiling of cancer cell line panels.

The use of large-scale genomic and drug response screening of cancer cell lines depends crucially on the reproducibility of results. Here we consider two previously published screens, plus a later critique of these studies. Using independent data, we show that consistency is achievable, and provide a systematic description of the best laboratory and analysis practices for future studies.

A resource for cell line authentication, annotation and quality control.

Cell line misidentification, contamination and poor annotation affect scientific reproducibility. Here we outline simple measures to detect or avoid cross-contamination, present a framework for cell line annotation linked to short tandem repeat and single nucleotide polymorphism profiles, and provide a catalogue of synonymous cell lines. This resource will enable our community to eradicate the use of misidentified lines and generate credible cell-based data.

Cell Lines as Biological Models: Practical Steps for More Reliable Research.

Research in toxicology relies on in vitro models such as cell lines. These living models are prone to change and may be described in publications with insufficient information or quality control testing. This article sets out recommendations to improve the reliability of cell-based research.

Authentication: A Standard Problem or a Problem of Standards?

Reproducibility and transparency in biomedical sciences have been called into question, and scientists have been found wanting as a result. Putting aside deliberate fraud, there is evidence that a major contributor to lack of reproducibility is insufficient quality assurance of reagents used in preclinical research. Cell lines are widely used in biomedical research to understand fundamental biological processes and disease states, yet most researchers do not perform a simple, affordable test to authenticate these key resources. Here, we provide a synopsis of the problems we face and how standards can contribute to an achievable solution.

Structure-guided discovery of a novel, potent, and orally bioavailable 3,5-dimethylisoxazole aryl-benzimidazole BET bromodomain inhibitor.

The bromodomain and extra-terminal (BET) family of proteins, consisting of the bromodomains containing protein 2 (BRD2), BRD3, BRD4, and the testis-specific BRDT, are key epigenetic regulators of gene transcription and has emerged as an attractive target for anticancer therapy. Herein, we describe the discovery of a novel potent BET bromodomain inhibitor, using a systematic structure-based approach focused on improving potency, metabolic stability, and permeability. The optimized dimethylisoxazole aryl-benzimidazole inhibitor exhibited high potency towards BRD4 and related BET proteins in biochemical and cell-based assays and inhibited tumor growth in two proof-of-concept preclinical animal models.

Authentication of M14 melanoma cell line proves misidentification of MDA-MB-435 breast cancer cell line.

A variety of analytical approaches have indicated that melanoma cell line UCLA-SO-M14 (M14) and breast carcinoma cell line MDA-MB-435 originate from a common donor. This indicates that at some point in the past, one of these cell lines became misidentified, meaning that it ceased to correspond to the reported donor and instead became falsely identified (through cross-contamination or other means) as a cell line from a different donor. Initial studies concluded that MDA-MB-435 was the misidentified cell line and M14 was the authentic cell line, although contradictory evidence has been published, resulting in further confusion. To address this question, we obtained early samples of the melanoma cell line (M14), a lymphoblastoid cell line from the same donor (ML14), and donor serum preserved at the originator's institution. M14 samples were cryopreserved in December 1975, before MDA-MB-435 cells were established in culture. Through a series of molecular characterizations, including short tandem repeat (STR) profiling and cytogenetic analysis, we demonstrated that later samples of M14 and MDA-MB-435 correspond to samples of M14 frozen in 1975, to the lymphoblastoid cell line ML14, and to the melanoma donor's STR profile, sex and blood type. This work demonstrates conclusively that M14 is the authentic cell line and MDA-MB-435 is misidentified. With clear provenance information and authentication testing of early samples, it is possible to resolve debates regarding the origins of problematic cell lines that are widely used in cancer research.

Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors.

Mutationally activated kinases define a clinically validated class of targets for cancer drug therapy. However, the efficacy of kinase inhibitors in patients whose tumours harbour such alleles is invariably limited by innate or acquired drug resistance. The identification of resistance mechanisms has revealed a recurrent theme­the engagement of survival signals redundant to those transduced by the targeted kinase. Cancer cells typically express multiple receptor tyrosine kinases (RTKs) that mediate signals that converge on common critical downstream cell-survival effectors­most notably, phosphatidylinositol-3-OH kinase (PI(3)K) and mitogen-activated protein kinase (MAPK). Consequently, an increase in RTK-ligand levels, through autocrine tumour-cell production, paracrine contribution from tumour stroma or systemic production, could confer resistance to inhibitors of an oncogenic kinase with a similar signalling output. Here, using a panel of kinase-'addicted' human cancer cell lines, we found that most cells can be rescued from drug sensitivity by simply exposing them to one or more RTK ligands. Among the findings with clinical implications was the observation that hepatocyte growth factor (HGF) confers resistance to the BRAF inhibitor PLX4032 (vemurafenib) in BRAF-mutant melanoma cells. These observations highlight the extensive redundancy of RTK-transduced signalling in cancer cells and the potentially broad role of widely expressed RTK ligands in innate and acquired resistance to drugs targeting oncogenic kinases.

Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events.

Lung cancer is a highly heterogeneous disease in terms of both underlying genetic lesions and response to therapeutic treatments. We performed deep whole-genome sequencing and transcriptome sequencing on 19 lung cancer cell lines and three lung tumor/normal pairs. Overall, our data show that cell line models exhibit similar mutation spectra to human tumor samples. Smoker and never-smoker cancer samples exhibit distinguishable patterns of mutations. A number of epigenetic regulators, including KDM6A, ASH1L, SMARCA4, and ATAD2, are frequently altered by mutations or copy number changes. A systematic survey of splice-site mutations identified 106 splice site mutations associated with cancer specific aberrant splicing, including mutations in several known cancer-related genes. RAC1b, an isoform of the RAC1 GTPase that includes one additional exon, was found to be preferentially up-regulated in lung cancer. We further show that its expression is significantly associated with sensitivity to a MAP2K (MEK) inhibitor PD-0325901. Taken together, these data present a comprehensive genomic landscape of a large number of lung cancer samples and further demonstrate that cancer-specific alternative splicing is a widespread phenomenon that has potential utility as therapeutic biomarkers. The detailed characterizations of the lung cancer cell lines also provide genomic context to the vast amount of experimental data gathered for these lines over the decades, and represent highly valuable resources for cancer biology.

PAK1 mediates pancreatic cancer cell migration and resistance to MET inhibition.

Pancreatic adenocarcinoma (PDAC) is a major unmet medical need and a deeper understanding of molecular drivers is needed to advance therapeutic options for patients. We report here that p21-activated kinase 1 (PAK1) is a central node in PDAC cells downstream of multiple growth factor signalling pathways, including hepatocyte growth factor (HGF) and MET receptor tyrosine kinase. PAK1 inhibition blocks signalling to cytoskeletal effectors and tumour cell motility driven by HGF/MET. MET antagonists, such as onartuzumab and crizotinib, are currently in clinical development. Given that even highly effective therapies have resistance mechanisms, we show that combination with PAK1 inhibition overcomes potential resistance mechanisms mediated either by activation of parallel growth factor pathways or by direct amplification of PAK1. Inhibition of PAK1 attenuated in vivo tumour growth and metastasis in a model of pancreatic adenocarcinoma. In human tissues, PAK1 is highly expressed in a proportion of PDACs (33% IHC score 2 or 3; n = 304) and its expression is significantly associated with MET positivity (p < 0.0001) and linked to a widespread metastatic pattern in patients (p = 0.067). Taken together, our results provide evidence for a functional role of MET/PAK1 signalling in pancreatic adenocarcinoma and support further characterization of therapeutic inhibitors in this indication.

Crosstalk between Aurora-A and p53: frequent deletion or downregulation of Aurora-A in tumors from p53 null mice.

The Aurora-A kinase gene is amplified in a subset of human tumors and in radiation-induced lymphomas from p53 heterozygous mice. Normal tissues from p53-/- mice have increased Aurora-A protein levels, but lymphomas from these mice exhibit heterozygous deletions of Aurora-A and/or reduced protein expression. A similar correlation between low p53 levels and Aurora-A gene deletions and expression is found in human breast cancer cell lines. In vitro studies using mouse embryo fibroblasts demonstrate that inhibition of Aurora-A can have either positive or negative effects on cell growth as a function of p53 status. These data have implications for the design of approaches to targeted cancer therapy involving the crosstalk between Aurora-A kinase and p53 pathways.

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.

A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes.

Recent studies suggest that thousands of genes may contribute to breast cancer pathophysiologies when deregulated by genomic or epigenomic events. Here, we describe a model "system" to appraise the functional contributions of these genes to breast cancer subsets. In general, the recurrent genomic and transcriptional characteristics of 51 breast cancer cell lines mirror those of 145 primary breast tumors, although some significant differences are documented. The cell lines that comprise the system also exhibit the substantial genomic, transcriptional, and biological heterogeneity found in primary tumors. We show, using Trastuzumab (Herceptin) monotherapy as an example, that the system can be used to identify molecular features that predict or indicate response to targeted therapies or other physiological perturbations.

Genomic and transcriptional aberrations linked to breast cancer pathophysiologies.

This study explores the roles of genome copy number abnormalities (CNAs) in breast cancer pathophysiology by identifying associations between recurrent CNAs, gene expression, and clinical outcome in a set of aggressively treated early-stage breast tumors. It shows that the recurrent CNAs differ between tumor subtypes defined by expression pattern and that stratification of patients according to outcome can be improved by measuring both expression and copy number, especially high-level amplification. Sixty-six genes deregulated by the high-level amplifications are potential therapeutic targets. Nine of these (FGFR1, IKBKB, ERBB2, PROCC, ADAM9, FNTA, ACACA, PNMT, and NR1D1) are considered druggable. Low-level CNAs appear to contribute to cancer progression by altering RNA and cellular metabolism.

A requirement for wild-type Ras isoforms in mutant KRas-driven signalling and transformation.

The mutant forms of KRas, NRas and HRas drive the initiation and progression of a number of human cancers, but less is known about the role of WT (wild-type) Ras alleles and isoforms in cancer. We used zinc-finger nucleases targeting HRas and NRas to modify both alleles of these genes in the mutant KRas-driven Hec1A endometrial cancer cell line, which normally expresses WT copies of these genes. The disruption of either WT isoform of Ras compromised growth-factor-dependent signalling through the ERK (extracellular-signal-regulated kinase) pathway. In addition, the disruption of HRas hindered the activation of Akt and subsequent downstream signalling. This was associated with decreased proliferation, increased apoptosis and decreased anchorage-independent growth in the HRas-disrupted cells. However, xenograft tumour growth was not significantly affected by the disruption of either NRas or HRas. As expected, deleting the mutant allele of KRas abolished tumour growth, whereas deletion of the remaining WT copy of KRas increased the tumorigenic properties of these cells; deleting a single copy of either HRas or NRas did not mimic this effect. The present study demonstrates that the WT copies of HRas, NRas and KRas play unique roles in the context of mutant KRas-driven tumours.

A conditional feedback loop regulates Ras activity through EphA2.

The EphA2 receptor tyrosine kinase is frequently overexpressed in many cancers, including 40% of breast cancers. Here, we show that EphA2 is a direct transcriptional target of the Ras-Raf-MAPK pathway and that ligand-stimulated EphA2 attenuates the growth factor-induced activation of Ras. Thus, a negative feedback loop is created that regulates Ras activity. Interestingly, the expression of EphA2 and ephrin-A1 is mutually exclusive in a panel of 28 breast cancer cell lines. We show that the MAPK pathway inhibits ephrin-A1 expression, and the ligand expression inhibits EphA2 levels contributing to the receptor-ligand reciprocal expression pattern in these cell lines. Our results suggest that an escape from the negative effects of this interaction may be important in the development of cancer.

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

BACKGROUND: An 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. RESULTS: We 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. CONCLUSIONS: The 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.

Epithelial mesenchymal transition traits in human breast cancer cell lines parallel the CD44(hi/)CD24 (lo/-) stem cell phenotype in human breast cancer.

We review here the recently emerging relationship between epithelial-mesenchymal transition (EMT) and breast cancer stem cells (BCSC), and provide analyses of published data on human breast cancer cell lines, supporting their utility as a model for the EMT/BCSC state. Genome-wide transcriptional profiling of these cell lines has confirmed the existence of a subgroup with mesenchymal tendencies and enhanced invasive properties ('Basal B'/Mesenchymal), distinct from subgroups with either predominantly luminal ('Luminal') or mixed basal/luminal ('Basal A') features (Neve et al. Cancer Cell, 2006). A literature-derived EMT gene signature has shown specific enrichment within the Basal B subgroup of cell lines, consistent with their over-expression of various EMT transcriptional drivers. Basal B cell lines are found to resemble BCSC, being CD44(high)CD24(low). Moreover, gene products that distinguish Basal B from Basal A and Luminal cell lines (Basal B Discriminators) showed close concordance with those that define BCSC isolated from clinical material, as reported by Shipitsin et al. (Cancer Cell, 2007). CD24 mRNA levels varied across Basal B cell lines, correlating with other Basal B Discriminators. Many gene products correlating with CD24 status in Basal B cell lines were also differentially expressed in isolated BCSC. These findings confirm and extend the importance of the cellular product of the EMT with Basal B cell lines, and illustrate the value of analysing these cell lines for new leads that may improve breast cancer outcomes. Gene products specific to Basal B cell lines may serve as tools for the detection, quantification, and analysis of BCSC/EMT attributes.

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.