Combination drug therapy is a widely used paradigm for managing numerous human malignancies. In cancer treatment, additive and/or synergistic drug combinations can convert weakly efficacious monotherapies into regimens that produce robust antitumor activity. This can be explained in part through pathway interdependencies that are critical for cancer cell proliferation and survival. However, identification of the various interdependencies is difficult due to the complex molecular circuitry that underlies tumor development and progression. Here, we present a highthroughput platform that allows for an unbiased identification of synergistic and efficacious drug combinations. In a screen of 22,737 experiments of 583 doublet combinations in 39 diverse cancer cell lines using a 4 by 4 dosing regimen, both wellknown and novel synergistic and efficacious combinations were identified. Here, we present an example of one such novel combination, a Wee1 inhibitor (AZD1775) and an mTOR inhibitor (ridaforolimus), and demonstrate that the combination potently and synergistically inhibits cancer cell growth in vitro and in vivo. This approach has identified novel combinations that would be difficult to reliably predict based purely on our current understanding of cancer cell biology.

During RNA interference (RNAi), long dsRNA is processed to approximately 21 nt duplexes, short interfering RNAs (siRNAs), which silence genes through a mRNA degradation pathway. Small temporal RNAs (stRNAs) and micro-RNAs (miRNAs) are approximately 21 nt RNAs that are processed from endogenously encoded hairpin-structured precursors, and function to silence genes via translational repression. Here we report that synthetic hairpin RNAs that mimic siRNAs and miRNA precursor molecules can target a gene for silencing, and the mechanism of silencing appears to be through mRNA degradation and not translational repression. The sequence and structural configuration of these RNAs are important, and even slight modification in structure can affect the silencing activity of the hairpins. Furthermore, these RNAs are active when expressed by DNA vectors containing polymerase III promoters, opening the possibility for new approaches in stable RNAi-based loss of function studies.

BackgroundHyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence.MethodsWe used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets.ResultsThe RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer.ConclusionsThese data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.

Introduction of small interfering RNAs (siRNAs) into a cell can cause a specific interference of gene expression known as RNA interference (RNAi). However, RNAi activity in lymphocytes and in normal primary mammalian cells has not been thoroughly demonstrated. In this report, we show that siRNAs complementary to CD4 and CD8α specifically reduce surface expression of these coreceptors and their respective mRNA in a thymoma cell line model. We show that RNAi activity is only caused by a subset of siRNAs complementary to the mRNA target and that ineffective siRNAs can compete with effective siRNAs. Using primary differentiated T lymphocytes, we provide the first evidence of siRNA-mediated RNAi gene silencing in normal nontransformed somatic mammalian lymphocytes.

Next generation sequencing was used to identify Notch mutations in a large collection of diverse solid tumors. NOTCH1 and NOTCH2 rearrangements leading to constitutive receptor activation were confined to triple negative breast cancers (TNBC, 6 of 66 tumors). TNBC cell lines with NOTCH1 rearrangements associated with high levels of activated NOTCH1 (N1-ICD) were sensitive to the gamma-secretase inhibitor (GSI) MRK-003, both alone and in combination with pacitaxel, in vitro and in vivo, whereas cell lines with NOTCH2 rearrangements were resistant to GSI. Immunohistochemical staining of N1-ICD in TNBC xenografts correlated with responsiveness, and expression levels of the direct Notch target gene HES4 correlated with outcome in TNBC patients. Activating NOTCH1 point mutations were also identified in other solid tumors, including adenoid cystic carcinoma (ACC). Notably, ACC primary tumor xenografts with activating NOTCH1 mutations and high N1-ICD levels were sensitive to GSI, whereas N1-ICD-low tumors without NOTCH1 mutations were resistant.

Fixed, paraffin-embedded (FPE) tissues are a potentially rich resource for studying the role of NOTCH1 in cancer and other pathologies, but tests that reliably detect activated NOTCH1 (NICD1) in FPE samples have been lacking. Here, we bridge this gap by developing an immunohistochemical (IHC) stain that detects a neoepitope created by the proteolytic cleavage event that activates NOTCH1. Following validation using xenografted cancers and normal tissues with known patterns of NOTCH1 activation, we applied this test to tumors linked to dysregulated Notch signaling by mutational studies. As expected, frequent NICD1 staining was observed in T lymphoblastic leukemia/lymphoma, a tumor in which activating NOTCH1 mutations are common. However, when IHC was used to gauge NOTCH1 activation in other human cancers, several unexpected findings emerged. Among B cell tumors, NICD1 staining was much more frequent in chronic lymphocytic leukemia than would be predicted based on the frequency of NOTCH1 mutations, while mantle cell lymphoma and diffuse large B cell lymphoma showed no evidence of NOTCH1 activation. NICD1 was also detected in 38% of peripheral T cell lymphomas. Of interest, NICD1 staining in chronic lymphocytic leukemia cells and in angioimmunoblastic lymphoma was consistently more pronounced in lymph nodes than in surrounding soft tissues, implicating factors in the nodal microenvironment in NOTCH1 activation in these diseases. Among carcinomas, diffuse strong NICD1 staining was observed in 3.8% of cases of triple negative breast cancer (3 of 78 tumors), but was absent from 151 non-small cell lung carcinomas and 147 ovarian carcinomas. Frequent staining of normal endothelium was also observed; in line with this observation, strong NICD1 staining was also seen in 77% of angiosarcomas. These findings complement insights from genomic sequencing studies and suggest that IHC staining is a valuable experimental tool that may be useful in selection of patients for clinical trials.

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Two genetically engineered, conditional mouse models of lung tumor formation, K-ras(LSL-G12D) and K-ras(LSL-G12D)/p53(LSL-R270H), are commonly used to model human lung cancer. Developed by Tyler Jacks and colleagues, these models have been invaluable to study in vivo lung cancer initiation and progression in a genetically and physiologically relevant context. However, heterogeneity, multiplicity and complexity of tumor formation in these models make it challenging to monitor tumor growth in vivo and have limited the application of these models in oncology drug discovery. Here, we describe a novel analytical method to quantitatively measure total lung tumor burden in live animals using micro-computed tomography imaging. Applying this methodology, we studied the kinetics of tumor development and response to targeted therapy in vivo in K-ras and K-ras/p53 mice. Consistent with previous reports, lung tumors in both models developed in a time- and dose (Cre recombinase)-dependent manner. Furthermore, the compound K-ras(LSL-G12D)/p53(LSL-R270H) mice developed tumors faster and more robustly than mice harboring a single K-ras(LSL-G12D) oncogene, as expected. Erlotinib, a small molecule inhibitor of the epidermal growth factor receptor, significantly inhibited tumor growth in K-ras(LSL-G12D)/p53(LSL-R270H) mice. These results demonstrate that this novel imaging technique can be used to monitor both tumor progression and response to treatment and therefore supports a broader application of these genetically engineered mouse models in oncology drug discovery and development.