Integrative multi-omics identifies high risk multiple myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways

Background

Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features.

Results

Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10⁻⁶)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e−5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways.

Conclusion

Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01140-5.

Genetic and pharmacological interrogation of cancer vulnerability using a multiplexed cell line screening platform

The multiplexed cancer cell line screening platform PRISM demonstrated its utility in testing hundreds of cell lines in a single run, possessing the potential to speed up anti-cancer drug discovery, validation and optimization. Here we described the development and implementation of a next-generation PRISM platform combining Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9-mediated gene editing, cell line DNA barcoding and next-generation sequencing to enable genetic and/or pharmacological assessment of target addiction in hundreds of cell lines simultaneously. Both compound and CRISPR-knockout PRISM screens well recapitulated the results from individual assays and showed high consistency with a public database.

Xia et al. report the development and optimization of a high-throughput screening platform to systematically determine cancer cell sensitivity to pharmacological and genetic perturbations, BMS-PRISM, based on PRISM and high-throughput CRISPR/Cas9 loss-of-function screen technologies using cell line barcoding.

CC-90009, a novel cereblon E3 ligase modulator, targets acute myeloid leukemia blasts and leukemia stem cells

A number of clinically validated drugs have been developed by repurposing the CUL4-DDB1-CRBN-RBX1 (CRL4CRBN) E3 ubiquitin ligase complex with molecular glue degraders to eliminate disease-driving proteins. Here, we present the identification of a first-in-class GSPT1-selective cereblon E3 ligase modulator, CC-90009. Biochemical, structural, and molecular characterization demonstrates that CC-90009 coopts the CRL4CRBN to selectively target GSPT1 for ubiquitination and proteasomal degradation. Depletion of GSPT1 by CC-90009 rapidly induces acute myeloid leukemia (AML) apoptosis, reducing leukemia engraftment and leukemia stem cells (LSCs) in large-scale primary patient xenografting of 35 independent AML samples, including those with adverse risk features. Using a genome-wide CRISPR-Cas9 screen for effectors of CC-90009 response, we uncovered the ILF2 and ILF3 heterodimeric complex as a novel regulator of cereblon expression. Knockout of ILF2/ILF3 decreases the production of full-length cereblon protein via modulating CRBN messenger RNA alternative splicing, leading to diminished response to CC-90009. The screen also revealed that the mTOR signaling and the integrated stress response specifically regulate the response to CC-90009 in contrast to other cereblon modulators. Hyperactivation of the mTOR pathway by inactivation of TSC1 and TSC2 protected against the growth inhibitory effect of CC-90009 by reducing CC-90009-induced binding of GSPT1 to cereblon and subsequent GSPT1 degradation. On the other hand, GSPT1 degradation promoted the activation of the GCN1/GCN2/ATF4 pathway and subsequent apoptosis in AML cells. Collectively, CC-90009 activity is mediated by multiple layers of signaling networks and pathways within AML blasts and LSCs, whose elucidation gives insight into further assessment of CC-90009s clinical utility. These trials were registered at www.clinicaltrials.gov as #NCT02848001 and #NCT04336982).

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

UBE2G1 governs the destruction of cereblon neomorphic substrates

The cereblon modulating agents (CMs) including lenalidomide, pomalidomide and CC-220 repurpose the Cul4-RBX1-DDB1-CRBN (CRL4^CRBN) E3 ubiquitin ligase complex to induce the degradation of specific neomorphic substrates via polyubiquitination in conjunction with E2 ubiquitin-conjugating enzymes, which have until now remained elusive. Here we show that the ubiquitin-conjugating enzymes UBE2G1 and UBE2D3 cooperatively promote the K48-linked polyubiquitination of CRL4^CRBN neomorphic substrates via a sequential ubiquitination mechanism. Blockade of UBE2G1 diminishes the ubiquitination and degradation of neomorphic substrates, and consequent antitumor activities elicited by all tested CMs. For example, UBE2G1 inactivation significantly attenuated the degradation of myeloma survival factors IKZF1 and IKZF3 induced by lenalidomide and pomalidomide, hence conferring drug resistance. UBE2G1-deficient myeloma cells, however, remained sensitive to a more potent IKZF1/3 degrader CC-220. Collectively, it will be of fundamental interest to explore if loss of UBE2G1 activity is linked to clinical resistance to drugs that hijack the CRL4^CRBN to eliminate disease-driving proteins.

Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors

SUMMARY

Comprehensive genomic profiling is expected to revolutionize cancer therapy. In this Prospective, we present the prevalence of mutations and copy-number alterations with predictive associations across solid tumors at different levels of stringency for gene-drug targetability. More than 90% of The Cancer Genome Atlas samples have potentially targetable alterations, the majority with multiple events, illustrating the challenges for treatment prioritization given the complexity of the genomic landscape. Nearly 80% of the variants in rarely mutated oncogenes are of uncertain functional significance, reflecting the gap in our understanding of the relevance of many alterations potentially linked to therapeutic actions. Access to targeted agents in early clinical trials could affect treatment decision in 75% of patients with cancer. Prospective implementation of large-scale molecular profiling and standardized reports of predictive biomarkers are fundamental steps for making precision cancer medicine a reality.

Functional kinomics identifies candidate therapeutic targets in head and neck cancer

PURPOSE

To identify novel therapeutic drug targets for p53-mutant head and neck squamous cell carcinoma (HNSCC).

EXPERIMENTAL DESIGN

RNAi kinome viability screens were performed on HNSCC cells, including autologous pairs from primary tumor and recurrent/metastatic lesions, and in parallel on murine squamous cell carcinoma (MSCC) cells derived from tumors of inbred mice bearing germline mutations in Trp53, and p53 regulatory genes: Atm, Prkdc, and p19(Arf). Cross-species analysis of cell lines stratified by p53 mutational status and metastatic phenotype was used to select 38 kinase targets. Both primary and secondary RNAi validation assays were performed on additional HNSCC cell lines to credential these kinase targets using multiple phenotypic endpoints. Kinase targets were also examined via chemical inhibition using a panel of kinase inhibitors. A preclinical study was conducted on the WEE1 kinase inhibitor, MK-1775.

RESULTS

Our functional kinomics approach identified novel survival kinases in HNSCC involved in G2-M cell-cycle checkpoint, SFK, PI3K, and FAK pathways. RNAi-mediated knockdown and chemical inhibition of the WEE1 kinase with a specific inhibitor, MK-1775, had a significant effect on both viability and apoptosis. Sensitivity to the MK-1775 kinase inhibitor is in part determined by p53 mutational status, and due to unscheduled mitotic entry. MK-1775 displays single-agent activity and potentiates the efficacy of cisplatin in a p53-mutant HNSCC xenograft model.

CONCLUSIONS

WEE1 kinase is a potential therapeutic drug target for HNSCC. This study supports the application of a functional kinomics strategy to identify novel therapeutic targets for cancer.

Abstract 5323: Integrated computational cell-line modeling of drug sensitivity and high-throughput siRNA screening reveals novel molecular biomarkers for conventional chemotherapy

Introduction: Identifying molecular aberrations that drive carcinogenesis remains a daunting problem. Uncovering important combinations of such aberrations, e.g. overexpression of a gene in concordance with copy number amplification, pose additional challenges. Publicly available large-scale cell line panels profiled for drug response often do not reflect the true tumor heterogeneity and therefore predictions made solely based on such data may be difficult to translate into therapeutic targets. Integrating predicted associations with drug sensitivity (resistance) on one hand, with siRNA screens with the same drug (patient-derived samples and cell lines) on the other hand, should allow for the identification of therapeutically relevant molecular traits.

Methods: We propose a novel Bayesian model for multi-task learning with a gene-wise prior, which accounts for multiple aberrations in a given gene (expression, mutation and copy number). Our method allows for the simultaneous learning of drug response for multiple drugs in a given panel (N_Sanger=138, N_CCLE=24 drugs). Our model aims to identifying sets of important aberrations, which individually occur at low frequencies. To train and evaluate the performance of our model we use the CCLE and Sanger datasets. Further, we use the Cisplatin and Doxorubicin drug responses from the Sanger dataset to rank genome-wise aberrations to find features best predictive of response to the chemotherapeutic agents. We then consider our findings in the context of high-throughput siRNA synthetic lethal screens in head and neck, ovarian and breast cancer with Cisplatin and Doxorubicin in patient-derived samples and cell lines.

Results: Our approach improves the quality of drug sensitivity prediction (Pearson correlation in five-fold cross validation) over methods that model individual drug responses (e.g. Elastic Net). It further allows for the subtyping of drugs by target/mechanism of action commonalities, which we recover closely for drugs with known mechanism of action. Our model recovers reasonably the targets of consensus drug interactions (e.g. Erlotinib and EGFR) and in addition identifies other known associations currently undergoing preclinical/clinical validation (i.e. Cisplatin and JAK2 mutation, and RUNX3 expression and Doxorubicin). Integrated analysis of the results from our predictive model in conjunction with the siRNA screens are currently under investigation.

Conclusions: We present an integrated approach that combines a novel Bayesian multi-task learning model with high-throughput siRNA screens. Our approach aims to uncover sets of important aberrations and allows for the subtyping of drugs based on similarities in targets and mechanisms of action. We integrate our results with high-throughput RNAi experiments to identify synthetic lethal events in specific therapeutic context.

Citation Format: Olga H. Nikolova, Mehmet Gönen, Rodrigo Dienstmann, In Sock Jang, Russell Moser, Silvia Cermelli, Chang Xu, Ryan M. Mitchell, Eduardo Mendez, Carla Grandori, Christopher Kemp, Stephen Friend, Justin Guinney, Adam Margolin. Integrated computational cell-line modeling of drug sensitivity and high-throughput siRNA screening reveals novel molecular biomarkers for conventional chemotherapy. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5323. doi:10.1158/1538-7445.AM2014-5323

Synthetic lethal screens as a means to understand and treat MYC-driven cancers

Although therapeutics against MYC could potentially be used against a wide range of human cancers, MYC-targeted therapies have proven difficult to develop. The convergence of breakthroughs in human genomics and in gene silencing using RNA interference (RNAi) have recently allowed functional interrogation of the genome and systematic identification of synthetic lethal interactions with hyperactive MYC. Here, we focus on the pathways that have emerged through RNAi screens and present evidence that a subset of genes showing synthetic lethality with MYC are significantly interconnected and linked to chromatin and transcriptional processes, as well as to DNA repair and cell cycle checkpoints. Other synthetic lethal interactions with MYC point to novel pathways and potentially broaden the repertoire of targeted therapies. The elucidation of MYC synthetic lethal interactions is still in its infancy, and how these interactions may be influenced by tissue-specific programs and by concurrent genetic change will require further investigation. Nevertheless, we predict that these studies may lead the way to novel therapeutic approaches and new insights into the role of MYC in cancer.

hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests

A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.

The Stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity

Computational efficiency is important for learning algorithms operating in the "large p, small n" setting. In computational biology, the analysis of data sets containing tens of thousands of features ("large p"), but only a few hundred samples ("small n"), is nowadays routine, and regularized regression approaches such as ridge-regression, lasso, and elastic-net are popular choices. In this paper we propose a novel and highly efficient Bayesian inference method for fitting ridge-regression. Our method is fully analytical, and bypasses the need for expensive tuning parameter optimization, via cross-validation, by employing Bayesian model averaging over the grid of tuning parameters. Additional computational efficiency is achieved by adopting the singular value decomposition reparametrization of the ridge-regression model, replacing computationally expensive inversions of large p × p matrices by efficient inversions of small and diagonal n × n matrices. We show in simulation studies and in the analysis of two large cancer cell line data panels that our algorithm achieves slightly better predictive performance than cross-validated ridge-regression while requiring only a fraction of the computation time. Furthermore, in comparisons based on the cell line data sets, our algorithm systematically out-performs the lasso in both predictive performance and computation time, and shows equivalent predictive performance, but considerably smaller computation time, than the elastic-net.

Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data

Large-scale pharmacogenomic screens of cancer cell lines have emerged as an attractive pre-clinical system for identifying tumor genetic subtypes with selective sensitivity to targeted therapeutic strategies. Application of modern machine learning approaches to pharmacogenomic datasets have demonstrated the ability to infer genomic predictors of compound sensitivity. Such modeling approaches entail many analytical design choices; however, a systematic study evaluating the relative performance attributable to each design choice is not yet available. In this work, we evaluated over 110,000 different models, based on a multifactorial experimental design testing systematic combinations of modeling factors within several categories of modeling choices, including: type of algorithm, type of molecular feature data, compound being predicted, method of summarizing compound sensitivity values, and whether predictions are based on discretized or continuous response values. Our results suggest that model input data (type of molecular features and choice of compound) are the primary factors explaining model performance, followed by choice of algorithm. Our results also provide a statistically principled set of recommended modeling guidelines, including: using elastic net or ridge regression with input features from all genomic profiling platforms, most importantly, gene expression features, to predict continuous-valued sensitivity scores summarized using the area under the dose response curve, with pathway targeted compounds most likely to yield the most accurate predictors. In addition, our study provides a publicly available resource of all modeling results, an open source code base, and experimental design for researchers throughout the community to build on our results and assess novel methodologies or applications in related predictive modeling problems.

Abstract A130: Joint cell-line (CCLE, Sanger) and patient (TCGA) modeling of drug sensitivity reveals novel molecular biomarkers for targeted therapy and conventional chemotherapy

Background: The unresponsiveness to anticancer drugs in patients outlines the need to identify novel and robust biomarkers of response to therapy. The recent release of large molecular cell line datasets labeled for drug sensitivity enables the development of such predictors. Cell line based models poorly reflect the extent of molecular heterogeneity and of tumor microenvironment relationships existing at the patient level. We aimed to overcome these limitations by combining cell line datasets with patient cohorts to uncover novel molecular traits associated with drug response at the patient level.

Methods: For building our model, we used the following publicly available datasets: CCLE (1057 cell lines, 24 drugs), Sanger (790 cell lines, 138 drugs) and TCGA (patients: breast cancer, colorectal cancer, lung squamous carcinoma, lung adenocarcinoma, ovarian cancer, kidney cancer and melanoma). A joint semi-supervised trained Elastic Net approach was used to model drug sensitivity (both conventional chemotherapy and targeted therapy) using gene-expression, copy-number alteration, and somatic mutation data. We introduced a false discovery rate estimation for selecting significant aberrations. As validation, we used pooled RNAi screens (NKI, Netherlands) and also a dataset of 120 patients prospectively enrolled in the ongoing MOSCATO molecular screening program (Gustave Roussy, France) (presented at ASCO 2013).

Results: Drug sensitivity models trained in the entire set of cell lines performed better than models trained using tissue specific cell lines, whatever the drug and the tissue type. Novel molecular traits (mutation, copy number variation) were identified by selecting highly performant models (AUC &gt; .75) across drug and tissue type combinations. We were able to recover all of the recognized landmark biomarkers across drug - tissue combination (e.g. Erlotinib: EGFR mutation in lung adenocarcinoma; Lapatanib: ERBB2 amplification in breast cancer; Vemurafenib: BRAF mutation in melanoma, etc). Moreover, we uncovered novel molecular traits (copy, number mutations) associated (FDR&lt;25%) with drug response (e.g. Erlotinib: CDKN2A deletion in lung adenocarcinoma; Lapatanib: CDH1 deletion in breast cancer; Vemurafenib: FH amplification in melanoma, etc.). The validation of our framework in the pooled RNAi screens of colorectal and lung adenocarcinoma cell lines and in the MOSCATO patient's cohort will be presented.

Conclusions: We provide a comprehensive annotation of mutations and copy-number events in tumors predicted to be associated with sensitivity and resistance in 142 anti-cancer drugs and across 7 tumor types. These molecular traits can be used to refine the results of large scale functional genomic experiments such as RNAi screens and ultimately in the early clinical trial setting.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A130.

Citation Format: Charles Ferté, Elias Chaibub Neto, Chong Sun, Cecilia Noecker, Frederic Commo, Olga Nikolova, In Sock Jang, Mehmet Gonen, Benjamin Besse, Fabrice André, Eric Angevin, Ludovic Lacroix, Clement Mazoyer, Antoine Hollebecque, Christophe Massard, Adam Margolin, Roderick Beijersbergen, Stephen Friend, Rene Bernards, Jean-Charles Soria, Justin Guinney. Joint cell-line (CCLE, Sanger) and patient (TCGA) modeling of drug sensitivity reveals novel molecular biomarkers for targeted therapy and conventional chemotherapy. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A130.

Improving breast cancer survival analysis through competition-based multidimensional modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Abstract IA11: Identification of therapeutic targets for MYC-driven cancers by functional genomics

The development of small molecules targeting MYC oncoproteins specifically in cancer cells has been a challenge, as MYC are DNA binding proteins required for proliferation of normal tissues. To circumvent these difficulties, we identified synthetic lethal interactions with MYC over-expression by high throughput siRNA screening (Toyoshima et al, 2012).

Here, we will present validation of specific MYC-synthetic lethal genes (MYC-SL) in MYCN amplified neuroblastoma and ovarian cancer with high level of MYC using both RNAi approaches as well as small molecule inhibitors. To prioritize genes for future drug development, we carried out meta-analysis of genomic tumor data and obtained statistical significant associations between MYC alterations (i.e. gene amplification or overexpression) for selected MYC-SL genes. We also addressed the need of identifying biomarkers to stratify patients with solid tumors for activation of MYC and we will discuss the challenges and potential strategies, with particular focus on ovarian and breast cancers.

Finally, to obtain a more global view of critical pathways that MYC-SL genes we performed network analysis of our targets. This analysis revealed that major points of vulnerabilities are linked to transcription and chromatin remodeling processes, DNA repair and cell-cycle checkpoints and metabolic functions. In addition, developmental, signaling and differentiation pathways represent unforeseen dependencies of MYC overexpressing cells. Altogether, these results provide a broad menu for therapeutic interventions to target MYC-driven cancers.

*Current affiliation: Tohoku University School of Medicine, Sendai, Japan.

Citation Format: Masafumi Toyoshima, In Sock Jang, Silvia Cermelli, Brady Bernard, Carla Grandori. Identification of therapeutic targets for MYC-driven cancers by functional genomics. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr IA11.

Abstract PR05: Construction of synthetic lethal networks for p53 tumor suppressor pathways identifies candidate therapeutic targets for metastatic, chemotherapy resistant HNSCC

p53 deficient cells are resistant to apoptosis, which contributes to malignant progression and therapy resistance of p53 mutant tumors. For this reason, identifying and targeting p53 independent cell death pathways is an important therapeutic goal. We previously reported that the DNA damage kinases Atm and DNA-PK exhibit p53 independent synthetic lethal interactions (Current Biology 11:191-194, 2001) and that DNA-PK regulates a DNA damage induced apoptotic response that is p53 independent (EMBO Reports, 10:87-93, 2009). To identify additional synthetic lethal interactions with the p53 pathway, we used high throughput siRNA screens to interrogate the kinomes of both human and mouse tumor cell cultures stratified for p53 status and metastatic potential. Mouse cells included low passage squamous cell carcinoma (SCC) cells isolated from tumors from five genotypes of mice: wild type, Atm-/-, DNA-PKcs-/-, p19/Arf -/- and p53+/-. Human cells were derived from both primary and metastatic head and neck squamous cell carcinomas (HNSCC). siRNA kinome screens were run in the presence of three concentrations of the chemotherapy agent doxorubicin to identify genotype specific sensitizers. We identified numerous additional synthetic partners with each p53 pathway mutation: Atm, DNA-PKcs, p19/Arf, and p53. These synthetic lethal interactions varied with doxorubicin exposure, thus defining gene targets that sensitize tumor cells of defined genotype to genotoxic therapy. Cross species comparison and independent validation was used to prioritize 20 survival kinases specific to p53 mutant metastatic HNSCC. These kinases are involved in phosphatidylinositol, Src, focal adhesion and integrin signaling pathways, and G2/M cell cycle transition. We selected the G2/M regulatory kinase, WEE1, for preclinical testing as a small molecule inhibitor is available with potential for clinical application. Relative to p53 wild type SCC cells, p53 deficient cells were more sensitive to the WEE1 inhibitor MK-1775, as well as the CHK1 inhibitor AZD7762 providing chemical confirmation of our siRNA screen results. Examination of 42 additional SCC cell lines revealed that p53 mutation conferred increased sensitivity to a dual WEE1/CHK1 inhibitor. Treatment of mice bearing xenografts of p53 mutant HNSCC cells with MK-1775, either alone or in conjunction with cisplatin, impaired tumor growth and in some cases led to tumor regression. These results demonstrate the utility of high throughput siRNA screens applied to genetically defined early passage tumor cells combined with cross species comparisons to efficiently pinpoint novel drug targets for p53 mutant cancers.

This abstract is also presented as Poster A04.

Citation Format: Russell Moser, Chang Xu, Michael Kao, In Sock Jang, Kay Gurley, Adam Margolin, Carla Grandori, Eddie Mendez, Chris Kemp. Construction of synthetic lethal networks for p53 tumor suppressor pathways identifies candidate therapeutic targets for metastatic, chemotherapy resistant HNSCC. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR05.

Abstract IA15: Predicting drug sensitivity from cancer cell lines

High-throughput molecular characterization technologies have enabled detailed genetic and genomic characterization of large panels of cancer cell lines, including profiling of gene expression, copy number variation (CNV), SNP, mutation, and RNA-seq. The same cell lines have also been profiled for sensitivity to potential anti-cancer compounds, allowing development of computational models used to predict genotype-specific drug treatments linked to tumor molecular subtypes.

Inferring such models is challenging because the model inputs contain far more features than observations, known as the p&gt;&gt;n problem, precluding the use of classical statistical models such as least squares (a.k.a. multiple linear regression). Recently, generalized linear models or penalized linear models have been proposed and extended for feature selection and overcoming the p&gt;&gt;n. Bayesian extension of such methods have also been developed, using algorithms such as Metropolis-Hasting and Markov Chain Monte Carlo, and demonstrated to improve prediction accuracy at the expense of increased computing cost.

A comprehensive evaluation of predictive modeling techniques is essential to leverage cell line studies to infer the most accurate genetic predictors of drug sensitivity, which can then be used to inform patient selection strategies in clinical trials and to identify functional genetic determinants of drug sensitivity or resistance. In this study, we evaluate state-of-the-art predictive models which utilize dimension reduction methods (e.g., partial least square, support vector machine, principal component regression), feature selection (e.g., LASSO, RIDGE, and elastic-net), and Bayesian feature selection (e.g., Bayesian LASSO and Bayesian RIDGE). We assess each method based on 1) the accuracy of sensitivity predictions in a cross validation setting and in an independent dataset, and 2) the biological coherence of inferred predictive features by comparison to publicly available pathway databases.

Citation Format: Adam Arne Margolin, In Sock Jang, Stephen Friend. Predicting drug sensitivity from cancer cell lines. [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA15.

Pregnenolone sulfate enhances spontaneous glutamate release by inducing presynaptic Ca2+-induced Ca2+ release

Pregnenolone sulfate (PS) acts as an excitatory neuromodulator and has a variety of neuropharmacological actions, such as memory enhancement and convulsant effects. In the present study, we investigated the effect of PS on glutamatergic spontaneous excitatory postsynaptic currents (sEPSCs) in acutely isolated dentate gyrus (DG) hilar neurons by use of a conventional whole-cell patch-clamp technique. PS significantly increased sEPSC frequency in a concentration-dependent manner without affecting the current amplitude, suggesting that PS acts presynaptically to increase the probability of spontaneous glutamate release. However, known molecular targets of PS, such as α7 nicotinic ACh, NMDA, σ1 receptors and voltage-dependent Ca(2+) channels, were not responsible for the PS-induced increase in sEPSC frequency. In contrast, the PS-induced increase in sEPSC frequency was completely occluded in a Ca(2+)-free external solution, and was significantly reduced by either the depletion of presynaptic Ca(2+) stores or the blockade of ryanodine receptors, suggesting that PS elicits Ca(2+)-induced Ca(2+) release (CICR) within glutamatergic nerve terminals. In addition, the PS-induced increase in sEPSC frequency was completely occluded by transient receptor potential (TRP) channel blockers. These data suggest that PS increases spontaneous glutamate release onto acutely isolated hilar neurons via presynaptic CICR, which was triggered by the influx of Ca(2+) through presynaptic TRP channels. The PS-induced modulation of excitatory transmission onto hilar neurons could have a broad impact on the excitability of hilar neurons and affect the pathophysiological functions mediated by the hippocampus.

Involvement of the protein kinase C pathway in thyrotropin-induced STAT3 activation in FRTL-5 thyroid cells

The binding of thyrotropin (TSH) to the TSH receptor (TSHR) activates two signaling pathways: the cAMP-protein kinase A (PKA) and the protein kinase C (PKC) systems. We have recently demonstrated that TSH activates the Janus kinases (JAK)/signal transducer and activator of transcription (STAT) pathway via TSHR. This study aimed to investigate whether the cAMP/PKA or the PKC system is involved in STAT3 activation in response to TSH. Treatment with TSH activated STAT3 phosphorylation in FRTL-5 thyrocytes and human TSHR-expressing Chinese hamster ovary cells. TSH-induced STAT3 activation was inhibited by a blocking antibody directed against TSHR that was isolated from patients with primary myxoedema. Increased intracellular cAMP activated STAT3 but inhibition of PKA did not affect STAT3 activation. On the other hand, the PKC stimulant PMA induced STAT3 phosphorylation and the PKC inhibitors inhibited it. Moreover, inhibition of PKC blocked STAT3 activation induced by a stimulator of cAMP. Our data suggest that TSH activates STAT3 via TSHR and cAMP- and PKC-dependent pathways, and provide evidence that PKC may be involved in the pathway downstream from cAMP.

Isoform-specific changes of adenylate cyclase mRNA expression in rat brains following chronic electroconvulsive shock

1. Electroconvulsive shock (ECS) has been reported to regulate the cAMP signaling system at various levels, suggesting that the cAMP system is involved in the therapeutic mechanism. 2. Chronic ECS has been suggested to change the expressions of adenylate cyclase (AC) genes, which constitute at least 9 families. However, little is known about its effect on the expression of AC. Therefore, to understand how chronic ECS alters the expression of AC genes in the brain, the authors analyzed the expression of 9 AC isoforms at the transcriptional level in rat hippocampus and cerebellum by quantitative RT-PCR following chronic ECS treatment. 3. Chronic ECS treatment was found to induce differential changes in the expression of AC isoforms in an isoform- and brain region-specific manner in the rat hippocampus and cerebellum. 4. Thus, it is concluded that chronic ECS induces differential changes in the expression of AC isoform mRNA in an isoform- and brain region-specific manner in the rat hippocampus and cerebellum. This suggests that the differential expression of AC isoforms might be an important mechanism by which chronic ECS treatment regulates the cAMP signaling system in rat brains.

Xenobiotic response in humanized double transgenic mice expressing tetracycline-controlled transactivator and human CYP1B1

The cytochrome P450 enzymes (P450s or CYPs) are a superfamily of hemeproteins that catalyze the monooxygenation of a wide range of endobiotic and xenobiotic substrates. A typical strategy in toxicological research and testing involves applying a toxicant at high doses for a short period to homogeneous animals under controlled conditions. However, the conditions of this approach have very little in common with actual human exposure. Transgenic (Tg) mice carrying human genes encoding a drug-metabolizing enzyme (CYP) offer a solution to many of the difficulties in the evaluation of chemical toxicity. It has been demonstrated that the expression of human CYP transgenes under the control of mammalian-inducible promoters exhibits relatively poor fold increases after induction. In this study, we used the tetracycline-regulated (tet) promoter system to increase the expression of the human CYP1B1 (hCYP1B1) gene in the tissues of transgenic mice. By mating two lineages of transgenic mice, double transgenic (dTg) mice expressing both tTA and hCYP1B1 genes under the control of the tet promoter were successfully produced, into which the two transgenes were introduced in an embryo. The expression pattern of tTA-driven hCYP1B1 transgene featured a fold induction of more than 3 to 12 in the brain, heart, and lung and 2- to 4-fold induction in the liver, kidney, and intestine upon doxycycline removal. Immunohistochemical staining with hCYP1B1 antibody was also increased by the removal of doxycycline. In addition, the activities of CYP liver microsomes in the dTg mice without doxycycline showed an increase compared to that in the dTg mice treated with doxycycline. The level of activities correspond to the levels of human CYP1B1 protein expression in the Tg mice (-dox) that was increased by 2-fold induction as compared to that of the dTg mice with doxycycline. Thus, overproduction in Tg can be purified and the activity of purified human CYP1B1 can be characterized by alterations to the coding sequence in order to solve the physiological function of this enzyme in a humanized in vivo system. It is also possible to examine the activity of purified human CYP1B1 using several environmental toxicants such as procarcinogens.

Developmental changes in P2X purinoceptors on glycinergic presynaptic nerve terminals projecting to rat substantia gelatinosa neurones

1. In mechanically dissociated rat spinal cord substantia gelatinosa (SG) neurones attached with native presynaptic nerve endings, glycinergic miniature inhibitory postsynaptic currents (mIPSCs) were recorded using nystatin perforated patch recording mode under voltage-clamp conditions. Under these conditions, it was tested whether the changes in P2X receptor subtype on the glycinergic presynaptic nerve terminals occur during postnatal development. 2. ATP facilitated glycinergic mIPSC frequency in a concentration-dependent manner through all developmental stages tested, whereas alphabeta-methylene-ATP (alphabeta-me-ATP) was only effective at later developmental stages. 3. alphabeta-me-ATP-elicited mIPSC frequency facilitation was completely occluded in the Ca2+-free external solution, but it was not affected by adding 10(-4) M Cd2+. 4. alphabeta-me-ATP still facilitated mIPSC frequency even in the presence of 10(-6) M thapsigargin, a Ca2+ pump blocker. 5. In later developmental stages, ATP-elicited presynaptic or postsynaptic responses were reversibly blocked by 10(-5) M pyridoxal-5-phosphate-6-azophenyl-2',4'-disulfonic acid (PPADS), but only partially blocked by 10(-7) M 2',3'-O-(2,4,6-trinitrophenyl)-ATP (TNP-ATP). However, alphabeta-me-ATP-elicited presynaptic or postsynaptic responses were completely and reversibly blocked by either 10(-5) M PPADS or 10(-7) M TNP-ATP. 6. alphabeta-me-ATP significantly reduced the evoked glycinergic IPSC amplitude in postnatal 28-30 day neurones, whereas it had no effect in 10-12 day neurones. 7. It was concluded that alphabeta-me-ATP-sensitive P2X receptors were functionally expressed on the glycinergic presynaptic nerve terminals projecting to SG neurones in later developmental stages. Such developmental changes of presynaptic P2X receptor subtypes might contribute to synaptic plasticity such as the regulation of neuronal excitability and the fine controlling of the pain signal in spinal dorsal horn neurones.

Thymidine-dependent attenuation of the mitochondrial apoptotic pathway in adenosine-induced apoptosis of HL-60 cells

OBJECTIVE

We previously reported that adenosine-induced apoptosis in HL-60 cells was attenuated by cotreating the cells with pyrimidine nucleosides. The mechanism involved in this adenosine-induced apoptosis by the differential supply of nucleosides is studied here with a particular focus on the regulation of apoptosis-associated mitochondrial events.

METHODS

Time-dependent changes in the mitochondrial membrane potential (MMP) after treatment with adenosine and/or thymidine were monitored.

RESULTS

The cells did not show any decrease of MMP level up to 2.5 h after 1 mM adenosine exposure, whereas cytochrome c release, caspase-9 and caspase-3 activity, and DNA fragmentation were already activated, suggesting that mitochondrial depolarization is not a prerequisite of other apoptosis-related mitochondrial events. In contrast, the translocation of Bax to mitochondria and the release of cytochrome c began within the first hour of adenosine treatment.

CONCLUSION

Thus, it is believed that adenosine-induced apoptosis is mediated by the activation of the caspase cascade by cytochrome c release with concomitant increase of Bax in the mitochondria, which implies that the translocation of Bax might be a leading event in the adenosine-induced apoptosis. Moreover, we found that most of the apoptotic parameters in adenosine-induced cellular changes, such as translocation of Bax, the release of cytochrome c, and the consequent activation of caspase-9 and caspase-3, were attenuated by thymidine supplement, thus indicating that the sensing of a nucleoside or nucleotide balance might be an upstream event of cytochrome c release. Therefore, it can be concluded that thymidine can attenuate adenosine-induced apoptosis by modulating the earliest stage of the mitochondrial apoptotic pathway.

Contribution of the Na-K-Cl cotransporter on GABA(A) receptor-mediated presynaptic depolarization in excitatory nerve terminals

GABA(A) receptor-mediated responses manifest as either hyperpolarization or depolarization according to the intracellular Cl(-) concentration ([Cl(-)](i)). Here, we report a novel functional interaction between the Na-K-Cl cotransporter (NKCC) and GABA(A) receptor actions on glutamatergic presynaptic nerve terminals projecting to ventromedial hypothalamic (VMH) neurons. The activation of presynaptic GABA(A) receptors depolarizes the presynaptic nerve terminals and facilitates spontaneous glutamate release by activating TTX-sensitive Na(+) channels and high-threshold Ca(2+) channels. This depolarizing action of GABA was caused by an outwardly directed Cl(-) driving force for GABA(A) receptors; that is, the [Cl(-)](i) of glutamatergic nerve terminals was higher than that predicted for a passive distribution. The higher [Cl(-)](i) was generated by bumetanide-sensitive NKCCs and was responsible for the GABA-induced presynaptic depolarization. Thus, GABA(A) receptor-mediated modulation of spontaneous glutamatergic transmission may contribute to the development and regulation of VMH function as well as to the excitability of VMH neurons themselves.

Histaminergic modulation of GABAergic transmission in rat ventromedial hypothalamic neurones

1. The ventromedial nucleus of the hypothalamus (VMH) is a key nucleus in the homeostatic regulation of neuroendocrine and behavioural functions. In mechanically dissociated rat VMH neurones with attached native presynaptic nerve endings, GABAergic spontaneous inhibitory postsynaptic currents (sIPSCs) were recorded using the nystatin perforated patch recording mode under voltage-clamp conditions. 2. Histamine reversibly inhibited the sIPSC frequency in a concentration-dependent manner without affecting the mean current amplitude. The selective histamine receptor type 3 (H(3)) agonist imetit (100 nM) mimicked this effect and it was completely abolished by the selective H(3) receptor antagonists clobenpropit (3 microM) and thioperamide (10 microM). 3. The GTP-binding protein inhibitor N-ethylmaleimide (10 microM) removed the histaminergic inhibition of GABAergic sIPSCs. 4. Elimination of external Ca(2+) reduced the GABAergic sIPSC frequency without affecting the distribution of current amplitudes. In this condition, the inhibitory effect of imetit on the sIPSC frequency completely disappeared, suggesting that the histaminergic inhibition requires extracellular Ca(2+). 5. The P/Q-type Ca(2+) channel blocker omega-agatoxin IVA (300 nM) attenuated the histaminergic inhibition of the GABAergic sIPSC frequency, but neither the N-type Ca(2+) channel blocker omega-conotoxin GVIA (3 microM) nor the L-type Ca(2+) channel blocker nicardipine (3 microM) was effective. 6. Activation of adenylyl cyclase with forskolin (10 microM) had no effect on histaminergic inhibition of the sIPSCs. 7. In conclusion, histamine inhibits spontaneous GABA release from presynaptic nerve terminals projecting to VMH neurones by inhibiting presynaptic P/Q-type Ca(2+) channels via a G-protein coupled to H(3) receptors and this may modulate the excitability of VMH neurones.

Adaptation of cAMP signaling system in SH-SY5Y neuroblastoma cells following expression of a constitutively active stimulatory G protein alpha, Q227L Gsalpha

Heterotrimeric GTP-binding proteins (G protein) are known to participate in the transduction of signals from ligand activated receptors to effector molecules to elicit cellular responses. Sustained activation of cAMP-G protein signaling system by agonist results in desensitization of the pathway at receptor levels, however it is not clear whether such receptor responses induce other changes in post-receptor signaling path that are associated with maintenance of AMP levels, i.e. cAMP-forming adenylate cyclase (AC), cAMP-degrading cyclic nucleotide phosphodiesterase (PDE) and cAMP-dependent protein kinase (PKA). Experiments were performed to determine the expression of AC, PDE, and PKA isoforms in SH-SY5Y neuroblastoma cells, in which cAMP system was activated by expressing a constitutively activated mutant of stimulatory G protein (Q227L Gsalpha). Expression of ACI mRNA was increased, but levels of ACVIII and ACIX mRNA were decreased. All of the 4 expressed isoforms of PDE (PDE1C, PDE2, PDE 4A, and PDE4B) were increased in mRNA expression; the levels of PKA RIalpha, RIbeta, and RIIbeta were increased moderately, however, those of RIIalpha and Calpha were increased remarkably. The activities of AC, PDE and PKA were also increased in the SH-SY5Y cells expressing Q227L Gsalpha. The similar changes in expression and activity of AC, PDE and PKA were observed in the SH-SY5Y cells treated with dbcAMP for 6 days. Consequently, it is concluded that the cAMP system adapts at the post-receptor level to a sustained activation of the system by differential expression of the isoforms of AC, PDE, and PKA in SH-SY5Y neuroblastoma. We also showed that an increase in cellular cAMP concentration might mediate the observed changes in the cAMP system.

Mammary gland tumor in transgenic mice expressing targeted beta-casein/HPV16E6 fusion gene

The human papillomaviruses (HPV)-16 and HPV-18 referred to as high-risk HPVs are strongly associated with anogenital malignancies as well as benign epithelial cysts. It has been demonstrated that transgenic mice carrying HPV-16 E6-E7 under the control of the MMTV LTR developed malignant tumors including salivary gland carcinoma, lymphoma, skin histiocytomas and testicular tumors in a non-mammary gland specific manner. Another regulatory unit of rat beta-casein gene can confer the expression of fusion gene preferentially in the mammary glands of transgenic mice in a developmentally regulated manner. In order to generate mammary tumor formation in transgenic mice directing HPV16E6 gene alone into the mammary gland, this regulatory unit was fused to the E6 gene of HPV-16 type to constructing fusion gene. By screening 51 newborn founder transgenic mice, three mice carrying transgenes were identified. One line termed TG32 developed in a mammary gland tumor with large subcutaneous mass in the left rib region at 17 months of age. The levels of E6 transcript in the mass-tumor of TG32 line were lower than those in non-tumor mammary gland of identical TG32 and of TG250. In each tissue of TG32 line, high expression of E6 transcript was detected both in the mammary gland and brain. Histological analysis showed that cells from mammary gland tumor of the TG32 line had also hyperplasia appearance, with irregular or increased total number of mitotic rate. These observations suggest that developing phenotype and the level of E6 transcripts in the process of malignant transformation may have different mechanisms involving the capacity to bind and destabilize p53, although for confirmation it is necessary to investigate many more transgenic mice.

Increased expression of Galphaq protein in the heart of streptozotocin-induced diabetic rats

Heart disease is one of the major cause of death in diabetic patients, but the pathogenesis of diabetic cardio-myopathy remains unclear. In this experiment, to assess the significance of G protein signaling pathways in the pathogenesis of diabetic cardiomyopathy, we analyzed the expression of G proteins and the activities of second messenger dependent protein kinases: cAMP-dependent protein kinase (PKA), DAG-mediated protein kinase C (PKC), and calmodulin dependent protein kinase II (CaM kinase II) in the streptozotocin induced diabetic rat heart. The expression of Galphaq was increased by slightly over 10% (P<0.05) in diabetic rat heart, while Galphas, Galphai, and Gbeta remained unchanged. The PKA activity in the heart did not change significantly but increased by 27% (P<0.01) in the liver. Insulin treatment did not restore the increased activity in the liver. Total PKC activity in the heart was increased by 56% (P<0.01), and insulin treatment did not restore such increase. The CaM kinase II activity in the heart remained at the same level but was slightly increased in the liver (14% increase, P<0.05). These findings of increased expression of Galphaq in the streptozotocin-diabetic rat heart that are reflected by the increased level of PKC activity and insensitivity to insulin demonstrate that alteration of Galphaq may underlie, at least partly, the cardiac dysfunction that is associated with diabetes.

Beta-glucuronidase-inhibitory activity and hepatoprotective effect of Ganoderma lucidum

To prove the relationship between the fluctuation in serum beta-glucuronidase level and hepatotoxicity, an inhibitor of beta-glucuronidase from G. lucidum was isolated and its hepatoprotective activity was investigated. The ether fraction of G. lucidum, which had potent beta-glucuronidase-inhibitory activity, protected against CCl4-induced liver injury. From this ether fraction, ganoderenic acid A, was isolated as the potent inhibitor of beta-glucuronidase. It had a potent hepatoprotective effect against CCl4-induced liver injury. These results suggest that the beta-glucuronidase seems to be closely related to liver injury, which could be prevented by beta-glucuronidase inhibitors.

Bacteroides J-37, a human intestinal bacterium, produces alpha-glucuronidase

beta-Glucuronidases of mammalian tissues metabolized glycyrrhizin (18 beta-glycyrrhetinic acid, beta-D-glucuronyl alpha-D-glucuronic acid, GL) to glycyrrhetinic acid (GA) via 18 beta-glycyrrhetinic acid alpha-D-glucuronic acid (GAMG); they hydrolyzed beta-glucuronic acid conjugates better than alpha-glucuronic acid conjugates. However, human intestinal bacteria directly metabolized GL to GA, and minorly to GA via GAMG. Bacteroides J-37, isolated from human intestinal bacteria, transformed GL or GAMG to GA, but not baicalin; it produced alpha-glucuroniase, which hydrolyzed the alpha-linkage of glucuronic acid conjugates. alpha-Glucuronidase of Bacteroides J-37 hydrolyzed alpha-glucuronic acid conjugates better than beta-glucuronic acid conjugates. beta-Glucuronidase from E. coli, a human intestinal bacterium, hydrolyzed baicalin to baicalein, but did not transform GL.

Purification and characterization of alpha-L-rhamnosidase from Bacteroides JY-6, a human intestinal bacterium

An alpha-L-rhamnosidase (EC 3.2.1.40) was purified 1500-fold from Bacteroides JY-6, an intestinal anaerobic bacterium of human. The specific activity of purified enzyme was 89.9 mumol/min/mg protein. The enzyme (M.W. 240000) is composed of two subunits of M.W. 120000 with pI and optimal pH values of 4.2 and 7.0, respectively. The apparent Kms for naringin, rutin and p-nitrophenyl-alpha-L-rhamnopyranoside were determined to be 0.89, 1.44 and 0.29 mM, respectively. The enzyme was strongly inhibited by L-rhamnose, L-fucose, saccharic acid 1,4-lactone, p-chlormercuriphenylsulfonic acid and Pb2+.