European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation

Atopic dermatitis (AD) is a common inflammatory skin condition and prior genome-wide association studies (GWAS) have identified 71 associated loci. In the current study we conducted the largest AD GWAS to date (discovery N = 1,086,394, replication N = 3,604,027), combining previously reported cohorts with additional available data. We identified 81 loci (29 novel) in the European-only analysis (which all replicated in a separate European analysis) and 10 additional loci in the multi-ancestry analysis (3 novel). Eight variants from the multi-ancestry analysis replicated in at least one of the populations tested (European, Latino or African), while two may be specific to individuals of Japanese ancestry. AD loci showed enrichment for DNAse I hypersensitivity and eQTL associations in blood. At each locus we prioritised candidate genes by integrating multi-omic data. The implicated genes are predominantly in immune pathways of relevance to atopic inflammation and some offer drug repurposing opportunities.

Rare variant analysis in eczema identifies exonic variants in DUSP1, NOTCH4 and SLC9A4

Previous genome-wide association studies revealed multiple common variants involved in eczema but the role of rare variants remains to be elucidated. Here, we investigate the role of rare variants in eczema susceptibility. We meta-analyze 21 study populations including 20,016 eczema cases and 380,433 controls. Rare variants are imputed with high accuracy using large population-based reference panels. We identify rare exonic variants in DUSP1, NOTCH4, and SLC9A4 to be associated with eczema. In DUSP1 and NOTCH4 missense variants are predicted to impact conserved functional domains. In addition, five novel common variants at SATB1-AS1/KCNH8, TRIB1/LINC00861, ZBTB1, TBX21/OSBPL7, and CSF2RB are discovered. While genes prioritized based on rare variants are significantly up-regulated in the skin, common variants point to immune cell function. Over 20% of the single nucleotide variant-based heritability is attributable to rare and low-frequency variants. The identified rare/low-frequency variants located in functional protein domains point to promising targets for novel therapeutic approaches to eczema.

Genetic studies of eczema to date have mostly explored common genetic variation. Here, the authors perform a large meta-analysis for common and rare variants and discover 8 loci associated with eczema. Over 20% of the heritability of the condition is attributable to rare variants.

Frailty trajectories in three longitudinal studies of aging: Is the level or the rate of change more predictive of mortality?

BACKGROUND

frailty shows an upward trajectory with age, and higher levels increase the risk of mortality. However, it is less known whether the shape of frailty trajectories differs by age at death or whether the rate of change in frailty is associated with mortality.

OBJECTIVES

to assess population frailty trajectories by age at death and to analyse whether the current level of the frailty index (FI) i.e. the most recent measurement or the person-specific rate of change is more predictive of mortality.

METHODS

3,689 individuals from three population-based cohorts with up to 15 repeated measurements of the Rockwood frailty index were analysed. The FI trajectories were assessed by stratifying the sample into four age-at-death groups: <70, 70-80, 80-90 and >90 years. Generalised survival models were used in the survival analysis.

RESULTS

the FI trajectories by age at death showed that those who died at <70 years had a steadily increasing trajectory throughout the 40 years before death, whereas those who died at the oldest ages only accrued deficits from age ~75 onwards. Higher level of FI was independently associated with increased risk of mortality (hazard ratio 1.68, 95% confidence interval 1.47-1.91), whereas the rate of change was no longer significant after accounting for the current FI level. The effect of the FI level did not weaken with time elapsed since the last measurement.

CONCLUSIONS

Frailty trajectories differ as a function of age-at-death category. The current level of FI is a stronger marker for risk stratification than the rate of change.

Taking the LEAP: study protocol for a randomized, multicentre, naturalistic, efficacy trial of the compuLsive Exercise Activity theraPy (LEAP) - a cognitive behavioral program specifically targeting compulsive exercise in patients with eating disorders

BACKGROUND

About half of Swedish eating disorder patients report exercising compulsively and compulsive exercise (CE) is prevalent in all diagnoses and both genders. Yet there are no systematic treatments targeting CE in specialist care. This study aims to evaluate the effects of The CompuLsive Exercise Activity TheraPy (LEAP) - a promising group treatment targeting compulsive exercise, in Swedish eating disorder patients.

METHOD

One hundred twenty-eight adult females and males suffering from anorexia nervosa, bulimia nervosa or other specified feeding and eating disorders (type 1, 2, or 4) with CE will be recruited via four specialist eating disorder treatment units. Participants will be randomized to receive treatment as usual (control group) or treatment as usual plus LEAP (intervention group). The groups will be assessed on key variables (e.g., BMI, eating disorder symptoms, exercise cognitions and behaviors) at three occasions: initially, after 3 months and after 6 months.

DISCUSSION

The project takes place in a clinical setting, including both male and female patients with different eating disorder diagnoses with CE, enabling a good indication of the efficacy of LEAP. If our results are positive, LEAP has the potential of benefiting about half of the eating disorder population, with remission and recovery hopefully improving as a result.

TRIAL REGISTRATION

The trial is registered with the ISRCTN registry (registration date 2020-03-25), trial ID: ISRCTN80711391 .

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

Integrated Human Evaluation of the Lysophosphatidic Acid Pathway as a Novel Therapeutic Target in Atherosclerosis

Variants in the PLPP3 gene encoding for lipid phosphate phosphohydrolase 3 have been associated with susceptibility to atherosclerosis independently of classical risk factors. PLPP3 inactivates lysophosphatidic acid (LPA), a pro-inflammatory, pro-thrombotic product of phospholipase activity. Here we performed the first exploratory analysis of PLPP3, LPA, and LPA receptors (LPARs 1–6) in human atherosclerosis. PLPP3 transcript and protein were repressed when comparing plaques versus normal arteries and plaques from symptomatic versus asymptomatic patients, and they were negatively associated with risk of adverse cardiovascular events. PLPP3 localized to macrophages, smooth muscle, and endothelial cells (ECs) in plaques. LPAR 2, 5, and especially 6 showed increased expression in plaques, with LPAR6 localized in ECs and positively correlated to PLPP3. Utilizing in situ mass spectrometry imaging, LPA and its precursors were found in the plaque fibrous cap, co-localizing with PLPP3 and LPAR6. In vitro, PLPP3 silencing in ECs under LPA stimulation resulted in increased expression of adhesion molecules and cytokines. LPAR6 silencing inhibited LPA-induced cell activation, but not when PLPP3 was silenced simultaneously. Our results show that repression of PLPP3 plays a key role in atherosclerosis by promoting EC activation. Altogether, the PLPP3 pathway represents a suitable target for investigations into novel therapeutic approaches to ameliorate atherosclerosis.

From drug response profiling to target addiction scoring in cancer cell models

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package.

Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells

Background

Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive type of cancer that lacks effective targeted therapy. Despite detailed molecular profiling, no targeted therapy has been established. Hence, with the aim of gaining deeper understanding of the functional differences of TNBC subtypes and how that may relate to potential novel therapeutic strategies, we studied comprehensive anticancer-agent responses among a panel of TNBC cell lines.

Method

The responses of 301 approved and investigational oncology compounds were measured in 16 TNBC cell lines applying a functional profiling approach. To go beyond the standard drug viability effect profiling, which has been used in most chemosensitivity studies, we utilized a multiplexed readout for both cell viability and cytotoxicity, allowing us to differentiate between cytostatic and cytotoxic responses.

Results

Our approach revealed that most single-agent anti-cancer compounds that showed activity for the viability readout had no or little cytotoxic effects. Major compound classes that exhibited this type of response included anti-mitotics, mTOR, CDK, and metabolic inhibitors, as well as many agents selectively inhibiting oncogene-activated pathways. However, within the broad viability-acting classes of compounds, there were often subsets of cell lines that responded by cell death, suggesting that these cells are particularly vulnerable to the tested substance. In those cases we could identify differential levels of protein markers associated with cytotoxic responses. For example, PAI-1, MAPK phosphatase and Notch-3 levels associated with cytotoxic responses to mitotic and proteasome inhibitors, suggesting that these might serve as markers of response also in clinical settings. Furthermore, the cytotoxicity readout highlighted selective synergistic and synthetic lethal drug combinations that were missed by the cell viability readouts. For instance, the MEK inhibitor trametinib synergized with PARP inhibitors. Similarly, combination of two non-cytotoxic compounds, the rapamycin analog everolimus and an ATP-competitive mTOR inhibitor dactolisib, showed synthetic lethality in several mTOR-addicted cell lines.

Conclusions

Taken together, by studying the combination of cytotoxic and cytostatic drug responses, we identified a deeper spectrum of cellular responses both to single agents and combinations that may be highly relevant for identifying precision medicine approaches in TNBC as well as in other types of cancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-016-0517-3) contains supplementary material, which is available to authorized users.

Abstract B21: Enhanced understanding of drug responses and drug-drug interactions in triple negative breast cancer cells with a multiplexed cell viability and cell death readout

Triple negative breast cancers (TNBC) are a heterogeneous group of cancers that remain a considerable clinical challenge with no known effective targeted therapy, limiting the therapies to cytotoxic chemotherapy, radiation and surgery. Molecular sub-classifications of TNBC have been established but none of these define therapy. With an aim to establish potential novel stratified therapeutic strategies to target TNBC, we applied the Drug Sensitivity and Resistance Testing (DSRT) (Pemovska et al., Cancer Discov. 2013) chemo-sensitivity profiling approach in which the responses of cells to 304 approved and investigational oncology drugs were explored. We studied a diverse panel of 15 TNBC cell lines in an attempt to functionally classify the TNBCs. To go beyond standard growth inhibition drug sensitivity testing we utilized a multiplexed cell viability and cytotoxicity readout allowing us to differentiate between cytostatic and cytotoxic drug responses.

Evaluating both types of drug responses, several discoveries were made: First, the multiplexed viability and cytotoxicity readout identified several drug classes that previously had been assumed to have cytotoxic effects based on their strong effects on cell viability but in fact only showed cytostatic effects in most cells. Drug classes exhibiting this type of response included anti-mitotics, mTOR, CDK, metabolic inhibitors, as well as many targeted agents selectively inhibiting oncogenically activated pathways in individual cell lines. Second, within the responses to these broad cytostatic-acting classes of drugs, there were subsets of the cell lines that responded by cell death, suggesting that these cell lines may represent the TNBC subtypes that might best respond to the drugs in the clinic. To explore this further, we tested whether the cytostatic vs. cytotoxic responses could be linked to expression of protein biomarkers using a published TNBC cell line reverse-phase protein array data set (Daemen et al, Genome Biol. 2013). This led to the identification of potential predictive biomarkers such as PAI-1, MAPK phosphatase and Notch-3 levels linking to toxic responses to mitotic and proteasome inhibitors, suggesting that these could be explored as predictors for clinical responses for the drugs. Third, drug response based clustering of cell lines was very distinctive to transcriptomics based TNBC subgroups and recurrent mutation patterns, highlighting the considerable challenges in converting genetic and transcriptomic data to therapeutic responses. Fourth, the cytotoxicity readout highlighted effective synergistic drug combinations that were not apparent using cell viability readouts. For instance, we identified selective synergistic combinations of the MEK inhibitor trametinib with PARP inhibitors or the tyrosine kinase inhibitor ponatinib in DU4475 cells; as well as of the rapamycin analog everolimus with the ATP-competitive mTOR inhibitor dactolisib in several mTOR addicted cell lines.

In conclusion, we showed that tracking drug-induced toxic responses in drug sensitivity testing and drug combination evaluation provides novel critical information on drug vulnerabilities. This argues that high throughput chemo-sensitivity profiling of cancer need to go beyond the current standard cell viability testing.

Citation Format: Prson Gautam, Leena Karhinen, Agnieszka Szwajda, Sawan Kumar Jha, Bhagwan Yadav, Tero Aittokallio, Krister Wennerberg. Enhanced understanding of drug responses and drug-drug interactions in triple negative breast cancer cells with a multiplexed cell viability and cell death readout. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B21.

Relevance Rank Platform (RRP) for Functional Filtering of High Content Protein-Protein Interaction Data

High content protein interaction screens have revolutionized our understanding of protein complex assembly. However, one of the major challenges in translation of high content protein interaction data is identification of those interactions that are functionally relevant for a particular biological question. To address this challenge, we developed a relevance ranking platform (RRP), which consist of modular functional and bioinformatic filters to provide relevance rank among the interactome proteins. We demonstrate the versatility of RRP to enable a systematic prioritization of the most relevant interaction partners from high content data, highlighted by the analysis of cancer relevant protein interactions for oncoproteins Pin1 and PME-1. We validated the importance of selected interactions by demonstration of PTOV1 and CSKN2B as novel regulators of Pin1 target c-Jun phosphorylation and reveal previously unknown interacting proteins that may mediate PME-1 effects via PP2A-inhibition. The RRP framework is modular and can be modified to answer versatile research problems depending on the nature of the biological question under study. Based on comparison of RRP to other existing filtering tools, the presented data indicate that RRP offers added value especially for the analysis of interacting proteins for which there is no sufficient prior knowledge available. Finally, we encourage the use of RRP in combination with either SAINT or CRAPome computational tools for selecting the candidate interactors that fulfill the both important requirements, functional relevance, and high confidence interaction detection.

Abstract 3746: Novel therapeutic possibilities for chemorefractory ovarian cancer patients identified by functional ex vivo drug sensitivity testing of primary cells from ascites

Ovarian cancer (OvCa) is the sixth most common cancer in women and a leading cause of death from gynecologic diseases. Poor prognosis in OvCa is due to late diagnosis and acquired resistance to conventional therapy. A significant setback for OvCa treatment is the lack of reliable biomarkers and the lack of effective targeted therapies.

Our aim is to discover novel therapeutic opportunities with approved and emerging drugs for OvCa and then translate actionable drug efficacies and combinations as personalized therapy suggestions for the clinic. We have previously pioneered drug sensitivity and resistance testing (DSRT) of patient cells from leukemias for personalized medicine and for drug repositioning (Pemovska et al., Cancer Discovery, 2013; Pemovska et al. Nature, in press, 2014). Here, we tested primary cell cultures from the ascites fluid of relapsed chemorefractory OvCa patient samples using the DSRT platform for 305 drugs, consisting of both emerging and established cancer drugs. All drugs were tested in 5 concentrations to achieve a dose-response over a 10,000-fold concentration range. Both viability and cell toxicity assays were applied and results compared to a variety of normal cells and a database of &gt;700 previously DSRT-tested cancer samples and cell lines of various types, including 30 OvCa cell lines. Genomic and transcriptomic profiling by next-gen sequencing was carried out in parallel. Most samples represent high-grade serous OvCa.

Results from 5 primary ascites cultures tested so far showed a distinct drug sensitivity profile as compared to the 30 OvCa cell lines. The results on patient cells led to the discovery of previously unanticipated therapeutic possibilities. For example, in a 51-year old chemorefractory serous OvCa patient, genomic and transcriptomic analyses revealed a fusion gene of NRG-1, a target that was recently reported to involve the NRG1/ERBB3 activation loop in OvCa (Sheng et al. Cancer Cell, 2010). We found high expression of ERBB2 and ERBB3 by RNA seq and phospho-ERBB3, phospho-ERBB2 and phospho-EGFR by immunohistochemistry. In agreement with the molecular mechanism, DSRT analysis identified significant sensitivity of patient tumor cells to EGFR inhibitors, such as erlotinib and afatinib. Furthermore, drug combination testing identified highest combinatorial potential of dasatinib with erlotinib and afatinib in both viability and cell killing assays.

In conclusion, DSRT testing together with genomic and transcriptomic profiling can identify and mechanistically validate tumor driver signals and pinpoint clinically actionable inhibitors and their combinations. Thus, this type of systems medicine profiling can significantly expand the power of current exclusively genomics-oriented personalized medicine approaches and help in drug repositioning to new indications.

Citation Format: Astrid Murumägi, Akira Hirasawa, Mariliina Arjama, Katja Välimäki, Bhagwan Yadav, Jing Tang, Agnieszka Szwajda, Laura Turunen, John Patrick Mpindi, Teijo Pellinen, Krister Wennerberg, Ralf Bützow, Tero Aittokallio, Olli Kallioniemi. Novel therapeutic possibilities for chemorefractory ovarian cancer patients identified by functional ex vivo drug sensitivity testing of primary cells from ascites. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3746. doi:10.1158/1538-7445.AM2015-3746

Systematic Mapping of Kinase Addiction Combinations in Breast Cancer Cells by Integrating Drug Sensitivity and Selectivity Profiles

Chemical perturbation screens offer the possibility to identify actionable sets of cancer-specific vulnerabilities. However, most inhibitors of kinases or other cancer targets result in polypharmacological effects, which complicate the identification of target dependencies directly from the drug-response phenotypes. In this study, we developed a chemical systems biology approach that integrates comprehensive drug sensitivity and selectivity profiling to provide functional insights into both single and multi-target oncogenic signal addictions. When applied to 21 breast cancer cell lines, perturbed with 40 kinase inhibitors, the subtype-specific addiction patterns clustered in agreement with patient-derived subtypes, while showing considerable variability between the heterogeneous breast cancers. Experimental validation of the top predictions revealed a number of co-dependencies between kinase targets that led to unexpected synergistic combinations between their inhibitors, such as dasatinib and axitinib in the triple-negative basal-like HCC1937 cell line.

Abstract P6-02-01: Identification of subgroups of triple negative breast cancer cells with selective responses to mTOR, CDK, mitotic and proteasome inhibitors

Triple negative breast cancer (TNBC) is characterized by the lack of of estrogen, progesterone and HER2/ErbB2 receptors. It is a highly heterogeneous class of breast cancer and transcriptomics has recently been used to define 6 major subtypes of TNBC. We studied a panel of 15 TNBC cell lines using a chemical biology approach where we measure the responses to 306 approved and investigational oncology drugs. Clustering of cell lines based on their overall drug responses resulted in a strikingly different grouping compared to the gene expression derived one, highlighting that the current TNBC subtyping is not easily converted to differential sensitivities to drugs.

To further evaluate the nature of the drug responses and to differentiate between their cell growth and cytotoxic effects, we multiplexed the standard cell viability readout in the cell line screening with detection of cytotoxicity. This simple multiplexed readout identified several drug classes that previously had been assumed to be cytotoxic based on strong effects on cell viability (cell numbers) while they in fact showed no or a very heterogeneous effect on cytotoxicity. Drug classes exhibiting this type of response included mTOR inhibitors, cyclin-dependent kinase inhibitors (eg. alvociclib), mitotic inhibitors (eg. paclitaxel) as well as proteasome inhibitors (eg. bortezomib) and RNA synthesis inhibitors (eg. dactinomycin).

Further investigation of these drug classes showed that their static effects were reversible and in some cases the cells even overcame the inhibitory effect in the presence of the drug in a matter of a few days.

Given the non-toxic responses to major classes of anticancer compounds such as mTOR inhibitors, we performed combination screens with these compounds to identify other drugs with which they may synergize to promote cancer cell specific killing. Surprisingly, we instead found that mTOR inhibitors had an antagonistic effect on the activity of many other cancer drugs such as different cytotoxic and antimitotic drugs, tyrosine kinase inhibitors, HDAC inhibitors and PARP inhibitors, suggesting that combining these classes of drugs may be counterproductive also in the clinic. We also found out that accessing a cytotoxic readout allowed us to identify effective synergistic drug combination concentrations that were not seen in cell viability readouts. For example, these synergistic toxic combination responses were seen in DU4475 cells when the MEK inhibitor trametinib was combined either with the PARP inhibitor iniparib or with the broad spectrum tyrosine kinase inhibitor ponatinib.

In conclusion, multiplexed cell viability cell death readouts in drug sensitivity testing yields novel critical information on single drug and drug combination activities and liabilities. With this we were able to conclude that antimitotic, mTOR, CDK, proteasome and metabolic inhibitors have a heterogeneous cytotoxic effect across the panel of TNBC cell lines in contrast to their homogenous effect on metabolic inactivation.

Citation Format: Prson Gautam, Leena Karhinen, Agnieszka Szwajda, Sawan Kumar Jha, Bhagwan Yadav, Tero Aittokallio, Krister Wennerberg. Identification of subgroups of triple negative breast cancer cells with selective responses to mTOR, CDK, mitotic and proteasome inhibitors [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-02-01.

Proteomic analysis of the palmitoyl protein thioesterase 1 interactome in SH-SY5Y human neuroblastoma cells

Neuronal ceroid lipofuscinoses (NCL) are a group of inherited progressive childhood disorders, characterized by early accumulation of autofluorescent storage material in lysosomes of neurons or other cells. Clinical symptoms of NCL include: progressive loss of vision, mental and motor deterioration, epileptic seizures and premature death. CLN1 disease (MIM#256730) is caused by mutations in the CLN1 gene, which encodes palmitoyl protein thioesterase 1 (PPT1). In this study, we utilised single step affinity purification coupled to mass spectrometry (AP-MS) to unravel the in vivo substrates of human PPT1 in the brain neuronal cells. Protein complexes were isolated from human PPT1 expressing SH-SY5Y stable cells, subjected to filter-aided sample preparation (FASP) and analysed on a Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer. A total of 23 PPT1 interacting partners (IP) were identified from label free quantitation of the MS data by SAINT platform. Three of the identified PPT1 IP, namely CRMP1, DBH, and MAP1B are predicted to be palmitoylated. Our proteomic analysis confirmed previously suggested roles of PPT1 in axon guidance and lipid metabolism, yet implicates the enzyme in novel roles including: involvement in neuronal migration and dopamine receptor mediated signalling pathway.

BIOLOGICAL SIGNIFICANCE

The significance of this work lies in the unravelling of putative in vivo substrates of human CLN1 or PPT1 in brain neuronal cells. Moreover, the PPT1 IP implicate the enzyme in novel roles including: involvement in neuronal migration and dopamine receptor mediated signalling pathway.

Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options.

Global analysis of S-nitrosylation sites in the wild type (APP) transgenic mouse brain-clues for synaptic pathology

Alzheimer's disease (AD) is characterized by an early synaptic loss, which strongly correlates with the severity of dementia. The pathogenesis and causes of characteristic AD symptoms are not fully understood. Defects in various cellular cascades were suggested, including the imbalance in production of reactive oxygen and nitrogen species. Alterations in S-nitrosylation of several proteins were previously demonstrated in various AD animal models and patients. In this work, using combined biotin-switch affinity/nano-LC-MS/MS and bioinformatic approaches we profiled endogenous S-nitrosylation of brain synaptosomal proteins from wild type and transgenic mice overexpressing mutated human Amyloid Precursor Protein (hAPP). Our data suggest involvement of S-nitrosylation in the regulation of 138 synaptic proteins, including MAGUK, CamkII, or synaptotagmins. Thirty-eight proteins were differentially S-nitrosylated in hAPP mice only. Ninety-five S-nitrosylated peptides were identified for the first time (40% of total, including 33 peptides exclusively in hAPP synaptosomes). We verified differential S-nitrosylation of 10 (26% of all identified) synaptosomal proteins from hAPP mice, by Western blotting with specific antibodies. Functional enrichment analysis linked S-nitrosylated proteins to various cellular pathways, including: glycolysis, gluconeogenesis, calcium homeostasis, ion, and vesicle transport, suggesting a basic role of this post-translational modification in the regulation of synapses. The linkage of SNO-proteins to axonal guidance and other processes related to APP metabolism exclusively in the hAPP brain, implicates S-nitrosylation in the pathogenesis of Alzheimer's disease.

Naturally occurring human phosphorylcholine antibodies are predominantly products of affinity-matured B cells in the adult

Phosphorylcholine (PC) is a classic T-independent Ag that is exposed on apoptotic cells, oxidized phospholipids, and bacterial polysaccharides. Experimental as well as epidemiological studies have over the past decade implicated Abs against PC (anti-PC) as anti-inflammatory and a strong protective factor in cardiovascular disease. Although clinically important, little is known about the development of anti-PC in humans. This study was conceived to dissect the human anti-PC repertoire and generate human mAbs. We designed a PC-specific probe to identify, isolate, and characterize PC-reactive B cells from 10 healthy individuals. The donors had all mounted somatically mutated Abs toward PC using a broad variety of Ig genes. PC-reactive B cells were primarily found in the IgM(+) memory subset, although significant numbers also were detected among naive, IgG(+), and CD27(+)CD43(+) B cells. Abs from these subsets were clonally related, suggesting a common origin. mAbs derived from the same donors exhibited equivalent or higher affinity for PC than the well-characterized murine T-15 clone. These results provide novel insights into the cellular and molecular ontogeny of atheroprotective PC Abs, thereby offering new opportunities for Ab-based therapeutic interventions.

Toward more realistic drug-target interaction predictions

A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors.

Chk1 targeting reactivates PP2A tumor suppressor activity in cancer cells

Checkpoint kinase Chk1 is constitutively active in many cancer cell types and new generation Chk1 inhibitors show marked antitumor activity as single agents. Here we present a hitherto unrecognized mechanism that contributes to the response of cancer cells to Chk1-targeted therapy. Inhibiting chronic Chk1 activity in cancer cells induced the tumor suppressor activity of protein phosphatase protein phosphatase 2A (PP2A), which by dephosphorylating MYC serine 62, inhibited MYC activity and impaired cancer cell survival. Mechanistic investigations revealed that Chk1 inhibition activated PP2A by decreasing the transcription of cancerous inhibitor of PP2A (CIP2A), a chief inhibitor of PP2A activity. Inhibition of cancer cell clonogenicity by Chk1 inhibition could be rescued in vitro either by exogenous expression of CIP2A or by blocking the CIP2A-regulated PP2A complex. Chk1-mediated CIP2A regulation was extended in tumor models dependent on either Chk1 or CIP2A. The clinical relevance of CIP2A as a Chk1 effector protein was validated in several human cancer types, including neuroblastoma, where CIP2A was identified as an NMYC-independent prognostic factor. Because the Chk1-CIP2A-PP2A pathway is driven by DNA-PK activity, functioning regardless of p53 or ATM/ATR status, our results offer explanative power for understanding how Chk1 inhibitors mediate single-agent anticancer efficacy. Furthermore, they define CIP2A-PP2A status in cancer cells as a pharmacodynamic marker for their response to Chk1-targeted therapy.

Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia

We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.

SIGNIFICANCE

Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing.

Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways

A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.

Drafting the CLN3 protein interactome in SH-SY5Y human neuroblastoma cells: a label-free quantitative proteomics approach

Neuronal ceroid lipofuscinoses (NCL) are the most common inherited progressive encephalopathies of childhood. One of the most prevalent forms of NCL, Juvenile neuronal ceroid lipofuscinosis (JNCL) or CLN3 disease (OMIM: 204200), is caused by mutations in the CLN3 gene on chromosome 16p12.1. Despite progress in the NCL field, the primary function of ceroid-lipofuscinosis neuronal protein 3 (CLN3) remains elusive. In this study, we aimed to clarify the role of human CLN3 in the brain by identifying CLN3-associated proteins using a Tandem Affinity Purification coupled to Mass Spectrometry (TAP-MS) strategy combined with Significance Analysis of Interactome (SAINT). Human SH-SY5Y-NTAP-CLN3 stable cells were used to isolate native protein complexes for subsequent TAP-MS. Bioinformatic analyses of isolated complexes yielded 58 CLN3 interacting partners (IP) including 42 novel CLN3 IP, as well as 16 CLN3 high confidence interacting partners (HCIP) previously identified in another high-throughput study by Behrends et al., 2010. Moreover, 31 IP of ceroid-lipofuscinosis neuronal protein 5 (CLN5) were identified (18 of which were in common with the CLN3 bait). Our findings support previously suggested involvement of CLN3 in transmembrane transport, lipid homeostasis and neuronal excitability, as well as link it to G-protein signaling and protein folding/sorting in the ER.

Abstract 895: Quantitative drug sensitivity and resistance testing (DSRT) of primary ex vivo AML blasts highlights mTOR and MEK as potential key molecular driver signals of therapeutic significance

Identification of signaling pathways that are required for the growth and differentiation block of cells from adult acute myeloid leukemia (AML) is urgently required to facilitate development of novel therapies. Here, we describe an approach to functionally determine molecular drivers of AML by quantitative drug sensitivity and resistance testing (DSRT) of AML blast cells in primary culture ex vivo. The selection of drugs covered the entire cancer pharmacopeia and much of the pipeline of drugs under development in the industry: 120 FDA approved small molecular cancer drugs and 120 emerging drugs, investigational compounds and signal transduction inhibitors. All compounds were tested over a 10,000-fold concentration range to generate quantitative and reliable dose-response data. In addition, whole exome and transcriptome sequencing and phophoproteomic profiling were also performed to derive a comprehensive understanding of the molecular AML-related aberrations on an individual basis. Comparison of 17 AML patient samples and 3 healthy bone marrow control samples based on ex vivo drug responses identified several classes of approved and investigational drugs that showed selective anti-AML activities: mTOR inhibitors (e.g. temsirolomus, everolimus, sirolimus), MEK inhibitors (e.g. AS703026, GSK1120212, RDEA119, selumetinib), tyrosine kinase inhibitors (e.g. dasatinib, ponatinib, sunitinib), Bcl-2 inhibitors (navitoclax) and HSP90 inhibitors (e.g. BIIB021, NVP-AUY922, tanespimycin). In particular, the rapamycin class of mTOR inhibitors and allosteric MEK inhibitors stood out as effective and selective inhibitors in 8/17 (47%) and 9/17 (52%) of the patients, respectively. Simultaneous data from other targeted inhibitors made it possible to dissect the critical steps in signaling and therapeutic efficacy. For example, PI3K and Akt inhibitors were not effective in these patients, suggesting that the mTOR dependency is mediated through a PI3K-Akt-independent pathway. Similarly, the dependency of MEK signaling appears to be through a Ras-Raf-independent pathway since Raf inhibitors were not effective. In conclusion, the DSRT platform allows us to derive quantitative data on the ex vivo drug response profiles of AML cells from individual patients. This information could be used as a diagnostic tool to optimize personalized therapies in the future. Our data demonstrate that mTOR and MEK signaling and the associated inhibitors are the most promising leads for improved AML therapeutics. This analysis also demonstrates gaps in our current understanding of the redundancy of key cancer cell signaling pathways and proves the significant value of data from experimental drug response testing ex vivo.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 895. doi:1538-7445.AM2012-895

Abstract 3188: Development of a cancer pharmacopeia-wide ex-vivo drug sensitivity and resistance testing (DSRT) platform for AML: Towards individually optimized therapy and improved understanding of drug resistance patterns

In order to discover unexpected anti-cancer efficacies of approved and emerging drugs, we established a diagnostic ex vivo drug sensitivity and resistance testing (DSRT) platform covering the entire cancer pharmacopeia as well as emerging anti-cancer compounds. Here, the platform was applied to analyze bone marrow (BM) mononuclear cells from 17 adult acute myeloid leukemia (AML) patients, 3 healthy donors as well as 7 AML cell lines. The DSRT panel covered FDA-approved small molecule oncology drugs (n=120), as well as emerging, investigational and pre-clinical oncology compounds (n=120), such as kinase (e.g. RTKs, checkpoint and mitotic kinases, Raf, MEK, JAKs, mTOR, PI3K), and non-kinase inhibitors (e.g. HSP, Bcl, activin, HDAC, PARP, Hh). To generate dose-response curves, each of the drugs was applied over a 10,000-fold concentration range. In addition, the samples underwent deep molecular profiling including exome- and transcriptome sequencing, as well as phosphoproteomic analysis. DSRT provided consistent and reliable data from ex vivo samples with a high correlation between data from individual healthy BM samples (r=0.93). Bioinformatic processing of the data from AML resulted in several key observations. First, overall drug response profiles of AML blast cells were distinctly different from healthy BM controls suggesting several potential leukemia-selective effects, such as multi-kinase (dasatinib), MEK, and mTOR inhibitors. Second, the overall drug responses from AML cell lines and the patient ex vivo samples showed differences, suggesting that ex vivo testing may reveal cancer-selective effects not previously seen in established cancer cell line panels. Third, the response data from patient samples clustered many drugs consistently into the expected functional classes (such as topoisomerase II inhibitors, MEK inhibitors and rapalogs), whereas other drug classes were more dispersed (such as FLT3 inhibitors with quizartinib clustering away from all other tyrosine kinase inhibitors), suggesting secondary targets playing a key role in drug efficacy. Fourth, analysis of serial samples from patients developing clinical resistance to targeted agents showed striking agreement between the ex-vivo DSRT profiles and clinical responses. In conclusion, comprehensive DSRT platform generated powerful novel insights on AML drug response and may enable individual optimization of therapies, particularly for recurrent leukemias. DSRT will also serve as a powerful hypothesis-generator for clinical trials, particularly for emerging drugs. The ability to correlate ex vivo response profiles for hundreds of drugs in clinical samples with deep molecular profiling data will yield exciting new translational and pharmacogenomic opportunities for cancer therapy.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3188. doi:1538-7445.AM2012-3188