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 .

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.

Age-of-onset information helps identify 76 genetic variants associated with allergic disease.

Risk factors that contribute to inter-individual differences in the age-of-onset of allergic diseases are poorly understood. The aim of this study was to identify genetic risk variants associated with the age at which symptoms of allergic disease first develop, considering information from asthma, hay fever and eczema. Self-reported age-of-onset information was available for 117,130 genotyped individuals of European ancestry from the UK Biobank study. For each individual, we identified the earliest age at which asthma, hay fever and/or eczema was first diagnosed and performed a genome-wide association study (GWAS) of this combined age-of-onset phenotype. We identified 50 variants with a significant independent association (P<3x10-8) with age-of-onset. Forty-five variants had comparable effects on the onset of the three individual diseases and 38 were also associated with allergic disease case-control status in an independent study (n = 222,484). We observed a strong negative genetic correlation between age-of-onset and case-control status of allergic disease (rg = -0.63, P = 4.5x10-61), indicating that cases with early disease onset have a greater burden of allergy risk alleles than those with late disease onset. Subsequently, a multivariate GWAS of age-of-onset and case-control status identified a further 26 associations that were missed by the univariate analyses of age-of-onset or case-control status only. Collectively, of the 76 variants identified, 18 represent novel associations for allergic disease. We identified 81 likely target genes of the 76 associated variants based on information from expression quantitative trait loci (eQTL) and non-synonymous variants, of which we highlight ADAM15, FOSL2, TRIM8, BMPR2, CD200R1, PRKCQ, NOD2, SMAD4, ABCA7 and UBE2L3. Our results support the notion that early and late onset allergic disease have partly distinct genetic architectures, potentially explaining known differences in pathophysiology between individuals.

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.

Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct.

The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n = 447,628), we found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h2g = 25.6%) than for AOA (onset at ages between 20 and 60 years; h2g = 10.6%). The genetic correlation (rg) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability (h2g = 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n = 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA- than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n = 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development.

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.

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.

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.

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.

Quantitative analysis of PPT1 interactome in human neuroblastoma cells.

Mutations in the CLN1 gene that encodes Palmitoyl protein thioesterase 1 (PPT1) or CLN1, cause Infantile NCL (INCL, MIM#256730). PPT1 removes long fatty acid chains such as palmitate from modified cysteine residues of proteins. The data shown here result from isolated protein complexes from PPT1-expressing SH-SY5Y stable cells that were subjected to single step affinity purification coupled to mass spectrometry (AP-MS). Prior to the MS analysis, we utilised a modified filter-aided sample preparation (FASP) protocol. Based on label free quantitative analysis of the data by SAINT, 23 PPT1 interacting partners (IP) were identified. A dense connectivity in PPT1 network was further revealed by functional coupling and extended network analyses, linking it to mitochondrial ATP synthesis coupled protein transport and thioester biosynthetic process. Moreover, the terms: inhibition of organismal death, movement disorders and concentration of lipid were predicted to be altered in the PPT1 network. Data presented here are related to Scifo et al. (J. Proteomics, 123 (2015) 42-53).

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.

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.

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.

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.

Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis.

We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available.

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.

UNLABELLED: 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.