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.Peer reviewe

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.Peer reviewe

Quantitative analysis of PPT1 interactome in human neuroblastoma cells

Peer reviewe

Drafting the CLN3 Protein Interactome in SH-SY5Y Human Neuroblastoma Cells: A Label-free Quantitative Proteomics Approach

Peer reviewe

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.</p

Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

Peer reviewe

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.Peer reviewe

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

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.</p

Bioinformatic identification of disease driver networks using functional profiling data

Genomics-based drug discovery utilizing sequencing data for elucidation of candidate targets has led to the development of a number of successful treatments in the last decades. However, the molecular driver signals for many complex diseases cannot be easily derived from genome sequencing. Functional profiling studies, such as those involving the detection of protein interaction networks or the effects of perturbations with small molecules or siRNAs on cellular phenotypes, offer a complementary approach for the identification of molecular vulnerabilities that can be exploited in the development of new treatment strategies. The goal of this thesis was to develop computational systems biology methods for supporting such functional endeavors, and through their application use cases, to elucidate novel disease driver signals in cancer and Alzheimer’s disease networks. The availability of functional profiling data (such as biochemical target selectivity information or efficacy readouts) for numerous small molecule compounds has enabled building interaction network models to predict cancer addictions i.e. genes that are essential for disease progression but are not necessarily mutated. In this work, network-based computational methods (such as kinase inhibition sensitivity score – KISS) were developed to infer disease addictions (either single genes or sub-networks) using functional data from high-throughput drug sensitivity screens, and applied in breast cancer cell lines. Further extension of the KISS method, named combinatorial KISS, was introduced as a novel approach to predict synergistic drug combinations and their underlying co-essential target pairs. Driver deconvolution from drug response profiles relies on extensive and reliable drug-target interaction networks. Therefore, a systematic evaluation of target selectivity profiles was performed among recently published large-scale biochemical assays of kinase inhibitors, combined with data reported in the drug-target databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among various bioactivity types, including IC50, Ki, and Kd. To make better use of the complementary information captured by the various bioactivity types, we developed a model-based integration approach, termed KIBA, and demonstrated how it can be used to classify kinase inhibitor targets. As a result, we created kinome-level, quantitative drug–target interaction network for further modeling studies. Besides the analysis of drug responses, another way to find novel disease drivers or molecular vulnerabilities is to explore the interaction partners of known oncogenes, since the existence of a protein-protein interaction suggests their involvement in the same biological pathway and thereby in the same biological process. However, one of the major challenges in the protein-protein interaction screens is the identification of functionally relevant interactions from the long hit list, in particular when their functional annotations are missing. This motivated the development of Relevance Rank Platform (RRP) approach that can suggest the candidate proteins from the high throughput screens that most likely contribute to the function of the bait protein. The method predicts functionally similar candidate interactors regardless of either the reliability of the mass spectrometry-based identification, or the knowledge of the biological function of the putative interactor. RRP was applied and validated in PIN1 (Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1) and PME-1 (Protein Phosphatase Methylesterase 1) interaction networks in prostate cancer. Finally, we carried out functional comparison of nitrosylated proteins in the brain synaptosomes of Alzheimer’s disease (AD) mouse models and their healthy controls, with the aim to reveal the disease processes in which this posttranslational modification plays a role. We also elucidated the amyloid precursor protein (APP) - centered Alzheimer’s disease network of differentially nitrosylated proteins that are likely to be implicated in this neurological disorder. Taken together, this thesis work introduces novel experimental-computational strategies for the deconvolution or prioritization of potential disease drivers, either single proteins or their subnetworks. These methods are applicable to various cell lines or patient-derived samples. They can provide directly druggable therapeutic targets for personalized treatment applications and may be used in the development of novel therapeutic options.Genomisekvenssointiin perustuva lääkeainekehitys missä lähdetään liikkeelle potentiaalisista tautigeeneistä on tuottanut uudensukupolven kohdennettuja hoitomuotoja eri sairauksiin, etenkin syöpäsairauksiin, joiden kehitys pohjaa tunnettuihin syöpägeeneihin. On kuitenkin useita sairauksia, sekä myös syövän eri alimuotoja, joiden syntymekanismit eivät ilmene tai ole helposti löydettävissä genomisekvenssin tasolla. Funktionaaliset profiloinnit, jotka perustuvat proteiinimolekyylien välisiin vuorovaikutussuhteisiin tai geenien ilmenemisen hiljentämiseen joko RNA-interferenssi-tekniikalla tai lääkemolekyylien avulla mahdollistavat vaihtoehtoisen tavan etsiä uusia kohdemolekyylejä sairauksien kohdennettuun hoitoon. Tämän väitöskirjatyön tavoitteena oli kehittää laskennallisia, systeemibiologian menetelmiä, joiden avulla funktionaalisten profilointien tuloksia voidaan paremmin käyttää etsittäessä eri syöpämuotojen sekä Alzheimer-taudin mekanismeja sekä niihin kohdennettuja hoitokeinoja