Predicting cancer drug mechanisms of action using molecular network signatures

Functional genomics reveals an off-target dependency of drug synergy in gastric cancer therapy

BACKGROUND

Integrating molecular-targeted agents into combination chemotherapy is transformative for enhancing treatment outcomes in cancer. However, realizing the full potential of this approach requires a clear comprehension of the genetic dependencies underlying drug synergy. While the interactions between conventional chemotherapeutics are well-explored, the interplay of molecular-targeted agents with conventional chemotherapeutics remains a frontier in cancer treatment. Hence, we leveraged a powerful functional genomics approach to decode genomic dependencies that drive synergy in molecular-targeted agent/chemotherapeutic combinations in gastric adenocarcinoma, addressing a critical need in gastric cancer therapy.

METHODS

We screened pharmacological interactions between fifteen molecular-targeted agent/conventional chemotherapeutic pairs in gastric adenocarcinoma cells, and examined the genome-scale genetic dependencies of synergy integrating genome-wide CRISPR screening with the shRNA-based signature assay. We validated the synergy in cell death using fluorescence-based and lysis-dependent inference of cell death kinetics assay, and validated the genetic dependencies by single-gene knockout experiments.

RESULTS

Our combination screen identified SN-38/erlotinib as the drug pair with the strongest synergism. Functional genomics assays unveiled a genetic dependency signature of SN-38/erlotinib identical to SN-38. Remarkably, the enhanced cell death with improved kinetics induced by SN-38/erlotinib was attributed to erlotinib's off-target effect, inhibiting ABCG2, rather than its on-target effect on EGFR.

CONCLUSION

In the era of precision medicine, where emphasis on primary drug targets prevails, our research challenges this paradigm by showcasing a robust synergy underpinned by an off-target dependency. Further dissection of the intricate genetic dependencies that underlie synergy can pave the way to developing more effective combination strategies in gastric cancer therapy.

Inference of drug off-target effects on cellular signaling using interactome-based deep learning

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

Functional genomics reveals an off-target dependency of drug synergy in gastric cancer therapy

The rational combination of anticancer agents is critical to improving patient outcomes in cancer. Nonetheless, most combination regimens in the clinic result from empirical methodologies disregarding insight into the mechanism of action and missing the opportunity to improve therapy outcomes incrementally. Deciphering the genetic dependencies and vulnerabilities responsible for synergistic interactions is crucial for rationally developing effective anticancer drug combinations. Hence, we screened pairwise pharmacological interactions between molecular-targeted agents and conventional chemotherapeutics and examined the genome-scale genetic dependencies in gastric adenocarcinoma cell models. Since this type of cancer is mainly chemoresistant and incurable, clinical situations demand effective combination strategies. Our pairwise combination screen revealed SN38/erlotinib as the drug pair with the most robust synergism. Genome-wide CRISPR screening and a shRNA-based signature assay indicated that the genetic dependency/vulnerability signature of SN38/erlotinib is the same as SN38 alone. Additional investigation revealed that the enhanced cell death with improved death kinetics caused by the SN38/erlotinib combination is surprisingly due to erlotinib's off-target effect that inhibits ABCG2 but not its on-target effect on EGFR. Our results confirm that a genetic dependency signature different from the single-drug application may not be necessary for the synergistic interaction of molecular-targeted agents with conventional chemotherapeutics in gastric adenocarcinoma. The findings also demonstrated the efficacy of functional genomics approaches in unveiling biologically validated mechanisms of pharmacological interactions.

Self-assembled ruthenium and osmium nanosystems display a potent anticancer profile by interfering with metabolic activity

Amphiphilic ruthenium and osmium complexes auto-assemble to nanosystems that poison mitochondria and show highly promising in vitro and in vivo anticancer activity.

Synthesis, structure and anticancer properties of new biotin- and morpholine-functionalized ruthenium and osmium half-sandwich complexes

Ruthenium (Ru) and osmium (Os) complexes are of sustained interest in cancer research and may be alternative to platinum-based therapy. We detail here three new series of ruthenium and osmium complexes, supported by physico-chemical characterizations, including time-dependent density functional theory, a combined experimental and computational study on the aquation reactions and the nature of the metal-arene bond. Cytotoxic profiles were then evaluated on several cancer cell lines although with limited success. Further investigations were, however, performed on the most active series using a genetic approach based on RNA interference and highlighted a potential multi-target mechanism of action through topoisomerase II, mitotic spindle, HDAC and DNMT inhibition.

The primary mechanism of cytotoxicity of the chemotherapeutic agent CX-5461 is topoisomerase II poisoning

Small molecules can affect many cellular processes. The disambiguation of these effects to identify the causative mechanisms of cell death is extremely challenging. This challenge impacts both clinical development and the interpretation of chemical genetic experiments. CX-5461 was developed as a selective RNA polymerase I inhibitor, but recent evidence suggests that it may cause DNA damage and induce G-quadraplex formation. Here we use three complimentary data mining modalities alongside biochemical and cell biological assays to show that CX-5461 exerts its primary cytotoxic activity through topoisomerase II poisoning. We then show that acquired resistance to CX-5461 in previously sensitive lymphoma cells confers collateral resistance to the topoisomerase II poison doxorubicin. Doxorubicin is already a frontline chemotherapy in a variety of hematopoietic malignancies, and CX-5461 is being tested in relapse/refractory hematopoietic tumors. Our data suggest that the mechanism of cell death induced by CX-5461 is critical for rational clinical development in these patients. Moreover, CX-5461 usage as a specific chemical genetic probe of RNA polymerase I function is challenging to interpret. Our multimodal data-driven approach is a useful way to detangle the intended and unintended mechanisms of drug action across diverse essential cellular processes.

AXL Targeting Abrogates Autophagic Flux and Induces Immunogenic Cell Death in Drug-Resistant Cancer Cells

INTRODUCTION

Acquired cancer therapy resistance evolves under selection pressure of immune surveillance and favors mechanisms that promote drug resistance through cell survival and immune evasion. AXL receptor tyrosine kinase is a mediator of cancer cell phenotypic plasticity and suppression of tumor immunity, and AXL expression is associated with drug resistance and diminished long-term survival in a wide range of malignancies, including NSCLC.

METHODS

We aimed to investigate the mechanisms underlying AXL-mediated acquired resistance to first- and third-generation small molecule EGFR tyrosine kinase inhibitors (EGFRi) in NSCLC.

RESULTS

We found that EGFRi resistance was mediated by up-regulation of AXL, and targeting AXL reduced reactivation of the MAPK pathway and blocked onset of acquired resistance to long-term EGFRi treatment in vivo. AXL-expressing EGFRi-resistant cells revealed phenotypic and cell signaling heterogeneity incompatible with a simple bypass signaling mechanism, and were characterized by an increased autophagic flux. AXL kinase inhibition by the small molecule inhibitor bemcentinib or siRNA mediated AXL gene silencing was reported to inhibit the autophagic flux in vitro, bemcentinib treatment blocked clonogenicity and induced immunogenic cell death in drug-resistant NSCLC in vitro, and abrogated the transcription of autophagy-associated genes in vivo. Furthermore, we found a positive correlation between AXL expression and autophagy-associated gene signatures in a large cohort of human NSCLC (n = 1018).

CONCLUSION

Our results indicate that AXL signaling supports a drug-resistant persister cell phenotype through a novel autophagy-dependent mechanism and reveals a unique immunogenic effect of AXL inhibition on drug-resistant NSCLC cells.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

Aneuploid Cell Survival Relies upon Sphingolipid Homeostasis

Aneuploidy, a hallmark of cancer cells, poses an appealing opportunity for cancer treatment and prevention strategies. Using a cell-based screen to identify small molecules that could selectively kill aneuploid cells, we identified the compound N-[2-hydroxy-1-(4-morpholinylmethyl)-2-phenylethyl]-decanamide monohydrochloride (DL-PDMP), an antagonist of UDP-glucose ceramide glucosyltransferase. DL-PDMP selectively inhibited proliferation of aneuploid primary mouse embryonic fibroblasts and aneuploid colorectal cancer cells. Its selective cytotoxic effects were based on further accentuating the elevated levels of ceramide, which characterize aneuploid cells, leading to increased apoptosis. We observed that DL-PDMP could also enhance the cytotoxic effects of paclitaxel, a standard-of-care chemotherapeutic agent that causes aneuploidy, in human colon cancer and mouse lymphoma cells. Our results offer pharmacologic evidence that the aneuploid state in cancer cells can be targeted selectively for therapeutic purposes, or for reducing the toxicity of taxane-based drug regimens. Cancer Res; 77(19); 5272-86. ©2017 AACR.

Induction of Necroptosis in Cancer Stem Cells using a Nickel(II)-Dithiocarbamate Phenanthroline Complex

The cytotoxic properties of a series of nickel(II)-dithiocarbamate phenanthroline complexes is reported. The complexes 1-6 kill bulk cancer cells and cancer stem cells (CSCs) with micromolar potency. Two of the complexes, 2 and 6, kill twice as many breast cancer stem cell (CSC)-enriched HMLER-shEcad cells as compared to breast CSC-depleted HMLER cells. Complex 2 inhibits mammosphere formation to a similar extent as salinomycin (a CSC-specific toxin). Detailed mechanistic studies suggest that 2 induces CSC death by necroptosis, a programmed form of necrosis. Specifically, 2 triggers MLKL phosphorylation, oligomerization, and translocation to the cell membrane. Additionally, 2 induces necrosome-mediated propidium iodide (PI) uptake and mitochondrial membrane depolarisation, as well as morphological changes consistent with necroptotosis. Strikingly, 2 does not evoke necroptosis by intracellular reactive oxygen species (ROS) production or poly(ADP) ribose polymerase (PARP-1) activation.

A subset of platinum-containing chemotherapeutic agents kills cells by inducing ribosome biogenesis stress

Cisplatin and its platinum analogs, carboplatin and oxaliplatin, are some of the most widely used cancer chemotherapeutics. Although cisplatin and carboplatin are used primarily in germ cell, breast and lung malignancies, oxaliplatin is instead used almost exclusively to treat colorectal and other gastrointestinal cancers. Here we utilize a unique, multi-platform genetic approach to study the mechanism of action of these clinically established platinum anti-cancer agents, as well as more recently developed cisplatin analogs. We show that oxaliplatin, unlike cisplatin and carboplatin, does not kill cells through the DNA-damage response. Rather, oxaliplatin kills cells by inducing ribosome biogenesis stress. This difference in drug mechanism explains the distinct clinical implementation of oxaliplatin relative to cisplatin, and it might enable mechanistically informed selection of distinct platinum drugs for distinct malignancies. These data highlight the functional diversity of core components of front-line cancer therapy and the potential benefits of applying a mechanism-based rationale to the use of our current arsenal of anti-cancer drugs.

Mechanistic Studies of the Anticancer Activity of An Octahedral Hexanuclear Pt(II) Cage

The cellular response evoked by a hexanuclear platinum complex, Pt6L4 (1), is reported. Compound 1, a 3-nm octahedral cage formed by self-assembly of six Pt(II) centers and four 2,4,6-tris(4-pyridyl)-1,3,5-triazine ligands (L), exhibits promising in vitro potency against a panel of human cancer cell lines. Unlike classical platinum-based anticancer agents, 1 interacts with DNA in a non-covalent, intercalative manner and promotes DNA condensation. In cancer cells, 1 induces DNA damage, upregulates p53, its phosphorylated form phospho-p53 and its downstream effector, p21, as well as both apoptosis and senescence.

Using an RNAi Signature Assay To Guide the Design of Three-Drug-Conjugated Nanoparticles with Validated Mechanisms, In Vivo Efficacy, and Low Toxicity

Single-nanoparticle (NP) combination chemotherapeutics are quickly emerging as attractive alternatives to traditional chemotherapy due to their ability to increase drug solubility, reduce off-target toxicity, enhance blood circulation lifetime, and increase the amount of drug delivered to tumors. In the case of NP-bound drugs, that is, NP-prodrugs, the current standard of practice is to assume that the subcellular mechanism of action for each drug released from the NP mirrors that of the unbound, free-drug. Here, we use an RNAi signature assay for the first time to examine the mechanism of action of multidrug-conjugated NP prodrugs relative to their small molecule prodrugs and native drug mechanisms of action. Additionally, the effective additive contribution of three different drugs in a single-NP platform is characterized. The results indicate that some platinum(IV) cisplatin prodrugs, although cytotoxic, may not have the expected mechanism of action for cisplatin. This insight was utilized to develop a novel platinum(IV) oxaliplatin prodrug and incorporate it into a three-drug-conjugated NP, where each drug's mechanism of action is preserved, to treat tumor-bearing mice with otherwise lethal levels of chemotherapy.

The Potent Inhibitory Effect of a Naproxen-Appended Cobalt(III)-Cyclam Complex on Cancer Stem Cells

We report the potency against cancer stem cells (CSCs) of a new cobalt(III)-cyclam complex (1) that bears the nonsteroidal anti-inflammatory drug, naproxen. The complex displays selective potency for breast CSC-enriched HMLER-shEcad cells over breast CSC-depleted HMLER cells. Additionally, it inhibited the formation of three-dimensional tumour-like mammospheres, and reduced their viability to a greater extent than clinically used breast cancer drugs (vinorelbine, cisplatin and paclitaxel). The anti-mammosphere potency of 1 was enhanced under hypoxia-mimicking conditions. Detailed mechanistic studies revealed that DNA damage and cyclooxygenase-2 (COX-2) inhibition contribute to the cytotoxic mechanism of 1. To the best of our knowledge, 1 is the first cobalt-containing compound to show selective potency for CSCs over bulk cancer cells.

Transcriptional response networks for elucidating mechanisms of action of multitargeted agents

Drug discovery is moving away from the single target-based approach towards harnessing the potential of polypharmacological agents that modulate the activity of multiple nodes in the complex networks of deregulations underlying disease phenotypes. Computational network pharmacology methods that use systems-level drug-response phenotypes, such as those originating from genome-wide transcriptomic profiles, have proved particularly effective for elucidating the mechanisms of action of multitargeted compounds. Here, we show, via the case study of the natural product pinosylvin, how the combination of two complementary network-based methods can provide novel, unexpected mechanistic insights. This case study also illustrates that elucidating the mechanism of action of multitargeted natural products through transcriptional response-based approaches is a challenging endeavor, often requiring multiple computational-experimental iterations.

The breast cancer stem cell potency of copper(ii) complexes bearing nonsteroidal anti-inflammatory drugs and their encapsulation using polymeric nanoparticles

This is the first report to demonstrate that polymeric nanoparticles can be used to effectively deliver CSC-potent metal complexes into CSCs.

A Pt(IV) Pro-drug Preferentially Targets Indoleamine-2,3-dioxygenase, Providing Enhanced Ovarian Cancer Immuno-Chemotherapy

Expression of indoleamine-2,3-dioxygenase (IDO), an immunosuppressive enzyme in human tumors, leads to immune evasion and tumor tolerance. IDO is therefore a tumor immunotherapeutic target, and several IDO inhibitors are currently undergoing clinical trials. IDO inhibitors can enhance the efficacy of common cancer chemotherapeutics. Here we investigate Pt(IV)-(D)-1-methyltryptophan conjugates 1 and 2 for combined immunomodulation and DNA cross-link-triggered apoptosis for cancer "immuno-chemotherapy". Compound 2 effectively kills hormone-dependent, cisplatin-resistant human ovarian cancer cells, inhibiting IDO by transcriptional deregulation of the autocrine-signaling loop IDO-AHR-IL6, which blocks kynurenine production and promotes T-cell proliferation. Additionally, 1 and 2 display low toxicity in mice and are stable in blood. To our knowledge, this construct is the first Pt drug candidate with immune checkpoint blockade properties.

Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic prediction of effective therapeutic combinations. We focus specifically on anticancer applications that use in vivo and in vitro functional phenotypic measurements, such as genome-wide transcriptomic response profiles, which enable a global modelling of the multi-target activity at the level of the biological pathways and interaction networks. We also provide representative examples of other disease applications, databases and tools as well as existing and emerging resources, which may prove useful for future natural product research. Finally, we offer our personal view of the current limitations, prospective developments and open questions in this exciting field.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Necroptosis-inducing rhenium(V) oxo complexes

Rhenium(V) oxo complexes of general formula [ReO(OMe)(N^N)Cl2], where N^N = 4,7-diphenyl-1,10-phenanthroline, 1, or 3,4,7,8-tetramethyl-1,10-phenanthroline, 2, effectively kill cancer cells by triggering necroptosis, a non-apoptotic form of cell death. Both complexes evoke necrosome (RIP1-RIP3)-dependent intracellular reactive oxygen species (ROS) production and propidium iodide uptake. The complexes also induce mitochondrial membrane potential depletion, a possible downstream effect of ROS production. Apparently, 1 and 2 are the first rhenium complexes to evoke cellular events consistent with programmed necrosis in cancer cells. Furthermore, 1 and 2 display low acute toxicity in C57BL/6 mice and reasonable stability in fresh human blood.

A multichannel nanosensor for instantaneous readout of cancer drug mechanisms

Screening methods that use traditional genomic, transcriptional, proteomic and metabonomic signatures to characterize drug mechanisms are known. However, they are time consuming and require specialized equipment. Here, we present a high-throughput multichannel sensor platform that can profile the mechanisms of various chemotherapeutic drugs in minutes. The sensor consists of a gold nanoparticle complexed with three different fluorescent proteins that can sense drug-induced physicochemical changes on cell surfaces. In the presence of cells, fluorescent proteins are rapidly displaced from the gold nanoparticle surface and fluorescence is restored. Fluorescence 'turn on' of the fluorescent proteins depends on the drug-induced cell surface changes, generating patterns that identify specific mechanisms of cell death induced by drugs. The nanosensor is generalizable to different cell types and does not require processing steps before analysis, offering an effective way to expedite research in drug discovery, toxicology and cell-based sensing.

Bidentate ligands on osmium(VI) nitrido complexes control intracellular targeting and cell death pathways

The cellular response evoked by antiproliferating osmium(VI) nitrido compounds of general formula OsN(N^N)Cl3 (N^N = 2,2'-bipyridine 1, 1,10-phenanthroline 2, 3,4,7,8-tetramethyl-1,10-phenanthroline 3, or 4,7-diphenyl-1,10-phenanthroline 4) can be tuned by subtle ligand modifications. Complex 2 induces DNA damage, resulting in activation of the p53 pathway, cell cycle arrest at the G2/M phase, and caspase-dependent apoptotic cell death. In contrast, 4 evokes endoplasmic reticulum (ER) stress leading to the upregulation of proteins of the unfolded protein response pathway, increase in ER size, and p53-independent apoptotic cell death. To the best of our knowledge, 4 is the first osmium compound to induce ER stress in cancer cells.

Cancer-related networks: a help to understand, predict and change malignant transformation

Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.