High-Order Drug Combinations Are Required to Effectively Kill Colorectal Cancer Cells

Light emitting diode (LED) irradiation of liposomes enhances drug encapsulation and delivery for improved cancer eradication

Liposomes are widely used as drug delivery nanoplatforms because of their versatility and biocompatibility; however, their ability to load certain drugs may be suboptimal. In this study, we generated liposomes using a combination of DSPE and DSPE-PEG-2 k lipids and loaded them with doxorubicin (DOX) and paclitaxel (PTX), to investigate the effects of light emitting diode (LED) irradiation on liposome structure and drug loading efficiency. Scanning and transmission electron microscopy revealed that the surface of liposomes irradiated with blue or near-infrared LEDs (LsLipo) was rougher and more irregular than that of non-LED-irradiated liposomes (NsLipo). Nuclear magnetic resonance analysis showed that the hydrogen peak originating from the lipid head groups was lower in LsLipo than in NsLipo preparations, indicating that LED irradiation changed the chemical and physical properties of the liposome. Structural changes, such as reduced rigidity, induced by LED irradiation, increased the loading efficiency of DOX and PTX. In vitro and in vivo experiments showed that LsLipo were more effective at inhibiting the growth of cancer cells than NsLipo. Our findings suggest that LED irradiation enhances the drug delivery efficacy of liposomes and offer new possibilities for improving drug delivery systems.

A landscape of response to drug combinations in non-small cell lung cancer

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Integrated CRISPR screening and drug profiling identifies combination opportunities for EGFR, ALK, and BRAF/MEK inhibitors

Anti-tumor efficacy of targeted therapies is variable across patients and cancer types. Even in patients with initial deep response, tumors are typically not eradicated and eventually relapse. To address these challenges, we present a systematic screen for targets that limit the anti-tumor efficacy of EGFR and ALK inhibitors in non-small cell lung cancer and BRAF/MEK inhibitors in colorectal cancer. Our approach includes genome-wide CRISPR screens with or without drugs targeting the oncogenic driver ("anchor therapy"), and large-scale pairwise combination screens of anchor therapies with 351 other drugs. Interestingly, targeting of a small number of genes, including MCL1, BCL2L1, and YAP1, sensitizes multiple cell lines to the respective anchor therapy. Data from drug combination screens with EGF816 and ceritinib indicate that dasatinib and agents disrupting microtubules act synergistically across many cell lines. Finally, we show that a higher-order-combination screen with 26 selected drugs in two resistant EGFR-mutant lung cancer cell lines identified active triplet combinations.

Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma

BACKGROUND

We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results.

METHODS

The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso.

RESULTS

The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI.

CONCLUSIONS

Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe.

The future of antibiotics begins with discovering new combinations

Antibiotic resistance is a worldwide and growing clinical problem. With limited drug development in the antibacterial space, combination therapy has emerged as a promising strategy to combat multidrug-resistant bacteria. Antibacterial combinations can improve antibiotic efficacy and suppress antibacterial resistance through independent, synergistic, or even antagonistic activities. Combination therapies are famously used to treat viral and mycobacterial infections and cancer. However, antibacterial combinations are only now emerging as a common treatment strategy for other bacterial infections owing to challenges in their discovery, development, regulatory approval, and commercial/clinical deployment. Here, we focus on discovery-where the sheer scale of combinatorial chemical spaces represents a significant challenge-and discuss how combination therapy can impact the treatment of bacterial infections. Despite these challenges, recent advancements, including new in silico methods, theoretical frameworks, and microfluidic platforms, are poised to identify the new and efficacious antibacterial combinations needed to revitalize the antibacterial drug pipeline.

Optimized low-dose combinatorial drug treatment boosts selectivity and efficacy of colorectal carcinoma treatment

The current standard of care for colorectal cancer (CRC) is a combination of chemotherapeutics, often supplemented with targeted biological drugs. An urgent need exists for improved drug efficacy and minimized side effects, especially at late‐stage disease. We employed the phenotypically driven therapeutically guided multidrug optimization (TGMO) technology to identify optimized drug combinations (ODCs) in CRC. We identified low‐dose synergistic and selective ODCs for a panel of six human CRC cell lines also active in heterotypic 3D co‐culture models. Transcriptome sequencing and phosphoproteome analyses showed that the mechanisms of action of these ODCs converged toward MAP kinase signaling and cell cycle inhibition. Two cell‐specific ODCs were translated to in vivo mouse models. The ODCs reduced tumor growth by ~80%, outperforming standard chemotherapy (FOLFOX). No toxicity was observed for the ODCs, while significant side effects were induced in the group treated with FOLFOX therapy. Identified ODCs demonstrated significantly enhanced bioavailability of the individual components. Finally, ODCs were also active in primary cells from CRC patient tumor tissues. Taken together, we show that the TGMO technology efficiently identifies selective and potent low‐dose drug combinations, optimized regardless of tumor mutation status, outperforming conventional chemotherapy.

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines.

Inactivation of NF1 Promotes Resistance to EGFR Inhibition in KRAS/NRAS/BRAFV600 -Wild-Type Colorectal Cancer

Through the use of an unbiased, genome-scale CRISPR modifier screen, we identified NF1 suppression as a mechanism of resistance to EGFR inhibition in NRAS/KRAS/BRAFV600 -wild-type colorectal cancer cells. Reduced NF1 expression permitted sustained signaling through the MAPK pathway to promote cell proliferation in the presence of EGFR inhibition. Targeting of MEK in combination with EGFR inhibition leads to synergistic antiproliferative activity. Human KRAS/NRAS/BRAFV600 -wild-type colorectal cancer cell lines with NF1 mutations displayed reduced NF1 mRNA or protein expression and were resistant to EGFR blockade by gefitinib or cetuximab. Cooccurring loss-of-function mutations in PTEN were associated with resistance to dual EGFR/MEK inhibition but cotreatment with a PI3K inhibitor further suppressed proliferation. Loss of NF1 may be a useful biomarker to identify patients that are less likely to benefit from single-agent anti-EGFR therapy in colorectal cancer and may direct potential combination strategies. IMPLICATIONS: This study suggests that further clinical validation of NF1 status as predictor of response to anti-EGFR targeting antibodies in patients with colorectal cancer with KRAS/NRAS/BRAFV600 -wild-type tumors is warranted.

Identification of low-dose multidrug combinations for sunitinib-naive and pre-treated renal cell carcinoma

Background

Combinations of drugs can improve the efficacy of cancer treatment, enable the reduction of side effects and the occurrence of acquired drug resistance.

Methods

We approached this challenge mathematically by using the validated technology called the Therapeutically Guided Multidrug Optimization (TGMO) method. In a set of genetically distinct human renal cell carcinoma (RCC) cell lines, either treated chronically with sunitinib (−ST) or sunitinib-naive, we identified cell line-specific low-dose-optimised drug combinations (ODC).

Results

Six cell-type-specific low-dose drug combinations for three sunitinib-naive as well as three sunitinib pre-treated cells were established. These ODCs effectively inhibited the RCC cell metabolic activity while being ineffective in non-cancerous cells. Based on a single screening test and three searches, starting with ten drugs, we identified highly efficacious drug mixtures containing four drugs. All ODCs contained AZD4547 (FGFR signalling pathway inhibitor) and pictilisib (pan-phosphatidylinositol 3-kinase inhibitor), but varied in the third and fourth drug. ODC treatment significantly decreased cell metabolic activity (up to 70%) and induced apoptosis, independent of the pretreatment with sunitinib. The ODCs outperformed sunitinib, the standard care for RCC. Moreover, short-term starvation potentiated the ODC activity. The translation of the 2D-based results to 3D heterotypic co-culture models revealed significant inhibition of the spheroid growth (up to 95%).

Conclusion

We demonstrate a promising low-dose drug combination development to obtain drug combinations effective in naive as well as resistant tumours. Nevertheless, we emphasise the need for further mechanistic investigation and preclinical development.

Charting the Fragmented Landscape of Drug Synergy

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.

MDM2-p53 Interaction Inhibitors: The Current State-of-Art and Updated Patent Review (2010-Present)

Background:

Mouse Double Minute 2 protein (MDM2) is a cellular regulator of p53 tumor suppressor (p53). Inhibition of the interaction between MDM2 and p53 proteins is a promising anticancer therapy.

Objective:

This updated patent review is an attempt to compile the research and achievements of the various researchers working on small molecule MDM2 inhibitors from 2010 to date. We provide an outlook into the future for therapy based on MDM2 inhibition by presenting an overview of the most relevant patents which have recently appeared in the literature.

Methods:

Literature and recent patents focusing on the anticancer potential of MDM2-p53 interaction inhibitors and its applications have been analyzed. We put the main emphasis on the most perspective compounds which are or were examined in clinical trials.

Results:

Literature data indicated that MDM2 inhibitors are therapeutically effective in specific types of cancer or non-cancer diseases. A great number of patents and research work around new MDM2- p53 interaction inhibitors, possible combinations, new indications, clinical regimens in previous years prove that this targeted therapy is in the scope of interest for many business and academic research groups.

Conclusion:

Novel MDM2 inhibitors thanks to higher potency and better ADME properties have shown effectiveness in preclinical and clinical development however the final improvement of therapeutic potential for MDM2 inhibitors might depend on the useful combination therapy and exploring new cancer and non-cancer indications.

Pyrazole-4-Carboxamide (YW2065): A Therapeutic Candidate for Colorectal Cancer via Dual Activities of Wnt/β-Catenin Signaling Inhibition and AMP-Activated Protein Kinase (AMPK) Activation

Dysregulation of the Wnt/β-catenin signaling pathway has been widely recognized as a pathogenic mechanism for colorectal cancer (CRC). Although numerous Wnt inhibitors have been developed, they commonly suffer from toxicity and unintended effects. Moreover, concerns have been raised in targeting this pathway because of its critical roles in maintaining stem cells and regenerating tissues and organs. On the basis of the anthelmintic drug pyrvinium and previous lead FX1128, we have developed a compound YW2065 (1c) which demonstrated excellent anti-CRC effects in vitro and in vivo. YW2065 achieves its inhibitory activity for Wnt signaling by stabilizing Axin-1, a scaffolding protein that regulates proteasome degradation of β-catenin. Simultaneously, YW2065 also led to the activation of the tumor suppressor AMPK, providing an additional anticancer mechanism. In addition, YW2065 showed favorable pharmacokinetic properties without obvious toxicity. The anti-CRC effect of YW2065 was highlighted by its promising efficacy in a mice xenograft model.

Prediction of drug combination effects with a minimal set of experiments

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

Identification of a Synergistic Multi-Drug Combination Active in Cancer Cells via the Prevention of Spindle Pole Clustering

A major limitation of clinically used cancer drugs is the lack of specificity resulting in toxicity. To address this, we performed a phenotypically-driven screen to identify optimal multidrug combinations acting with high efficacy and selectivity in clear cell renal cell carcinoma (ccRCC). The search was performed using the Therapeutically Guided Multidrug Optimization (TGMO) method in ccRCC cells (786-O) and nonmalignant renal cells and identified a synergistic low-dose four-drug combination (C2) with high efficacy and negligible toxicity. We discovered that C2 inhibits multipolar spindle pole clustering, a survival mechanism employed by cancer cells with spindle abnormalities. This phenotype was also observed in 786-O cells resistant to sunitinib, the first line ccRCC treatment, as well as in melanoma cells with distinct percentages of supernumerary centrosomes. We conclude that C2-treatment shows a high efficacy in cells prone to form multipolar spindles. Our data suggest a highly effective and selective C2 treatment strategy for malignant and drug-resistant cancers.

Therapeutic strategies to target RAS-mutant cancers

RAS genes are the most commonly mutated oncogenes in cancer, but effective therapeutic strategies to target RAS-mutant cancers have proved elusive. A key aspect of this challenge is the fact that direct inhibition of RAS proteins has proved difficult, leading researchers to test numerous alternative strategies aimed at exploiting RAS-related vulnerabilities or targeting RAS effectors. In the past few years, we have witnessed renewed efforts to target RAS directly, with several promising strategies being tested in clinical trials at different stages of completion. Important advances have also been made in approaches designed to indirectly target RAS by improving inhibition of RAS effectors, exploiting synthetic lethal interactions or metabolic dependencies, using therapeutic combination strategies or harnessing the immune system. In this Review, we describe historical and ongoing efforts to target RAS-mutant cancers and outline the current therapeutic landscape in the collective quest to overcome the effects of this crucial oncogene.

Noise-precision tradeoff in predicting combinations of mutations and drugs

Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem.

Sometimes a combination of drugs works much better than each drug alone. Finding such drug cocktails is a pressing challenge in order to combat drug resistance and to improve drug effects. However, it is impossible to test all combinations of multiple drug experimentally. Therefore, researchers are looking for computational rather than experimental approaches to overcome this problem. One approach is to measure the effect of few drugs and plug it into a formula that predicts the effect of many drugs together. Existing prediction formulae typically perform best on the dataset that they were developed on, but less well on other datasets. Here we explain this observation and give a guide for the choice of an optimal prediction formula for a given dataset. The optimal formula depends on two main properties of the dataset: 1) The interaction strength between the drugs and 2) The experimental noise in the data. This study may help researchers discover effective combinations of multiple drugs and multiple perturbations in general.

COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies

BACKGROUND

Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements.

RESULTS

COMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer. This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use.

CONCLUSIONS

COMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument.

The potent and selective cyclin-dependent kinases 4 and 6 inhibitor ribociclib (LEE011) is a versatile combination partner in preclinical cancer models

Inhibition of cyclin-dependent kinases 4 and 6 (CDK4/6) is associated with robust antitumor activity. Ribociclib (LEE011) is an orally bioavailable CDK4/6 inhibitor that is approved for the treatment of hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer, in combination with an aromatase inhibitor, and is currently being evaluated in several additional trials. Here, we report the preclinical profile of ribociclib. When tested across a large panel of kinase active site binding assays, ribociclib and palbociclib were highly selective for CDK4, while abemaciclib showed affinity to several other kinases. Both ribociclib and abemaciclib showed slightly higher potency in CDK4-dependent cells than in CDK6-dependent cells, while palbociclib did not show such a difference. Profiling CDK4/6 inhibitors in large-scale cancer cell line screens in vitro confirmed that RB1 loss of function is a negative predictor of sensitivity. We also found that routinely used cellular viability assays measuring adenosine triphosphate levels as a proxy for cell numbers underestimated the effects of CDK4/6 inhibition, which contrasts with assays that assess cell number more directly. Robust antitumor efficacy and combination benefit was detected when ribociclib was added to encorafenib, nazartinib, or endocrine therapies in patient-derived xenografts.

Modeling the impact of drug interactions on therapeutic selectivity

Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity-enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors.

Diagonal Method to Measure Synergy Among Any Number of Drugs

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs. Several recent studies have reported drug interaction measurements using a diagonal sampling of the traditional checkerboard assay. This alternative methodology greatly decreases the cost of drug interaction experiments and allows interaction measurement for combinations with many drugs. Here, we describe a protocol to measure the three pairwise interactions and one three-way interaction among three antibiotics in duplicate, in five days, using only three 96-well microplates and standard laboratory equipment. We present representative results showing that the three-antibiotic combination of Levofloxacin + Nalidixic Acid + Penicillin G is synergistic. Our protocol scales up to measure interactions among many drugs and in other biological contexts, allowing for efficient screens for multi-drug synergies against pathogens and tumors.

Prediction of drug cocktail effects when the number of measurements is limited

Cocktails of drugs can be more effective than single drugs, because they can potentially work at lower doses and avoid resistance. However, it is impossible to test all drug cocktails drawn from a large set of drugs because of the huge number of combinations. To overcome this combinatorial explosion problem, one can sample a relatively small number of combinations and use a model to predict the rest. Recently, Zimmer and Katzir et al. presented a model that accurately predicted the effects of cocktails at all doses based on measuring pairs of drugs. This model requires measuring each pair at several different doses and uses interpolation to reduce experimental noise. However, often, it is not possible to measure each pair at multiple doses (for example, in scarce patient-derived tumor material or in large screens). Here, we ask whether measurements at only a single dose can also predict high-order drug cocktails. To address this, we present a fully factorial experimental dataset on all drug cocktails built of 6 chemotherapy drugs on 2 cancer cell lines. We develop a formula that uses only pair measurements at a single dose to predict much of the variation up to 6-drug cocktails in the present data, outperforming commonly used Bliss independence and regression approaches. This model, called the pairs model, is an extension of the Bliss independence model to pairs: For M drugs, it equals the product of all pair effects to the power 1/(M−1). The pairs model also shows good agreement with previously published data on antibiotic triplets and quadruplets. The present model can only predict combinations at the same doses in which the pairs were measured and is not able to predict effects at other doses. This study indicates that pair-based approaches might be able to usefully predict and prioritize high-order combinations, even in large screens or when material for testing is limited.

Drug cocktails are a promising strategy for diseases such as cancer and infections, because cocktails can be more effective than individual drugs and can overcome problems of drug resistance. However, finding the best cocktail comprising a given set of drugs is challenging because the number of experiments needed is huge and grows exponentially with the number of drugs. This problem is exacerbated when the experiments are expensive and the material for testing is rare. Here, we present a way to address this challenge using a mathematical formula, called the pairs model, that requires relatively few experimental tests in order to estimate the effects of cocktails and to predict which cocktail is most effective. The formula does well on experimental data generated in this study using combinations of between 3 and 6 anticancer drugs, as well as on existing data that use combinations of antibiotics.

The Atypical Kinase RIOK1 Promotes Tumor Growth and Invasive Behavior

Despite being overexpressed in different tumor entities, RIO kinases are hardly characterized in mammalian cells. We investigated the role of these atypical kinases in different cancer cells. Using isogenic colon-, breast- and lung cancer cell lines, we demonstrate that knockdown of RIOK1, but not of RIOK2 or RIOK3, strongly impairs proliferation and invasiveness in conventional and 3D culture systems. Interestingly, these effects were mainly observed in RAS mutant cancer cells. In contrast, growth of RAS wildtype Caco-2 and Bcr-Abl-driven K562 cells is not affected by RIOK1 knockdown, suggesting a specific requirement for RIOK1 in the context of oncogenic RAS signaling. Furthermore, we show that RIOK1 activates NF-κB signaling and promotes cell cycle progression. Using proteomics, we identified the pro-invasive proteins Metadherin and Stathmin1 to be regulated by RIOK1. Additionally, we demonstrate that RIOK1 promotes lung colonization in vivo and that RIOK1 is overexpressed in different subtypes of human lung- and breast cancer. Altogether, our data suggest RIOK1 as a potential therapeutic target, especially in RAS-driven cancers.

Resistance mechanisms to TP53-MDM2 inhibition identified by in vivo piggyBac transposon mutagenesis screen in an Arf-/- mouse model

Inhibitors of double minute 2 protein (MDM2)-tumor protein 53 (TP53) interaction are predicted to be effective in tumors in which the TP53 gene is wild type, by preventing TP53 protein degradation. One such setting is represented by the frequent CDKN2A deletion in human cancer that, through inactivation of p14ARF, activates MDM2 protein, which in turn degrades TP53 tumor suppressor. Here we used piggyBac (PB) transposon insertional mutagenesis to anticipate resistance mechanisms occurring during treatment with the MDM2-TP53 inhibitor HDM201. Constitutive PB mutagenesis in Arf^-/- mice provided a collection of spontaneous tumors with characterized insertional genetic landscapes. Tumors were allografted in large cohorts of mice to assess the pharmacologic effects of HDM201. Sixteen out of 21 allograft models were sensitive to HDM201 but ultimately relapsed under treatment. A comparison of tumors with acquired resistance to HDM201 and untreated tumors identified 87 genes that were differentially and significantly targeted by the PB transposon. Resistant tumors displayed a complex clonality pattern suggesting the emergence of several resistant subclones. Among the most frequent alterations conferring resistance, we observed somatic and insertional loss-of-function mutations in transformation-related protein 53 (Trp53) in 54% of tumors and transposon-mediated gain-of-function alterations in B-cell lymphoma-extra large (Bcl-xL), Mdm4, and two TP53 family members, resulting in expression of the TP53 dominant negative truncations ΔNTrp63 and ΔNTrp73. Enhanced BCL-xL and MDM4 protein expression was confirmed in resistant tumors, as well as in HDM201-resistant patient-derived tumor xenografts. Interestingly, concomitant inhibition of MDM2 and BCL-xL demonstrated significant synergy in p53 wild-type cell lines in vitro. Collectively, our findings identify several potential mechanisms by which TP53 wild-type tumors may escape MDM2-targeted therapy.