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

Challenges in validation of combination treatment strategies for CRC using patient-derived organoids

Patient-derived organoids (PDOs) established from tissues from various tumor types gave the foundation of ex vivo models to screen and/or validate the activity of many cancer drug candidates. Due to their phenotypic and genotypic similarity to the tumor of which they were derived, PDOs offer results that effectively complement those obtained from more complex models. Yet, their potential for predicting sensitivity to combination therapy remains underexplored. In this review, we discuss the use of PDOs in both validation and optimization of multi-drug combinations for personalized treatment strategies in CRC. Moreover, we present recent advancements in enriching PDOs with diverse cell types, enhancing their ability to mimic the complexity of in vivo environments. Finally, we debate how such sophisticated models are narrowing the gap in personalized medicine, particularly through immunotherapy strategies and discuss the challenges and future direction in this promising field.

HDAC6 mediates tumorigenesis during mitosis and the development of targeted deactivating agents

Epigenetics, particularly deacetylation, plays a critical role in tumorigenesis as many carcinogens are under tight control by post-translational modification. HDAC6, an important and special histone deacetylase (HDAC) family member, has been indicated to increase carcinogenesis through various functions. Recent studies demonstrated the effects of HDAC6 inhibitors in mitotic arrest, however, detailed mechanisms still remain unknown. Herein, we review and summarize HDAC6-associated proteins that have been implicated in important roles in mitosis. We also discuss the development of medicinal agents targeting HDAC6.

Selectively cross-linked hydrogel-based cocktail drug delivery micro-chip for colon cancer combinatorial drug screening using AI-CSR platform for precision medicine

Cancer, ranked as the second leading cause of global mortality with a prevalence of 1 in 6 deaths, necessitates innovative approaches for effective treatment. Combinatorial drug therapy for cancer treatment targets several key pathways simultaneously and potentially enhances anti-cancer efficacy without intolerable side effects. However, it demands precise and accurate control of drug-dose combinations and their release. In this study, we demonstrated a selectively cross-linked hydrogel-based platform that can quantify and release drugs simultaneously for in-parallel cocktail drug screening. PDMS was used as the flow channel substrate and the poly (ethylene glycol) diacrylate (PEGDA) hydrogel array was formed by UV exposure using the photomask. Employing our platform, cocktails of anticancer drugs are precisely loaded and simultaneously released in-parallel into HCT-116 colon cancer cells, facilitating combinatorial drug screening. The integration of an artificial intelligence-based complex system response (AI-CSR) platform successfully identifies optimal drug-dose combinations from a pool of ten approved drugs. Notably, our cocktail drug chip demonstrates exceptional efficiency, screening 155 drug-dose combinations within a brief two and a half hours, a marked improvement over traditional methods. Furthermore, the device exhibits low drug consumption, requiring a mere 1 μL per patch of chip. Thus, our developed PDMS drug-loaded hydrogel platform presents a novel and expedited approach to quantifying drug concentrations, promising to be a faster, efficient and more precise approach for conducting cocktail drug screening experiments.

Data-driven learning of structure augments quantitative prediction of biological responses

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.

Impact of UV radiation on Mxene-mediated tubulin dissociation and mitochondrial apoptosis in breast cancer cells

Breast cancer is a global health concern that requires personalized therapies to prevent relapses, as conventional treatments may develop resistance over time. Photothermal therapy using spectral radiation or intense light emission is a broad-spectrum treatment that induces hyperthermia-mediated cancer cell death. MXene, a two-dimensional material, has been reported to have potential biological applications in photothermal therapy for cancer treatment. In this study, we investigated the apoptotic activity of MXene and UV-irradiated MXene in MCF-7 breast cancer cells by treating them with varying concentrations of MXene. The cytotoxicity of MXene and UV was evaluated by analyzing cellular morphology, nuclei condensation, caspase activation, and apoptotic cell death. We also assessed the effect of the combined treatment on the expression and cellular distribution of Tubulin, a key component of microtubules required for cell division. At low concentrations of MXene (up to 100 µg/ml), the level of cytotoxicity in MCF-7 cells was low. However, the combined treatment of MXene and UV resulted in a synergistic increase in cytotoxicity, causing rounded cellular morphology, condensed nuclei, caspase activation, and apoptotic cell death. Furthermore, the treatment reduced Tubulin protein expression and cellular distribution, indicating a potent inducer of cell death with potential application for cancer treatment. The study demonstrates that the combined treatment of MXene and UVB irradiation is a promising strategy for inducing apoptotic cell death in breast cancer cells, suggesting its potential as a therapeutic intervention for breast cancer.

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.

Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer

Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

FOLFOXIRI Resistance Induction and Characterization in Human Colorectal Cancer Cells

FOLFOXIRI, i.e., the combination of folinic acid, 5-fluorouracil, oxaliplatin, and irinotecan, is a first-line treatment for colorectal carcinoma (CRC), yet non-personalized and aggressive. In this study, to mimic the clinical situation of patients diagnosed with advanced CRC and exposed to a chronic treatment with FOLFOXIRI, we have generated the CRC cell clones chronically treated with FOLFOXIRI. A significant loss in sensitivity to FOLFOXIRI was obtained in all four cell lines, compared to their treatment-naïve calls, as shown in 2D cultures and heterotypic 3D co-cultures. Acquired drug resistance induction was observed through morphometric changes in terms of the organization of the actin filament. Bulk RNA sequencing revealed important upregulation of glucose transporter family 5 (GLUT5) in SW620 resistant cell line, while in the LS174T-resistant cell line, a significant downregulation of protein tyrosine phosphatase receptor S (PTPRS) and oxoglutarate dehydrogenase-like gene (OGDHL). This acquired resistance to FOLFOXIRI was overcome with optimized low-dose synergistic drug combinations (ODCs) acting via the Ras-Raf-MEK-ERK pathway. The ODCs inhibited the cell metabolic activity in SW620 and LS174T 3Dcc, respectively by up to 82%.

Combination of Talazoparib and Calcitriol Enhanced Anticancer Effect in Triple−Negative Breast Cancer Cell Lines

Monotherapy for triple−negative breast cancer (TNBC) is often ineffective. This study aimed to investigate the effect of calcitriol and talazoparib combination on cell proliferation, migration, apoptosis and cell cycle in TNBC cell lines. Monotherapies and their combination were studied for (i.) antiproliferative effect (using real−time cell analyzer assay), (ii.) cell migration (CIM−Plate assay), and (iii.) apoptosis and cell cycle analysis (flow cytometry) in MDA−MB−468 and BT−20 cell lines. The optimal antiproliferative concentration of talazoparib and calcitriol in BT−20 was 91.6 and 10 µM, respectively, and in MDA−MB−468, it was 1 mM and 10 µM. Combined treatment significantly increased inhibition of cell migration in both cell lines. The combined treatment in BT−20 significantly increased late apoptosis (89.05 vs. control 0.63%) and S and G2/M populations (31.95 and 24.29% vs. control (18.62 and 12.09%)). Combined treatment in MDA−MB−468 significantly increased the S population (45.72%) and decreased G0/G1 (45.86%) vs. the control (26.79 and 59.78%, respectively). In MDA−MB−468, combined treatment significantly increased necrosis, early and late apoptosis (7.13, 33.53 and 47.1% vs. control (1.5, 3.1 and 2.83%, respectively)). Talazoparib and calcitriol combination significantly affected cell proliferation and migration, induction of apoptosis and necrosis in TNBC cell lines. This combination could be useful as a formulation to treat TNBC.

Programmed cell death lives

Research on cell death mechanisms gets a lot of attention. This is understandable as it underlies biology in general, as well as the insight in pathological conditions and the development of opportunities for therapeutic intervention. Over the last years a steady rise in the number of scientific reports and in the impact of this literature on the different mechanisms of programmed cell death can be observed. A number of new concepts are highlighted.

Orthogonal array composite designs for drug combination experiments with applications for tuberculosis

The aim of this article is to provide an overview of the orthogonal array composite design (OACD) methodology, illustrate the various advantages, and provide a real-world application. An OACD combines a two-level factorial design with a three-level orthogonal array and it can be used as an alternative to existing composite designs for building response surface models. We compare the D $$ D $$ -efficiencies of OACDs relative to the commonly used central composite design (CCD) when there are a few missing observations and demonstrate that OACDs are more robust to missing observations for two scenarios. The first scenario assumes one missing observation either from one factorial point or one additional point. The second scenario assumes two missing observations either from two factorial points or from two additional points, or from one factorial point and one additional point. Furthermore, we compare OACDs and CCDs in terms of I $$ I $$ -optimality for precise predictions. Lastly, a real-world application of an OACD for a tuberculosis drug combination study is provided.

The Influence of CYP3A4 Genetic Polymorphism and Proton Pump Inhibitors on Osimertinib Metabolism

The aim of this study was to 1) investigate the effects of 27 CYP3A4 variants on the metabolism of osimertinib and 2) study the interactions between osimertinib and others as well as the underlying mechanism. A recombinant human CYP3A4 enzymatic incubation system was developed and employed to determine the kinetic profile of CYP3A4 variants. Ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) was applied to detect the concentration of the main metabolite, AZ5104. The results demonstrated that the relative clearance rates of CYP3A4.19, 10, 18, 5, 16, 14, 11, 2, 13, 12, 7, 8, and 17 in catalyzing osimertinib were significantly reduced to a minimum of 25.68% compared to CYP3A4.1, while those of CYP3A4.29, 32, 33, 28, 15, 34, and 3 were obviously enhanced, ranging from 114.14% to 284.52%. The activities of the remaining variants were almost equal to those of CYP3A4.1. In addition, 114 drugs were screened to determine the potential interaction with osimertinib based on the rat liver microsome (RLM) reaction system. Sixteen of them inhibited the production of AZ5104 to 20% or less, especially proton pump inhibitors, among which the IC₅₀ of rabeprazole was 6.49 ± 1.17 μM in RLM and 20.39 ± 2.32 μM in human liver microsome (HLM), with both following competitive and non-competitive mixed mechanism. In an in vivo study, Sprague–Dawley (SD) rats were randomly divided into groups, with six animals per group, receiving osimertinib with or without rabeprazole, omeprazole, and lansoprazole. We found that the AUC_(0–t), AUC_(0–∞), and C_max of osimertinib decreased significantly after co-administration with rabeprazole orally, but they increased remarkably when osimertinib was administered through intraperitoneal injection. Taken together, our data demonstrate that the genetic polymorphism and proton pump inhibitors remarkably influence the disposition of osimertinib, thereby providing basic data for the precise application of osimertinib.

Drug Repurposing to Identify a Synergistic High-Order Drug Combination to Treat Sunitinib-Resistant Renal Cell Carcinoma

Simple Summary

In this study, drug combination screening was used to design a multidrug combination consisting of repurposed drugs to treat sunitinib-resistant clear cell renal cell carcinoma. In the frame of this project, the multidrug combination has been optimized and validated and an insight into the mechanism of action is given. The multidrug combinations significantly altered the transcription of genes related to apoptosis and metabolic pathways. Further analysis of the metabolism revealed strong upregulation of the presence of sphingolipids after multidrug combination treatment. Final evaluation for translation of the multidrug combination in ex vivo organoid-like cultures demonstrated significant anti-cancer efficacy.

Abstract

Repurposed drugs have been evaluated for the management of clear cell renal cell carcinoma (ccRCC), but only a few have influenced the overall survival of patients with advanced disease. To combine repurposed non-oncology with oncological drugs, we applied our validated phenotypic method, which consisted of a reduced experimental part and data modeling. A synergistic optimized multidrug combination (ODC) was identified to significantly reduce the energy levels in cancer remaining inactive in non-cancerous cells. The ODC consisted of Rapta-C, erlotinib, metformin and parthenolide and low doses. Molecular and functional analysis of ODC revealed a loss of adhesiveness and induction of apoptosis. Gene-expression network analysis displayed significant alterations in the cellular metabolism, confirmed by LC-MS based metabolomic analysis, highlighting significant changes in the lipid classes. We used heterotypic in vitro 3D co-cultures and ex vivo organoids to validate the activity of the ODC, maintaining an efficacy of over 70%. Our results show that repurposed drugs can be combined to target cancer cells selectively with prominent activity. The strong impact on cell adherence and metabolism indicates a favorable mechanism of action of the ODC to treat ccRCC.

Molecular and Functional Analysis of Sunitinib-Resistance Induction in Human Renal Cell Carcinoma Cells

Resistance in clear cell renal cell carcinoma (ccRCC) against sunitinib is a multifaceted process encompassing numerous molecular aberrations. This induces clinical complications, reducing the treatment success. Understanding these aberrations helps us to select an adapted treatment strategy that surpasses resistance mechanisms, reverting the treatment insensitivity. In this regard, we investigated the dominant mechanisms of resistance to sunitinib and validated an optimized multidrug combination to overcome this resistance. Human ccRCC cells were exposed to single or chronic treatment with sunitinib to obtain three resistant clones. Upon manifestation of sunitinib resistance, morphometric changes in the cells were observed. At the molecular level, the production of cell membrane and extracellular matrix components, chemotaxis, and cell cycle progression were dysregulated. Molecules enforcing the cell cycle progression, i.e., cyclin A, B1, and E, were upregulated. Mass spectrometry analysis revealed the intra- and extracellular presence of N-desethyl sunitinib, the active metabolite. Lysosomal sequestration of sunitinib was confirmed. After treatment with a synergistic optimized drug combination, the cell metabolic activity in Caki-1-sunitinib-resistant cells and 3D heterotypic co-cultures was reduced by >80%, remaining inactive in non-cancerous cells. These results demonstrate geno- and phenotypic changes in response to sunitinib treatment upon resistance induction. Mimicking resistance in the laboratory served as a platform to study drug responses.

Signaling pathways in cancer-associated fibroblasts and targeted therapy for cancer

To flourish, cancers greatly depend on their surrounding tumor microenvironment (TME), and cancer-associated fibroblasts (CAFs) in TME are critical for cancer occurrence and progression because of their versatile roles in extracellular matrix remodeling, maintenance of stemness, blood vessel formation, modulation of tumor metabolism, immune response, and promotion of cancer cell proliferation, migration, invasion, and therapeutic resistance. CAFs are highly heterogeneous stromal cells and their crosstalk with cancer cells is mediated by a complex and intricate signaling network consisting of transforming growth factor-beta, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin, mitogen-activated protein kinase, Wnt, Janus kinase/signal transducers and activators of transcription, epidermal growth factor receptor, Hippo, and nuclear factor kappa-light-chain-enhancer of activated B cells, etc., signaling pathways. These signals in CAFs exhibit their own special characteristics during the cancer progression and have the potential to be targeted for anticancer therapy. Therefore, a comprehensive understanding of these signaling cascades in interactions between cancer cells and CAFs is necessary to fully realize the pivotal roles of CAFs in cancers. Herein, in this review, we will summarize the enormous amounts of findings on the signals mediating crosstalk of CAFs with cancer cells and its related targets or trials. Further, we hypothesize three potential targeting strategies, including, namely, epithelial-mesenchymal common targets, sequential target perturbation, and crosstalk-directed signaling targets, paving the way for CAF-directed or host cell-directed antitumor therapy.

Characterization of Renal Cell Carcinoma Heterotypic 3D Co-Cultures with Immune Cell Subsets

Two-dimensional cell culture-based platforms are easy and reproducible, however, they do not resemble the heterotypic cell-cell interactions or the complex tumor microenvironment. These parameters influence the treatment response and the cancer cell fate. Platforms to study the efficacy of anti-cancer treatments and their impact on the tumor microenvironment are currently being developed. In this study, we established robust, reproducible, and easy-to-use short-term spheroid cultures to mimic clear cell renal cell carcinoma (ccRCC). These 3D co-cultures included human endothelial cells, fibroblasts, immune cell subsets, and ccRCC cell lines, both parental and sunitinib-resistant. During spheroid formation, cells induce the production and secretion of the extracellular matrix. We monitored immune cell infiltration, surface protein expression, and the response to a treatment showing that the immune cells infiltrated the spheroid co-cultures within 6 h. Treatment with an optimized drug combination or the small molecule-based targeted drug sunitinib increased immune cell infiltration significantly. Assessing the therapeutic potential of this drug combination in this platform, we revealed that the expression of PD-L1 increased in 3D co-cultures. The cost- and time-effective establishment of our 3D co-culture model and its application as a pre-clinical drug screening platform can facilitate the treatment validation and clinical translation.

Forcing dividing cancer cells to die; low-dose drug combinations to prevent spindle pole clustering

Mitosis, under the control of the microtubule-based mitotic spindle, is an attractive target for anti-cancer treatments, as cancer cells undergo frequent and uncontrolled cell divisions. Microtubule targeting agents that disrupt mitosis or single molecule inhibitors of mitotic kinases or microtubule motors kill cancer cells with a high efficacy. These treatments have, nevertheless, severe disadvantages: they also target frequently dividing healthy tissues, such as the haematopoietic system, and they often lose their efficacy due to primary or acquired resistance mechanisms. An alternative target that has emerged in dividing cancer cells is their ability to “cluster” the poles of the mitotic spindle into a bipolar configuration. This mechanism is necessary for the specific survival of cancer cells that tend to form multipolar spindles due to the frequent presence of abnormal centrosome numbers or other spindle defects. Here we discuss the recent development of combinatorial treatments targeting spindle pole clustering that specifically target cancer cells bearing aberrant centrosome numbers and that have the potential to avoid resistance mechanism due their combinatorial nature.

Optimized Combination of HDACI and TKI Efficiently Inhibits Metabolic Activity in Renal Cell Carcinoma and Overcomes Sunitinib Resistance

Clear cell renal cell carcinoma (ccRCC) is characterized by high histone deacetylase (HDAC) activity triggering both cell motility and the development of metastasis. Therefore, there is an unmet need to establish innovative strategies to advance the use of HDAC inhibitors (HDACIs). We selected a set of tyrosine kinase inhibitors (TKIs) and HDACIs to test them in combination, using the validated therapeutically guided multidrug optimization (TGMO) technique based on experimental testing and in silico data modeling. We determined a synergistic low-dose three-drug combination decreasing the cell metabolic activity in metastatic ccRCC cells, Caki-1, by over 80%. This drug combination induced apoptosis and showed anti-angiogenic activity, both in original Caki-1 and in sunitinib-resistant Caki-1 cells. Through phosphoproteomic analysis, we revealed additional targets to improve the translation of this combination in 3-D (co-)culture systems. Cell-cell and cell-environment interactions increased, reverting the invasive and metastatic phenotype of Caki-1 cells. Our data suggest that our optimized low-dose drug combination is highly effective in complex in vitro settings and promotes the activity of HDACIs.

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.

Integrating Phenotypic Search and Phosphoproteomic Profiling of Active Kinases for Optimization of Drug Mixtures for RCC Treatment

Combined application of multiple therapeutic agents presents the possibility of enhanced efficacy and reduced development of resistance. Definition of the most appropriate combination for any given disease phenotype is challenged by the vast number of theoretically possible combinations of drugs and doses, making extensive empirical testing a virtually impossible task. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, which converges to optimized drug combinations (ODC) within a few experimental steps. Phosphoproteomics analysis coupled to kinase activity analysis using the novel INKA (integrative inferred kinase activity) pipeline was performed to evaluate ODC mechanisms in a panel of renal cell carcinoma (RCC) cell lines. We identified different ODC with up to 95% effectivity for each RCC cell line, with low doses (ED_5-25) of individual drugs. Global phosphoproteomics analysis demonstrated inhibition of relevant kinases, and targeting remaining active kinases with additional compounds improved efficacy. In addition, we identified a common RCC ODC, based on kinase activity data, to be effective in all RCC cell lines under study. Combining s-FSC with a phosphoproteomic profiling approach provides valuable insight in targetable kinase activity and allows for the identification of superior drug combinations for the treatment of RCC.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.

DNA Replication Stress and Chromosomal Instability: Dangerous Liaisons

Chromosomal instability (CIN) is associated with many human diseases, including neurodevelopmental or neurodegenerative conditions, age-related disorders and cancer, and is a key driver for disease initiation and progression. A major source of structural chromosome instability (s-CIN) leading to structural chromosome aberrations is "replication stress", a condition in which stalled or slowly progressing replication forks interfere with timely and error-free completion of the S phase. On the other hand, mitotic errors that result in chromosome mis-segregation are the cause of numerical chromosome instability (n-CIN) and aneuploidy. In this review, we will discuss recent evidence showing that these two forms of chromosomal instability can be mechanistically interlinked. We first summarize how replication stress causes structural and numerical CIN, focusing on mechanisms such as mitotic rescue of replication stress (MRRS) and centriole disengagement, which prevent or contribute to specific types of structural chromosome aberrations and segregation errors. We describe the main outcomes of segregation errors and how micronucleation and aneuploidy can be the key stimuli promoting inflammation, senescence, or chromothripsis. At the end, we discuss how CIN can reduce cellular fitness and may behave as an anticancer barrier in noncancerous cells or precancerous lesions, whereas it fuels genomic instability in the context of cancer, and how our current knowledge may be exploited for developing cancer therapies.

Drug-Drug Interactions of Irinotecan, 5-Fluorouracil, Folinic Acid and Oxaliplatin and Its Activity in Colorectal Carcinoma Treatment

The combination of folinic acid, 5-fluorouracil, oxaliplatin and/or irinotecan (FOLFOXIRI) is the standard of care for metastatic colorectal cancer (CRC). This strategy inhibits tumor growth but provokes drug resistance and serious side effects. We aimed to improve FOLFOXIRI by optimization of the dosing and the sequence of drug administration. We employed an orthogonal array composite design and linear regression analysis to obtain cell line-specific drug combinations for four CRC cell lines (DLD1, SW620, HCT116, LS174T). Our results confirmed the synergy between folinic acid and 5-fluorouracil and additivity, or even antagonism, between the other drugs of the combination. The drug combination administered at clinical doses resulted in significantly higher antagonistic interactions compared to the low-dose optimized drug combination (ODC). We found that the concomitant administration of the optimized drug combination (ODC) was comparatively active to sequential administration. However, the administration of oxaliplatin or the active metabolite of irinotecan seemed to sensitize the cells to the combination of folinic acid and 5-fluorouracil. ODCs were similarly active in non-cancerous cells as compared to the clinically used doses, indicating a lack of reduction of side effects. Interestingly, ODCs were inactive in CRC cells chronically pretreated with FOLFOXIRI, suggesting the occurrence of resistance. We were unable to improve FOLFOXIRI in terms of efficacy or specificity. Improvement of CRC treatment should come from the optimization of targeted drugs and immunotherapy strategies.

Patient-Derived In Vitro Models for Drug Discovery in Colorectal Carcinoma

Lack of relevant preclinical models that reliably recapitulate the complexity and heterogeneity of human cancer has slowed down the development and approval of new anti-cancer therapies. Even though two-dimensional in vitro culture models remain widely used, they allow only partial cell-to-cell and cell-to-matrix interactions and therefore do not represent the complex nature of the tumor microenvironment. Therefore, better models reflecting intra-tumor heterogeneity need to be incorporated in the drug screening process to more reliably predict the efficacy of drug candidates. Classic methods of modelling colorectal carcinoma (CRC), while useful for many applications, carry numerous limitations. In this review, we address the recent advances in in vitro CRC model systems, ranging from conventional CRC patient-derived models, such as conditional reprogramming-based cell cultures, to more experimental and state-of-the-art models, such as cancer-on-chip platforms or liquid biopsy.

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.