Robust scoring of selective drug responses for patient-tailored therapy selection.

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia.

Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.

Systematic review of computational methods for drug combination prediction.

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

Inhibited effect of an RGD peptide hydrogel on the expression of ß1-integrin, FAK, and Akt in Tenon's capsule fibroblasts.

Tenon's capsule fibroblasts are the main cellular components of filtration tract scar that limit the success rate of glaucoma filtration surgery. Scar formation results from infiltration and proliferation of fibroblasts into damaged areas, meanwhile synthesis of extracellular matrix glycoproteins. Integrins are cell surface receptors for extracellular molecules that mediate cell adhesion, spreading, migration, and invasion. They bind their ligands often through recognition of short amino-acid sequences-arginine-glycine-aspartic acid (RGD). Peptides that contain RGD sequence can compete with RGD containing insoluble matrix proteins for binding to the integrin receptor and thus prevent the downstream signaling pathway. Increasing evidence supports that ß1-integrin/focal adhesion kinase (FAK)/Akt signal pathway plays an important role in fibrogenesis and scar formation in different tissues. In consideration of advantages of peptide hydrogel, that is well biocompatibility, gel state, degradability, good drug loading, we designed and fabricated an RGD peptide hydrogel, and hypothesized that it could inhibit the expression of ß1-integrin, FAK, and Akt in Tenon's capsule fibroblasts. Rheology results showed that 1% wt Fmoc-FFGGRGD peptide solution could self-assemble into hydrogel. Western blot analysis revealed that there were statistical differences between control group and 1% wt group in ß1-integrin/ß-actin, FAK/ß-actin, Akt/ß-actin respectively (*p < .05). The relative mRNA expression of ß1-integrin, FAK, Akt in control group and 1% wt group were also statistically different respectively (*p < .05). We proved that 1% wt Fmoc-FFGGRGD self-assembly peptide hydrogel could inhibit the expression of ß1-integrin, FAK and Akt in Tenon's capsule fibroblasts. It is a promising way to solve scar formation of glaucoma filter channel.

Characterizing the Quality of Insight by Interactions: A Case Study.

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study's implications, lessons learned, and future research opportunities.

Comparison of inflammatory cytokines levels in the aqueous humor with diabetic retinopathy.

PURPOSE: Evidence has accumulated suggesting that various inflammatory cytokines are involved in the progress of diabetic retinopathy (DR), but there are few studies concerning the relationship between individual cytokines levels in the aqueous humor (AH) and the severity of DR. This study aimed to explore the differences of interleukin (IL)-23, IL-17, IL-10 and transforming growth factor (TGF)-ß in AH form patients with different proliferative stages of DR. METHODS: From June 2016 to June 2019, patients for senile cataract surgery were enrolled with the informed consent. All cases were graded into 4 groups: the control group (patients without diabetes), non-retinopathy (NDR) group (diabetic patients without retinopathy), non-proliferative diabetic retinopathy (NPDR) group, and proliferative diabetic retinopathy (PDR) group. The concentrations of IL-23, IL-17, IL-10, and TGF-ß in AH were measured using ELISA and compared them within four groups by ANOVA. RESULTS: In this study, 20 (28.57%), 18 (25.71%), 17 (24.29%), and 15 (21.43%) patients were included in the control, NDR, NPDR, and PDR groups, respectively. There had no significant differences in demographic characteristics (P > 0.05). Comparison of these cytokines within four groups revealed: the IL-23 level was increased in NDR group initially and raised along with the progression of DR (P < 0.01). The IL-17 level was significantly higher in NPDR and PDR groups compared to NDR and the control groups, and positively correlated with more-severe DR (P < 0.01). By contrast, The IL-10 level was significantly lower in diabetic patients than in non-diabetic controls, and decreased as the severity of DR increased (P < 0.05). In addition, TGF-ß was also elevated in diabetic patients, but showed no differences based on the presence or severity of DR (P > 0.05). CONCLUSIONS: The over-expression of IL-23 and IL-17 in AH might have a synergistic effect on the pathogenesis well before the proliferative stage, and was typical positively correlated with the severity of DR. These results offer a novel early therapeutic target for DR.

Protective effects of carnosic acid on retinal ganglion cells in acute ocular hypertension rats.

OBJECTIVE: To observe the protective effects of carnosic acid on rat retinal ganglion cells (RGCs) among acute ocular hypertension rats. METHODS: Sixty male SPF (specific-pathogen-free) SD rats (10 weeks) were randomly assigned to untreated group, carnosic-acid-treated group and hypertensive group with 20 rats for each. The acute ocular hypertension animal model was induced by the perfusion of normal saline solution into anterior chamber of eyes to elevate the intraocular pressure (IOP) to 110 mmHg for 60 min in the rats of the carnosic-acid-treated group and hypertensive group. Then, the carnosic acid dissolving in dimethyl sulfoxide (DMSO) was intraperitoneally injected for consecutive 7 days in the carnosic-acid-treated group, and only DMSO was used in the same way in the hypertensive group. The rats were killed 2 weeks after experiment, and retinal sections were prepared for histopathological and apoptotic retinal ganglion cells (RGCs) examination by hemotoxylin and eosin staining and TUNEL staining. Use immunofluorescence employed to examine the survival of RGCs. This study protocol was approved by the Ethic Committee for Experimental Animal of Three Gorges University. RESULTS: The retinal morphology and structure were clear in the untreated group. The edema of retinal tissue, loosely arranged RGCs and swollen nucleus were seen in the hypertensive group. In the carnosic-acid-treated group, the retinal morphology and structure were regular. The retinal nerve fiber layer (RNFL) thickness was (32.96 ± 1.63), (58.96 ± 1.57) and (50.11 ± 2.37) µm, and the apoptotic cell number was (6.92 ± 2.96), (29.85 ± 6.40) and (14.69 ± 2.98)/field, and the survived cell number was (2363.17 ± 148.45), (1308.67 ± 106.02) and (1614.17 ± 96.39)/0.235 mm2 in the untreated group, hypertensive group and carnosic-acid-treated group, respectively, showing significant differences among groups (F = 339.284, 81.583, 122.68, all at P < 0.01). Compared with the untreated group, the RNFL thickness was thickened, the number of apoptotic RGCs was much more, and the number of survived RGCs was decreased in the hypertensive group. In the carnosic-acid-treated group, the RNFL thickness was thinner, the number of apoptotic RGCs was reduced, and the number of survived RGCs was increased in comparison with the untreated group (all at P < 0.01). CONCLUSIONS: Carnosic acid plays a protective effect on RGCs by inhibiting the cell apoptosis in acute ocular hypertension rats.

Inhibitive effect of TAK-242 on Tenon's capsule fibroblasts proliferation in rat eyes.

AIM: To study the inhibition effect of TAK-242 on the proliferation of rat eye Tenon's capsule fibroblasts via the toll-like receptor 4 (TLR4) signaling pathway. METHODS: SD rat Tenon's capsule fibroblasts were extracted and cultured, then the cells were divided into normal control group, lipopolysaccharide (LPS) group (10 g/mL LPS) and TAK-242 group (1 µmol/L TAK-242, and 10 µg/mL LPS after 30min). The expressions of TLR4, transforming growth factor-ß1 (TGF-ß1) and interleukin-6 (IL-6) in each group were detected by Western blot and reverse transcriptase-polymerase chain reaction (RT-PCR). Cell proliferation was detected by cell counting kit-8 (CCK-8). RESULTS: Double immunofluorescent labeling in the extracted cells showed negative keratin staining and positive vimentin staining. Western blot showed that the LPS group had the highest expression of TLR4 and TGF-ß1 (P<0.01). Enzyme linked immunosorbent assay (ELISA) also showed that the secretion of IL-6 was the highest in LPS group (P<0.01). But there was no significant difference in TLR4 and TGF-1, as well as IL-6 expressions between the TAK-242 group and the normal control group (P>0.05). RT-PCR showed that the IL-6 mRNA expression in LPS group was the highest in the three groups (P<0.01). CONCLUSION: TAK-242 inhibits the proliferation of LPS-induced Tenon's capsule fibroblasts and the release of inflammatory factors by regulating the TLR4 signaling pathway, providing a new idea for reducing the scarring of the filter passage after glaucoma filtration surgery.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

Corneal hysteresis and glaucoma.

PURPOSE: To review and summarize the characteristics of corneal hysteresis (CH) and its relationship with glaucoma. METHODS: A PubMed search was carried out using the terms "corneal hysteresis", "glaucoma", and "biomechanics". Up to March 2018, all studies published in English are included in this review. RESULTS: The value of CH reflects the ability of corneal tissue to absorb and release energy during bidirectional flattening. It is an important biomechanical parameter of the cornea. The CH value of healthy adults is about 11 mmHg. The measurement of CH is reproducible and different. People have different CH values, which are determined by the shape of the individual's cornea. The study found that all types of glaucoma, including primary open angle glaucoma, angle-closure glaucoma, normal tension glaucoma, congenital glaucoma, binocular asymmetrical glaucoma, CH values are lower than normal people, therefore, CH is therefore a good indicator of glaucoma diagnosis and screening. Lower CH values are associated with thinner retinal nerve fiber layer (RNFL), larger linear cup/disk ratio (LCDR) and degree of optic disc defect. A lower CH value can also result in a lower visual field index. CH and the basic intraocular pressure play a synergistic role in the progression of glaucoma. The study found that CH can change with changes in basic intraocular pressure, means CH increases when intraocular pressure decreases, while the CH decreases conversely when intraocular pressure increases. Most clinical case studies have shown a decrease in CH after LASER refractive surgery. CH has its limitations, such as corneal damage or corneal surgery, but in general, CH is a risk factor for glaucoma progression. CONCLUSION: CH is used as a predictor of glaucoma risk and may help to assess the effect of corneal thickness on intraocular pressure. The clinical significance of CH in the diagnosis and efficacy of glaucoma will become more explicit. In the future, CH can also play an important role in the diagnosis and treatment of glaucoma.

Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients.

The molecular pathways that drive cancer progression and treatment resistance are highly redundant and variable between individual patients with the same cancer type. To tackle this complex rewiring of pathway cross-talk, personalized combination treatments targeting multiple cancer growth and survival pathways are required. Here we implemented a computational-experimental drug combination prediction and testing (DCPT) platform for efficient in silico prioritization and ex vivo testing in patient-derived samples to identify customized synergistic combinations for individual cancer patients. DCPT used drug-target interaction networks to traverse the massive combinatorial search spaces among 218 compounds (a total of 23,653 pairwise combinations) and identified cancer-selective synergies by using differential single-compound sensitivity profiles between patient cells and healthy controls, hence reducing the likelihood of toxic combination effects. A polypharmacology-based machine learning modeling and network visualization made use of baseline genomic and molecular profiles to guide patient-specific combination testing and clinical translation phases. Using T-cell prolymphocytic leukemia (T-PLL) as a first case study, we show how the DCPT platform successfully predicted distinct synergistic combinations for each of the three T-PLL patients, each presenting with different resistance patterns and synergy mechanisms. In total, 10 of 24 (42%) of selective combination predictions were experimentally confirmed to show synergy in patient-derived samples ex vivo The identified selective synergies among approved drugs, including tacrolimus and temsirolimus combined with BCL-2 inhibitor venetoclax, may offer novel drug repurposing opportunities for treating T-PLL.Significance: An integrated use of functional drug screening combined with genomic and molecular profiling enables patient-customized prediction and testing of drug combination synergies for T-PLL patients. Cancer Res; 78(9); 2407-18. ©2018 AACR.

Methods for High-throughput Drug Combination Screening and Synergy Scoring.

Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.

SynergyFinder: a web application for analyzing drug combination dose-response matrix data.

SUMMARY: Rational design of drug combinations has become a promising strategy to tackle the drug sensitivity and resistance problem in cancer treatment. To systematically evaluate the pre-clinical significance of pairwise drug combinations, functional screening assays that probe combination effects in a dose-response matrix assay are commonly used. To facilitate the analysis of such drug combination experiments, we implemented a web application that uses key functions of R-package SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user-friendly interface for visualizing the drug combination landscapes in an interactive manner. AVAILABILITY AND IMPLEMENTATION: The SynergyFinder web application is freely accessible at https://synergyfinder.fimm.fi ; The R-package and its source-code are freely available at http://bioconductor.org/packages/release/bioc/html/synergyfinder.html . CONTACT: jing.tang@helsinki.fi.

Comprehensive analysis of microRNA-Seq and target mRNAs of rice sheath blight pathogen provides new insights into pathogenic regulatory mechanisms.

MicroRNAs (miRNAs) are ∼22 nucleotide non-coding RNAs that regulate gene expression by targeting mRNAs for degradation or inhibiting protein translation. To investigate whether miRNAs regulate the pathogenesis in necrotrophic fungus Rhizoctonia solani AG1 IA, which causes significant yield loss in main economically important crops, and to determine the regulatory mechanism occurring during pathogenesis, we constructed hyphal small RNA libraries from six different infection periods of the rice leaf. Through sequencing and analysis, 177 miRNA-like small RNAs (milRNAs) were identified, including 15 candidate pathogenic novel milRNAs predicted by functional annotations of their target mRNAs and expression patterns of milRNAs and mRNAs during infection. Reverse transcription-quantitative polymerase chain reaction results for randomly selected milRNAs demonstrated that our novel comprehensive predictions had a high level of accuracy. In our predicted pathogenic protein-protein interaction network of R. solani, we added the related regulatory milRNAs of these core coding genes into the network, and could understand the relationships among these regulatory factors more clearly at the systems level. Furthermore, the putative pathogenic Rhi-milR-16, which negatively regulates target gene expression, was experimentally validated to have regulatory functions by a dual-luciferase reporter assay. Additionally, 23 candidate rice miRNAs that may involve in plant immunity against R. solani were discovered. This first study on novel pathogenic milRNAs of R. solani AG1 IA and the recognition of target genes involved in pathogenicity, as well as rice miRNAs, participated in defence against R. solani could provide new insights into revealing the pathogenic mechanisms of the severe rice sheath blight disease.

TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.

UNLABELLED: Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logic-based network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drug-target interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications. AVAILABILITY AND IMPLEMENTATION: TIMMA-R source code is freely available at http://cran.r-project.org/web/packages/timma/.