Computational Prediction of Drug Responses in Cancer Cell Lines From Cancer Omics and Detection of Drug Effectiveness Related Methylation Sites

Multidimensional Transcriptomics Unveils RNF34 as a Prognostic Biomarker and Potential Indicator of Chemotherapy Sensitivity in Wilms' Tumour

Nephroblastoma, colloquially known as Wilms' tumour (WT), is the predominant malignant renal neoplasm arising in the paediatric population. Modern therapeutic approaches for WT incorporate a synergistic combination of surgical intervention, radiotherapy, and chemotherapy, which substantially ameliorate the overall patient survival rate. Despite this, the optimal sequence of chemotherapy and surgical intervention remains a matter of contention, with each strategy presenting its own strengths and weaknesses that could influence clinical decision-making. To make some headway on this clinical dilemma, we deployed a multidimensional transcriptomics integration approach by analysing bulk RNA sequencing data with 136 samples, as well as single-nucleus RNA sequencing (snRNA-seq) and paired spatial transcriptome sequencing (stRNA) data from 32 WT specimens. Our findings identified a distinct elevation of RNF34 expression within WT samples, which correlated with unfavourable prognostic outcomes. Leveraging the Genomics of Drug Sensitivity in Cancer (GDSC), we simultaneously revealed that patients with high expression of RNF34 have higher sensitivity to commonly used chemotherapy drugs for WT. Furthermore, our analysis of snRNA and stRNA data unveiled a reduced proportion of RNF34 expression in neoplastic cells after chemotherapy. Moreover, stRNA data delineated a significant association between a higher proportion of RNF34 expression in cancer cells and adverse features such as anaplastic histology and tumour recurrence. Intriguingly, we also observed a close association between elevated RNF34 expression and a characteristic exhausted tumour immune microenvironment. Collectively, our findings underscore the pivotal role of RNF34 in the prognostic prediction potential and treatment sensitivity of WT. This comprehensive analysis can potentially inform and refine clinical decision-making for WT patients and guide future studies towards the development of optimized, rational therapeutic strategies.

Factors related to tumor response rate from TCGA three omics data-variants, expression, methylation

The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.

The pharmacoepigenomic landscape of cancer cell lines reveals the epigenetic component of drug sensitivity

Aberrant DNA methylation accompanies genetic alterations during oncogenesis and tumour homeostasis and contributes to the transcriptional deregulation of key signalling pathways in cancer. Despite increasing efforts in DNA methylation profiling of cancer patients, there is still a lack of epigenetic biomarkers to predict treatment efficacy. To address this, we analyse 721 cancer cell lines across 22 cancer types treated with 453 anti-cancer compounds. We systematically detect the predictive component of DNA methylation in the context of transcriptional and mutational patterns, i.e., in total 19 DNA methylation biomarkers across 17 drugs and five cancer types. DNA methylation constitutes drug sensitivity biomarkers by mediating the expression of proximal genes, thereby enhancing biological signals across multi-omics data modalities. Our method reproduces anticipated associations, and in addition, we find that the NEK9 promoter hypermethylation may confer sensitivity to the NEDD8-activating enzyme (NAE) inhibitor pevonedistat in melanoma through downregulation of NEK9. In summary, we envision that epigenomics will refine existing patient stratification, thus empowering the next generation of precision oncology.

CTDN (Convolutional Temporal Based Deep- Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction

The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.

DROEG: a method for cancer drug response prediction based on omics and essential genes integration

Predicting therapeutic responses in cancer patients is a major challenge in the field of precision medicine due to high inter- and intra-tumor heterogeneity. Most drug response models need to be improved in terms of accuracy, and there is limited research to assess therapeutic responses of particular tumor types. Here, we developed a novel method DROEG (Drug Response based on Omics and Essential Genes) for prediction of drug response in tumor cell lines by integrating genomic, transcriptomic and methylomic data along with CRISPR essential genes, and revealed that the incorporation of tumor proliferation essential genes can improve drug sensitivity prediction. Concisely, DROEG integrates literature-based and statistics-based methods to select features and uses Support Vector Regression for model construction. We demonstrate that DROEG outperforms most state-of-the-art algorithms by both qualitative (prediction accuracy for drug-sensitive/resistant) and quantitative (Pearson correlation coefficient between the predicted and actual IC50) evaluation in Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia datasets. In addition, DROEG is further applied to the pan-gastrointestinal tumor with high prevalence and mortality as a case study at both cell line and clinical levels to evaluate the model efficacy and discover potential prognostic biomarkers in Cisplatin and Epirubicin treatment. Interestingly, the CRISPR essential gene information is found to be the most important contributor to enhance the accuracy of the DROEG model. To our knowledge, this is the first study to integrate essential genes with multi-omics data to improve cancer drug response prediction and provide insights into personalized precision treatment.

Promises and challenges in pharmacoepigenetics

Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.

Hybrid Sea Lion Crow Search Algorithm-Based Stacked Autoencoder for Drug Sensitivity Prediction From Cancer Cell Lines

Cancer is the most dreadful diseases across world and providing better therapy to cancer patients is still remains as a major challenging task due to drug resistance of tumor cells. This paper proposes a Sea Lion Crow Search Algorithm (SLCSA) for drug sensitivity prediction. The drug sensitivity from cultured cell lines is predicted using stacked autoencoder and proposed SLCSA is derived by combination of Sea Lion Optimization (SLnO) and Crow Search Algorithm (CSA).The implemented approach has offered superior results with maximum value of testing accuracy for normal are 0.920, leukemia is 0.920, NSCLC is 0.912, and urogenital is 0.914.

Integration of transcriptomics and system pharmacology to reveal the therapeutic mechanism underlying Qingfei Xiaoyan Wan to treat allergic asthma

ETHNOPHARMACOLOGICAL RELEVANCE

Asthma is a chronic inflammatory disease, characterized by airway inflammation, hyperresponsiveness, and bronchial smooth muscle contraction. Qingfei Xiaoyan Wan (QFXYW), a traditional Chinese formula, has been shown to exert anti-asthma effects and immune response in multiple diseases.

AIM OF THIS STUDY

In this study, we evaluated the therapeutic mechanism of QFXYW in the suppression of allergic asthma by integrating of transcriptomics and system pharmacology.

MATERIALS AND METHODS

BALB/c mice were sensitized with ovalbumin (OVA) to establish the allergic asthma model, and its success was confirmed with behavioral observations. Lung histopathological analysis, inflammatory pathology scores, transcription factors were used to evaluate the effects of QFXYW on allergic asthma. The therapeutic mechanism of QFXYW in treating allergic asthma through integrated transcriptomics and system pharmacology was then determined: hub genes were screened out by topological analysis and functional enrichment analysis were performed to identify key signaling pathway. Subsequently, quantitative RP-PCR and protein array were performed to detect the mRNA of hub genes and to predict the key pathway in OVA-induced allergic asthma, respectively.

RESULTS

Our results demonstrated that QFXYW could significantly attenuate inflammatory cell infiltration, mucus secretion, and epithelial damage. The transcriptomics analysis found the six hub genes with the highest values- CXCL10, CXCL2, CXCL1, IL-6, CCL-5, and CCL-4 were screened out. Functional enrichment analysis showed that the differentially expressed genes (DEGs) were mainly enriched in the inflammatory response and cytokine signaling pathway. Moreover, the quantitative RT-PCR verification experiment found the CXCL2 and CXCL1 were significantly suppressed after treatment with QFXYW. The results of protein array showed that QFXYW inhibited the multi-cytokines of OVA-induced allergic asthma via cytokine signaling pathway.

CONCLUSIONS

QFXYW may have mediated OVA-induced allergic asthma mainly through the hub genes CXCL2, CXCL1, and the cytokine signaling pathway. This finding will offer a novel strategy to explore effective and safe mechanism of Traditional Chinese Medicine (TCM) formula to treat allergic asthma.

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach-DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of "hybrid" chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug-gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity.

Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.

A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study

Introduction

Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility.

Methods

The protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI.

Results

The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics.

Conclusion

The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-021-00330-y.

A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists’ confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4–8.8 for targeted therapy-naïve patients and 3.3–26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis—a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.