Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer

LncRNA CARMN suppresses EMT through inhibiting transcription of MMP2 activated by DHX9 in breast cancer

Long non-coding RNAs (lncRNAs) have been shown to drive cancer progression. However, the function of lncRNAs and the underlying mechanism in early-stage breast cancer(BC) have rarely been investigated. Datasets of pre-invasive ductal carcinoma in situ (DCIS), invasive ductal BC (IDC) and normal breast tissue from TCGA and GEO databases were used to conduct bioinformatics analysis. LncRNA CARMN was identified as a tumor suppressor in early-stage BC and related to a better prognosis. CARMN over-expression inhibited MMP2 mediated migration and EMT in BC. Further analysis showed that CARMN was located in the nucleus and functioned as an enhancer RNA (eRNA) in mammary epithelial cell. Mechanically, CARMN binding protein DHX9 was identified by RNA pull-down and mass spectrometry (MS) assays and it also bound to the MMP2 promoter to activate its transcription. As a decoy, CARMN competitively bound to DHX9 and blocked MMP2 transcriptional activation, thereby inhibiting metastasis and EMT of BC cells. These findings reveal the important role of CARMN as a tumor suppressor in the metastasis and a potential biomarker for progression in early-stage BC.

Decoding Systems Biology of Inflammation Signatures in Cancer Pathogenesis: Pan-Cancer Insights from 12 Common Cancers

Chronic inflammation is an important contributor to tumorigenesis in many tissues. However, the underlying mechanisms of inflammatory signaling in the tumor microenvironment are not yet fully understood in various cancers. Therefore, this study aimed to uncover the gene expression signatures of inflammation-associated proteins that lead to tumorigenesis, and with an eye to discovery of potential system biomarkers and novel drug candidates in oncology. Gene expression profiles associated with 12 common cancers (e.g., breast invasive carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma) from The Cancer Genome Atlas were retrieved and mapped to inflammation-related gene sets. Subsequently, the inflammation-associated differentially expressed genes (i-DEGs) were determined. The i-DEGs common in all cancers were proposed as tumor inflammation signatures (TIS) after pan-cancer analysis. A TIS, consisting of 45 proteins, was evaluated as a potential system biomarker based on its prognostic forecasting and secretion profiles in multiple tissues. In addition, i-DEGs for each cancer type were used as queries for drug repurposing. Narciclasine, parthenolide, and homoharringtonine were identified as potential candidates for drug repurposing. Biomarker candidates in relation to inflammation were identified such as KNG1, SPP1, and MIF. Collectively, these findings inform precision diagnostics development to distinguish individual cancer types, and can also pave the way for novel prognostic decision tools and repurposed drugs across multiple cancers. These new findings and hypotheses warrant further research toward precision/personalized medicine in oncology. Pan-cancer analysis of inflammatory mediators can open up new avenues for innovation in cancer diagnostics and therapeutics.

Informatics on Drug Repurposing for Breast Cancer

Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

HPV16 status predicts potential protein biomarkers and therapeutics in head and neck squamous cell carcinoma

Human papillomavirus (HPV) infection, especially HPV16, is one of the causative factors for the development of head and neck squamous cell (HNSC) carcinoma. HPV-positive and HPV-negative HNSC patients differ significantly in their molecular profiles and clinical features, so they should be evaluated differently depending on their HPV status. Given the tremendous variation in HNSC cancers depending on HPV, our goal in this study was to present biomarkers and treatment options tailored to the patient's HPV status. Gene expression levels of HPV16-positive and -negative patients were used as proxies, and the differential interactome algorithm was employed to identify the differential interacting proteins (DIPs). By assessing the prognostic capabilities and druggabilities of DIPs and their interacting partners (DIP-centered modules), we introduce eight modules as potential biomarkers specialized for either positive or negative phenotype. Finally, raloxifene was repositioned for the first time as a drug candidate for the treatment of HPV16-positive HNSC patients.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

Medical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer.

METHODS

Data on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced breast MRI were included. Response to NAC was assessed using the Miller-Payne system on the excised tumor. Tumor segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator regression. A support vector machine learning model was used to classify response to NAC.

RESULTS

74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity < 90%, n = 44) and an excellent response (> 90% reduction in cellularity, n = 30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor status improved the accuracy of the model with an AUC of 0.811.

CONCLUSION

This study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.

Drug Repositioning Identifies Six Drug Candidates for Systemic Autoimmune Diseases by Integrative Analyses of Transcriptomes from Scleroderma, Systemic Lupus Erythematosus, and Sjogren's Syndrome

The mechanisms of systemic autoimmune diseases (ADs) are still not clearly understood. Understanding the etiology of systemic ADs and identifying new therapeutic targets require a systems science approach. Using publicly available transcriptome data and bioinformatic analysis, we investigated the differential gene expression profiles of patients with scleroderma, systemic lupus erythematosus, and Sjogren's syndrome. Of these common differentially expressed gene signatures, 208 were regulated in the same direction (either upregulated or downregulated in all datasets) and used for drug repositioning. Six small molecule drug candidates (KU-0063794, YM-155 [sepantronium bromide], MST-312 [telomerase inhibitor IX], PLX-4720, ZM 336372, and 528116.cdx [PIK-75]) were discovered by drug repositioning as potential therapeutics for systemic ADs. The Search Tool for Chemical Interactions was used to find the anticipated target genes of the repositioned molecules. The PI3K/AKT pathway topped the list of common enriched pathways with the most anticipated target genes of the six repositioned small molecules. We also report here the molecular docking findings on the binding affinity between the repositioned drug candidates and genes from the protein-protein interaction network modules of anticipated target genes. Taken together, this study provides new insights and opens up new possibilities on both pathogenesis and treatment of systemic ADs through drug repositioning.

Integrative Analysis of Motor Neuron and Microglial Transcriptomes from SOD1G93A Mice Models Uncover Potential Drug Treatments for ALS

Amyotrophic lateral sclerosis (ALS) is a fatal disease of motor neurons that mainly affects the motor cortex, brainstem, and spinal cord. Under disease conditions, microglia could possess two distinct profiles, M1 (toxic) and M2 (protective), with the M2 profile observed at disease onset. SOD1 (superoxide dismutase 1) gene mutations account for up to 20% of familial ALS cases. Comparative gene expression differences in M2-protective (early) stage SOD1^G93A microglia and age-matched SOD1^G93A motor neurons are poorly understood. We evaluated the differential gene expression profiles in SOD1^G93A microglia and SOD1^G93A motor neurons utilizing publicly available transcriptomics data and bioinformatics analyses, constructed biomolecular networks around them, and identified gene clusters as potential drug targets. Following a drug repositioning strategy, 5 small compounds (belinostat, auranofin, BRD-K78930611, AZD-8055, and COT-10b) were repositioned as potential ALS therapeutic candidates that mimic the protective state of microglia and reverse the toxic state of motor neurons. We anticipate that this study will provide new insights into the ALS pathophysiology linking the M2 state of microglia and drug repositioning.

Functional stratification of cancer drugs through integrated network similarity

Drugs not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored networks modulated by several drugs across multiple cancer cell lines by integrating their targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing a similar mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation and found literature evidence for a set of drug pairs. Overall, network-level exploration of drug-modulated pathways and their deep comparison may potentially help optimize treatment strategies and suggest new drug combinations.

Past, Present, and Future of Therapies for Pituitary Neuroendocrine Tumors: Need for Omics and Drug Repositioning Guidance

Innovation roadmaps are important, because they encourage the actors in an innovation ecosystem to creatively imagine multiple possible science future(s), while anticipating the prospects and challenges on the innovation trajectory. In this overarching context, this expert review highlights the present unmet need for therapeutic innovations for pituitary neuroendocrine tumors (PitNETs), also known as pituitary adenomas. Although there are many drugs used in practice to treat PitNETs, many of these drugs can have negative side effects and show highly variable outcomes in terms of overall recovery. Building innovation roadmaps for PitNETs' treatments can allow incorporation of systems biology approaches to bring about insights at multiple levels of cell biology, from genes to proteins to metabolites. Using the systems biology techniques, it will then be possible to offer potential therapeutic strategies for the convergence of preventive approaches and patient-centered disease treatment. Here, we first provide a comprehensive overview of the molecular subtypes of PitNETs and therapeutics for these tumors from the past to the present. We then discuss examples of clinical trials and drug repositioning studies and how multi-omics studies can help in discovery and rational development of new therapeutics for PitNETs. Finally, this expert review offers new public health and personalized medicine approaches on cases that are refractory to conventional treatment or recur despite currently used surgical and/or drug therapy.

Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Simple Summary

The heterogeneity of complicated diseases like cancer negatively affects patients’ responses to treatment. Finding homogeneous subgroups of patients within the cancer population and finding the appropriate treatment for each subgroup will improve patients’ survival. In this study, we focus on triple-negative breast cancer (TNBC), where approximately 80% of patients do not entirely respond to chemotherapy. Our aim is to find subgroups of TNBC patients and identify drugs that have the potential to tailor treatments for each group through drug repositioning. After applying our method to TNBC, we found that different targeted mechanisms were suggested for different groups of patients. Our findings could help the research community to gain a better understanding of different subgroups within the TNBC population and can help the drugs to be repurposed with explainable results regarding the targeted mechanism.

Abstract

Breast cancer (BC) is the leading cause of death among female patients with cancer. Patients with triple-negative breast cancer (TNBC) have the lowest survival rate. TNBC has substantial heterogeneity within the BC population. This study utilized our novel patient stratification and drug repositioning method to find subgroups of BC patients that share common genetic profiles and that may respond similarly to the recommended drugs. After further examination of the discovered patient subgroups, we identified five homogeneous druggable TNBC subgroups. A drug repositioning algorithm was then applied to find the drugs with a high potential for each subgroup. Most of the top drugs for these subgroups were chemotherapy used for various types of cancer, including BC. After analyzing the biological mechanisms targeted by these drugs, ferroptosis was the common cell death mechanism induced by the top drugs in the subgroups with neoplasm subdivision and race as clinical variables. In contrast, the antioxidative effect on cancer cells was the common targeted mechanism in the subgroup of patients with an age less than 50. Literature reviews were used to validate our findings, which could provide invaluable insights to streamline the drug repositioning process and could be further studied in a wet lab setting and in clinical trials.

Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

Artificial Intelligence as Accelerator for Genomic Medicine and Planetary Health

Genomic medicine has made important strides over the past several decades, but as new insights and technologies emerge, the applications of genomics in medicine and planetary health continue to evolve and expand. An important grand challenge is harnessing and making sense of the genomic big data in ways that best serve public and planetary health. Because human health is inextricably intertwined with the health of planetary ecosystems and nonhuman animals, genomic medicine is in need of high throughput bioinformatics analyses to harness and integrate human and ecological multiomics big data. It is in this overarching context that artificial intelligence (AI), particularly machine learning and deep learning, offers enormous potentials to advance genomic medicine in a spirit of One Health. This expert review offers an analysis of the rapidly emerging role of AI in genomic medicine, including its current drivers, levers, opportunities, and challenges. The scope of AI applications in genomic medicine is broad, ranging from efficient and automated data analysis to drug repurposing and precision medicine, as with its challenges such as veracity of the big data that AI sorely depends on, social biases that the AI-driven algorithms can introduce, and how best to incorporate AI with human intelligence. The road ahead for AI in genomic medicine is complex and arduous and yet worthy of cautious optimism as we face future pandemics and ecological crises in the 21st century. Now is a good time to think about the role of AI in genomic medicine and planetary health.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

Differential Protein Interactome in Esophageal Squamous Cell Carcinoma Offers Novel Systems Biomarker Candidates with High Diagnostic and Prognostic Performance

Esophageal squamous cell carcinoma (ESCC) is among the most dangerous cancers with high mortality and lack of robust diagnostics and personalized/precision therapeutics. To achieve a systems-level understanding of tumorigenesis, unraveling of variations in the protein interactome and determination of key proteins exhibiting significant alterations in their interaction patterns during tumorigenesis are crucial. To this end, we have described differential protein-protein interactions and differentially interacting proteins (DIPs) in ESCC by utilizing the human protein interactome and transcriptome. Furthermore, DIP-centered modules were analyzed according to their potential in elucidation of disease mechanisms and improvement of efficient diagnostic, prognostic, and treatment strategies. Seven modules were presented as potential diagnostic, and 16 modules were presented as potential prognostic biomarker candidates. Importantly, our findings also suggest that 30 out of the 53 repurposed drugs were noncancer drugs, which could be used in the treatment of ESCC. Interestingly, 25 of these, proposed as novel drug candidates here, have not been previously associated in a context of esophageal cancer. In this context, risperidone and clozapine were validated for their growth inhibitory potential in three ESCC lines. Our findings offer a high potential for the development of innovative diagnostic, prognostic, and therapeutic strategies for further experimental studies in line with predictive diagnostics, targeted prevention, and personalization of medical services in ESCC specifically, and personalized cancer care broadly.

New drug candidates for osteosarcoma: Drug repurposing based on gene expression signature

Osteosarcoma (OS) is an aggressive bone malignancy and the third most common cancer in adolescence. Since the late 1970s, OS therapy and prognosis had only modest improvements, making it appealing to explore new tools that could help ameliorate the treatment. We present a meta-analysis of the gene expression signature of primary OS, and propose small molecules that could reverse this signature. The meta-analysis was performed using GEO microarray series. We first compared gene expression from eleven primary OS against osteoblasts to obtain the differentially expressed genes (DEGs). We later filtered those DEGs by verifying which ones had a concordant direction of differential expression in a validation group of 82 OS samples versus 30 bone marrow mesenchymal stem cells (BM-MSC) samples. A final gene expression signature of 266 genes (98 up and 168 down regulated) was obtained. The L1000CDS² engine was used for drug repurposing. The top molecules predicted to reverse the signature were afatinib (PubChem CID 10184653), BRD-K95196255 (PubChem CID 3242434), DG-041 (PubChem CID 11296282) and CA-074 Me (PubChem CID 23760717). Afatinib (Gilotrif™) is currently used for metastatic non-small-cell lung cancer with EGFR mutations, and in vitro evidence shows antineoplastic potential in OS cells. The other three molecules have reports of antineoplastic effects, but are not currently FDA-approved. Further studies are necessary to establish the potential of these drugs in OS treatment. We believe our results can be an important contribution for the investigation of new therapeutic genetic targets and for selecting new drugs to be tested for OS.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas

Although many studies have been conducted on single gene therapies in cancer patients, the reality is that tumor arises from different coordinating protein groups. Unveiling perturbations in protein interactome related to the tumor formation may contribute to the development of effective diagnosis, treatment strategies, and prognosis. In this study, considering the clinical and transcriptome data of three Renal Cell Carcinoma (RCC) subtypes (ccRCC, pRCC, and chRCC) retrieved from The Cancer Genome Atlas (TCGA) and the human protein interactome, the differential protein–protein interactions were identified in each RCC subtype. The approach enabled the identification of differentially interacting proteins (DIPs) indicating prominent changes in their interaction patterns during tumor formation. Further, diagnostic and prognostic performances were generated by taking into account DIP clusters which are specific to the relevant subtypes. Furthermore, considering the mesenchymal epithelial transition (MET) receptor tyrosine kinase (PDB ID: 3DKF) as a potential drug target specific to pRCC, twenty-one lead compounds were identified through virtual screening of ZINC molecules. In this study, we presented remarkable findings in terms of early diagnosis, prognosis, and effective treatment strategies, that deserve further experimental and clinical efforts.

The enigmatic helicase DHX9 and its association with the hallmarks of cancer

Much interest has been expended lately in characterizing the association between DExH-Box helicase 9 (DHX9) dysregulation and malignant development, however, the enigmatic nature of DHX9 has caused conflict as to whether it regularly functions as an oncogene or tumor suppressor. The impact of DHX9 on malignancy appears to be cell-type specific, dependent upon the availability of binding partners and activation of inter-connected signaling pathways. Realization of DHX9's pivotal role in the development of several hallmarks of cancer has boosted the enzyme's potential as a cancer biomarker and therapeutic target, opening up novel avenues for exploring DHX9 in precision medicine applications. Our review discusses the ascribed functions of DHX9 in cancer, explores its enigmatic nature and potential as an antineoplastic target.

Drug Repositioning for P-Glycoprotein Mediated Co-Expression Networks in Colorectal Cancer

Colorectal cancer (CRC) is one of the most fatal types of cancers that is seen in both men and women. CRC is the third most common type of cancer worldwide. Over the years, several drugs are developed for the treatment of CRC; however, patients with advanced CRC can be resistant to some drugs. P-glycoprotein (P-gp) (also known as Multidrug Resistance 1, MDR1) is a well-identified membrane transporter protein expressed by ABCB1 gene. The high expression of MDR1 protein found in several cancer types causes chemotherapy failure owing to efflux drug molecules out of the cancer cell, decreases the drug concentration, and causes drug resistance. As same as other cancers, drug-resistant CRC is one of the major obstacles for effective therapy and novel therapeutic strategies are urgently needed. Network-based approaches can be used to determine specific biomarkers, potential drug targets, or repurposing approved drugs in drug-resistant cancers. Drug repositioning is the approach for using existing drugs for a new therapeutic purpose; it is a highly efficient and low-cost process. To improve current understanding of the MDR-1-related drug resistance in CRC, we explored gene co-expression networks around ABCB1 gene with different network sizes (50, 100, 150, 200 edges) and repurposed candidate drugs targeting the ABCB1 gene and its co-expression network by using drug repositioning approach for the treatment of CRC. The candidate drugs were also assessed by using molecular docking for determining the potential of physical interactions between the drug and MDR1 protein as a drug target. We also evaluated these four networks whether they are diagnostic or prognostic features in CRC besides biological function determined by functional enrichment analysis. Lastly, differentially expressed genes of drug-resistant (i.e., oxaliplatin, methotrexate, SN38) HT29 cell lines were found and used for repurposing drugs with reversal gene expressions. As a result, it is shown that all networks exhibited high diagnostic and prognostic performance besides the identification of various drug candidates for drug-resistant patients with CRC. All these results can shed light on the development of effective diagnosis, prognosis, and treatment strategies for drug resistance in CRC.

A genome-wide analysis of copy number variation in Murciano-Granadina goats

BACKGROUND

In this work, our aim was to generate a map of the copy number variations (CNV) segregating in a population of Murciano-Granadina goats, the most important dairy breed in Spain, and to ascertain the main biological functions of the genes that map to copy number variable regions.

RESULTS

Using a dataset that comprised 1036 Murciano-Granadina goats genotyped with the Goat SNP50 BeadChip, we were able to detect 4617 and 7750 autosomal CNV with the PennCNV and QuantiSNP software, respectively. By applying the EnsembleCNV algorithm, these CNV were assembled into 1461 CNV regions (CNVR), of which 486 (33.3% of the total CNVR count) were consistently called by PennCNV and QuantiSNP and used in subsequent analyses. In this set of 486 CNVR, we identified 78 gain, 353 loss and 55 gain/loss events. The total length of all the CNVR (95.69 Mb) represented 3.9% of the goat autosomal genome (2466.19 Mb), whereas their size ranged from 2.0 kb to 11.1 Mb, with an average size of 196.89 kb. Functional annotation of the genes that overlapped with the CNVR revealed an enrichment of pathways related with olfactory transduction (fold-enrichment = 2.33, q-value = 1.61 × 10^-10), ABC transporters (fold-enrichment = 5.27, q-value = 4.27 × 10^-04) and bile secretion (fold-enrichment = 3.90, q-value = 5.70 × 10^-03).

CONCLUSIONS

A previous study reported that the average number of CNVR per goat breed was ~ 20 (978 CNVR/50 breeds), which is much smaller than the number we found here (486 CNVR). We attribute this difference to the fact that the previous study included multiple caprine breeds that were represented by small to moderate numbers of individuals. Given the low frequencies of CNV (in our study, the average frequency of CNV is 1.44%), such a design would probably underestimate the levels of the diversity of CNV at the within-breed level. We also observed that functions related with sensory perception, metabolism and embryo development are overrepresented in the set of genes that overlapped with CNV, and that these loci often belong to large multigene families with tens, hundreds or thousands of paralogous members, a feature that could favor the occurrence of duplications or deletions by non-allelic homologous recombination.

Novel molecular signatures and potential therapeutics in renal cell carcinomas: Insights from a comparative analysis of subtypes

Renal cell carcinomas (RCCs) are among the highest causes of cancer mortality. Although transcriptome profiling studies in the last decade have made significant molecular findings on RCCs, effective diagnosis and treatment strategies have yet to be achieved due to lack of adequate screening and comparative profiling of RCC subtypes. In this study, a comparative analysis was performed on RNA-seq based transcriptome data from each RCC subtype, namely clear cell RCC (KIRC), papillary RCC (KIRP) and kidney chromophobe (KICH), and mutual or subtype-specific reporter biomolecules were identified at RNA, protein, and metabolite levels by the integration of expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers in RCCs as well as novel biomarker candidates and potential therapeutic targets. Our findings also pointed out the incorporation of the molecular mechanisms of KIRC and KIRP, whereas KICH was shown to have distinct molecular signatures. Furthermore, considering the Dipeptidyl Peptidase 4 (DPP4) receptor as a potential therapeutic target specific to KICH, several drug candidates such as ZINC6745464 were identified through virtual screening of ZINC molecules. In this study, we reported valuable data for further experimental and clinical efforts, since the proposed molecules have significant potential for screening and therapeutic purposes in RCCs.

New Machine Learning Applications to Accelerate Personalized Medicine in Breast Cancer: Rise of the Support Vector Machines

Artificial intelligence, machine learning, health care robots, and algorithms for clinical decision-making are currently being sought after in diverse fields of clinical medicine and bioengineering. The field of personalized medicine stands to benefit from new technologies so as to harness the omics big data, for example, to individualize and accelerate cancer diagnostics and therapeutics in particular. In this overarching context, breast cancer is one of the most common malignancies worldwide with multiple underlying molecular etiologies and each subtype displaying diverse clinical outcomes. Disease stratification for breast cancer is, therefore, vital to its effective and individualized clinical care. The support vector machine (SVM) is a rising machine learning approach that offers robust classification of high-dimensional big data into small numbers of data points (support vectors), achieving differentiation of subgroups in a short amount of time. Considering the rapid timelines required for both diagnosis and treatment of most aggressive cancers, this new machine learning technique has important clinical and public applications and implications for high-throughput data analysis and contextualization. This expert review describes and examines, first, the SVM models employed to forecast breast cancer subtypes using diverse systems science data, including transcriptomics, epigenetics, proteomics, and radiomics, as well as biological pathway, clinical, pathological, and biochemical data. Then, we compare the performance of the present SVM and other diagnostic and therapeutic prediction models across the data types. We conclude by emphasizing that data integration is a critical bottleneck in systems science, cancer research and development, and health care innovation and that SVM and machine learning approaches offer new solutions and ways forward in biomedical, bioengineering, and clinical applications.

Pan-cancer mapping of differential protein-protein interactions

Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.