Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs.

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Pharmacomicrobiomics-Guided Precision Oncology: A New Frontier of P4 (Predictive, Personalized, Preventive, and Participatory) Medicine and Microbiome-Based Therapeutics.

Pharmacomicrobiomics is a rapidly developing field that promises to make significant contributions to predictive, personalized, preventive, and participatory (P4) medicine. This is becoming evident particularly in the field of precision (P4) oncology by taking seriously the crucial role microbiome plays in health and disease. Several studies have already shown that clinicians can harness insights from the microbiome to better predict treatment response, reduce side effects, and improve overall outcomes for cancer patients. Furthermore, pharmacomicrobiomics will undoubtedly play a crucial role in shaping the future of cancer treatment in the era of P4 oncology as we continue to unravel the intricate relationships between the microbiome and cancer. This perspective and innovation analysis discusses the emerging intersection of P4 medicine and P4 oncology, as seen through a lens of pharmacomicrobiomics. A key promise of pharmacomicrobiomics is the development of personalized microbiome-based therapeutics. In all, we suggest that optimizing cancer treatment and prevention by harnessing pharmacomicrobiomics has vast potentials for precision oncology, and personalized medicine using the right drug, at the right dose, for the right patient, and at the right time.

Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

A Differential Transcriptional Regulome Approach to Unpack Cancer Biology: Insights on Renal Cell Carcinoma Subtypes.

Cancer research calls for new approaches that account for the regulatory complexities of biology. We present, in this study, the differential transcriptional regulome (DIFFREG) approach for the identification and prioritization of key transcriptional regulators and apply it to the case of renal cell carcinoma (RCC) biology. Of note, RCC has a poor prognosis and the biomarker and drug discovery studies to date have tended to focus on gene expression independent from mutations and/or post-translational modifications. DIFFREG focuses on the differential regulation between transcription factors (TFs) and their target genes rather than differential gene expression and integrates transcriptome profiling with the human transcriptional regulatory network to analyze differential gene regulation between healthy and RCC cases. In this study, RNA-seq tissue samples (n = 1020) from the Cancer Genome Atlas (TCGA), including healthy and tumor subjects, were integrated with a comprehensive human TF-gene interactome dataset (1122603 interactions between 1289 TFs and 25177 genes). Comparative analysis of DIFFREG profiles, consisting of perturbed TF-gene interactions, from three common subtypes (clear cell RCC, papillary RCC and chromophobe RCC) revealed subtype-specific alterations, supporting the hypothesis that these signatures in the transcriptional regulome profiles may be considered potential biomarkers that may play an important role in elucidating the molecular mechanisms of RCC development and translating knowledge about the genetic basis of RCC into the clinic. In addition, these indicators may help oncologists make the best decisions for diagnosis and prognosis management.

Toward Precision Oncology in Glioblastoma with a Personalized Cancer Genome Reporting Tool and Genetic Changes Identified by Whole Exome Sequencing.

Precision/personalized medicine in oncology has two key pillars: molecular profiling of the tumors and personalized reporting of the results in ways that are clinically contextualized and triangulated. Moreover, neurosurgery as a field stands to benefit from precision/personalized medicine and new tools for reporting of the molecular findings. In this context, glioblastoma (GBM) is a highly aggressive brain tumor with limited treatment options and poor prognosis. Precision/personalized medicine has emerged as a promising approach for personalized therapy in GBM. In this study, we performed whole exome sequencing of tumor tissue samples from six newly diagnosed GBM patients and matched nontumor control samples. We report here the genetic alterations identified in the tumors, including single nucleotide variations, insertions or deletions (indels), and copy number variations, and attendant mutational signatures. Additionally, using a personalized cancer genome-reporting tool, we linked genomic information to potential therapeutic targets and treatment options for each patient. Our findings revealed heterogeneity in genetic alterations and identified targetable pathways, such as the PI3K/AKT/mTOR pathway. This study demonstrates the prospects of precision/personalized medicine in GBM specifically, and neurosurgical oncology more generally, including the potential for genomic profiling coupled with personalized cancer genome reporting. Further research and larger studies are warranted to validate these findings and advance the treatment options and outcomes for patients with GBM.

Three candidate anticancer drugs were repositioned by integrative analysis of the transcriptomes of species with different regenerative abilities after injury.

Regeneration is a homeostatic process that involves the restoration of cells and body parts. Most of the molecular mechanisms and signalling pathways involved in wound healing, such as proliferation, have also been associated with cancer cell growth, suggesting that cancer is an over/unhealed wound. In this study, we examined differentially expressed genes in spinal cord samples from regenerative organisms (axolotl and zebrafish) and nonregenerative organisms (mouse and rat) compared to intact control spinal cord samples using publicly available transcriptomics data and bioinformatics analyses. Based on these gene signatures, we investigated 3 small compounds, namely cucurbitacin I, BMS-754807, and PHA-793887 as potential candidates for the treatment of cancer. The predicted target genes of the repositioned compounds were mainly enriched with the greatest number of genes in cancer pathways. The molecular docking results on the binding affinity between the repositioned compounds and their target genes are also reported. The repositioned 3 small compounds showed anticancer effect both in 2D and 3D cell cultures using the prostate cancer cell line as a model. We propose cucurbitacin I, BMS-754807, and PHA-793887 as potential anticancer drug candidates. Future studies on the mechanisms associated with the revealed gene signatures and anticancer effects of these three small compunds would allow scientists to develop therapeutic approaches to combat cancer. This research contributes to the evaluation of mechanisms and gene signatures that either limit or cause cancer, and to the development of new cancer therapies by establishing a link between regeneration and carcinogenesis.

The Versatility of Plectin in Cancer: A Pan-Cancer Analysis on Potential Diagnostic and Prognostic Impacts of Plectin Isoforms.

Plectin, encoded by PLEC, is a cytoskeletal and scaffold protein with a number of unique isoforms that act on various cellular functions such as cell adhesion, signal transduction, cancer cell invasion, and migration. While plectin has been shown to display high expression and mislocalization in tumor cells, our knowledge of the biological significance of plectin and its isoforms in tumorigenesis remain limited. In this study, we first performed pathway enrichment analysis to identify cancer hallmark proteins associated with plectin. Then, a pan-cancer analysis was performed using RNA-seq data collected from the Cancer Genome Atlas (TCGA) to detect the mRNA expression levels of PLEC and its transcript isoforms, and the prognostic as well as diagnostic significance of the transcript isoforms was evaluated considering cancer stages. We show here that several tissue specific PLEC isoforms are dysregulated in different cancer types and stages but not the expression of PLEC. Among them, PLEC 1d and PLEC 1f are potential biomarker candidates and call for further translational and personalized medicine research. This study makes a contribution as a stride to unravel the molecular mechanisms underpinning plectin isoforms in cancer development and progression by revealing the potent plectin isoforms in different stages of cancer as potential early cancer detection biomarkers. Importantly, uncovering how plectin isoforms guide malignancy and particular cancer types by comprehensive functional studies might open new avenues toward novel cancer therapeutics.

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.

Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer.

Introduction: Integrating interaction data with biological knowledge can be a critical approach for drug development or drug repurposing. In this context, host-pathogen-protein-protein interaction (HP-PPI) networks are useful instrument to uncover the phenomena underlying therapeutic effects in infectious diseases, including cervical cancer, which is almost exclusively due to human papillomavirus (HPV) infections. Cervical cancer is one of the second leading causes of death, and HPV16 and HPV18 are the most common subtypes worldwide. Given the limitations of traditionally used virus-directed drug therapies for infectious diseases and, at the same time, recent cancer statistics for cervical cancer cases, the need for innovative treatments becomes clear. Methods: Accordingly, in this study, we emphasize the potential of host proteins as drug targets and identify promising host protein candidates for cervical cancer by considering potential differences between HPV subtypes (i.e., HPV16 and HPV18) within a novel bioinformatics framework that we have developed. Subsequently, subtype-specific HP-PPI networks were constructed to obtain host proteins. Using this framework, we next selected biologically significant host proteins. Using these prominent host proteins, we performed drug repurposing analysis. Finally, by following our framework we identify the most promising host-oriented drug candidates for cervical cancer. Results: As a result of this framework, we discovered both previously associated and novel drug candidates, including interferon alfacon-1, pimecrolimus, and hyaluronan specifically for HPV16 and HPV18 subtypes, respectively. Discussion: Consequently, with this study, we have provided valuable data for further experimental and clinical efforts and presented a novel bioinformatics framework that can be applied to any infectious disease.

A Transcriptomic and Reverse-Engineering Strategy Reveals Molecular Signatures of Arachidonic Acid Metabolism in 12 Cancers.

Cancer and arachidonic acid (AA) have important linkages. For example, AA metabolites regulate several critical biological functions associated with carcinogenesis: angiogenesis, apoptosis, and cancer invasion. However, little is known about the comparative changes in metabolite expression of the arachidonic acid pathway (AAP) in carcinogenesis. In this study, we examined transcriptome data from 12 cancers, such as breast invasive carcinoma, colon adenocarcinoma, lung adenocarcinoma, and prostate adenocarcinoma. We also report here a reverse-engineering strategy wherein we estimated metabolic signatures associated with AAP by (1) making deductive inferences through transcriptome-level data extraction, (2) remodeling AA metabolism, and (3) performing a comparative analysis of cancer types to determine the similarities and differences between different cancer types with respect to AA metabolic alterations. We identified 77 AAP gene signatures differentially expressed in cancers and 37 AAP metabolites associated with them. Importantly, the metabolite 15(S)-HETE was identified in almost all cancers, while arachidonate, 5-HETE, PGF2α, 14,15-EET, 8,9-EET, 5,6-EET, and 20-HETE were discovered as other most regulated metabolites. This study shows that the 12 cancers studied herein, although in different branches of the AAP, have altered expression of AAP gene signatures. Going forward, AA related-cancer research generally, and the molecular signatures and their estimated metabolites reported herein specifically, hold broad promise for precision/personalized medicine in oncology as potential therapeutic and diagnostic targets.

SVM-DO: identification of tumor-discriminating mRNA signatures via support vector machines supported by Disease Ontology.

Background/aim: The complicated nature of tumor formation makes it difficult to identify discriminatory genes. Recently, transcriptome-based supervised classification methods using support vector machines (SVMs) have become popular in this field. However, the inclusion of less significant variables in the construction of classification models can lead to misclassification. To improve model performance, feature selection methods such as enrichment analysis can be used to extract useful variable sets. The detection of genes that can discriminate between normal and tumor samples in the association of cancer and disease remains an area of limited information. We therefore aimed to discover novel and practical sets of discriminatory biomarkers by utilizing the association of cancer and disease. Materials and methods: In this study, we employed an SVM classification method for differentially expressed genes enriched by Disease Ontology and filtered nondiscriminatory features using Wilk's lambda criterion prior to classification. Our approach uses the discovery of disease-associated genes as a viable strategy to identify gene sets that discriminate between tumor and normal states. We analyzed the performance of our algorithm using comprehensive RNA-Seq data for adenocarcinoma of the colon, squamous cell carcinoma of the lung, and adenocarcinoma of the lung. The classification performance of the obtained gene sets was analyzed by comparison with different expression datasets and previous studies using the same datasets. Results: It was found that our algorithm extracts stable small gene sets that provide high accuracy in predicting cancer status. In addition, the gene sets generated by our method perform well in survival analyses, indicating their potential for prognosis. Conclusion: By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor (https://doi.org/10.18129/B9.bioc.SVMDO) and GitHub (https://github.com/robogeno/SVMDO).

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins.

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems' biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.

Human oncogenic viruses: an overview of protein biomarkers in viral cancers and their potential use in clinics.

INTRODUCTION: Although the idea that carcinogenesis might be caused by viruses was first voiced about 100 years ago, today's data disappointingly show that we have not made much progress in preventing and/or treating viral cancers in a century. According to recent studies, infections are responsible for approximately 13% of cancer development in the world. Today, it is accepted and proven by many authorities that Epstein-Barr virus (EBV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), Human Herpesvirus 8 (HHV8), Human T-cell Lymphotropic virus 1 (HTLV1) and highly oncogenic Human Papillomaviruses (HPVs) cause or/and contribute to cancer development in humans. AREAS COVERED: Considering the insufficient prevention and/or treatment strategies for viral cancers, in this review we present the current knowledge on protein biomarkers of oncogenic viruses. In addition, we aimed to decipher their potential for clinical use by evaluating whether the proposed biomarkers are expressed in body fluids, are druggable, and act as tumor suppressors or oncoproteins. EXPERT OPINION: Consequently, we believe that this review will shed light on researchers and provide a guide to find remarkable solutions for the prevention and/or treatment of viral cancers.

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 SOD1G93A microglia and age-matched SOD1G93A motor neurons are poorly understood. We evaluated the differential gene expression profiles in SOD1G93A microglia and SOD1G93A 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.

Precision Oncology: An Ensembled Machine Learning Approach to Identify a Candidate mRNA Panel for Stratification of Patients with Breast Cancer.

The rise of machine learning (ML) has recently buttressed the efforts for big data-driven precision oncology. This study used ensemble ML for precision oncology in breast cancer, which is one of the most common malignancies worldwide with marked heterogeneity of the underlying molecular mechanisms. We analyzed clinical and RNA-seq data from The Cancer Genome Atlas (TCGA) (844 patients with breast cancer and 113 healthy individuals) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (1784 patients with breast cancer and 202 healthy individuals). We evaluated six algorithms in the context of ensemble modeling and identified a candidate mRNA diagnostic panel that can differentiate patients from healthy controls, and stratify breast cancer into molecular subtypes. The ensemble model included 50 mRNAs and displayed 82.55% accuracy, 79.22% specificity, and 84.55% sensitivity in stratifying patients into molecular subtypes in TCGA cohort. Its performance was markedly higher, however, in distinguishing the basal, LumB, and Her2+ breast cancer subtypes from healthy individuals. In overall survival analysis, the mRNA panel showed a hazard ratio of 2.25 (p = 5 × 10-7) for breast cancer and was significantly associated with molecular pathways related to carcinogenesis. In conclusion, an ensemble ML approach, including 50 mRNAs, was able to stratify patients with different breast cancer subtypes and differentiate them from healthy individuals. Future prospective studies in large samples with deep phenotyping can help advance the ensemble ML approaches in breast cancer. Advanced ML methods such as ensemble learning are timely additions to the precision oncology research toolbox.

Repositioning of Anti-Inflammatory Drugs for the Treatment of Cervical Cancer Sub-Types.

Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.