A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

Reproductomics: Exploring the Applications and Advancements of Computational Tools

Over recent decades, advancements in omics technologies, such as proteomics, genomics, epigenomics, metabolomics, transcriptomics, and microbiomics, have significantly enhanced our understanding of the molecular mechanisms underlying various physiological and pathological processes. Nonetheless, the analysis and interpretation of vast omics data concerning reproductive diseases are complicated by the cyclic regulation of hormones and multiple other factors, which, in conjunction with a genetic makeup of an individual, lead to diverse biological responses. Reproductomics investigates the interplay between a hormonal regulation of an individual, environmental factors, genetic predisposition (DNA composition and epigenome), health effects, and resulting biological outcomes. It is a rapidly emerging field that utilizes computational tools to analyze and interpret reproductive data, with the aim of improving reproductive health outcomes. It is time to explore the applications of reproductomics in understanding the molecular mechanisms underlying infertility, identification of potential biomarkers for diagnosis and treatment, and in improving assisted reproductive technologies (ARTs). Reproductomics tools include machine learning algorithms for predicting fertility outcomes, gene editing technologies for correcting genetic abnormalities, and single cell sequencing techniques for analyzing gene expression patterns at the individual cell level. However, there are several challenges, limitations and ethical issues involved with the use of reproductomics, such as the applications of gene editing technologies and their potential impact on future generations are discussed. The review comprehensively covers the applications and advancements of reproductomics, highlighting its potential to improve reproductive health outcomes and deepen our understanding of reproductive molecular mechanisms.

Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes

This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (¹⁸F-FDG PET) image features of NSCLC patients. RNA-sequencing data and ¹⁸F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein-protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker.

EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer

Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a single type of cancer can make accurate classification a challenging problem. Therefore, improved and robust methods for classification are absolutely critical. Although deep learning-based methods for cancer classification have been proposed earlier, they all provide point estimates for predictions without any measure of confidence and thus, can fall short in real-world applications where key decisions are to be made based on the predictions of the classifier. Here we report a Bayesian neural network-based model for classification of cancer types as well as sub-types from transcriptomic data. This model reported a measure of confidence with each prediction through analysis of epistemic uncertainty. We incorporated an uncertainty correction step with the Bayesian network-based model to greatly enhance prediction accuracy of cancer types (> 97% accuracy) and sub-types (> 80%). Our work suggests that reporting uncertainty measure with each classification can enable more accurate and informed decision-making that can be highly valuable in clinical settings.

Machine learning in medical applications: A review of state-of-the-art methods

Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.

Predictive Modeling for Clinical Features Associated With Neurofibromatosis Type 1

Objective

To perform a longitudinal analysis of clinical features associated with neurofibromatosis type 1 (NF1) based on demographic and clinical characteristics and to apply a machine learning strategy to determine feasibility of developing exploratory predictive models of optic pathway glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a pediatric NF1 cohort.

Methods

Using NF1 as a model system, we perform retrospective data analyses using a manually curated NF1 clinical registry and electronic health record (EHR) information and develop machine learning models. Data for 798 individuals were available, with 578 comprising the pediatric cohort used for analysis.

Results

Males and females were evenly represented in the cohort. White children were more likely to develop OPG (odds ratio [OR]: 2.11, 95% confidence interval [CI]: 1.11–4.00, p = 0.02) relative to their non-White peers. Median age at diagnosis of OPG was 6.5 years (1.7–17.0), irrespective of sex. Males were more likely than females to have a diagnosis of ADHD (OR: 1.90, 95% CI: 1.33–2.70, p < 0.001), and earlier diagnosis in males relative to females was observed. The gradient boosting classification model predicted diagnosis of ADHD with an area under the receiver operator characteristic (AUROC) of 0.74 and predicted diagnosis of OPG with an AUROC of 0.82.

Conclusions

Using readily available clinical and EHR data, we successfully recapitulated several important and clinically relevant patterns in NF1 semiology specifically based on demographic and clinical characteristics. Naive machine learning techniques can be potentially used to develop and validate predictive phenotype complexes applicable to risk stratification and disease management in NF1.

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDF PET/CT, EZRIN and KI67

Simple Summary

Pediatric osteosarcoma is one of the most aggressive cancers, and predictions of metastasis and chemotherapy response have a significant impact on pediatric patient survival. Radiogenomics, as methods of analyzing gene expression or image texture features, have previously been used for the diagnosis of chemotherapy responses and metastasis and can reveal the current state of cancer. In this study, we aimed to generate a predictive model using gene expression and ¹⁸F-FDG PET/CT image texture features in pediatric osteosarcoma in relation to metastasis and chemotherapy response. A predictive model using radiogenomics technology that incorporates both imaging features and gene expression can accurately predict metastasis and chemotherapy responses to improve patient outcomes.

Abstract

Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. ¹⁸F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data.

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

BACKGROUND

Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level.

RESULTS

By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples' expression profiles we systematically interrogated the signatures associated with combinations of mutations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize over a hundred cases where subsets of a gene's mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions.

CONCLUSIONS

The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.

Implications of new understandings of gliomas in children and adults with NF1: report of a consensus conference

Gliomas are the most common primary central nervous system tumors occurring in children and adults with neurofibromatosis type 1 (NF1). Over the past decade, discoveries of the molecular basis of low-grade gliomas (LGGs) have led to new approaches for diagnosis and treatments. However, these new understandings have not been fully applied to the management of NF1-associated gliomas. A consensus panel consisting of experts in NF1 and gliomas was convened to review the current molecular knowledge of NF1-associated low-grade “transformed” and high-grade gliomas; insights gained from mouse models of NF1-LGGs; challenges in diagnosing and treating older patients with NF1-associated gliomas; and advances in molecularly targeted treatment and potential immunologic treatment of these tumors. Next steps are recommended to advance the management and outcomes for NF1-associated gliomas.

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

Identification of pan-cancer Ras pathway activation with deep learning

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1.

The Role of RUNX1 in NF1-Related Tumors and Blood Disorders

Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder. NF1 patients are predisposed to formation of several type solid tumors as well as to juvenile myelomonocytic leukemia. Loss of NF1 results in dysregulation of MAPK, PI3K and other signaling cascades, to promote cell proliferation and to inhibit cell apoptosis. The RUNX1 gene is associated with stem cell function in many tissues, and plays a key role in the fate of stem cells. Aberrant RUNX1 expression leads to context-dependent tumor development, in which RUNX1 may serve as a tumor suppressor or an oncogene in specific tissue contexts. The co-occurrence of mutation of NF1 and RUNX1 is detected rarely in several cancers and signaling downstream of RAS-MAPK can alter RUNX1 function. Whether aberrant RUNX1 expression contributes to NF1-related tumorigenesis is not fully understood. This review focuses on the role of RUNX1 in NF1-related tumors and blood disorders, and in sporadic cancers.

APD optimal bias voltage compensation method based on machine learning

The signal-to-noise ratio (SNR) of avalanche photodiode (APD) in optical detection system is greatly influenced by background radiation and operating temperature, so an APD optimal bias voltage compensation method based on machine learning is designed to accurately judge the current laser emission state and APD working state so that dichotomy compensation can be carried out to make APD work in optimal state. By means of cross-verification, the accuracy of judging laser emission state and APD working state is as high as 100% and 99.3% separately, then the number of input variables in the model is reduced appropriately by experiment and the prediction speed of the algorithm is further improved. Finally, road detection application is taken as the experimental background and comparison between the proposed method and the most widely used signal amplitude feedback compensation method is carried out. The results of this study suggest that the proposed APD optimal bias voltage compensation method based on machine learning offers a new and promising approach.

Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools

BACKGROUND

Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the ability of tumor cells to deeply infiltrate the surrounding tissue makes complete resection quite impossible, and, in consequence, the probability of tumor recurrence is high, and the prognosis is not positive. GBM is highly heterogeneous and adapts to treatment in most individuals. Nevertheless, these mechanisms of adaption are unknown.

RECENT FINDINGS

In this review, we will discuss the recent discoveries in molecular and cellular heterogeneity, mechanisms of therapeutic resistance, and new technological approaches to identify new treatments for GBM. The combination of biology and computer resources allow the use of algorithms to apply artificial intelligence and machine learning approaches to identify potential therapeutic pathways and to identify new drug candidates.

CONCLUSION

These new approaches will generate a better understanding of GBM pathogenesis and will result in novel treatments to reduce or block the devastating consequences of brain cancers.

Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer.

Discovering Pathway and Cell Type Signatures in Transcriptomic Compendia with Machine Learning

Pathway and cell type signatures are patterns present in transcriptome data that are associated with biological processes or phenotypic consequences. These signatures result from specific cell type and pathway expression but can require large transcriptomic compendia to detect. Machine learning techniques can be powerful tools for signature discovery through their ability to provide accurate and interpretable results. In this review, we discuss various machine learning applications to extract pathway and cell type signatures from transcriptomic compendia. We focus on the biological motivations and interpretation for both supervised and unsupervised learning approaches in this setting. We consider recent advances, including deep learning, and their applications to expanding bulk and single-cell RNA data. As data and computational resources increase, there will be more opportunities for machine learning to aid in revealing biological signatures.

Machine Learning in Neuro-Oncology: Can Data Analysis From 5346 Patients Change Decision-Making Paradigms?

BACKGROUND

Machine learning (ML) is an application of artificial intelligence (AI) that gives computer systems the ability to learn data, without being explicitly programmed. Currently, ML has been successfully used for optical character recognition, spam filtering, and face recognition. The aim of the present study was to review the current applications of ML in the field of neuro-oncology.

METHODS

We conducted a systematic literature review using the PubMed and Cochrane databases using a keyword search for January 30, 2000 to March 31, 2018. The data were clustered for neuro-oncology scope of ML into 3 categories: patient outcome predictors, imaging analysis, and gene expression.

RESULTS

Data from 5346 patients in 29 studies were used to develop ML-based algorithms (MLBAs) in neuro-oncology. MLBAs were used to predict the outcomes for 2483 patients, with a sensitivity range of 78%-98% and specificity range of 76%-95%. In all studies, the MLBAs had greater accuracy than the conventional ones. MLBAs for image analysis showed accuracy in diagnosing low-grade versus high-grade gliomas, ranging from 80% to 93% and 90% for diagnosing high-grade glioma versus lymphoma. Seven studies used MLBAs to analyze gene expression in neuro-oncology.

CONCLUSIONS

MLBAs in neuro-oncology have been shown to predict patients' outcomes more accurately than conventional parameters in a retrospective analysis. If their high diagnostic accuracy in imaging analysis and detection of somatic mutations are corroborated in prospective studies, the use of tissue diagnosis or liquid biopsy might be curtailed. Finally, MLBAs are promising to help guide targeted therapy, can lead to personalized medicine, and open areas of study in the cancer cellular signaling system, not otherwise known.

Cutaneous neurofibromas in the genomics era: current understanding and open questions

Cutaneous neurofibromas (cNF) are a nearly ubiquitous symptom of neurofibromatosis type 1 (NF1), a disorder with a broad phenotypic spectrum caused by germline mutation of the neurofibromatosis type 1 tumour suppressor gene (NF1). Symptoms of NF1 can include learning disabilities, bone abnormalities and predisposition to tumours such as cNFs, plexiform neurofibromas, malignant peripheral nerve sheath tumours and optic nerve tumours. There are no therapies currently approved for cNFs aside from elective surgery, and the molecular aetiology of cNF remains relatively uncharacterised. Furthermore, whereas the biallelic inactivation of NF1 in neoplastic Schwann cells is critical for cNF formation, it is still unclear which additional genetic, transcriptional, epigenetic, microenvironmental or endocrine changes are important. Significant inroads have been made into cNF understanding, including NF1 genotype-phenotype correlations in NF1 microdeletion patients, the identification of recurring somatic mutations, studies of cNF-invading mast cells and macrophages, and clinical trials of putative therapeutic targets such as mTOR, MEK and c-KIT. Despite these advances, several gaps remain in our knowledge of the associated pathogenesis, which is further hampered by a lack of translationally relevant animal models. Some of these questions may be addressed in part by the adoption of genomic analysis techniques. Understanding the aetiology of cNF at the genomic level may assist in the development of new therapies for cNF, and may also contribute to a greater understanding of NF1/RAS signalling in cancers beyond those associated with NF1. Here, we summarise the present understanding of cNF biology, including the pathogenesis, mutational landscape, contribution of the tumour microenvironment and endocrine signalling, and the historical and current state of clinical trials for cNF. We also highlight open access data resources and potential avenues for future research that leverage recently developed genomics-based methods in cancer research.

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.

Molecular Subgroups of Glioblastoma- an Assessment by Immunohistochemical Markers

Comprehensive molecular characterization of and novel therapeutic approaches to glioblastoma have been explored as a result of advancements in biotechnologies. In this study, we aimed to bring basic research discoveries closer to clinical practice and ultimately incorporate molecular classification into the routine histopathological evaluation of grade IV gliomas. Integrated results of genome-wide sequencing, transcriptomic and epigenomic analyses by The Cancer Genome Atlas Network defined the classic, proneural, neural and mesenchymal subtypes of this tumor. In a retrospective cohort, we analyzed selected subgroup-defining molecular markers in formalin-fixed paraffin-embedded surgical specimens by immunohistochemistry. Quantitative and qualitative scores of marker expression were tested in hierarchical cluster analyses to evaluate segregations of the molecular subgroups, which then were correlated with clinical parameters including patients' age, gender and overall survival. Our study has confirmed the separation of molecular glioblastoma subgroups with clear trends regarding clinical correlations. Future analyses in a larger, prospective cohort using similar methods are expected to facilitate the development of a molecular diagnostic panel that may complement routine histological work up and support prognostication as well as treatment decisions in glioblastoma.